Дисертації з теми "Battery state-of-health"
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1
Grube, Ryan J. "Automotive Battery State-of-Health Monitoring Methods." Wright State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=wright1229787557.
Повний текст джерела
2
Söderhielm, Camilla. "Investigation of Battery Parameters for Li-ion Battery State of Health Estimation." Thesis, KTH, Kemiteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299432.
Повний текст джерелаАнотація:
Miljöpåverkan från konventionella förbränningsmotorer har bidragit till en övergång till elmotorer. I denna övergång spelar litiumjonbatterier en viktig roll som energilagringssystem, men på grund av sin reaktiva kemi kan de utgöra en säkerhetsrisk. I likhet med civilsamhället står Försvarsmakten inför ett skifte där förbränningsmotorer ska bytas ut mot el- och hybridmotorer. För en säker militär tillämpning är det därför viktigt att förstå hur litiumjonbatterier beter sig vid åldrande och bortom ramen för normal användning. Detta projekt syftar till att identifiera batteriparametrar (impedans, resistans, kapacitet och yttemperatur) att använda för bedömning av batteriets hälsotillstånd. Vidare syftar projektet till att värdera de identifierade batteriparametrarnas lämplighet för militära applikationer. Som en del av syftet undersöker detta projekt omgivningstemperaturens effekt på batteriparametrarna, samt använder batteriparametrarna för att uppskatta när ett batteri kan klassas som förbrukat. Kommersiella NMC/grafit-litiumjonbatterier åldrades genom full upp- och urladdning. Varje batteri utsattes för maximalt 250 upp- och urladdningscykler vid laddningsströmmar om 4 A och urladdningsströmmar om 10 A. Åldrandet övervakades genom regelbunden mätning av impedans, resistans, kapacitet och yttemperatur. Batterierna cyklades vid antingen 52 ± 3 °C, 21 ± 3 °C eller −15 ± 3 °C för att studera omgivningstemperaturens effekt på de undersökta batteriparametrarna. Impedansmätningar vid 980 Hz var stabilast med avseende på variationer i omgivningstemperatur samt batteriets laddningsnivå, och ansågs därför vara den lämpligaste batteriparametern att använda för uppskattning av batteriets hälsotillstånd när tillämpningen kräver stor flexibilitet. Förändringar i resistans och kapacitet vid givna omgivningstemperaturer ansågs å andra sidan bättre återspegla batteriets åldringsgrad. Därför ansågs resistans och kapacitet vara de lämpligaste batteriparametrarna för uppskattning av batteriets hälsotillstånd med avseende på precision. Mätning av yttemperatur gav otillräcklig information för att uppskatta batteriernas hälsotillstånd med precision. En sänkning av omgivningstemperaturen från 21 °C till −15 °C hade en stor påverkan på resistans och kapacitet; resistansen ökade medan kapaciteten minskade, vilket motsvarar en reducerad batteriprestanda. Med avseende på kapacitetsförlust så förbrukades inget av batterierna som förvarades i 21 °C under cyklingen. Batterier som förvarades i 52 °C och −15 °C var förbrukade efter 150–200 cyklingar. Med avseende på resistansökning var ett av batterierna som förvarades vid 21 °C förbrukat efter 200 cyklingar. Samtliga batterier förvarade vid 52 °C var förbrukade efter 150–200 cyklingar, medan batterier förvarade vid −15 °C var förbrukade efter 200–250 cyklingar. Slutligen, med avseende på impedansmätning vid 980 Hz så tog det 200 cyklingar tills dess att ett av batterierna som förvarades i 21 °C var förbrukat. Ett av batterierna som förvarades i 52 °C var förbrukat efter 150 cyklingar. Batterier förvarade vid −15 °C var förbrukade efter 200–250 cyklingar.Environmental concerns associated with greenhouse gas emissions from conventional combustion engines have contributed to a transition towards electric mobility. In this transition, lithium-ion (Li-ion) batteries play an important part as an energy storage system. However, Li-ion batteries can pose a safety risk due to their reactive chemistry. The Swedish Armed Forces are approaching a transition towards electric mobility, therefore, understanding Li-ion battery behavior with regard to non-normal use and ageing is critical for safe military applications. This project aimed to identify and evaluate battery parameters (impedance, resistance, capacity and surface temperature) suitable for State of Health (SOH) estimation of Li-ion batteries in military applications. Furthermore, this project aimed to investigate the ambient temperature’s effect on battery parameters, and identify the battery’s end of life (EOL) based on battery parameter tracking. Commercial NMC/graphite Li-ion batteries were exposed to ageing through repeated charge and discharge cycles. A critical application was mimicked, where the batteries operated at 1C charge rate (4 A) and 2.5C discharge rate (10 A) between 100 % and 0 % state of charge, for up to 250 charge/discharge cycles. The ageing process was tracked through regular measurements of impedance, resistance, capacity and surface temperature. In order to investigate the ambient temperature’s effect on the investigated battery parameters, the batteries were aged at either 52 ± 3 °C, 21 ± 3 °C or −15 ± 3 °C. Impedance measured at 980 Hz was the most stable battery parameter with respect to variations in state of charge and temperature, and was therefore regarded as the most suitable parameter for SOH estimation with respect to flexibility. Measurements of resistance and capacity at given temperatures were likely reflecting electrochemical ageing phenomena more accurately, hence the most suitable battery parameters for SOH estimation with respect to accuracy. Tracking of surface temperature provided insufficient information for accurate estimation of the batteries SOH. Decreasing the ambient temperature from 21 °C to −15 °C had a major effect on capacity and resistance; the resistance increased and the capacity decreased, corresponding to a decrease in battery performance. With respect to capacity fade, neither of the batteries aged at 21 °C reached their EOL within 250 cycles, while batteries aged at 52 °C or −15 °C reached their EOL after 150–200 cycles. With respect to resistance, one battery kept at 21 °C reached their EOL after 200 cycles, all batteries kept at 52 °C reached their EOL after 150–200 cycles, and batteries kept at −15 °C reached their EOL between 200–250 cycles. Finally, with respect to impedance measured at 980 Hz, one battery kept at 21 °C reached their EOL after 200 cycles, one battery kept at 52 °C reached their EOL after 150 cycles, and batteries kept at −15 °C reached their EOL between 200–250 cycles.
3
Kerley, Ross Andrew. "Automotive Lead-Acid Battery State-of-Health Monitoring System." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/64870.
Повний текст джерелаАнотація:
This thesis describes the development of a system to continuously monitor the battery in a car and warn the user of an upcoming battery failure. An automotive battery endures enormous strain when it starts the engine, and when it supplies loads without the engine running. Note that the current during a cranking event often exceeds 500 Amperes. Despite the strains, a car battery still typically lasts 4-6 years before requiring replacement. There is often no warning of when a battery should be replaced and there is never a good time for a battery failure.
All currently available lead-acid battery monitoring systems use voltage and current sensing to monitor battery impedance and estimate battery health. However, such a system is costly due to the current sensor and typically requires an expert to operate the system. This thesis describes a prototype system to monitor battery state of health and provide advance warning of an upcoming battery failure using only voltage sensing. The prototype measures the voltage during a cranking event and determines if the battery is healthy or not. The voltage of an unhealthy battery will drop lower than a healthy one, and it will not recover as quickly.
The major contributions of the proposed research to the field are an algorithm to predict automotive battery state-of-health that is temperature-dependent and a prototype implementation of the algorithm on an ARM processor development board.Master of Science
4
Suozzo, Christopher. "Lead-Acid Battery Aging and State of Health Diagnosis." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1212002134.
Повний текст джерела
5
Samolyk, Mateusz, and Jakub Sobczak. "Development of an algorithm for estimating Lead-Acid Battery State of Charge and State of Health." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2937.
Повний текст джерелаАнотація:
In this paper, a state of charge (SOC) and a state of health (SOH) estimation method for lead-acid batteries are presented. In the algorithm the measurements of battery’s terminal voltage, current and temperature are used in the process of SOC calculation. The thesis was written in cooperation with Micropower AB. The algorithm was designed to fulfill the specific requirements of the electric vehicles application: an error below 5% of SOC, computational simplicity and the possibility of being implemented in a basic programming languages. The current used method at Micropower, Coulomb counting, is compared with a method presented by Chiasson and Vairamohan 2005 based on modified Thevein circuit during charging and discharging of the battery. The Thevenin based method gave better result compared to Coulomb counting but seems not to fulfill Micropowers requirements. A correction method based on periods of no charging or discharging, possible to be used together with Coulomb counting as well as with the Thevenin method was developed. The evaluation method indicates that when using also the correction method the Micropowers requirements are fulfilled.I detta papper, är ett laddningstillstånd (SOC) och hälsotillstånd (SOH) skattningsmetod för blybatterier presenteras. I algoritmen mätningarna av batteriets polspänning, ström och temperatur används i processen för SOC beräkning. Avhandlingen är skriven i samarbete med Micropower AB. Algoritmen har utformats för att uppfylla de särskilda kraven för elektriska fordon: ett fel under 5% av SOC, computational enkelhet och möjligheten att genomföras i ett grundläggande programmeringsspråk. Den nuvarande metoden vid Micropower, Coulomb räkning, jämförs med en metod som presenteras av Chiasson och Vairamohan 2005 baserad på modifierad Thevein kretsen under laddning och urladdning av batteriet.
6
Salyer, Zachary M. "Identification of Optimal Fast Charging Control based on Battery State of Health." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587037951166857.
Повний текст джерела
7
Cordoba, Arenas Andrea Carolina. "Aging Propagation Modeling and State-of-Health Assessment in Advanced Battery Systems." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1385967836.
Повний текст джерела
8
Klass, Verena. "Battery Health Estimation in Electric Vehicles." Doctoral thesis, KTH, Tillämpad elektrokemi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-173544.
Повний текст джерелаАнотація:
For the broad commercial success of electric vehicles (EVs), it is essential to deeply understand how batteries behave in this challenging application. This thesis has therefore been focused on studying automotive lithium-ion batteries in respect of their performance under EV operation. Particularly, the need for simple methods estimating the state-of-health (SOH) of batteries during EV operation has been addressed in order to ensure safe, reliable, and cost-effective EV operation. Within the scope of this thesis, a method has been developed that can estimate the SOH indicators capacity and internal resistance. The method is solely based on signals that are available on-board during ordinary EV operation such as the measured current, voltage, temperature, and the battery management system’s state-of-charge estimate. The approach is based on data-driven battery models (support vector machines (SVM) or system identification) and virtual tests in correspondence to standard performance tests as established in laboratory testing for capacity and resistance determination. The proposed method has been demonstrated for battery data collected in field tests and has also been verified in laboratory. After a first proof-of-concept of the method idea with battery pack data from a plug-in hybrid electric vehicle (PHEV) field test, the method was improved with the help of a laboratory study where battery electric vehicle (BEV) operation of a battery cell was emulated under controlled conditions providing a thorough validation possibility. Precise partial capacity and instantaneous resistance estimations could be derived and an accurate diffusion resistance estimation was achieved by including a current history variable in the SVM-based model. The dynamic system identification battery model gave precise total resistance estimates as well. The SOH estimation method was also applied to a data set from emulated hybrid electric vehicle (HEV) operation of a battery cell on board a heavy-duty vehicle, where on-board standard test validation revealed accurate dynamic voltage estimation performance of the applied model even during high-current situations. In order to exhibit the method’s intended implementation, up-to-date SOH indicators have been estimated from driving data during a one-year time period.QC 20150914
9
Schmidt, Alexander Patrick [Verfasser]. "A Novel Electrochemical Battery Model For State Of Charge And State Of Health Estimation / Alexander Patrick Schmidt." Aachen : Shaker, 2010. http://d-nb.info/1084536315/34.
Повний текст джерела
10
Hyun, Ji Hoon. "State of Health Estimation System for Lead-Acid Car Batteries Through Cranking Voltage Monitoring." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71796.
Повний текст джерелаАнотація:
The work in this thesis is focused on the development and validation of an automotive battery monitoring system that estimates the health of a lead-acid battery during engine cranking and provides a low state of health (SOH) warning of potential battery failure. A reliable SOH estimation should assist users in preventing a sudden battery failure and planning for battery replacement in a timely manner.
Most commercial battery health estimation systems use the impedance of a battery to estimate the SOH with battery voltage and current; however, using a current sensor increases the installation cost of a system due to parts and labor. The battery SOH estimation method with the battery terminal voltage during engine cranking was previously proposed. The proposed SOH estimation system intends to improve existing methods. The proposed method requires battery voltages and temperature for a reliable SOH estimation. Without the need for a costly current sensor, the proposed SOH monitoring system is cost-effective and useful for automotive applications.
Measurement results presented in this thesis show that the proposed SOH monitoring system is more effective in evaluating the health of a lead-acid battery than existing methods. A low power microcontroller equipped prototype implements the proposed SOH algorithm on a high performance ARM Cortex-M4F based MCU, TM4C123GH6PM. The power dissipation of the final prototype is approximately 144 mW during an active state and 36 mW during a sleep state. With the reliability of the proposed method and low power dissipation of the prototype, the proposed system is suitable for an on-board battery monitoring as there is no on-board warning that estimates the health of a battery in modern cars.Master of Science
11
Juhlin, Olof. "Modeling of Battery Degradation in Electrified Vehicles." Thesis, Linköpings universitet, Fordonssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-134114.
Повний текст джерелаАнотація:
This thesis provides an insight into battery modeling in electric vehicles which includes degradation mechanisms as in automotive operation in electric vehicles. As electric vehicles with lithium ion batteries increase in popularity there is an increased need to study and model the capacity losses in such batteries. If there is a good understanding of the phenomena involved and an ability to predict these losses there is also a foundation to take measures to minimize these losses. In this thesis a battery model for lithium ion batteries which includes heat dissipation is used as groundwork. This model is expanded with the addition of capacity losses due to usage as well as storage. By combining this with a simple vehicle model one can use these models to achieve an understanding as to how a battery or pack of several batteries would behave in a specific driving scenario. Much of the focus in the thesis is put into comparing the different factors of degradation to highlight what the major contributors are. The conclusion is drawn that heat is the main cause for degradation for batteries in electric vehicles. This applies for driving usage as well as during storage. As heat is generated when a battery is used, the level of current is also a factor, as well as in which state of charge region the battery is used.
12
Quintero, Cedeño Vanessa Lisbeth. "Design of a medium-access-control protocol for wireless sensor networks considering the battery state of charge and state of health." Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/170130.
Повний текст джерелаАнотація:
Tesis para optar al grado de Doctora en Ingeniería EléctricaLa disponibilidad de energía es una de las limitaciones que presentan las Redes de Sensores Inalámbricas (WSN, Wireless Sensor Network). Tradicionalmente, las baterías han sido utilizadas para proveer energía a los nodos de sensores y al tener una vida útil limitada afectan el tiempo de vida de la red. Soluciones como el uso de baterías de gran tamaño o el reemplazo de ellas no son viables, debido al gran número de sensores que componen la red y a que pueden ser desplegados en zonas de difícil acceso. Esta situación ha motivado que las soluciones para la conservación de la energía en las WSNs se enfoquen en el desarrollo de técnicas que actúen a nivel de las capas física y de enlace de datos, como es el caso de los protocolos de Control de Acceso al Medio (MAC, Medium Access Control). Los protocolos MAC son una de las soluciones ampliamente estudiadas y utilizadas porque permiten un equilibrio entre la conservación de energía y otros parámetros críticos de la red, como el rendimiento, latencia, reducción de colisiones y mensajes de control. También tienen la facilidad de adaptarse a las nuevas aristas de trabajo que surgen al incorporar nuevas tecnologías como lo son los Dispositivos de Recolección de Energía (EHD, Energy Harvesting Device). Otro aspecto que está siendo considerado y estudiado en el diseño de los protocolos MAC es la información que se puede extraer de la batería, ya que al estimar la capacidad disponible de la misma, el mecanismo del Duty Cycling (DuC) puede ser ajustado con el propósito de aumentar la eficiencia energética y por lo tanto, extender la vida útil de la red. Es necesario desarrollar técnicas que incorporen un mecanismo de conservación de energía que integre información de la batería a través de indicadores como el Estado de Carga (SOC, State of Charge) y Estado de Salud (SOH, State of Health) para mejorar la eficiencia energética en WSN. La idea de incorporar información de la batería se debe a que la capa MAC está a cargo de controlar los modos de operación del nodo sensor, lo que está directamente relacionado con la cantidad de corriente exigida a la batería. Conocidos los perfiles de uso de la batería es posible estimar los indicadores SOC y SOH que se han utilizado ampliamente en diversas aplicaciones para conocer la cantidad de energía disponible en la batería y la degradación que ha sufrido la misma. En este trabajo se propuso el desarrollo de un protocolo que actúa en la subcapa MAC y que considera la información de la batería para tomar decisiones con respecto al tiempo activo y de reposo del nodo de sensor, con la finalidad de promover el uso eficiente de la energía y extender la vida útil de la red. Los resultados obtenidos validan esta nueva propuesta de algoritmo y establecer pautas para guiar el diseño de protocolos MAC que se centren en minimizar el consumo de energía teniendo en cuenta los dispositivos de recolección de energía y la información de la batería.
13
Zhang, Klaus. "Comparison of Nonlinear Filtering Methods for Battery State of Charge Estimation." ScholarWorks@UNO, 2014. http://scholarworks.uno.edu/td/1896.
Повний текст джерелаАнотація:
In battery management systems, the main figure of merit is the battery's SOC, typically obtained from voltage and current measurements. Present estimation methods use simplified battery models that do not fully capture the electrical characteristics of the battery, which are useful for system design. This thesis studied SOC estimation for a lithium-ion battery using a nonlinear, electrical-circuit battery model that better describes the electrical characteristics of the battery. The extended Kalman filter, unscented Kalman filter, third-order and fifth-order cubature Kalman filter, and the statistically linearized filter were tested on their ability to estimate the SOC through numerical simulation. Their performances were compared based on their root-mean-square error over one hundred Monte Carlo runs as well as the time they took to complete those runs. The results show that the extended Kalman filter is a good choice for estimating the SOC of a lithium-ion battery.
14
El, outmani Sohaïb. "On-line estimation of thermodynamic properties for Lithium-Ion battery state of health and composition analysis." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT111.
Повний текст джерелаАнотація:
Les batteries rechargeables et notamment les batteries Li-Ion sont déjà présentes dans une multitude d'applications parmi lesquelles on peut citer le stockage d'énergie éolienne ou solaire, les téléphones portables, les avions et les automobiles. Dans ce dernier secteur, la part de marché des véhicules hybrides ou électriques devrait bondir de 1% à 30% dans les prochaines années, les batteries vont donc connaître un essor sans précédent.Dans toutes ces applications, le diagnostic des batteries représente un enjeu majeur en termes de performance et de sécurité. L'actualité est riche d'exemples de problèmes de surchauffe, voire d'incendie ou d'explosion de batteries.Il est donc primordial de développer des outils qui permettent de mieux diagnostiquer l’état des batteries, et notamment l’état de santé des batteries.On sait qu'il existe un lien entre l'état interne d'une batterie et les grandeurs thermodynamiques qui lui sont associées, en particulier la variation d'entropie. En effet, il a été observé que les profils thermodynamiques présentent une dépendance avec certaines caractéristiques de la batterie. Ces profils dépendent du vieillissement par charge-décharge, du vieillissement par surcharge et du vieillissement thermique. Ils dépendent aussi de la composition chimique de la batterie. Une connaissance des données thermodynamiques est donc une riche source d’informations sur l’état et le passé d’une batterie. A partir de ce constat, trois contributions majeures ont été apportées dans cette étude.La première contribution est la détermination des éléments chimiques d’une cellule lithium-ion. La connaissance des éléments composants les électrodes d’une cellule peut jouer un rôle critique dans l’évaluation de la qualité à différentes étapes du processus de fabrication. De plus, pour une deuxième vie des cellules de batterie, il est également important de connaître les caractéristiques de la batterie, telles que sa composition, pour pouvoir les assembler en pack. Nous proposons une nouvelle approche pour déterminer la composition chimique d’une cellule. Cette méthodologie consiste à mesurer des profils thermodynamiques de différentes cellules. Ensuite par apprentissage, des algorithmes de Machine Learning reconnaissent la composition chimique des batteries à partir de ces profils. Le travail effectué dans le cadre de cette étude est surtout une première approche qui nécessite des approfondissements.La deuxième contribution est l’estimation de l’état de santé (SOH). L'état de santé de la batterie est un paramètre clé, car il contrôle l'énergie et les performances de la batterie, ainsi que son cycle et sa durée de vie. Une évaluation précise du SOH est très importante pour la performance et la prévision de la durée de vie. Dans ce travail, nous avons développé une nouvelle méthode pour évaluer le SOH de la batterie par analyse de profil de variation d'entropie ∆S. Une relation entre ∆S et SOH est ensuite établie par des algorithmes de Machine Learning.La dernière contribution est l’estimation des grandeurs thermodynamiques en temps réel. Il est apparu nécessaire de développer une méthodologie d’estimation in-situ et en temps réel des grandeurs thermodynamiques d’une cellule lithium-ion. Cela permettra d’augmenter l'intérêt de la mesure thermodynamique dans le domaine de la batterie. En effet la méthodologie existante de référence est longue, il faut plusieurs jours/semaines pour obtenir les profils thermodynamiques. Nous proposons une approche d’identification de système pour estimer la variation d’entropie. Cela nous permet d’accéder aux valeurs thermodynamiques rapidement et de les mettre à jour pendant l’utilisation de la celluleRechargeable batteries and especially Li-Ion batteries are already present in a multitude of applications among which we can mention the storage of wind or solar energy, mobile phones, airplanes and automotive. In the latter sector, since the market share of hybrid or electric vehicles is expected to increase from 1% to 30% in the coming years, batteries will experience unprecedented growth.In all these applications, battery diagnosis represents a major challenge in terms of performance and safety. The news is full of battery issue examples: overheating, fire or even explosion.It is therefore essential to develop tools that provide a better diagnose of the state of the batteries, and in particular the state of health.There is a relationship between the internal state of a battery and the thermodynamic quantities associated with it, in particular the entropy variation. Indeed, it has been observed that the thermodynamic profiles are dependent on some battery characteristics. These profiles depend on cycle, overcharge and thermal ageing. They also depend on the chemical composition of the battery. A knowledge of thermodynamic data is therefore a valuable source of information on the state and the past of a battery. From this observation, three major contributions have been done in this study.The first contribution is the determination of a lithium-ion cell chemical elements. Electrode cell components can play a critical role in assessing quality at different stages of the manufacturing process. In addition, for a second life of battery cells, it is important to know the battery characteristics, such as its composition, to be able to assemble them in battery packs. We propose a new approach to determine the chemical composition of a cell. This methodology consists of measuring thermodynamic profiles of different cells. Then by training, machine learning algorithms recognize the chemical composition of the batteries from these profiles. The work done in this study is a first approach that requires confirmation.The second contribution the state of health (SOH) estimation. The battery state of health is a key parameter because it controls the energy and performance of the battery, as well as its cycle and its life. An accurate assessment of SOH is very important for life performance and prediction. In this work, we have developed a new methodology to evaluate the SOH of the battery by entropy variation (ΔS) profile analysis. A relationship between ΔS and SOH is then established by Machine Learning algorithms.The last contribution is the real-tie estimation of battery cell thermodynamic quantities. It has become necessary to develop a methodology for in-situ and real-time estimation of lithium-ion cell thermodynamic quantities. This will emphasize the interest of the thermodynamic measurement in the battery field. Indeed the existing standard methodology is long, it takes several days / weeks to obtain the thermodynamic profiles. We propose a system identification approach to estimate entropy variation. This allow us to access the thermodynamic value quickly and to update it while the cell is in use
15
Huhman, Brett Michael. "A Single-Frequency Impedance Diagnostic for State of Health Determination in Li-ion 4P1S Battery Packs." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/80573.
Повний текст джерелаАнотація:
State-of-Health (SoH), a specified measure of stability, is a critical parameter for determining the safe operating area of a battery cell and battery packs to avoid abuse and prevent failure and accidents. A series of experiments were performed to evaluate the performance of a 4P1S battery array using electrochemical impedance spectroscopy to identify key frequencies that may describe battery state of health at any state of charge. Using a large sample number of cells, the state of health frequency, fSoH, for these LiFePO4 26650 cells is found to be 158 Hz. Four experiments were performed to evaluate the lifetime in different configurations: single-cell at 1C (2.6A), single-cell at 10C (26A), four cells in parallel at 10C (ideal match), and four cells in parallel (manufacturer match). The lifetime for each experiment set degraded substantially, with the final parallel series reaching end of life at 400 cycles, a 75.32% reduction in life compared to operating solo. Analysis of the fSoH data for these cells revealed a change in imaginary impedance at the critical frequency that corresponded to changes in the capacity and current data, supporting the development of a single-frequency diagnostic tool. An electrochemical model of the battery was generated, and it indicated the anode material was aging faster than the SEI layer, the opposite of normal cell degradation. A post-mortem analysis of cells from three configurations (baseline, single-cell, and parallel-cell) supported the modeling, as physical damage to the copper current collector in the anode was visible in the parallel-connected cell.Ph. D.
16
Lièvre, Aurélien. "Développement d'un système de gestion de batterie lithium-ion à destination de véhicules "mild hybrid" : détermination des indicateurs d'état (SoC, SoH et SoF)." Thesis, Lyon 1, 2015. http://www.theses.fr/2015LYO10065/document.
Повний текст джерелаАнотація:
Les véhicules hybrides se démocratisent avec une utilisation croissante des éléments de stockage à base de lithium-ion. Dans ce contexte d'exploitation, le type d'usage est atypique et dépend fortement des stratégies de répartition des énergies au sein du véhicule. Parmi les hybridations, la catégorie "mild hybrid" conserve la motorisation thermique pour l'autonomie qu'elle apporte, et lui adjoint une machine électrique associée à un élément de stockage réversible, afin de permettre une récupération de l'énergie cinétique du véhicule. L'objet de ces travaux porte sur la mise en place d'algorithmes destinés à la détermination des états de charge (SoC), de santé (SoH) et de fonction (SoF) de chacune des cellules qui compose un pack batterie lithium-ion. Ces fonctionnalités sont implantées dans un système de gestion dénommé BMS pour Battery Management System. Dans un souci de réduction des coûts de production, nos travaux s'attachent à limiter la puissance de calcul et les moyens de mesure nécessaires à la détermination de ces états. À partir de mesures effectuées lors d'une utilisation de la batterie dans une application "mild hybrid", les méthodes développées permettent la détermination des états, ainsi que d'une partie des paramètres internes aux cellules. Cette utilisation est caractérisée par de forts courants et un maintien de l'état de charge autour de 50 %, ceci afin de maximiser la disponibilité de la batterie et d'en minimiser le vieillissement. L'utilisation d'observateurs et de méthodes en boucle ouverte, à partir d'une modélisation simplifiée de cellule, nous permet d'obtenir des résultats satisfaisants avec une puissance de calcul réduiteHybrid vehicles are developing with increasing use of energy storage elements based on lithium-ion battery. In this context, the use of battery is atypical and highly dependent on energy allocation strategies within the vehicle. Among these vehicles, the mild hybrid category retains heat engine for the autonomy that offer and adds to it an electric machine associated with a reversible storage system, to allow the kinetic energy recovery of the vehicle. The object of this work involves the development of algorithms for determining the states of charge (SoC) and health (SoH) and function (SoF) of each cell that compose a lithium-ion battery pack. These features are implemented in a Battery Management System (BMS) for industrial production. In order to reduce production costs, our work attempts to limit the computing power and the measuring sensors necessary for these states determination. From battery measurements in a "mild hybrid" use, developed methods allow the states determination, as well as some of the internal parameters of cells. This application is characterized by high currents and maintaining a SoC of around 50%, in order to maximize the availability of the battery and to minimize aging. The use of observers and estimators, using a simplified model cell, allows us to achieve satisfactory results with a reduced computing power
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Riesco, Refoyo Javier. "Development of battery models for on-board health estimation in hybrid vehicles." Thesis, KTH, Materialvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-211680.
Повний текст джерелаАнотація:
Following the positive reception of electric and hybrid transport solutions in the market, manufacturers keep developing their vehicles further, while facing previously undertaken challenges. Knowing the way lithium-ion batteries behave is still one of the key factors for hybrid electric vehicles (HEVs) development, especially for the requirements of the battery management system during their operation. Hence, this project focuses on the necessity of robust yet reasonably simple and cost-effective models of the battery for estimating the health status during the operation of the vehicles. With this aim, the procedure and models to calculate the state-of-health (SOH) indicators, internal resistance and capacity, are proposed and the results discussed. Two machine-learning based models are presented, a support vector machine (SVM) and a neural network (NN), together with one equivalent circuit model (ECM). The data used for training and validating the models comes from testing the batteries in the laboratory with standard performance tests and real driving cycles along the battery lifespan. However, data sets measured in actual heavy-duty vehicles during their operation for three years is also analysed and compared. With respect to this matter, a study of the battery materials, behaviour and operation attributes is carried out, highlighting the main aspects and issues that affect the development of the models. The inputs for the models are signals that can be measured on-board in the vehicles, as current, voltage or temperature, and other derived from them as the state-of-charge (SOC) calculated by the internal battery management unit. Time-series of the variables are used for simulation purposes. The management of signals and implementation of the models is done in the environment of Matlab-Simulink, using some of its in-built functions and other specifically developed. The models are evaluated and compared by means of the normalized root mean squared error (NRMSE) of the voltage output profile compared to that of the tested batteries, but also the error of the internal resistance calculations calculated from the voltage profile for the three models, and the internal parameters in case of the ECM. While despite the difficulties faced with the data, the models can eventually perform accurate estimations of the resistance, the results of the capacity estimations are omitted in the document due to the lack of useful information derived. Nevertheless, the calculation procedure and other considerations to take into account regarding the capacity estimation and data sets are undertaken. Finally, the conclusions about the data used, battery materials and methods evaluated are drawn, laying down recommendations as to design the performance tests following the conditions of the driving cycles, and indicating the higher general performance of the SVM respect the other two methods, while asserting the usefulness of the ECM. Moreover, the battery with NMC material composition is observed to be easier to predict by the models than LFP, also showing different evolution of its internal resistance.
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Christophersen, Jon Petter. "Battery state-of-health assessment using a near real-time impedance measurement technique under no-load and load conditions." Diss., Montana State University, 2011. http://etd.lib.montana.edu/etd/2011/christophersen/ChristophersenJ0511.pdf.
Повний текст джерелаАнотація:
The reliability of battery technologies has become a critical issue as the United States seeks to reduce its dependence on foreign oil. One of the significant limitations of in-situ battery health and reliability assessments, however, has been the inability to rapidly acquire information on power capability during aging. The Idaho National Laboratory has been collaborating with Montana Tech of the University of Montana and Qualtech Systems, Incorporated, on the development of a Smart Battery Status Monitor. This in-situ device will track changes in battery performance parameters to estimate its state-of-health and remaining useful life. A key component of this onboard monitoring system will be rapid, in-situ impedance measurements from which the available power can be estimated. A novel measurement technique, known as Harmonic Compensated Synchronous Detection, has been developed to acquire a wideband impedance spectrum based on an input sum-of-sines signal that contains frequencies separated by octave harmonics and has a duration of only one period of the lowest frequency. For this research, studies were conducted with high-power lithium-ion cells to examine the effectiveness and long-term impact of in-situ Harmonic Compensated Synchronous Detection measurements. Cells were cycled using standardized methods with periodic interruptions for reference performance tests to gauge degradation. The results demonstrated that in-situ impedance measurements were benign and could be successfully implemented under both no-load and load conditions. The acquired impedance spectra under no-load conditions were highly correlated to the independently determined pulse resistance growth and power fade. Similarly, the impedance measurements under load successfully reflected changes in cycle-life pulse resistance at elevated test temperatures. However, both the simulated and measured results were corrupted by transient effects and, for the under-load spectra, a bias voltage error. These errors mostly influenced the impedance at low frequencies, while the mid-frequency charge transfer resistance was generally retained regardless of current level. It was further demonstrated that these corrupting influences could be minimized with additional periods of the lowest frequency. Therefore, the data from these studies demonstrate that Harmonic Compensated Synchronous Detection is a viable in-situ impedance measurement technique that could be implemented as part of the overall Smart Battery Status Monitor.
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Ovejas, Benedicto Victòria Júlia. "Determination of the state of health of Li-ion batteries : the irreversible entropy production approach." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/461681.
Повний текст джерелаАнотація:
In recent years, portable applications have experienced an exponential growth and consequently, the demand of batteries has increased accordingly. It is widely known, though, that the performance of batteries decreases with time and use. This loss of performance is easured by the State-of-Health (SoH) of the cells. However, there is no consensus in defining this parameter. Experimental, theoretical or even heuristic approaches can be found in literature and commercial systems, but usually, they only work for particular conditions and they are not linked to the degradation suffered by the cells themselves.
The aim of this study is to find a parameter directly related to this degradation. For this purpose, we investigate the irreversible entropy production in Li-ion cells because irreversible entropy is related to energy dissipation and thus, to irrversibilities due to system or energy degradation.
In order to evaluate the degradation of the cells and its correspondence to irreversible entropy generation, we studied different Li-ion chemistries (NMC, LFP and LCO). Batteries were cycled at different discharge rates (close to and far from equilibrium) and evaluated at different SoHs. Therefore, capacity fade and impedance rise (the most commonly used techniques in SoH determination) were characterized and related to irreversible entropy generation. In addition, post-mortem analysis was carried out to achieve a deeper knowledge of the causes and effects of degradation.
As a result of this study, we introduced a new parameter for system degradation characterization, the Relative-Entropy-Production (REP), defined as the irreversible entropy generation ratio at actual state and the initial state. In particular, we found irreversible entropy production evaluated at low discharge rates was higher as more degraded were the NMC cells. In the case of LFP cells, irreversible entropy production decreased during initial cycles but then increased towards the EoL. This behavior coincided with a capacity increase during initial cycles. In addition, we found a relationship between irreversible entropy generation and the phase transformations taking place during the discharge processes in all the evaluated cells because the materials undergoing phase transformations expand and contract yielding to cracks and other structural.
Irreversible entropy production is found to be a promising magnitude to characterize battery aging. Even though much research has still to be carried out, the idea is to define, in the future, a threshold in irreversible entropy production that the cells can stand before considering their EoL is reached.En els darrers anys, la demanda de bateries ha augmentat considerablement gràcies a la creixent proliferació de dispositius portàtils. Tot i això, és ben sabut que el funcionament de les bateries empitjora amb el temps i l'ús. Aquesta pèrdua de rendiment es mesura amb un paràmetre anomenat State-oh-Health (SoH) encara que, avui dia, no s'ha arribat a un consens per a definir-lo. A la literatura o als mateixos sistemes comercials s'hi poden trobar aproximacions experimentals, teòriques o heurístiques, que generalment funcionen en situacions particulars i que, moltes sovint, no estan directament relacionades amb la degradació que pateixen les cel·les. L'objectiu d'aquest estudi és trobar un paràmetre que estigui directament relacionat amb la degradació patida per les cel·les. Per aquest motiu, ens hem centrat en la producció d'entropia irreversible perquè aquesta està relacionada amb la dissipació d'energia i, per tant, amb les irreversibilitats degudes a la degradació del sistema o de l'energia. Es va treballar amb vàries químiques de bateries d'ions de liti (NMC, LFP i LCO) per tal d’avaluar la degradació patida per aquestes i la correspondència amb la generació d'entropia irreversible. Aquestes cel·les van ser avaluades a taxes baixes i elevades a diferents nivells de SoH. En particular, la disminució de capacitat i l’augment d’impedància, que són les tècniques més utilitzades per a determinar el SoH, van ser determinades i posteriorment relacionades amb la generació d’entropia irreversible. A més a més, l’anàlisi post-mortem de les cel·les ens va permetre obtenir un coneixement major de les causes i els efectes de la degradació. Com a resultat d’aquest estudi, hem introduït un nou paràmetre per a la caracterització de la degradació d’un sistema. Aquest paràmetre l’hem anomenat Relative-Entropy-Production (REP) i l’hem definit com la relació entre la generació d’entropia irreversible en el moment actual i l’estat inicial. En particular, hem trobat que la producció d’entropia irreversible a taxes baixes de descàrrega és més gran com més degradades estan les cel·les de NMC. En canvi, en el cas de les cel·les de LFP, hem trobat que la generació d’entropia irreversible disminueix durant els primers cicles per després augmentar fins al final de la seva vida útil. S’ha vist que aquesta disminució coincideix amb un increment de la capacitat. A més a més, a totes les cel·les amb les que hem treballat, hem trobat una relació entre la producció d’entropia irreversible i les transformacions de fase que tenen lloc als elèctrodes durant la descàrrega. Aquesta relació ha sigut associada al fet de que els materials que pateixen una canvi de fase s’expandeixen i es contrauen el que fa que es produeixin fractures o esquerdes o altres modificacions estructurals. Totes elles produeixen degradació i, per tant, generen entropia irreversible. S’ha trobat que REP i la generació d’entropia irreversible són magnituds prometedores per a caracteritzar l’envelliment de bateries. Encara que queda molta feina per fer, la idea és, en un futur, poder definir un llindar de REP o de generació d’entropia irreversible que les cel·les siguin capaces de suportar abans no es consideri que han assolit el final de les seves vides útils.
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Savvidis, Charalampos, and Zeyang Geng. "Onboard Impedance Diagnostics Method of Li-ion Traction Batteries using Pseudo-Random Binary Sequence." Thesis, Linköpings universitet, Tekniska fakulteten, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-118970.
Повний текст джерелаАнотація:
Environmental and economic reasons have lead automotive companies towards the direction of EVs and HEVs. Stricter emission legislations along with the consumer needs for more cost-efficient and environmental friendly vehicles have increased immensely the amount of hybrid and electric vehicles available in the market. It is essential though for Li-ion batteries, the main propulsion force of EVs and HEVs, to be able to read the battery characteristics in a high accuracy manner, predict life expectancy and behaviour and act accordingly. The following thesis constitutes a concept study of a battery diagnostics method. The method is based on the notion of a pseudo-random binary signal used as the current input and from its voltage response, the impedance is used for the estimation of parameters such as the state of charge and more. The feasibility of the PRBS method at a battery cell has been examined through various tests, both in an experimental manner at the lab but also in a simulation manner. The method is compared for validation against the electrochemical impedance spectroscopy method which is being used as a reference. For both the experimental and the simulation examinations, the PRBS method has been validated and proven to work. No matter the change in the parameters of the system, the method behaves in a similar manner as in the reference EIS method. The level of detail in the research and the performed experiments is what makes the significance of the results of high importance. The method in all ways has been proven to work in the concept study and based on the findings, if implemented on an EV’s or HEV’s electric drive line and the same functionality is observed, be used as a diagnostics method of the battery of the vehicle.
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Hashemi, Seyed Reza. "An Intelligent Battery Managment System For Electric And Hybrid Electric Aircraft." University of Akron / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=akron1615732366021405.
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Singh, Baljot. "A case study about the potential of battery storage in Culture house : Investigation on the economic viability of battery energy storage system with peak shaving & time-of-use application for culture house in Skellefteå." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-52998.
Повний текст джерелаАнотація:
The energy demand is steadily increasing, and the electricity sector is undergoing a severe change in this decade. The primary drivers, such as the need to decarbonize the power industry and megatrends for more distributed and renewable systems, are resulting in revolutionary changes in our lifestyle and industry. The power grid cannot be easily or quickly be upgraded, as investment decisions, construction approvals, and payback time are the main factors to consider. Therefore, new technology, energy storage, tariff reform, and new business models are rapidly changing and challenging the conventional industry. In recent times, industrial peak shaving application has sparked an increased interest in battery energy storage system (BESS). This work investigated BESS’s potential from peak shaving and Time-of-use (TOU) applications for a Culture-house in Skellefteå. Available literature provides the knowledge of various BESS applications, tariff systems, and how battery degradation functions. The predicted electrical load demand of the culture-house for 2019 is obtained from a consultant company Incoord. The linear optimization was implemented in MATLAB using optimproblem function to perform peak shaving and time-of-use application for the Culture-hose BESS. A cost-optimal charging/discharging strategy was derived through an optimization algorithm by analyzing the culture-house electrical demand and Skellefteå Kraft billing system. The decisional variable decides when to charge/discharge the battery for minimum battery degradation and electricity purchase charges from the grid. Techno-economic viability is analyzed from BESS investment cost, peak-power tariff, battery lifespan, and batter aging perspective. Results indicate that the current BESS price and peak-power tariff of Skellefteå Kraft are not suitable for peak shaving. Electricity bill saving is too low to consider TOU application due to high battery degradation. However, combining peak shaving & TOU does generate more profit annually due to additional savings from the electricity bill. However, including TOU also leads to higher battery degradation, making it not currently a viable application. A future scenario suggests a decrease in investment cost, resulting in a shorter payback period. The case study also analyses the potential in the second-life battery, where they are purchased at 80 % State of Health (SoH) for peak shaving application. Second-life batteries are assumed to last until 70 % or 60 % before End of Life (EOL). The benefit-cost ratio indicates that second-life batteries are an attractive investment if batteries can perform until 60% end of life, it would be an excellent investment from an economic and sustainability perspective. Future work suggests integrating more BESS applications into the model to make BESS an economically viable project.
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Varia, Adhyarth C. "In-Situ Capacity and Resistance Estimation Algorithm Development for Lithium-Ion Batteries Used in Electrified Vehicles." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1408665208.
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Schlasza, Christian. "Analysis of aging mechanisms in Li-ion cells used for traction batteries of electric vehicles and development of appropriate diagnostic concepts for the quick evaluation of the battery condition." Thesis, Belfort-Montbéliard, 2016. http://www.theses.fr/2016BELF0155.
Повний текст джерелаАнотація:
Dans cette thèse, les mécanismes de vieillissement des cellules Li-ion sont analysés sur un niveau théorique,assisté par une AMDEC (Analyse des modes de défaillance, de leurs effets et de leur criticité). L'accent est mis surla famille des cellules lithium fer phosphate (LFP) utilisées comme batteries de traction dans les applicationsvéhicules électriques.L'objectif de la partie xpérimentale de cette thèse est le développement d'un concept d'un outil de diagnostic pourla détermination rapide d'état de la batterie. Une expérience de vieillissement accélérée est réalisée avec un groupede cellules LFP de haute capacité (70Ah). Les cellules sont analysées en utilisant des méthodes de mesured'impédance dans les domaines temporel et fréquentiel. La pectroscopie d'Impédance Électrochimique (SIE, ouEIS en anglais) s'est trouvée être un bon outil pour révéler des informations intéressantes sur l'état de santé (Stateof-Health, SOH) de la batterie.Des modèles de batterie sont utilisés pour l'interprétation des résultats de mesure. En comparant différents modèlesdu circuit équivalent (ECMs), un modèle est choisi. Ce modèle est utilisé pour la détermination du SOC et étendupour la détermination du SOH. Un concept pour la détermination du SOH est développé, permettant uneapproximation de la capacité de la batterie dans une période de temps de moins de 30s, si les onditions de labatterie et d'environnement, comme la température et l'état de charge de la batterie, sont connusIn this thesis, the aging mechanisms withing Li-ion cells are analyzed on a theoretical level, supported by an FMEA(Failure ode and Effects Analysis). The focus lies on the group of lithium iron phosphate (LFP) cells used fortraction batteries in electric vehicles. Scope of the experimental part of the thesis is the development of a diagnosticconcept for the quick battery state determination. A group of high capacity LFP cells (70Ah) designed for tractionpurposes in electric vehicles is aged artificially and investigated afterwards by impedance measurements in the timeand frequency domain. Electrochemical impedance spectroscopy (EIS) is found to reveal interesting information onthe battery's State-of-Health (SOH).For the interpretation of the measurement results, battery models are employed. Different equivalent circuit models(ECMs) are compared and an appropriate model is chosen, which is used for the SOC (State-of-Charge)determination and extended for the SOH (State-of-Health) determination. An SOH determination concept isdeveloped, which allows the approximation of the cell capacity in less than 30s, if the battery and environmentalconditions, such as the temperature and the cell's SOC, are known
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Andersson, Joakim. "Lifetime estimation of lithium-ion batteries for stationary energy storage system." Thesis, KTH, Skolan för kemivetenskap (CHE), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212987.
Повний текст джерелаАнотація:
With the continuing transition to renewable inherently intermittent energy sources like solar- and wind power, electrical energy storage will become progressively more important to manage energy production and demand. A key technology in this area is Li-ion batteries. To operate these batteries efficiently, there is a need for monitoring of the current battery state, including parameters such as state of charge and state of health, to ensure that adequate safety and performance is maintained. Furthermore, such monitoring is a step towards the possibility of the optimization of battery usage such as to maximize battery lifetime and/or return on investment. Unfortunately, possible online measurements during actual operation of a lithium-ion battery are typically limited to current, voltage and possibly temperature, meaning that direct measurement of battery status is not feasible. To overcome this, battery modeling and various regression methods may be used. Several of the most common regression algorithms suggested for estimation of battery state of charge and state of health are based on Kalman filtering. While these methods have shown great promise, there currently exist no thorough analysis of the impact of so-called filter tuning on the effectiveness of these algorithms in Li-ion battery monitoring applications, particularly for state of health estimation. In addition, the effects of only adjusting the cell capacity model parameter for aging effects, a relatively common approach in the literature, on overall state of health estimation accuracy is also in need of investigation. In this work, two different Kalman filtering methods intended for state of charge estimation: the extended Kalman filter and the extended adaptive Kalman filter, as well as three intended for state of health estimation: the dual extended Kalman filer, the enhanced state vector extended Kalman filer, and the single weight dual extended Kalman filer, are compared from accuracy, performance, filter tuning and practical usability standpoints. All algorithms were used with the same simple one resistor-capacitor equivalent circuit battery model. The Li-ion battery data used for battery model development and simulations of filtering algorithm performance was the “Randomized Battery Usage Data Set” obtained from the NASA Prognostics Center of Excellence. It is found that both state of charge estimators perform similarly in terms of accuracy of state of charge estimation with regards to reference values, easily outperforming the common Coulomb counting approach in terms of precision, robustness and flexibility. The adaptive filter, while computationally more demanding, required less tuning of filter parameters relative to the extended Kalman filter to achieve comparable performance and might therefore be advantageous from a robustness and usability perspective. Amongst the state of health estimators, the enhanced state vector approach was found to be most robust to initialization and was also least taxing computationally. The single weight filter could be made to achieve comparable results with careful, if time consuming, filter tuning. The full dual extended Kalman filter has the advantage of estimating not only the cell capacity but also the internal resistance parameters. This comes at the price of slow performance and time consuming filter tuning, involving 17 parameters. It is however shown that long-term state of health estimation is superior using this approach, likely due to the online adjustment of internal resistance parameters. This allows the dual extended Kalman filter to accurately estimate the SoH over a full test representing more than a full conventional battery lifetime. The viability of only adjusting the capacity in online monitoring approaches therefore appears questionable. Overall the importance of filter tuning is found to be substantial, especially for cases of very uncertain starting battery states and characteristics.
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Maillard, Florian. "Méthodologie de diagnostic des batteries Li-ion par la mesure des bruits électrochimiques." Thesis, Poitiers, 2015. http://www.theses.fr/2015POIT2302.
Повний текст джерелаАнотація:
Ce travail concerne les fluctuations électrochimiques de tension des batteries Li-ion, communément appelées bruit électrochimique.L'idée est d'utiliser la mesure de bruit électrochimique en fonctionnement pour générer, via du traitement de signal, des descripteurs statistiques permettant de caractériser le SOH (état de santé). L'objectif consiste à développer une méthode innovante de diagnostic non intrusif permettant de compléter les méthodes traditionnelles (impédancemétrie,...).DCNS St-Tropez a participé et compte développer cette approche dans le cadre d'une application d'alimentation d'armes sous-marines, qui nécessite un très haut niveau de sécurité et de fiabilité. La mesure de bruit des batteries Li-ion est difficile à cause des très bas niveaux du signal et nécessite des appareils performants. Nous avons installé une chaîne de mesure permettant d'acquérir les fluctuations de tension en décharge. Puis nous avons extrait le bruit grâce à une méthode numérique robuste. La tension de décharge est non-stationnaire, ce qui nécessite un traitement spécifique. L'analyse à court-terme par les moments d'ordre 2, 3 et 4 montre qu'il y a trois zones dans lesquelles les bruits sont complètement différents. Le milieu de la décharge présente une répartition uniforme caractérisé par une forme en V (minimum à SOC = 55%), des structures cohérentes tempo-fréquentielles sur les bords révélées par l'analyse en ondelettes. Notre modèle permet de trouver les sources de bruit prépondérantes et d'identifier les paramètres responsables du bruit électrochimique. Les applications futures concernent la caractérisation du vieillissement et la qualité de fabrication des batteriesThis work concerns the electrochemical voltage fluctuations Li-ion batteries, commonly known as electrochemical noise. The idea is to use the electrochemical measurement noise in operation to generate, via signal processing, statistical descriptors to characterize the SOH (health). The objective is to develop an innovative method noninvasive diagnostic to complement traditional methods (impedance,...). DCNS St Tropez has participated and intends to develop this approach in the context of an arms supply subsea application, which requires a very high level of security and reliability. The measurement of Li-ion batteries is difficult because of very low signal levels and requires efficient appliances. We installed a measurement system for acquiring voltage fluctuations landfill. Then we extracted noise due to robust numerical method. The discharge voltage is non-stationary, which requires a specific treatment. The short-term analysis by moments of order 2, 3 and 4 shows that there are three areas in which the noises are completely different. The middle of the discharge has a uniform distribution characterized by a V-shape (minimum to SOC = 55 %), tempo-frequency coherent structures on the edges revealed by wavelet analysis. Our model allows to find the predominant noise sources and identify the parameters responsible for the electrochemical noise. Future applications include the characterization of aging and quality of manufacture of batteries
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Vichard, Loic. "Contribution à l’étude du vieillissement des composants batterie et pile à combustible en usage réel." Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCA018.
Повний текст джерелаАнотація:
Les travaux de thèse sont associés au projet AsDeCoEUR (Analyses De Composants Energétiques en Usage Réel). Ce projet s’inscrit dans le contexte actuel de la mise en œuvre des nouvelles technologies de l’énergie pour la mobilité et porte plus spécifiquement sur l’étude des composants batterie et pile à combustible situés au coeur des chaines de traction électriques. Le projet souhaite s’appuyer sur les travaux déjà réalisés par les acteurs UTBM et IFSTTAR de la fédération de recherche FCLAB dans le projet Mobypost qui a permis l’expérimentation en usage réel sur deux sites de La Poste en région Franche-Comté d’une flotte de 10 véhicules électriques à pile à combustible et batterie.Pendant l’expérimentation Mobypost, tous les véhicules du projet enregistrent les nombreuses données physiques de leur chaine de traction. L’ensemble de ces informations constitue aujourd’hui une base très riche à exploiter. Dans le projet AsDeCoEUR, nous proposons une démarche scientifique menée autour d’un travail de thèse visant à comprendre le comportement dynamique, d'étudier le vieillissement et d'estimer l'état de santé des composants énergétiques batterie et pile à combustible en usage réel. Cette démarche est basée sur l’analyse des données enregistrées sur ces composants dans le projet Mobypost et est renforcée par des expérimentations spécifiques et maitrisées en laboratoire. La mise en oeuvre des compétences et des outils d’analyse numériques développés notamment pour les batteries par le laboratoire Ampère et l’IFSTTAR vise à comprendre et reproduire les phénomènes de dégradation des composants étudiés. Les travaux permettent au final, en associant les compétences des enseignants-chercheurs et chercheurs de l’UFC, de l’UTBM, de l’IFSTTAR et du laboratoire Ampère sur ce sujet, de contribuer à l’étude du vieillissement et d'estimer l'état de santé en usage réel des batteries et des piles à combustible ce qui constituent une avancée remarquable dans ce domaine, notamment en vue de l’industrialisation de véhicules équipés de ce type de composantsThese thesis works are associated to the AsDeCoEUR project. This project is part of the current context of the development of new energy technologies for mobility and focuses more specifically on the study of battery and fuel cell components located in the heart of electric power train. The project is based on the work already carried out by the UTBM and IFSTTAR actors of the FCLAB research federation among the Mobypost project. Mobypost european project has allowed the experimentation of a fleet of 10 fuel cell electric vehicles under actual operating conditions on two postal platforms in the Franche-Comté region.During the Mobypost experiment, a deep monorting was performed on all the vehicles so numerous physical data of their power train were recorded. All of these information now constitutes a very rich database to exploit. Among the AsDeCoEUR project, we propose a scientific approach carried out around a thesis work wich aims at understanding dynamic behavior, studying aging and estimating the state of health of batteries and fuel cells in real use. This approach is based on the analysis of the data recorded on these components among the Mobypost project and is reinforced by specific experiments set up in the laboratory. The implementation of skills and digital analysis tools developed especially for batteries by the Ampère laboratory and IFSTTAR aims at understanding and reproducing the degradation phenomena. The works should finally allow, by combining the skills of UFC, UTBM, IFSTTAR and Ampère laboratory researchers, to contribute to the study of batteries and fuel cells aging and to estimate their state of health under actual operating conditions. This constitutes a remarkable advance in this field, particularly with a view to the industrialization of vehicles equipped with this type of component
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Eddahech, Akram. "Modélisation du vieillissement et détermination de l’état de santé de batteries lithium-ion pour application véhicule électrique et hybride." Thesis, Bordeaux 1, 2013. http://www.theses.fr/2013BOR14992/document.
Повний текст джерелаАнотація:
Cette thèse se concentre sur la fiabilité des batteries lithium pour des applications véhicules à faible émission en CO2. Pour cela, des méthodologies de caractérisation électriques et thermiques, des protocoles et des tests de vieillissement de batteries lithium sous différents modes (cyclage actif, calendaire simple et cyclage/calendaire) ont été mis en œuvre.Une première partie de ces travaux de thèse s’attache à la modélisation et à l’estimation des états de charge et de santé de la batterie.La deuxième partie est consacrée à l’étude du vieillissement calendaire des batteries lithium utilisant la spectroscopie d’impédance comme méthode de caractérisation. Ensuite, une méthode originale pour l’évaluation de l’état santé de la batterie est proposée. Elle est basée sur l’exploitation de l’étape de charge à tension constante lors d’une recharge complète et est donc bien adaptée à une intégration au sein d’un système de gestion de batterie. L’approche introduite est validée sur des données réelles de vieillissement allant jusqu’à deux ans de test.Enfin, une étude du phénomène de régénération de la capacité suite à un vieillissement des batteries de type combiné cyclage/calendaire est menée. Cette dernière partie constitue une ouverture pour le développement de stratégies d’usage des batteries lithium en incluant leur comportement thermiqueIn this thesis, we focus on the reliability of lithium batteries used for automotive applications. For this purpose, electric and thermal characterization methodologies as well as aging tests under several modes (calendar, power cycling, calendar/power cycling) are carried out.In a first part of the work, battery modeling and battery state estimation (state-of-charge and state-of-health) are considered.Then, based on periodic characterization from electrochemical impedance spectroscopy, calendar aging is investigated. Next, we proposed an original process for precise battery state-of-health determination that exploits a full recharge and mainly constant-voltage charge step which allows easily its integration within a battery management system. Our experimental results, up to two years real-life data, confirm effectiveness of our technique.Finally, we study the capacity recovery phenomenon occurring due to combined battery aging (calendar/power cycling). This final part is almost dedicated to introduce strategies for battery use presenting at the same time a thermal behavior study
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Zenati, Ali. "Gestion haut niveau et suivi en ligne de l'état de santé des batteries lithium-ion." Thesis, Université de Lorraine, 2012. http://www.theses.fr/2012LORR0391/document.
Повний текст джерелаАнотація:
Les batteries lithium-ion sont considérées de nos jours comme la solution optimale pour les systèmes de stockage d'énergie, et cela est dû principalement à leurs hautes densités d'énergie et de puissance. Leurs performances, durées de vie, et fiabilités sont liées et dépendent des conditions d'utilisations telles que la température et les courants demandés par l'application. Et afin d'avoir un suivi de l'évolution du vieillissement de la batterie, la détermination de son état de santé (State-Of-Health -SOH-) est une fonction majeure à considérer. Ce mémoire expose les méthodologies ou techniques développées pour la gestion de la durée de vie de la batterie lithium-ion, et plus particulièrement pour la détermination de son état de santé, en se basant sur ses propres paramètres principaux qui sont la capacité et la résistance ohmique. Cette démarche permet de basculer d'un SOH statique (basé sur un modèle prédéfini de vieillissement tenant compte du calendaire et du cyclage en fonction des caractéristiques telles que la température et le courant de la batterie suivies en temps réel) vers un SOH dynamique ou auto-adaptatif, puisqu'il est basé sur un modèle du composant électrochimique dont les paramètres précédents sont évaluées en temps réel en fonction des conditions d?utilisation. Le premier chapitre revient sur les généralités concernant la technologie lithium-ion : caractéristiques, performances, constitution de l'élément de stockage, choix et nature des électrodes... Le principe de fonctionnement avec les équations générales des phénomènes électrochimiques sont aussi développés. Le second chapitre est un état de l'art des méthodologies de prédiction de la durée de vie avec les différentes classifications des modèles et des techniques de prédiction. Puis lors du troisième chapitre, nous aborderons nos méthodologies développées et les techniques utilisées, telles que le calcul statistique, la logique floue et les lois de vieillissement pour la détermination d'un état de santé dynamique de la batterie, qui en plus de la prise en compte de l'état de santé statique, c'est-à-dire basé seulement sur le vieillissement calendaire et en cyclage, considérera aussi l'évolution de la capacité et de la résistance ohmique de la batterie, en fonction du temps et des conditions d'utilisation, permettant ainsi de considérer les phénomènes improbables. Enfin dans le dernier chapitre, nous exposerons les résultats obtenus lors des tests de validations sur banc de puissance et de prototypage rapide sur des éléments réelsLithium-ion batteries are considered nowadays as the optimal issue for the energy storage systems, it is mainly due to their high energy and power density. Their performances, lifetime, and reliability are related and depend on the operating conditions such as the temperature and requested current by the application. And in order to track the evolution of the ageing of the battery, the determination of its State-Of-Health -SOH- is a major function to consider. This thesis presents both methodologies and techniques developed for the management of the lifetime of lithium-ion battery, and more particularly the assessment of its state-of-health, based on its own main parameters which are the capacity and the ohmic resistance. This approach allows to switch from a static SOH (based on a predefined ageing model, which take into account the calendar and cycling ageing of the battery, according to some characteristics such as the temperature and the courant of the battery tracked in real time) to a dynamic SOH (self-adaptive) using an online assessment of the previous parameters according to the operating conditions. The first chapter is an overview about the lithium-ion technology: characteristics, performances, cell design, choice and nature of the electrodes... The operating principle with the general equations are also developed. The second chapter is a state of the art of the lifetime prediction methodologies with the different kinds of classification of models and prediction techniques. Then in the third chapter, we will discuss our methodologies and the developed techniques, such as the use of statistics, fuzzy logic and rules of ageing to assess a dynamic state of health of the battery, which not only does take into account the static SOH (calendar and cycling ageing), but also considers the evolution of the ohmic resistance and the capacity of the battery, depending on the time and the operating conditions. This allows taking into consideration unlikely phenomena. Finally, in the last chapter, we will expose obtained results from validation tests. These tests were done under a power electrical testbench and a rapid prototyping testbench with real cells
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Farfan-Ramos, Luis. "Real-time Fault Diagnosis of Automotive Electrical Power Generation and Storage System." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1303129393.
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Riviere, Elie. "Détermination in-situ de l'état de santé de batteries lithium-ion pour un véhicule électrique." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAI048/document.
Повний текст джерелаАнотація:
Les estimations précises des états de charge (« State of Charge » - SoC) et de santé (« State of Health » - SoH) des batteries au lithium sont un point crucial lors d’une utilisation industrielle de celles-ci. Ces estimations permettent d’améliorer la fiabilité et la robustesse des équipements embarquant ces batteries. Cette thèse CIFRE est consacrée à la recherche d’algorithmes de détermination de l’état de santé de batteries lithium-ion, en particulier de chimie Lithium Fer Phosphate (LFP) et Lithium Manganèse Oxyde (LMO).Les recherches ont été orientées vers des solutions de détermination du SoH directement embarquables dans les calculateurs des véhicules électriques. Des contraintes fortes de coût et de robustesse constituent ainsi le fil directeur des travaux.Or, si la littérature actuelle propose différentes solutions de détermination du SoH, celles embarquées ou embarquables sont encore peu étudiées. Cette thèse présente donc une importante revue bibliographique des différentes méthodes d’estimation du SoH existantes, qu’elles soient embarquables ou non. Le fonctionnement détaillé ainsi que les mécanismes de vieillissement d’une batterie lithium-ion sont également explicités.Une partie majoritaire des travaux est consacrée à l’utilisation de l’analyse incrémentale de la capacité (« Incremental Capacity Analysis » - ICA) en conditions réelles, c’est-à-dire avec les niveaux de courant présents lors d’un profil de mission classique d’un véhicule électrique, avec les mesures disponibles sur un BMS (« Battery Management System ») industriel et avec les contraintes de robustesses associées, notamment une gamme étendue de température de fonctionnement. L’utilisation de l’ICA pour déterminer la capacité résiduelle de la batterie est mise en œuvre de façon totalement innovante et permet d’obtenir une grande robustesse aux variations des conditions d’utilisation de la batterie.Une seconde méthode est, elle, dédiée à la chimie LMO et exploite le fait que le potentiel aux bornes de la batterie soit représentatif de son état de charge. Un compteur coulométrique partiel est ainsi proposé, intégrant une gestion dynamique des bornes d’intégration en fonction de l’état de la batterie.A l’issue des travaux, une méthode complète et précise de détermination du SoH est disponible pour chacune des chimies LFP et LMO. La détermination de la capacité résiduelle de ces deux familles de batteries est ainsi possible à 4 % prèsAccurate lithium-ion battery State of Charge (SoC) and State of Health (SoH) estimations are nowadays a crucial point, especially when considering an industrial use. These estimations enable to improve robustness and reliability of hardware using such batteries. This thesis focuses on researching lithium-ion batteries state of health estimators, in particular considering Lithium Iron Phosphate (LFP) and Lithium Manganese Oxide (LMO) chemistries.Researches have been targeted towards SoH estimators straight embeddable into electric vehicles (EV) computers. Cost and reliability constraints are thus the main guideline for this work.Although existing literature offers various SoH estimators, those who are embedded or embeddable are still little studied. A complete literature review about SoH estimators, embedded or not, is therefore proposed. Lithium-ion batteries detailed operation and ageing mechanisms are also presented.The main part of this work is dedicated to Incremental Capacity Analysis (ICA) use with electric vehicle constraints, such as current levels available with a typical EV mission profile or existing measurements on the Battery Management System (BMS). Incremental Capacity Analysis is implemented in an innovative way and leads to a remaining capacity estimator with a high robustness to conditions of use variations, including an extended temperature range.A second method, dedicated to LMO chemistry, take advantage of the fact that the battery potential is representative of its state of charge. Partial Coulomb counting is thus performed, with a dynamic management of integration limits, depending on the battery state.Outcomes of this work are two complete and accurate SoH estimators, one for each chemistry, leading to a remaining capacity estimation accurate within 4 %
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Urbain, Matthieu. "Modélisation électrique et énergétique des accumulateurs Li-Ion. Estimation en ligne de la SOC et de la SOH." Thesis, Vandoeuvre-les-Nancy, INPL, 2009. http://www.theses.fr/2009INPL028N/document.
Повний текст джерелаАнотація:
Ce mémoire traite de la modélisation électrique des accumulateurs lithium-ion, de l’estimation de leur état de charge (SOC) et de leur état de santé (SOH). Le premier chapitre revient sur les généralités concernant la technologie lithium-ion : caractéristiques, performances, constitution de l’élément de stockage, choix et nature des électrodes, conséquences qui en découlent d’un point de vue énergétique. Le principe de fonctionnement et les équations générales des phénomènes électrochimiques sont aussi développés. Des exemples d’application dans différents secteurs industriels sont ensuite proposés pour plusieurs gammes de puissance et d’énergie. Le second volet aborde la modélisation électrique des accumulateurs lithium-ion. Pour une meilleure compréhension des phénomènes complexes mis en jeu au sein des batteries, des éléments de modélisation physique sont exposés. Puis nous envisageons une synthèse des différents modèles de nature électrique rencontrés dans la littérature. Sur la base de campagnes de mesures menées sur un élément lithium-ion de 6,8 Ah, nous proposons, dans un troisième chapitre, notre propre modèle électrique équivalent valable pour les phases de décharge et de relaxation. En particulier nous déclinons plusieurs solutions pour distribuer l’énergie et rendre compte des différents effets de ligne. Les outils de caractérisation et les procédures d’extractions des paramètres sont traités en détail. Dans un dernier chapitre nous étudions les possibilités d’estimer en ligne l’état de charge (SOC) et l’état de santé (SOH) d’un élément lithium-ion en cours d’exploitation. Après un bref rappel des méthodes académiques et industrielles actuelles, nous nous orientons vers l’emploi d’un filtre de Kalman. Afin d’estimer ses performances par rapport au coulombmètre, nous proposons un modèle et un algorithme que nous évaluons par simulation et testons sur élément réelThis dissertation of thesis deals with the electrical modelling of lithium-ion accumulators and the determination of both state-of-charge (SOC) and state-of-health (SOH). The first chapter is focused on generalities about lithium-ion technology: characteristics, qualities, constitution of the storage device, choice and nature of the electrodes and their consequences on energetical features. The principle and the general equations of the electrochemical phenomena are developed as well. Application examples from different industrial areas are displayed for several power and energy ranges. The second section is about the electrical modelling of lithium-ion accumulators. With a view to better understand the complex electrochemical phenomena, elements of physical modelling are proposed. Then, the synthesis of different electrical models released in the press is considered. On the basis of experimental campaigns lead on a 6.8 Ah lithium-element, we proposed, in a third chapter, our own equivalent electrical model suitable for both discharge phases and relaxation period. In particular, we depict several alternatives to distribute the energy and describe the different line effects. Both characterization tools and parameters extraction procedure are clearly detailed. In the last section, we tackle both SOC and SOH on-line determination. After a short review of academicals and industrial solutions, we rapidly head towards the use of a Kalman filter. In order to compare its features versus the coulombmeter, we propose a model and an algorithm, numerical simulations and experimental tests are performed
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Nazer, Rouba Al. "Système de mesure d'impédance électrique embarqué, application aux batteries Li-ion." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT007/document.
Повний текст джерелаАнотація:
La mesure d'impédance électrique en embarqué sur véhicule est un sujet clé pour améliorer les fonctions de diagnostic d'un pack batterie. On cherche en particulier à fournir ainsi des mesures supplémentaires à celles du courant pack et des tensions cellules, afin d'enrichir les indicateurs de vieillissement dans un premier temps, et d'état de santé et de charge dans un second temps. Une méthode classique de laboratoire pour obtenir des mesures d'impédance d'une batterie est la spectroscopie d'impédance électrochimique (ou EIS). Elle consiste à envoyer un signal sinusoïdal en courant (ou tension) de fréquence variable balayant une gamme de fréquences d'intérêt et mesurer ensuite la réponse en tension (ou courant) pour chaque fréquence. Une technique d'identification active basée sur l'utilisation des signaux large bande à motifs carrés est proposée. En particulier, des simulations ont permis de comparer les performances d'identification de différents signaux d'excitation fréquemment utilisés dans le domaine de l'identification et de vérifier les conditions correspondant à un comportement linéaire et invariant dans le temps de l'élément électrochimique. L'évaluation de la qualité d'estimation est effectuée en utilisant une grandeur spécifique : la cohérence. Cette grandeur statistique permet de déterminer un intervalle de confiance sur le module et la phase de l'impédance estimée. Elle permet de sélectionner la gamme de fréquence où la batterie respecte les hypothèses imposées par la méthode d'identification large bande. Afin de valider les résultats, une électronique de test a été conçue. Les résultats expérimentaux permettent de mettre en valeur l'intérêt de cette approche par motifs carrés. Un circuit de référence est utilisé afin d'évaluer les performances en métrologie des méthodes. L'étude expérimentale est ensuite poursuivie sur une batterie Li-ion soumise à un courant de polarisation et à différents états de charge. Des essais comparatifs avec l'EIS sont réalisés. Le cahier de charge établi à l'aide d'un simulateur de batterie Li-ion a permis d'évaluer les performances de la technique large bande proposée et de structurer son utilité pour l'estimation des états de vieillissement et de chargeEmbedded electrical impedance measurement is a key issue to enhance battery monitoring and diagnostic in a vehicle. It provides additional measures to those of the pack's current and cell's voltage to enrich the aging's indicators in a first time, and the battery states in a second time. A classical method for battery impedance measurements is the electrochemical impedance spectroscopy (EIS). At each frequency, a sinusoidal signal current (or voltage) of a variable frequency sweeping a range of frequencies of interest is at the input of the battery and the output is the measured voltage response (or current). An active identification technique based on the use of wideband signals composed of square patterns is proposed. Particularly, simulations were used to compare the performance of different excitation signals commonly used for system identification in several domains and to verify the linear and time invariant behavior for the electrochemical element. The evaluation of the estimation performance is performed using a specific quantity: the spectral coherence. This statistical value is used to give a confidence interval for the module and the phase of the estimated impedance. It allows the selection of the frequency range where the battery respects the assumptions imposed by the non-parametric identification method. To experimentally validate the previous results, an electronic test bench was designed. Experimental results are used to evaluate the wideband frequency impedance identification. A reference circuit is first used to evaluate the performance of the used methodology. Experimentations are then done on a Li–ion battery. Comparative tests with EIS are realized. The specifications are established using a simulator of Li-ion battery. They are used to evaluate the performance of the proposed wide band identification method and fix its usefulness for the battery states estimation: the state of charge and the state of health
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Mallangi, Siva Sai Reddy. "Low-Power Policies Based on DVFS for the MUSEIC v2 System-on-Chip." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229443.
Повний текст джерелаАнотація:
Multi functional health monitoring wearable devices are quite prominent these days. Usually these devices are battery-operated and consequently are limited by their battery life (from few hours to a few weeks depending on the application). Of late, it was realized that these devices, which are currently being operated at fixed voltage and frequency, are capable of operating at multiple voltages and frequencies. By switching these voltages and frequencies to lower values based upon power requirements, these devices can achieve tremendous benefits in the form of energy savings. Dynamic Voltage and Frequency Scaling (DVFS) techniques have proven to be handy in this situation for an efficient trade-off between energy and timely behavior. Within imec, wearable devices make use of the indigenously developed MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). This system is optimized for efficient and accurate collection, processing, and transfer of data from multiple (health) sensors. MUSEIC v2 has limited means in controlling the voltage and frequency dynamically. In this thesis we explore how traditional DVFS techniques can be applied to the MUSEIC v2. Experiments were conducted to find out the optimum power modes to efficiently operate and also to scale up-down the supply voltage and frequency. Considering the overhead caused when switching voltage and frequency, transition analysis was also done. Real-time and non real-time benchmarks were implemented based on these techniques and their performance results were obtained and analyzed. In this process, several state of the art scheduling algorithms and scaling techniques were reviewed in identifying a suitable technique. Using our proposed scaling technique implementation, we have achieved 86.95% power reduction in average, in contrast to the conventional way of the MUSEIC v2 chip’s processor operating at a fixed voltage and frequency. Techniques that include light sleep and deep sleep mode were also studied and implemented, which tested the system’s capability in accommodating Dynamic Power Management (DPM) techniques that can achieve greater benefits. A novel approach for implementing the deep sleep mechanism was also proposed and found that it can obtain up to 71.54% power savings, when compared to a traditional way of executing deep sleep mode.Nuförtiden så har multifunktionella bärbara hälsoenheter fått en betydande roll. Dessa enheter drivs vanligtvis av batterier och är därför begränsade av batteritiden (från ett par timmar till ett par veckor beroende på tillämpningen). På senaste tiden har det framkommit att dessa enheter som används vid en fast spänning och frekvens kan användas vid flera spänningar och frekvenser. Genom att byta till lägre spänning och frekvens på grund av effektbehov så kan enheterna få enorma fördelar när det kommer till energibesparing. Dynamisk skalning av spänning och frekvens-tekniker (såkallad Dynamic Voltage and Frequency Scaling, DVFS) har visat sig vara användbara i detta sammanhang för en effektiv avvägning mellan energi och beteende. Hos Imec så använder sig bärbara enheter av den internt utvecklade MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). Systemet är optimerat för effektiv och korrekt insamling, bearbetning och överföring av data från flera (hälso) sensorer. MUSEIC v2 har begränsad möjlighet att styra spänningen och frekvensen dynamiskt. I detta examensarbete undersöker vi hur traditionella DVFS-tekniker kan appliceras på MUSEIC v2. Experiment utfördes för att ta reda på de optimala effektlägena och för att effektivt kunna styra och även skala upp matningsspänningen och frekvensen. Eftersom att ”overhead” skapades vid växling av spänning och frekvens gjordes också en övergångsanalys. Realtidsoch icke-realtidskalkyler genomfördes baserat på dessa tekniker och resultaten sammanställdes och analyserades. I denna process granskades flera toppmoderna schemaläggningsalgoritmer och skalningstekniker för att hitta en lämplig teknik. Genom att använda vår föreslagna skalningsteknikimplementering har vi uppnått 86,95% effektreduktion i jämförelse med det konventionella sättet att MUSEIC v2-chipets processor arbetar med en fast spänning och frekvens. Tekniker som inkluderar lätt sömn och djupt sömnläge studerades och implementerades, vilket testade systemets förmåga att tillgodose DPM-tekniker (Dynamic Power Management) som kan uppnå ännu större fördelar. En ny metod för att genomföra den djupa sömnmekanismen föreslogs också och enligt erhållna resultat så kan den ge upp till 71,54% lägre energiförbrukning jämfört med det traditionella sättet att implementera djupt sömnläge.
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Yuan, Hsiang-Fu, and 原祥富. "A Study on Battery State-of-Health Management Technology for Battery Second-Use." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/4ke9yf.
Повний текст джерелаАнотація:
博士國立交通大學
電控工程研究所
104
Battery second-use (B2U) is a new business model in the future electric vehicle (EV) market to offset the high EV cost to customers by repurposing used EV batteries to a second-life application, such as a battery energy storage system (BESS) with a renewable energy source (RES), for maximizing its economic benefits. For B2U, battery state-of-health (SOH) managements are essential technology for evaluating the remaining value of an aging EV battery and maximizing the battery second lifetime. In this dissertation, an off-line SOH estimation based on the charge transfer resistance is proposed for high power and high capacity lithium-ion battery cells, such as lithium iron phosphate or LFP battery cells, to estimate an unbiased and reliable SOH, so it can be a useful information for EV manufacturers and second-hand battery buyers in trading evaluation. As shown in the experimental results, the charge transfer resistance has a great aging change with battery degradation and good abilities against the state-of-charge (SOC) effect and external resistance variation in impedance parameters of a single time-constant equivalent circuit model (ECM) including ohmic resistance, charge transfer resistance, double-layer capacitance, and time constant, for SOH estimation. A fast and efficient three-point impedance extraction (TPIE) method is further proposed in this dissertation for accurately extracting the charge transfer resistance in off-line SOH estimation. The results of long-term cycling test demonstrate that the TPIE method can successfully indicate the SOH of LFP battery cells with low estimation error of 6.1%. This dissertation also presents a dual second-life battery energy storage system (DSLBESS) with the battery depth-of-discharge (DOD) management for extending battery cycle life and system service time. The proposed system consists of a power management system, a dual-battery energy storage unit (ESU), a fuel cell (FC) stack, and two DC-DC unidirectional power converters. Unlike conventional BESSs, the dual-battery ESU in the DSLBESS serves as the primary power source, while the FC stack serves as the secondary power source. The proposed power management is based on a state-machine-based mechanism, using a battery interchanging strategy to limit batteries operating within a given DOD range. The state-machine-based mechanism is employed for reducing the effects of battery overcharging or overdischarging; thus, it can significantly improve the battery life time. In addition, this algorithm has the ability to distribute the power flow efficiently between the dual batteries and the FC stack, and also to keep providing a stable and continuous energy output for the load. A current-mode control DC-DC boost converter is designed to regulate the FC current and try to maximize the FC power for battery charging time reduction. The proposed system is designed and implemented with a 20W proton exchange membrane fuel cell (PEMFC) stack and two 12Ah LiFePO4 batteries. The experimental results of capacity fades on various DOD tests verify the usefulness of the proposed DSLBESS. The comparison of life improvement shows that the cycle life has a 66.93% improvement when the normal operating region is limited within 60% DOD.
36
Liang, Yi-Min, and 梁翊民. "On-line State-of-Health Estimation for LiFePO4 Battery." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/sp3q7x.
Повний текст джерелаАнотація:
碩士國立中山大學
電機工程學系研究所
104
The demand of batteries for electric vehicle (EV) and Energy Storage System (ESS) is increasing. After battery has been used for a long time, the actual available capacity of battery will decrease, so State-of-Health (SOH) estimation is important in EV and ESS operations. An on-line SOH estimation method is proposed in the thesis. It is different from the conventional off-line estimation methods that need to remove battery from the system and connect to other devices. The key component in the proposed SOH estimation procedure is to obtain aging indicators according to the data from aging experiment performed off-line, and then use the indicators, including model parameters in a battery equivalent circuit to estimate SOH. Test data are used to determine the model parameter values during different battery ages by least square error method. The battery characteristic parameters computed at each age of the battery are then used in an Artificial Neural Network (ANN) to train and setup the automatic SOH estimator. In the proposed procedure, a regression model is used to determine the relationship of battery open-circuit voltage with State-of-Charge (SOC) and SOH. An on-line SOH estimation can be achieved after the battery open-circuit voltage and the equivalent circuit model parameters are calculated real time and fed into the ANN model. Test results indicate that the average absolute error of the proposed SOH estimator under different usage scenarios is 1.7732% based on 5 LiFePO4 batteries.
37
GUO, GUAN-DE, and 郭冠德. "The Development of Detection Methods for Battery State of Health." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/72423278053198512596.
Повний текст джерелаАнотація:
碩士高苑科技大學
電機工程研究所
104
A novel detection method for the health of a battery is proposed. Combined DC resistance and AC impedance method calculate the internal resistance of the battery. The internal resistance of a battery includes the ohmic resistance, the charge transfer resistance and the mass transfer impedance. When the battery is aging, the reduction of electrode active material, the separator obstruction, the electrolyte aging will be the reaction to the internal resistance of the battery.Since the ohmic resistance of a battery typically only a few milliohms, this paper uses DC resistance method to measure the ohmic resistance. DC resistance method could be obtained with a high accuracy compared with AC impedance method. Moreover, this paper uses AC impedance method with low frequency AC test signals is proposed to measure the charge transfer resistance and the mass transfer impedance. Based on the ohmic resistance, the charge transfer resistance and the mass transfer impedance that estimate the battery state of health compared with the battery aging database, as estimated remaining life of the battery.
38
Liu, Sung-Hsun, and 劉松洵. "Model-Based State of Charge and State of Health Estimation Method for Lithium-Ion Battery." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7dpawd.
Повний текст джерелаАнотація:
碩士大同大學
電機工程學系(所)
106
In this thesis, a state of charge (SOC) and state of health (SOH) estimation method for lithium-ion (Li-ion) battery is proposed. The proposed estimation method can be divided into three parts: (1) parameters identification; (2) SOH estimation; (3) and SOC estimation. A first-order RC model is adopted as the equivalent circuit model (ECM) of the battery in this thesis. The aging models are established by experiments. The parameters of the battery are identified by the proposed aging level estimation method and the recursive least square (RLS) method. A proper aging model is chosen according to the aging level estimation. Based on the aging level estimation, this thesis proposes a resistance-capacity fusion method to estimate the usable capacity of the battery and the SOH. The SOC estimation is fulfilled by the adaptive extended Kalman filter (AEKF). To experimentally validate the proposed estimation method, the Li-ion battery cells with unknown aging level have been discharged by the federal urban driving schedule (FUDS) and dynamic stress test (DST) profile under room temperature condition. According to the validation results, the proposed estimation method has good performance.
39
Huang, Sheng-Yu, and 黃勝煜. "Estimation of State of Health of Lithium-ion Battery Using Deep Learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/zr8h48.
Повний текст джерелаАнотація:
碩士國立臺灣大學
機械工程學研究所
106
In development of Battery models are vital for the development of electric vehicles. The battery life change the battery response. As a result, the model, which has battery degradation factors helps improve the efficiency of other control algorithms and maintain the safe usage of the battery. In this thesis, use charge-discharge cycle that is similar to a vehicle testing pattern and TensorFlow™ to build artificial neural network of battery cell which can predict the battery life. The battery model considers different parameters like patterns of charge current and the number of cycle which have an influence on the result of prediction. We have training data that is for a Deep Neural Network and test data that is identified for the model accuracy. The best result is that it can predict state of health within 0.1 of mean square error. In this thesis, the other model that is Recurrent Neural Network has the ability of remembering previous data. It has also good performance on estimating SOH.
40
Chao, Liang-Chieh, and 趙良傑. "Evaluation of Battery Model, State of Charge, and State of Health with Measurement of DC Internal Resistance." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/x992sh.
Повний текст джерелаАнотація:
碩士國立虎尾科技大學
電機工程系碩士班
104
Lead-acid battery can be used for the energy storage systems of solar power, UPS of the power system and the battery of hybrid vehicles and electric vehicles…etc. Therefore, the measurement of the lead-acid battery internal resistance has a high place in the application of battery. It is proposed a kind of the technology to measure the lead-acid battery internal resistance in this paper. The circuit architecture in this paper is not complicated, so the cost is cheaper than the normal measurement of DC internal resistance. It is convenient and not loss the accuracy. And it can help the people to know the battery status to prevent low power battery. The measurement of DC internal resistance in this paper is based on the Electrochemistry Impedance Spectroscopy (EIS) to establish the First-order battery model. With the First-order battery model, it can get the Ohmic resistance(R_ohm), Polarization resistance(R_ct) and Electric double layer capacitor(C_dl) in this measurement of Direct-Current Resistance(DCR) to establish the First-order dy battery models. After the establishment of the first-order dynamical battery models, it can calculate the State Of Charge (SOC) and State Of Health (SOH).
41
Chen, Yu-chieh, and 陳育傑. "ONLINE STATE OF CHARGE AND STATE OF HEALTH ESTIMATION FOR LITHIUM-ION BATTERY BY THE NEURAL NETWORK MODEL." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/p6cx36.
Повний текст джерелаАнотація:
碩士大同大學
電機工程學系(所)
107
The online estimation of the state of charge and the state of health of lithium-ion battery (Li-ion battery) is studied in this thesis. The state of health (SOH) and state of charge (SOC) of Li-ion battery are two important items in Li-ion battery management systems. It is known that the usable capacity of a Li-ion battery varies with the SOH and the ambient temperature. In order to obtain good estimation of the SOC, it is necessary to have good estimation of the SOH of the Li-ion battery. A first-order RC equivalent circuit model (ECM) is adopted as dynamic model of the Li-ion battery. We design a characteristic test to find the relationship between the open-circuit voltage (Voc) and the SOC of the battery; the relationship between the ECM parameters (Rs) and the SOC of the battery under different SOH and different ambient temperatures. A back-propagation neural network is applied to estimate the SOH of the battery. The value of the Rs obtained from the characteristic test, SOC and ambient temperature (T) are adopted as the input data of the back-propagation neural network, and the usable capacity as output data. Thus, the SOH can be estimated by the usable capacity. Then, an online parameter update method is employed to find the values of the parameters (Rp, Cp) of the ECM. Finally, the adaptive extended Kalman filter (AEKF) is applied to estimate the SOC. The experimental results show the modeling error that caused by SOH and temperature effect can be compensated, and the SOC of the Li-ion battery can be accurately estimated by the proposed method.
42
Chuang, Kuo-Shun, and 莊國順. "Estimation of State of charge and State of health for lithium-ion battery pack using dual extended Kalman filter." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/ds7u43.
Повний текст джерелаАнотація:
碩士國立臺北科技大學
車輛工程系所
105
A dual extended Kalman filter is employed to estimate the state of charge (SOC) and the state of health (SOH) for a lithium-ion battery pack in this thesis. The sample time of the parameter estimator is larger than that of the state estimator. Due to the lack of the experiment data of the aged battery pack, a virtual battery pack is established using MapleSim. First, a cell model is established using the experiment data of the new cell. The aged cell model is then established according to the characteristics of the aged cell. The battery pack model can then obtained by connecting cells in series and parallel according to the specification of the target battery pack. Measurement resolutions and noises of the current and voltage are also considered in this thesis. An adaptive law is proposed to adjust the measurement noise covariance matrix of the parameter estimator according to the estimated SOC to reduce the noise effect to the estimator. Incorrect initial conditions of the parameters and states, and the battery pack with 3% parameter variations are used to evaluate the performance and robustness of the proposed approach. Simulation results show that the proposed adaptive law can accurately estimate the SOC and SOH of the aged battery pack with 3% parameter variations, and effectively reduce the noise sensitivity and enhance the robustness.
43
LIN, DAO-QIN, and 林道勤. "Estimation of State of Charge and State of Health for Aging Lithium-ion Battery Pack using Automatic Data Update." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/f39g85.
Повний текст джерела
44
Lin, Chiung-Ming, and 林熲珉. "State of Health Prediction for Lithium Ion Battery Using a New Equivalent Circuit Model." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/07156700457980708285.
Повний текст джерелаАнотація:
碩士國立虎尾科技大學
電機工程研究所
102
This paper is using a new equivalent circuit model; Meanwhile analyze the state of health for the battery. Because the high frequency dynamic response has been ignored in equivalent circuit model, we can further simplify the equivalent circuit model that Kalman filter calculation can be easier. We will verify the above calculation. The new equivalent circuit model with dual circuit will be got exact value prediction by Kalman filter. Finally,Finally, we use the estimate state of health to amend the state of charge after battery aging and getting more accurate state of charge. Improve reliability of the battery, and get more accurate value. The reliability of the battery can be improved if we avoided the battery overcharge or over discharge.
45
Lin, Chiung-Ming, and 林熲? "State of Health Prediction for Lithium Ion Battery Using a New Equivalent Circuit Model." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/vvruzj.
Повний текст джерелаАнотація:
碩士國立虎尾科技大學
電機工程研究所
102
This paper is using a new equivalent circuit model; Meanwhile analyze the state of health for the battery. Because the high frequency dynamic response has been ignored in equivalent circuit model, we can further simplify the equivalent circuit model that Kalman filter calculation can be easier. We will verify the above calculation. The new equivalent circuit model with dual circuit will be got exact value prediction by Kalman filter. Finally,Finally, we use the estimate state of health to amend the state of charge after battery aging and getting more accurate state of charge. Improve reliability of the battery, and get more accurate value. The reliability of the battery can be improved if we avoided the battery overcharge or over discharge.
46
Bibin, Nataraja Pattel. "An evaluation of the moving horizon estimation algorithm for online estimation of battery state of charge and state of health." Thesis, 2014. http://hdl.handle.net/1805/6293.
Повний текст джерелаАнотація:
Indiana University-Purdue University Indianapolis (IUPUI)Moving Horizon Estimation (MHE) is a powerful estimation technique for tackling the estimation problems of the state of dynamic systems in the presence of constraints, nonlinearities and disturbances and measurement noises. In this work, the Moving Horizon Estimation approach is applied in estimating the State of Charge (SOC) and State of Health (SOH) of a battery and the results are compared against those for the traditional estimation method of Extended Kalman Filter (EKF). The comparison of the results show that MHE provides improvement in performance over EKF in terms of different state initial conditions, convergence time, and process and sensor noise variations. An equivalent circuit battery model is used to capture the dynamics of the battery states, experimental data is used to identify the parameters of the battery model. MHE based state estimation technique is applied to estimates the states of the battery model, subjected to various estimated initial conditions, process and measurement noises and the results are compared against the traditional EKF based estimation method. Both experimental data and simulations are used to evaluate the performance of the MHE. The results shows that MHE performs better than EKF estimation even with unknown initial state of the estimator, MHE converges faster to the actual states,and also MHE is found to be robust to measurement and process noises.
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Li, Li-gang, and 李立綱. "STATE OF CHARGE AND STATE OF HEALTH ESTIMATION OF LITHIUM-ION BATTERY BY THE NEURAL NETWORK AND ADAPTIVE EXTENDED KALMAN FILTER." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/p589vc.
Повний текст джерелаАнотація:
碩士大同大學
電機工程學系(所)
107
In this thesis, a hybrid SOC and SOH estimator for the Lithium-ion battery in different ambient temperature is proposed. In order to obtain good estimation of the SOC, it is necessary to have good estimation of the SOH of the Lithium-ion battery. A first-order RC model is adopted as the equivalent circuit model (ECM) of the Lithium-ion battery. It is known that such an ECM can well describe the static characteristics and dynamic characteristics of the Lithium-ion battery. However, it is seen that all the numerical values of the parameters in the ECM vary with the SOH, SOC and the ambient temperature T. In this thesis, we will design the characteristic tests, under different ambient temperature, for the Lithium-ion batteries with different SOH to find the relations between the open circuit voltage (OCV) and the SOC, the relations between the numerical values of the parameters in the ECM and the SOC. A back-propagation neural network (BPNN) will be used to mimic the relations between the parameters in the ECM, the ambient temperature T with the SOH of the Lithium-ion battery. Therefore, the numerical values of the parameters in the ECM and the ambient temperature T are adopted as the input of BPNN and the SOH as the output of BPNN. Finally, the adaptive extended Kalman filter (AEKF) is applied to estimate the SOC of the lithium-ion battery. According to the experimental results, the absolute values of the SOC estimation errors of the tested batteries can be maintained in a 3% band, which means that the proposed estimator can accurately estimate the SOC.
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Locorotondo, Edoardo, Luca Pugi, Lorenzo Berzi, and Marco Pierini. "Advanced modeling and development of mathematical methods for real-time diagnosis on lithium-ion batteries and state prediction." Doctoral thesis, 2021. http://hdl.handle.net/2158/1238304.
Повний текст джерелаАнотація:
The capability to assess and monitor the state of charge (SOC) and state of health (SOH) of lithium-based cells is a highly demanded feature for advanced battery management systems.
The main purpose of this thesis is to develop advanced models and methodologies to have a real-time diagnostic of battery state. In this regard, electrical, electrochemical, and aging characteristics have been analyzed, in order to develop accurate and low-computational cost models in the time and frequency domain. In particular, a novel electrical lithium-based battery performance model with aging characteristics has been defined and validated through experimental tests. The virtual model developed and implemented in Simulink platform has an interesting peculiarity. Model parameters have been evaluated from experimental tests performed on several end-of-life (EOL) automotive cells, at different SOHs. Nowadays, there is a lack of reliable and accurate battery models to assess the applicability of EOL batteries, giving them a second-life in stationary applications. Consequently, the battery model developed in this work could simulate the performance of a real second-life battery system.
Moreover, this work has been presented a set of algorithms for the estimation of SOC, specifically deployed for lithium-ferrum-phosphate (LFP) batteries. The algorithms proposed are founded on state-observers model-based approach. Especially for LFP batteries, key factor is the introduction of a hysteresis property inside the battery voltage model for an adequate treatment of SOC estimation error.
Finally, an innovative SOH diagnosis method for lithium-based cells is proposed. Battery SOH can be detected by exploiting impedance measurements obtained by fast active electrochemical impedance spectroscopy test. Key factors are the following: first, the excitation on the battery has been generated by a novel electronic prototype. This prototype is made up of cheap facilities and can perform EIS tests with reduced time duration and low energy consumption. Second, the experimental EIS test has been shown the possibility to determine frequency points in which the impedance measurements dramatically change due to different cell SOH. As a consequence, these peculiar frequencies can be adopted as a reference for cluster separation and SOH determination.
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Reddy, T. Mohan. "Capacity and Life Estimation of Flooded Lead Acid Batteries using Eddy Current Sensors." Thesis, 2016. http://etd.iisc.ernet.in/handle/2005/2971.
Повний текст джерелаАнотація:
Lead acid batteries are widely used in domestic, industrial and automotive applications. Even after lot of advancements in battery technologies, lead acid cells are still in use because of their high capacity and low cost. To use any battery effectively, first we should be able to identify the available capacity or State of Charge (SoC). There are many techniques available to measure SoC of a lead acid battery. One such unique method is to measure the capacity using eddy current sensors. This method is unique because it is non-obtrusive and online. Eddy current sensors (ECS) are wire wound inductors which work on the principle of electromagnetic induction. Eddy currents are the currents generated on a conductive material when it is kept in a varying magnetic. Eddy current sensors generate varying magnetic eldest and will be able to identify the properties of conductive materials like thickness, conductivity, material composition etc. Also they can be used as proximity sensors.
Lead acid batteries use lead metal as cathode. Upon usage(discharge) the lead metal converts to lead sulfate and revert back to lead after charging. These changes in lead electrode can be monitored using eddy current sensors. The impedance of an eddy current sensor will change when it is kept close to the lead electrode when the battery is charging or discharging. These impedance parameters can be monitored to determine the battery SoC. When lead is deposited on cathode, there will be more eddy current loss in the target and the total resistance of coil increases. On the other hand, when lead is deposited on the electrode because of increase in the magnitude of eddy currents which oppose the source magnetic, the total inductance of coil decreases. We can observe exactly opposite behaviour of coil resistance and inductance when the lead electrode is converted to less conductive lead sulfate. There is a lot of research on using ECS to measure SoC of lead acid batteries and there are still many challenges to be addressed.
First we have explained about different circuit designs we have used to monitor the battery capacity using eddy current sensors. After that, we have explained about our complete experimental setup and the procedure to measure the sensor parameters using the setup. Then, we have discussed about different issues involved in the eddy current sensing based state of charge measurement.
Eddy current sensors are affected by temperature variations. We have studied the coil resistance behaviour with temperature at different frequencies using simulations and experiments. We have obtained the conditions for linear variation of coil resistance with temperature. The measured temperature compensation scheme is applied and the results are discussed. We have also modified the measurement system design in order to minimize the lift o errors. We have used a metallic clamp structure to minimize the lift o errors.
We have used finite element analysis based simulations to study different design parameters and their effect on the sensitivity of eddy current sensor. We have created 2D eddy current models and the sensitivity of coil resistance is computed by changing the coil dimensions and the core permeability. We have also performed error analysis and computed the error due to the tilt angle shift between coil and electrode. We have also computed the error due to the internal heating of battery.
We have also studied the effect of acid strati cation on state of charge for both sealed and hooded batteries. We have proposed a multi coil method to minimize the errors in SoC measurement due to acid strati cation for Flooded type batteries. We have used finite element analysis based simulations to compute the error due to acid strati cation by increasing the number of coils.
Finally we have derived the equation for electrode Q factor using the transformer model of eddy current sensor. The derived Q factor equation is then used to study the aging of lead acid batteries both by using experiments and simulations. Finally we have explained a detail procedure to measure the state of charge(SoC) and state of health(SoH) of a hooded lead acid battery using eddy current sensing method.
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Silva, Bruno Joel Ribeiro da. "Desenvolvimento de um Posto de Carregamento Lento com BMS e Interface com o Cockpit para o CEPIUM." Master's thesis, 2013. http://hdl.handle.net/1822/39835.
Повний текст джерелаАнотація:
Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e de ComputadoresAtualmente, os veículos elétricos têm vindo a ganhar notoriedade dentro do ramo da indústria automóvel. Assim, existe um interesse crescente na substituição dos veículos com motor de combustão interna por veículos elétricos, devido às vantagens inerentes aos mesmos. No entanto, o desenvolvimento do veículo elétrico exige novas infraestruturas e soluções ao nível do carregamento, bem como novas soluções para o armazenamento de energia. O recurso às baterias para o armazenamento de energia nos veículos elétricos tem sido a solução mais adotada, assim, nesta dissertação descreve-se a implementação de um sistema de gestão e monitorização de baterias. Este sistema visa proteger as baterias contra abusos durante o seu funcionamento (não permite que os seus valores máximos e mínimos sejam excedidos), aumentando assim o seu tempo de vida útil, mas tentando sempre obter o melhor desempenho possível das baterias. Além disso, este sistema também informa o utilizador do veículo elétrico sobre diversos parâmetros do sistema de baterias, necessários para uma condução agradável e desprovida de ansiedade da autonomia, provocada pela falta de conhecimento do estado de carga das mesmas. Nesta dissertação, numa fase inicial apresenta-se o estado de arte sobre as tecnologias de baterias. De seguida, descreve-se a estrutura e constituição de um sistema de gestão de baterias. Posteriormente, realizam-se várias simulações para validar a topologia de equalização. Por último, implementa-se e testa-se o sistema de gestão de baterias proposto.
Currently, electric vehicles have been gaining notoriety within the field of car industry. Thus, there is a growing interest in the replacement of vehicles with internal combustion engine for electric vehicles, due to the inherent advantages of these. However, the development of the electric vehicle requires new infrastructures and solutions in what concerns loading, as well as new solutions for energy storage. The use of batteries for energy storage is the main solution adopted for electric vehicles. Thus, this work describes the implementation of a battery management system. This system aims to protect the batteries from abuse during operation (not allowing their maximum and minimum values to be exceeded), thereby increasing their lifetime, but always trying to get the maximum performance from the batteries. In addition, this system also informs the user of the electric vehicle about various parameters of the battery system required for a pleasant driving, and devoid of range anxiety, caused by the unawareness of the battery status. This work, initially presents the state of the art on battery technologies. Afterwards, describes the structure and constitution of a battery management system. Thereafter, several simulations are carried out to validate the topology of equalization. Finally, it presents the implementation and testing of the battery management system.