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Artykuły w czasopismach na temat "PASSIVE CELL BALANCING"
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Udupa T S, Rakshak, Shashank K Holla i Kariyappa B S. "Design and Performance Analysis of Active and Passive Cell Balancing for Lithium-Ion Batteries". Journal of University of Shanghai for Science and Technology 23, nr 06 (17.06.2021): 476–88. http://dx.doi.org/10.51201/jusst/21/05246.
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Electric Vehicles (EV) are growing areas of research since the demand for clean transportation is ever-increasing. Batteries form an integral part of EVs. Battery Management systems (BMS) need to support many features, including charge balancing to improve battery life and longevity. Among passive cell balancing and active cell balancing, the latter provides better battery life and efficiency. Among different active and passive cell balancing techniques, popular techniques like Flyback transformer-based active cell balancing and switched capacitor-based active cell balancing are used. These methods are not only easy to implement but also provide good performance. These balancing circuits are integrated with non-ideal RC models of a lithium-ion battery. The bleed resistor-based passive cell balancing took more than 16000 seconds to reach a 0.01V difference for capacitors with 5F capacitance, whereas the switched capacitor design is estimated to take 500 seconds. The multi-winding flyback active cell balancing system reached a 2% difference in SOC in 1800 seconds. There was a visible increase in time taken for balancing the cells using multi-winding active cell balancing as the cell temperature increased. A 2.32% increase in the time taken for balancing the cells was observed when cell temperature increased from 293K to 313K.
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Kumar, Sonu, S. Koteswara Rao, Arvind R. Singh i Raj Naidoo. "Switched-Resistor Passive Balancing of Li-Ion Battery Pack and Estimation of Power Limits for Battery Management System". International Journal of Energy Research 2023 (17.06.2023): 1–21. http://dx.doi.org/10.1155/2023/5547603.
Pełny tekst źródłaStreszczenie:
The battery pack performance and expected lifespan are crucial in electric vehicle applications. Balancing the charge on a battery pack connected in series and parallel is crucial due to manufacturing discrepancies and distinct performance of each cell in a standard battery pack. In this paper, a switched-resistor passive balancing-based method is proposed for balancing cells in a battery management system (BMS). The value of the available voltage at the battery cell terminals is balanced using resistors in an electrical circuit, and the excess voltage is eliminated. The cell balancing outcome demonstrates that the electrical circuit can maintain an even voltage across each cell. The procedure of balancing involves individually adjusting each cell’s level of charge. Passive balancing releases energy as heat by draining charge from cells that have too much charge. A passive cell balancer is a cost-effective solution and easy to install, but due to thermal loss from a resistor, it has a low energy efficiency for cell balancing and necessitates a lengthy balancing process. This passive cell balancer is an effective and reliable method for low-power devices and portable applications such as electrical vehicles. The power limits during charging and discharging are estimated using the bisection method.
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Duraisamy, Thiruvonasundari, i Kaliyaperumal Deepa. "Evaluation and Comparative Study of Cell Balancing Methods for Lithium-Ion Batteries Used in Electric Vehicles". International Journal of Renewable Energy Development 10, nr 3 (10.02.2021): 471–79. http://dx.doi.org/10.14710/ijred.2021.34484.
Pełny tekst źródłaStreszczenie:
Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.
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Duraisamy, Thiruvonasundari, i Kaliyaperumal Deepa. "Evaluation and Comparative Study of Cell Balancing Methods for Lithium-Ion Batteries Used in Electric Vehicles". International Journal of Renewable Energy Development 10, nr 3 (10.02.2021): 471–79. http://dx.doi.org/10.14710/ijred.0.34484.
Pełny tekst źródłaStreszczenie:
Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.
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KOCYIGIT, Davut, i Umut Engin AYTEN. "Hybrid Battery Balancing System for Electric Drive Vehicles". Eurasia Proceedings of Science Technology Engineering and Mathematics 19 (14.12.2022): 47–54. http://dx.doi.org/10.55549/epstem.1219150.
Pełny tekst źródłaStreszczenie:
In electric vehicles, cell and module voltage equalization plays a vital role in Battery Management System (BMS). The capacity, temperature, and aging imbalances in the cells and modules of electric vehicles battery packs restrict the amount of power that can be delivered to the vehicle. Spurred by this issue, we propose a new class of battery balancing systems, called hybrid balancing, capable of simultaneously equalizing battery capacity while enabling cost-effectiveness of cell-level passive balancing and module-level active balancing, modules consist of a number of cells connected in series, with cell-level passive balancing performed in a module, together with the module level switched capacitor that performs active balancing among the modules. The strategy is called hybrid balancing because it pursues goals beyond conventional state-of-charge equalization, including temperature and power capability equalization, and minimization of energy losses. Design details and MATLAB Simulink simulation results are provided for a hybrid balancing system implemented on a lithium-ion battery pack.
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Uzair, Muhammad, Ghulam Abbas i Saleh Hosain. "Characteristics of Battery Management Systems of Electric Vehicles with Consideration of the Active and Passive Cell Balancing Process". World Electric Vehicle Journal 12, nr 3 (13.08.2021): 120. http://dx.doi.org/10.3390/wevj12030120.
Pełny tekst źródłaStreszczenie:
Energy shortage and environmental pollution issues can be reduced considerably with the development and usage of electric vehicles (EVs). However, electric vehicle performance and battery lifespan depend on a suitable battery arrangement to meet the various battery performance demands. The safety, reliability, and efficiency of EVs largely depends on the constant monitoring of the batteries and management of battery packs. This work comprehensively reviews different aspects of battery management systems (BMS), i.e., architecture, functions, requirements, topologies, fundamentals of battery modeling, different battery models, issues/challenges, recommendations, and active and passive cell balancing approaches, etc., as compared to the existing works which normally discuss one or two aspects only. The work describes BMS functions, battery models and their comparisons in detail for an efficient operation of the battery pack. Similarly, the work presents a comprehensive overview of issues and challenges faced by BMS and also provides recommendations to address these challenges. Cell balancing is very important for the battery performance and in this work various cell balancing methodologies and their comparisons are also presented in detail. Modeling of a cell balancer is presented and a comparative study is also carried out for active and passive cell balance technique in MATLAB/Simulink with an eight cell battery packcell balancing approach. The result shows that the active cell balancing technique is more advantageous than passive balancing for electrical vehicles using lithium-ion batteries.
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B.T., Prashant Singh, Babu Bobba Phaneendra i K. Suresh. "Extensive review on Supercapacitor cell voltage balancing". E3S Web of Conferences 87 (2019): 01010. http://dx.doi.org/10.1051/e3sconf/20198701010.
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This paper explains about the supercapacitor cell voltage balancing circuits by comparing different topologies with regard to parameters like cost, balancing time, weight of the components used and control of switches. The advantage of supercapacitor over battery made to overcome weight and faster responding source problems. In supercapacitor bank cell voltages differ from each other which effects the performance of the device. Passive circuits consume power from cell for balancing but active circuits consume power from source. Many topologies are considered in this paper for different ratings and with different components. Balancing circuit is selected based upon total number of components in the balancing circuit, many components make circuit less reliable, complex and also increase the cost for balancing.
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Rao K, Shubha. "System Modelling and Simulation Analysis of Battery Pack with Passive Cell Balancing". International Journal of Latest Technology in Engineering, Management & Applied Science XII, nr VII (2023): 77–85. http://dx.doi.org/10.51583/ijltemas.2023.12707.
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This paper presents system modelling and simulation of lithium battery pack with passive cell balancing technique. A battery pack of 57.6 V, 27 Ah is modelled and simulated in MATLAB/Simulink environment. The balancing algorithm is triggered whenever the difference in State of charge (SoC) of series connected cells modules exceeds the threshold value of 0.1% of SoC. The balancing algorithm also provides an optimum value of shunt resistor value which is selected based on time taken to balance the cells and minimum power consumption. Graphs of balancing time and power consumption versus resistor value were obtained. A shunt resistor of 4 Ω chosen as an optimum value among a set of resistors as its balancing time of 9636.9s and power loss of 26.2462W was satisfactory. The performance of battery pack was analyzed during charging phase using Constant Charging- Constant Voltage (CC-CV) approach and discharging at constant current of 20A.
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Talha, Muhammad, Furqan Asghar i Sung Ho Kim. "Experimental Evaluation of Cell Balancing Algorithms with Arduino Based Monitoring System". Journal of Advanced Computational Intelligence and Intelligent Informatics 20, nr 6 (20.11.2016): 968–73. http://dx.doi.org/10.20965/jaciii.2016.p0968.
Pełny tekst źródłaStreszczenie:
The trend toward more electric vehicles has demanded the need for high efficiency, high voltage and long life battery systems [1,_2]. Also renewable energy systems carry huge battery backups to overcome the renewable source shortage. Battery systems are affected by many factors, cells unbalancing is one of most important among these factors. Without the balancing system, individual cell voltages will differ over time that will decrease the battery pack capacity quickly. This condition is especially severe when the battery has a long string of cells and frequent regenerative charging is done via battery pack. Cell balancing is a method of designing safer battery solutions that extends battery runtime as well as battery life. Balancing mechanism can help in equalizing the state of charge across the multiple cells, therefore increasing the performance of battery system. Different cell balancing methodologies have been proposed for battery pack in recent years. These methods have some merits and demerits in comparison to each other; e.g. balancing time, complexity and active or passive balancing etc. In this paper, current bypass active cell balancing and Arduino based monitoring system designing and implementation is carried out. In charging process, this balancing technique provides partial current bypass using charging slope for weak cells. Also the passive shunt resistor technique is implemented to compare and verify the proposed system efficient response. Output result shows that this proposed balancing technique can perform cell balancing in much effective and efficient way as compared to previous balancing techniques. Using this cell balancing technique, we can improve overall battery health and lifetime.
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Song, Heewook, i Seongjun Lee. "Study on the Systematic Design of a Passive Balancing Algorithm Applying Variable Voltage Deviation". Electronics 12, nr 12 (8.06.2023): 2587. http://dx.doi.org/10.3390/electronics12122587.
Pełny tekst źródłaStreszczenie:
A balancing circuit in a multi-series battery pack prevents a specific cell from being overcharged by reducing the voltage difference between the cells. Passive cell balancing is widely used for easy implementation and volume and size reduction. For optimal passive cell balancing, the charging/discharging current conditions and the state of charge (voltage condition) of the battery must be determined. In addition, the balancing algorithm must determine an allowable voltage deviation threshold between the cells connected in series to determine whether a specific cell performs a balancing operation. However, previous studies have not dealt with the design of balancing operating conditions in detail. In addition, the balancing time and efficiency improvement effect under specific conditions for arbitrary battery cells used in each previous study were mainly presented. Therefore, this study proposes a variable voltage deviation method in which the threshold for determining the voltage to be balanced is changed by reflecting the battery capacity, rated current specification, open-circuit voltage, and resistance of the balancing circuit. In addition, the voltage management performance and efficiency analysis results of the existing balancing algorithm and the proposed balancing method for the case where there is parameter deviation in the cells of the battery pack are also presented. The proposed method was verified through the simulation and experimental results of a reduced battery module in which three types of battery cells, INR 18650-30Q, INR 18650-29E, and INR 21700-50E, were arranged in 4-series.
Rozprawy doktorskie na temat "PASSIVE CELL BALANCING"
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Welsh, James Daniel Jr. "A Comparison of Active and Passive Cell Balancing Techniques for Series/Parallel Battery Packs". The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1253047792.
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Vo, Thomas V. "The Development of an Integrated Battery Management System and Charger". University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1406657703.
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Wu, Wenzhuo. "Charging time estimation and study of charging behavior for automotive Li-ion battery cells using a Matlab/Simulink model". Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-194490.
Pełny tekst źródłaStreszczenie:
An accurate estimation of the charging time of an automotive traction battery is possible only with the knowledge of different parameters of the battery and the vehicle. If this information is not available to the driver, the full time needed for charging of the battery may have to be assessed only from experience. A long route planning and estimation of required service life of the vehicle are therefore only roughly possible. Furthermore, with a better knowledge of estimated charging time, better management of public charging stations and better utilization of charging equipment can be achieved. An algorithm based on Matlab/Simulink model is made in the present thesis to estimate the charging time of a Li-ion battery pack which consists of 32 cells with 40 Ah each, as well as to investigate the impact of different cell balancing methods and different charging strategies on charging process. The theoretical background of the battery and charging modelling is investigated and different battery models are compared to get the best trade-off between the model accuracy and computation complexity. In the end, an electrical equivalent circuit model from reference [1], consists of a series resistor and two ZARC elements, is chosen to represent the battery cell. The parameters of the equivalent circuit are updated according to the SOC, current and temperature changes during the charging process. The whole simulation model of the algorithm consists of a charging controller (implementing the charging strategy), cell balancing logic controller, and cell balancing hardware simulation circuit and battery cell models. Different balancing criteria: based on SOC (with PWM drive) and based on terminal voltage (with/without advance) are implemented in the cell balancing logic controller, as well as different balancing windows, to investigate their impact on charging time. As for charging strategy, traditional CCCV is investigated, further investigation is conducted into improved CCCV method. The impact of initial SOC, charging rate and aging factor on charging behavior are investigated as well. Experiment results are validated by the comparison of the results with the ones got from a Hardware-in-the-loop simulation system.En noggrann estimering av laddtiden hos batterier avsedda för traktionsapplikationer kräver kunskap kring batteriets och dess tillhörande laddsystems parametervärden. Utan tillgång till denna information kan laddtiden endast uppskattas från fordonsägarens tidigare erfarenheter vilket försvårar t.ex. ruttplanering. En estimering av laddtiden med tillräcklig noggrannhet kan även möjliggöra bättre utnyttjade av laddutrusting inklusive nyttjandet av publika laddstationer. I detta examensarbete har en algoritm, implementerad i Matlab/Simulink, för att estimera laddtiden hos ett litiumjonbatteripack bestående av 32 celler på vardera 40 Ah tagits fram. Med hjälp av modellen har olika laddstrategier och metoder för att balansera cellerna studerats. Ett antal olika batterimodeller har jämförts i termer av noggrannhet och krav på beräkningsprestanda. En elektriskt ekvivalent krets från referens [1], bestående av en serieresistans samt två ZARC-element, valdes slutligen för att representera battericellen. Den ekvivalenta kretsens parametrar uppdateras vid förändringar i SOC, ström och temperatur. Hela simuleringsmodellen består av en laddregulator (i vilken laddstrategin är implementerad), cellbalanseringregulator och modeller för cell och cellbalanseringens hårdvara. Ett antal metoder för att balanser cellerna har jämförts med hänsyn till påverkan på den resulterande laddtiden. En traditionell samt modifierad CCCV laddstrategi har implementerats och jämförts med avseende på variationer i inledande SOC, total laddtid samt åldring. Experimentella resultat från en hardware-in-the-loop simulering har använts för att delvis kunna verifiera de framtagna resultaten.
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GUPTA, PRAPHULL PRAKASH. "PASSIVE CELL BALANCING AND ANALYSIS OF LLC CONVERTER WITH BUCK CONVERTER FOR BATTERY CHARGING USING CCCV TOPOLOGY". Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19290.
Pełny tekst źródłaStreszczenie:
This Project presents the passive cell balancing of a lithium-ion battery cells
and battery cell charging with the use CCCV (Constant current constant voltage
Topology). When battery cells are connected in series, they frequently become
unbalanced. The cell voltages aren't all the same. Variations in cell parameters such as
cell difference in SOC of battery cell, impedances, capacity, self-discharge rate, cell
temperature and so on can cause this unbalance. Passive cell balancing is constructed
and examined in a stand-alone state in this paper. The features of passive balancing
are determined in this study by conducting an on-line passive balancing experiment of
charge and discharge at various capacities (SOC). In this Project, circuitry for battery
charge controller circuit is also designed and the Constant voltage/constant current
(CCCV) topology is investigated to prevent overcharging issues.
Passive Balance method for Battery charging using CCCV topology is not
effective due to power wastage so I used Cascaded system of LLC resonant converter
with Buck converter for CCCV charging because LLC resonant converters are used in
various sectors due to their advantages of high efficiency, high energy density,
electrical isolation, low electromagnetic interference (EMI), wide output ranges and
high frequency. A Good power factor for the LLC resonant tank is required to achieve
high efficiency over a wide input voltage range. A unity voltage gain can be
accomplished over the whole loading conditions by having switching frequency equal
to resonant tank frequency in LLC converter, however it is not suited for constant
current – constant voltage (CC-CV) battery applications. The use of a buck converter
at the output of an LLC resonant converter to regulate the charging current and voltage
for battery charging applications is also proposed in this Project. The use of a buck
converter allows the output voltage of the LLC converter to be varied, ensuring CCCV
charging. This project is designed CC-CV charging for 48V/40Ah Battery. The
proposed system is analysed and studied in MATLAB/Simulink.
Części książek na temat "PASSIVE CELL BALANCING"
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Thiagarajan, Kripalakshmi, Mahadeedhar Marabathina, Maladhi D., Selvasundar K., Deepa T., angalaeswari S. i Subbulekshmi D. "Control of Cell Voltage Difference Balancing in Battery Management System Charging Circuits in Electric Vehicles". W Artificial Intelligence Applications in Battery Management Systems and Routing Problems in Electric Vehicles, 56–66. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6631-5.ch003.
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The growing development of battery technologies for the transposition of use of fuel in electric vehicles leads to the competent Battery Management System (BMS) design implementation. The chapter focuses on how the voltage difference perturbs the battery pack's performance by using an optimized control strategy. It helps in battery operated vehicles to charge and work efficiently. The control strategy involves the proper switching of the resistors connected to the charging circuit. The thermal balance of the resistors is balanced using the thermal power dissipation modelling in simulation. The models are tested under variable cell voltage conditions in MATLAB Simulink. The technique for voltage error balancing is termed passive cell balancing in BMS for the arrangement of a multiplicity of cells in stacks. The major issue of the passive cell balancing arises due to the State of Charge (SoC) imbalance in the batteries. The charging conditions of the battery are taken into the parameter estimation for the control technique of the balancing technique.
Streszczenia konferencji na temat "PASSIVE CELL BALANCING"
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Lee, Wai Chung, David Drury i Phil Mellor. "Comparison of passive cell balancing and active cell balancing for automotive batteries". W 2011 IEEE Vehicle Power and Propulsion Conference (VPPC). IEEE, 2011. http://dx.doi.org/10.1109/vppc.2011.6043108.
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Singh, Satya Vikram Pratap, i Prashant Agnihotri. "ANN Based Modelling of Optimal Passive Cell Balancing". W 2022 22nd National Power Systems Conference (NPSC). IEEE, 2022. http://dx.doi.org/10.1109/npsc57038.2022.10069440.
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Bashir, Hamza, Ammar Yaqoob, Farzana Kousar, Waqas Khalid, Saleem Akhtar i Wajahat Sultan. "A Comprehensive Review of Li-ion Battery Cell Balancing Techniques & Implementation of Adaptive Passive Cell Balancing". W 2022 International Conference on Electrical Engineering and Sustainable Technologies (ICEEST). IEEE, 2022. http://dx.doi.org/10.1109/iceest56292.2022.10077854.
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Gupta, Praphull Prakash, Narendra Kumar i Uma Nangia. "Passive Cell Balancing and Battery Charge Controller with CCCV Topology". W 2022 3rd International Conference for Emerging Technology (INCET). IEEE, 2022. http://dx.doi.org/10.1109/incet54531.2022.9825104.
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Shukla, Abhay P., i Rakesh A. Patel. "Battery Management System by Passive Cell Balancing for Electric vehicle". W 2022 2nd International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC). IEEE, 2022. http://dx.doi.org/10.1109/parc52418.2022.9726646.
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Soca, Mirela. "BATTERY INTERNAL RESISTANCE INFLUENCE ON VOLTAGE-BASED BALANCING ALGORITHMS". W eLSE 2021. ADL Romania, 2021. http://dx.doi.org/10.12753/2066-026x-21-118.
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Evolving technologies such as EVs and smart grids require their batteries to be operated safely and cost-effective and a battery management system is always used to achieve that. This study focused on building a passive balancer for four cells connected in series of a battery pack. Two voltage-based algorithms for passive balancing were tested and results were compared. Batteries premature failure can be caused by not making use of their full capacity. Variations in the physical volume, internal impedance and different self-discharge rates of the cells are a cause [2]. Then, as not all the cells are identical, they will charge and discharge at different rates and the charging/discharging process will stop as soon as the first one reached or finished its full capacity. To prolong the cell's life, their state of charge can be balanced during a charge or discharge process. The main methods available are passive and active balancing. Passive balancing dissipates the energy from the strongest cells discharging them through a resistor until the weakest ones reach their full capacity. The same principle is applied by active balancing, only that the energy is not wasted but transferred from the strongest to the weakest cell. In [3] the SoC profile for a car battery is compared before and after cell balancing over a 12h driving cycle. After one driving cycle, the cells' voltages vary and after a relaxation period, they will start to drift further away. These variations were translated in quite big SoC variations in individual cells. For the same current profile, cell balancing was introduced. After driving for 0.5h, the highest and the lowest charged cells were balanced, continuing with multiple cells balancing. Comparing the graphs, it can be seen how the active balancing circuit reduced the cells' variations. Balancing algorithms are based on SoC calculation. This can be achieved by voltage translation and/or coulomb counting" [1]. The main limitation of the voltage-based algorithm is that it assumes that the cells of different voltages are of different SoC, when the terminal voltage of a battery is not the same as the OCV (open circuit voltage). A voltage drop across its internal resistance can rise or drop the terminal voltage of the cell while charging or discharging. In their application notes for cell balancing ASICs (application specific integrated circuit), Texas Instruments also state that terminal-voltage-based balancing may not be accurate, due to the IR contribution of the internal resistance of the cells while charging/discharging. The relaxed voltage after charging (the true OCV) shows imbalances between the cells. Figure 1 is used to illustrate this [4].Some studies have discussed the problem associated with deriving the SoC from the terminal voltage. For example, David Andrea in his book [5], shows how the terminal voltage curve vs. the SoC is different for various discharge and charge rates as the current variation leads to a variation in the IR drop. However, knowing the current and the resistance for each cell, the OCV can be calculated. [6] also tried to approximate the SoC directly from the terminal voltage for a Lead Acid battery and have concluded that for different currents, the parameters for the approximation curve were different. Then, it proposes two equations to model the SoC as a function of both the terminal voltage and the internal resistance of the battery, as both vary with the SoC and the error for both were compared. Other methods for deducing SoC from the OCV are [7-9] or [10-11] and they require a long time for the voltage to relax to its open circuit value. However, all the studies mentioned above do not assess the performance of the SoC estimations for a balancing system. The novelty of this study is that it aims to confirm the importance of the internal resistance for the SoC estimation, comparing two balancing algorithms: one inferring the SoC of the cell from the terminal voltage only, and one using the derived OCV (calculated with the use of the known current and internal resistance).
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Hassan, Muhammad, Muhammad Jawad, Nisma Saleem, Ali Raza, Khurram Zaidi i Nadeem Rafiq. "Solar Power Assisted Passive and Active Cell Balancing System: A Comprehensive Analysis". W 2021 International Conference on Frontiers of Information Technology (FIT). IEEE, 2021. http://dx.doi.org/10.1109/fit53504.2021.00049.
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Babu, Prathibha S., i K. Ilango. "Comparative Analysis of Passive and Active Cell Balancing of Li Ion Batteries". W 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). IEEE, 2022. http://dx.doi.org/10.1109/icicict54557.2022.9917778.
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Bonora, Matteo, i Roberto Passerone. "Optimized Passive Battery Cell Balancing Algorithm for a Low-Cost Race Car". W 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE). IEEE, 2023. http://dx.doi.org/10.1109/isie51358.2023.10228086.
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Paidi, Ramkumar, i Satish Kumar Gudey. "Active and Passive Cell Balancing Techniques for Li-Ion Batteries used in EVs". W 2022 IEEE International Power and Renewable Energy Conference (IPRECON). IEEE, 2022. http://dx.doi.org/10.1109/iprecon55716.2022.10059573.
Pełny tekst źródłaRaporty organizacyjne na temat "PASSIVE CELL BALANCING"
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Tao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir i Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, grudzień 2014. http://dx.doi.org/10.32747/2014.7594391.bard.
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Extending the previous two years of research results (Mizarch, et al, 2012, Tao, 2011, 2012), the third year’s efforts in both Maryland and Israel were directed towards the engineering of the system. The activities included the robust chick handling and its conveyor system development, optical system improvement, online dynamic motion imaging of chicks, multi-image sequence optimal feather extraction and detection, and pattern recognition. Mechanical System Engineering The third model of the mechanical chick handling system with high-speed imaging system was built as shown in Fig. 1. This system has the improved chick holding cups and motion mechanisms that enable chicks to open wings through the view section. The mechanical system has achieved the speed of 4 chicks per second which exceeds the design specs of 3 chicks per second. In the center of the conveyor, a high-speed camera with UV sensitive optical system, shown in Fig.2, was installed that captures chick images at multiple frames (45 images and system selectable) when the chick passing through the view area. Through intensive discussions and efforts, the PIs of Maryland and ARO have created the protocol of joint hardware and software that uses sequential images of chick in its fall motion to capture opening wings and extract the optimal opening positions. This approached enables the reliable feather feature extraction in dynamic motion and pattern recognition. Improving of Chick Wing Deployment The mechanical system for chick conveying and especially the section that cause chicks to deploy their wings wide open under the fast video camera and the UV light was investigated along the third study year. As a natural behavior, chicks tend to deploy their wings as a mean of balancing their body when a sudden change in the vertical movement was applied. In the latest two years, this was achieved by causing the chicks to move in a free fall, in the earth gravity (g) along short vertical distance. The chicks have always tended to deploy their wing but not always in wide horizontal open situation. Such position is requested in order to get successful image under the video camera. Besides, the cells with checks bumped suddenly at the end of the free falling path. That caused the chicks legs to collapse inside the cells and the image of wing become bluer. For improving the movement and preventing the chick legs from collapsing, a slowing down mechanism was design and tested. This was done by installing of plastic block, that was printed in a predesign variable slope (Fig. 3) at the end of the path of falling cells (Fig.4). The cells are moving down in variable velocity according the block slope and achieve zero velocity at the end of the path. The slop was design in a way that the deacceleration become 0.8g instead the free fall gravity (g) without presence of the block. The tests showed better deployment and wider chick's wing opening as well as better balance along the movement. Design of additional sizes of block slops is under investigation. Slops that create accelerations of 0.7g, 0.9g, and variable accelerations are designed for improving movement path and images.