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Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Battery state-of-health"

1

Yang, Qingxia, Ke Ma, Liyou Xu, Lintao Song, Xiuqing Li, and Yefei Li. "A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health." Coatings 12, no. 8 (July 24, 2022): 1047. http://dx.doi.org/10.3390/coatings12081047.

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Анотація:
In a battery management system, the accurate estimation of the battery’s state of health (SOH) and state of capacity (SOC) are vital functions. The traditional estimation methods have limitations. To accurately estimate the SOC and SOH of power battery and improve the performance of the long-term estimation of a battery’s SOC, a joint estimation method based on a Kalman filter is proposed in this work. First, a second-order RC equivalent circuit model of a ternary lithium battery was built, whose parameters were identified online, and the model’s accuracy was verified. Then, the battery data under actual working conditions were collected. The SOC and SOH were estimated based on the Kalman filter algorithm, and the simulation was implemented using MATLAB. Finally, according to a time scale transformation, the battery state was jointly estimated, the SOC was estimated at a short-time scale, the SOH was estimated at a long-time scale, and the SOH estimation results were updated to the model parameters for SOC estimation. The results show that the accuracy of the method is very good, and it can effectively improve estimation accuracy and ensure batteries’ long-term estimation performance.
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2

Wei, Yupeng. "Prediction of State of Health of Lithium-Ion Battery Using Health Index Informed Attention Model." Sensors 23, no. 5 (February 26, 2023): 2587. http://dx.doi.org/10.3390/s23052587.

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State-of-health (SOH) is a measure of a battery’s capacity in comparison to its rated capacity. Despite numerous data-driven algorithms being developed to estimate battery SOH, they are often ineffective in handling time series data, as they are unable to utilize the most significant portion of a time series while predicting SOH. Furthermore, current data-driven algorithms are often unable to learn a health index, which is a measurement of the battery’s health condition, to capture capacity degradation and regeneration. To address these issues, we first present an optimization model to obtain a health index of a battery, which accurately captures the battery’s degradation trajectory and improves SOH prediction accuracy. Additionally, we introduce an attention-based deep learning algorithm, where an attention matrix, referring to the significance level of a time series, is developed to enable the predictive model to use the most significant portion of a time series for SOH prediction. Our numerical results demonstrate that the presented algorithm provides an effective health index and can precisely predict the SOH of a battery.
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3

Huang, Kai, Yong-Fang Guo, Ming-Lang Tseng, Kuo-Jui Wu, and Zhi-Gang Li. "A Novel Health Factor to Predict the Battery’s State-of-Health Using a Support Vector Machine Approach." Applied Sciences 8, no. 10 (October 2, 2018): 1803. http://dx.doi.org/10.3390/app8101803.

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The maximum available capacity is an important indicator for determining the State-of-Health (SOH) of a lithium-ion battery. Upon analyzing the experimental results of the cycle life and open circuit voltage tests, a novel health factor which can be used to characterize the maximum available capacity was proposed to predict the battery’s SOH. The health factor proposed contains the features extracted from the terminal voltage drop during the battery rest. In real applications, obtaining such health factor has the following advantages. The battery only needs to have a rest after it is charged or discharged, it is easy to implement. Charging or discharging a battery to a specific voltage rather than a specific state of charge which is difficult to obtain the accurate value, so the health factor has high accuracy. The health factor is not dependent on the cycle number of the cycle life test of the battery and it is less dependent on charging or discharging current rate, as a result, the working conditions have less effect on the health factor. Further, the paper adopted a support vector machine approach to connect the healthy factor to the maximum available battery capacity of the battery. The experimental results show that the proposed method can predict the SOH of the battery well.
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4

O. Hadi, Pradita, and Goro Fujita. "Battery Charge Control by State of Health Estimation." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 3 (March 1, 2017): 508. http://dx.doi.org/10.11591/ijeecs.v5.i3.pp508-514.

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Анотація:
Battery lifetime is one of importance consideration in smart system with energy storage system, because it is shorter than others. Extended of battery lifetime can give benefit to entire system, especially to reduce cost. The lifetime is commonly estimated by State of Health (SOH). Decreasing of SOH indicates degradation of battery. It can be influenced by the battery operation, so that operational management is needed. This study proposes control block for charging battery by using decreasing value of SOH as reference. The control block is implemented in battery system that connected to DC bus by bidirectional chopper. Numerical simulation study is performed by using PSIM software version 10.0. The result shows that the proposed block control is successfully used. Moreover, the relative error is less than 2% for delta SOH and less than 1% for battery power.<em> </em>
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5

Patel, Nisarg. "A Review of State of Health and State of Charge Estimation Methods." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 259–64. http://dx.doi.org/10.22214/ijraset.2021.38693.

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Abstract: The explosive growth of Electric Vehicles has made developing a robust system for managing batteries that are one of the crucial components of an EV, the need of the hour. Accuracy of the estimation models for determining the State of Charge and State of Health levels of the battery packs plays a key role. There are many sophisticated systems to determine these parameters we have tried to review a few of these systems in this paper. The complete electrification of the automotive industry heavily depends on the energy density and longevity that the battery packs provide and maintaining these packs in a safe operating condition can help achieve these goals. Keywords: BMS (Battery management system), SoC(state of charge), SoH (State of Health), Battery Thermal Management Systems (BTMS)
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6

Brunelli Lazzarin, Telles, and Ivo Barbi. "A system for state-of-health diagnosis of lead-acid batteries integrated with a battery charger." Eletrônica de Potência 17, no. 1 (February 1, 2012): 401–8. http://dx.doi.org/10.18618/rep.2012.1.401408.

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7

Zhang, Tao, Ningyuan Guo, Xiaoxia Sun, Jie Fan, Naifeng Yang, Junjie Song, and Yuan Zou. "A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles." Sustainability 13, no. 9 (May 5, 2021): 5166. http://dx.doi.org/10.3390/su13095166.

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Анотація:
Due to its advantages of high voltage level, high specific energy, low self-discharging rate and relatively longer cycling life, the lithium-ion battery has been widely used in electric vehicles. To ensure safety and reduce degradation during the lithium-ion battery’s service life, precise estimation of its states like state of charge (SOC), capacity and peak power is indispensable. This paper proposes a systematic co-estimation framework for the lithium-ion battery in electric vehicle applications. First, a linearized equivalent circuit-based battery model, together with an affine projection algorithm is used to estimate the model parameters. Then the state of health (SOH) estimator is triggered weekly or semi-monthly offline to update capacity based on the three-dimensional response surface open circuit voltage model and particle swarm optimization algorithm for accurate online SOC and state of power (SOP) estimation. At last, the Unscented Kalman Filter utilizes the estimated model parameters and updated capacity to estimate SOC online and the SOP estimator provides the power limitations considering SOC, current and voltage constraints, taking advantage of the information from both SOH and SOC estimators. Experiments show that the relative error of the SOH estimator is under 1% in all aging states whatever the loading profile is. The mean absolute SOC estimation error is under 1.6% even when the battery undergoes 744 aging cycles. The SOP estimator is validated by means of the calibrated battery model based on the HPPC test and its performance is ideal.
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Nuroldayeva, Gulzat, Yerkin Serik, Desmond Adair, Berik Uzakbaiuly, and Zhumabay Bakenov. "State of Health Estimation Methods for Lithium-Ion Batteries." International Journal of Energy Research 2023 (March 3, 2023): 1–21. http://dx.doi.org/10.1155/2023/4297545.

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Анотація:
Contemporary lithium-ion batteries (LIBs) are one of the main components of energy storage systems that need effective management to extend service life and increase reliability and safety. Their characteristics depend highly on internal and external conditions (ageing, temperature, and chemistry). Currently, the state of batteries is determined using two parameters: the state of charge (SOC) and the state of health (SOH). Applying these two parameters makes it possible to calculate the expected battery life and a battery’s performance. There are many methods for estimating the SOH of batteries, including experimental, model-based, and machine learning methods. By comparing model-based estimations with experimental techniques, it can be concluded that the use of experimental methods is not applicable for commercial cases. The electrochemical model-based SOH estimation method clearly explains processes in the battery with the help of multidifferential equations. The machine learning method is based on creating a program trained to predict the battery’s state of health with the help of past ageing data. In this review paper, we analyze the research available in the literature in this direction. It is found that all methods used to assess the SOH of an LIB play an essential role, and each method has its pros and cons.
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Yu, Zhilong, Na Liu, Yekai Zhang, Lihua Qi, and Ran Li. "Battery SOH Prediction Based on Multi-Dimensional Health Indicators." Batteries 9, no. 2 (January 24, 2023): 80. http://dx.doi.org/10.3390/batteries9020080.

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Battery capacity is an important metric for evaluating and predicting the health status of lithium-ion batteries. In order to determine the answer, the battery’s capacity must be, with some difficulty, directly measured online with existing methods. This paper proposes a multi-dimensional health indicator (HI) battery state of health (SOH) prediction method involving the analysis of the battery equivalent circuit model and constant current discharge characteristic curve. The values of polarization resistance, polarization capacitance, and initial discharge resistance are identified as the health indicators reflective of the battery’s state of health. Moreover, the retention strategy genetic algorithm (e-GA) selects the optimal voltage drop segment, and the corresponding equal voltage drop discharge time is also used as a health indicator. Based on the above health indicator selection strategy, a battery SOH prediction model based on particle swarm optimization (PSO) and LSTM neural network is constructed, and its accuracy is validated. The experimental results demonstrate that the suggested strategy is accurate and generalizable. Compared with the prediction model with single health indicator input, the accuracy is increased by 0.79%.
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Mei, Peng, Hamid Reza Karimi, Fei Chen, Shichun Yang, Cong Huang, and Song Qiu. "A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health." Sensors 22, no. 23 (December 4, 2022): 9474. http://dx.doi.org/10.3390/s22239474.

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Анотація:
The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles’ (EVs) complexity and changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration. Firstly, the indicator of battery performance degradation is extracted for SOH prediction according to the historical data; the Bayesian optimization approach is applied to the SOH prediction combined with Bi-LSTM. Then, the CNN-LSTM is implemented to provide direct and nonlinear mapping models for SOE. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Finally, the SOH correction in SOE estimation achieves the joint estimation with different time scales. With the validation of the National Aeronautics and Space Administration battery data set, as well as the established battery platform, the error of the proposed method is kept within 3%. The proposed vehicle-cloud approach performs high-precision joint estimation of battery SOE and SOH. It can not only use the battery historical data of the cloud platform to predict the SOH but also correct the SOE according to the predicted value of the SOH. The feasibility of vehicle-cloud collaboration is promising in future battery management systems.
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Дисертації з теми "Battery state-of-health"

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.

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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.

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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.
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3

Kerley, Ross Andrew. "Automotive Lead-Acid Battery State-of-Health Monitoring System." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/64870.

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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
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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.

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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.

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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.
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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.

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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.

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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.

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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

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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.

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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.

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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
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Частини книг з теми "Battery state-of-health"

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Fernandez, Carlos, Ji Wu, Lei Chen, Mingfang He, Peng Yu, Pu Ren, Shunli Wang, Xiao Yang, Siyu Jin, and Yangtao Wang. "Battery State of Health Estimation." In Multidimensional Lithium-Ion Battery Status Monitoring, 211–46. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003333791-4.

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Fotouhi, Abbas, Karsten Propp, Daniel J. Auger, and Stefano Longo. "State of Charge and State of Health Estimation Over the Battery Lifespan." In Behaviour of Lithium-Ion Batteries in Electric Vehicles, 267–88. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69950-9_11.

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Singh, Rupam, V. S. Bharath Kurukuru, and Mohammed Ali Khan. "Data-Driven Model for State of Health Estimation of Lithium-Ion Battery." In Computational Methods and Data Engineering, 279–93. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7907-3_21.

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Saqli, K., H. Bouchareb, M. Oudghiri, and N. K. M’Sirdi. "Critical Review of Ageing Mechanisms and State of Health Estimation Methods for Battery Performance." In Sustainability in Energy and Buildings, 507–18. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9868-2_43.

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Sun, Bingxiang, Yuzhe Chen, Shichang Ma, Zhengtao Cui, and Zhanguo Wang. "Estimating Contrast of State of Health for Lithium-Ion Battery Based on Accumulated Residual Energy." In The Proceedings of the 9th Frontier Academic Forum of Electrical Engineering, 83–96. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6609-1_8.

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Sudhakar Reddy, Mandeddu, and M. Monisha. "A Survey on Battery State of Charge and State of Health Estimation Using Machine Learning and Deep Learning Techniques." In Lecture Notes in Networks and Systems, 355–67. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6088-8_31.

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Li, Fan, Yusheng Wang, and Duzhi Wu. "Prognostics for State of Health Estimation of Battery System Under Uncertainty Based on Adaptive Learning Technique." In Advances in Intelligent Systems and Computing, 313–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47241-5_27.

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Sánchez, Luciano, José Otero, Manuela González, David Anseán, and Inés Couso. "Online Estimation of the State of Health of a Rechargeable Battery Through Distal Learning of a Fuzzy Model." In Advances in Intelligent Systems and Computing, 68–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20055-8_7.

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Wang, Shunli, Yongcun Fan, Daniel-Ioan Stroe, Carlos Fernandez, Chunmei Yu, Wen Cao, and Zonghai Chen. "Battery state-of-health estimation methods." In Battery System Modeling, 255–311. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-323-90472-8.00007-x.

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"Battery State of Health Estimation." In Advanced Battery Management Technologies for Electric Vehicles, 95–130. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119481652.ch4.

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Тези доповідей конференцій з теми "Battery state-of-health"

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Bandong, Steven, Muhammad Ihsan, and Endra Joelianto. "Chaotic Behavior of Battery State of Health." In 2019 6th International Conference on Electric Vehicular Technology (ICEVT). IEEE, 2019. http://dx.doi.org/10.1109/icevt48285.2019.8993986.

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Zhu, Xuetao, Qiongbin Lin, Shi You, Sixiong Chen, and Yiming Hong. "A Review of Battery State of Health Estimation." In 2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG). IEEE, 2019. http://dx.doi.org/10.1109/igbsg.2019.8886281.

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Sarikurt, Turev, Murat Ceylan, and Abdulkadir Balikci. "An analytical battery state of health estimation method." In 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE). IEEE, 2014. http://dx.doi.org/10.1109/isie.2014.6864855.

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Ramadan, M. Nisvo, Bhisma A. Pramana, Adha Cahyadi, and Oyas Wahyunggoro. "State of health estimation in lithium polymer battery." In PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON SYNCHROTRON RADIATION INSTRUMENTATION – SRI2015. Author(s), 2016. http://dx.doi.org/10.1063/1.4958523.

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Tairov, Stanislav, and Luiz Carlos Stevanatto. "Impedance measurements for battery state of health monitoring." In 2011 2nd International Conference on Control, Instrumentation, and Automation (ICCIA). IEEE, 2011. http://dx.doi.org/10.1109/icciautom.2011.6356634.

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He, Liang, Eugene Kim, Kang G. Shin, Guozhu Meng, and Tian He. "Battery state-of-health estimation for mobile devices." In ICCPS '17: ACM/IEEE 8th International Conference on Cyber-Physical Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3055004.3055018.

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Arif, Ameera, Muhammad Hassaan, Mujahid Abdullah, Ahmad Nadeem, and Naveed Arshad. "Estimating Battery State of Health using Machine Learning." In 2022 10th International Conference on Smart Grid and Clean Energy Technologies (ICSGCE). IEEE, 2022. http://dx.doi.org/10.1109/icsgce55997.2022.9953596.

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Natella, D., and F. Vasca. "Battery State of Health Estimation via Reinforcement Learning." In 2021 European Control Conference (ECC). IEEE, 2021. http://dx.doi.org/10.23919/ecc54610.2021.9655199.

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Guo, Qi, Wei Qu, Haoran Deng, Xueyuan Zhang, Yi Li, Xiaowei Wang, and Xiangwu Yan. "Estimation of electric vehicle battery state of health based on relative state of health evaluation." In 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2017. http://dx.doi.org/10.1109/iaeac.2017.8054365.

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Bashash, Saeid, and Hosam K. Fathy. "Battery State of Health and Charge Estimation Using Polynomial Chaos Theory." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-4088.

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Анотація:
In this effort, we use the generalized Polynomial Chaos theory (gPC) for the real-time state and parameter estimation of electrochemical batteries. We use an equivalent circuit battery model, comprising two states and five parameters, and formulate the online parameter estimation problem using battery current and voltage measurements. Using a combination of the conventional recursive gradient-based search algorithm and gPC framework, we propose a novel battery parameter estimation strategy capable of estimating both battery state-of-charge (SOC) and parameters related to battery health, e.g., battery charge capacity, internal resistance, and relaxation time constant. Using a combination of experimental tests and numerical simulations, we examine and demonstrate the effectiveness of the proposed battery estimation method.
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