Passive Cell Balancing of Li-Ion batteries used for Automotive Applications
01 Dec 2020-Vol. 1716, Iss: 1, pp 012005
TL;DR: This research paper begins with battery modelling using passive components and discusses the major factors which are important while designing an effective BMS, and provides simulation to help better understand the functioning of a BMS.
Abstract: Due to the increasing demand of Li-ion batteries in automobiles and home applications due to their high volumetric energy density, high gravimetric energy density, low self-discharge and high efficiency. However, due to their high energy carrying capability, they tend to be more unstable compared to other batteries like lead acid batteries and hence need extensive monitoring to make sure they are operating within their specified safe operating limits; failing to do so may result in fire hazards and explosions. Hence this creates a demand for sophisticated Battery Management System (BMS) which will not only optimize power draw from the batteries but also keep them operating within safe limits, thus not putting the users at risk. This research paper begins with battery modelling using passive components and discusses the major factors which are important while designing an effective BMS. It also provides simulation to help better understand the functioning of a BMS.
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TL;DR: In this paper, the optimal selection of balancing resistor with respect to degree of cell imbalance, balancing time, C-rate, and temperature rise using machine learning (ML) based balancing control algorithm is proposed to improve the balancing time and optimal power loss management.
Abstract: Cell balancing is a vital function of battery management system (BMS), which is implemented to extend the battery run time and service life. Various cell balancing techniques are being focused due to the growing requirements of larger and superior performance battery packs. The passive balancing approach is the most popular because of its low cost and easy implementation. As the balancing energy is dissipated as heat by the balancing resistors, an appropriate thermal scheme of the balancing system is necessary, to keep the BMS board temperature under a tolerable limit. In this paper, optimum selection of balancing resistor with respect to degree of cell imbalance, balancing time, C- rate, and temperature rise using machine learning (ML) based balancing control algorithm is proposed to improve the balancing time and optimal power loss management. Variable resistors are utilised in the passive balancing system, in order to optimize the power loss and to obtain optimal thermal characterization. The performance of the proposed system is evaluated using back propagation neural network (BPNN), radial basis neural network (RBNN), and long short term memory (LSTM). Error analysis of the balancing system is done to optimize balancing parameters and the proposed algorithms are compared using performance indices such as mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) to validate the balancing model performance. The possible optimization scope for implementing passive balancing using machine learning algorithms are experimented in the Matlab-Simscape environment.
17 citations
23 Sep 2022
TL;DR: In this paper , a Proportional Integral Controller (PIC) was implemented in a MATLAB Simulink environment and the results obtained show remarkable improvement in the SoC and voltage levels of the batteries after the balancing operation compared to the previous works.
Abstract: To save mankind from the impending disaster of global warming, the transition of transport away from fossil fuels is crucial. Experimentations are going on for the development of some appropriate substitutes for fossil fuel-based vehicles, which has led to the emergence of Electric Vehicles (EVs), From the literature, it is found that the Battery Management System (BMS) is of vital concern to the researchers since the battery is one of the primary components of EV. BMS comprises the cell balancing phenomenon for balancing the battery's State of Charge (SoC) and thereby voltage levels. There are mainly two cell balancing methods, namely active and passive cell balancing methods, of which the passive method has proved to be more efficient for low-power system applications. Work has been done on conventional logic-based Passive Cell Balancing (PCB). This work focuses on introducing and implementing a Proportional Integral controller in PCB. It is executed in a MATLAB Simulink environment and the results obtained show remarkable improvement in the SoC and voltage levels of the batteries after the balancing operation compared to the previous works. Also, the balancing time required has reduced drastically, proving it to be a very effective technique for cell balancing in EVs.
2 citations
07 Apr 2022
TL;DR: In this paper , an active cell balancer that supports bidirectional cell balancing operation with very high efficiency is proposed, which is designed using MATLAB/Simulink for a commercially available 14S10P 48V/32Ah battery pack.
Abstract: The remaining useful life of a lithium ion cell module is determined by its rate of getting charged and discharged. To extend this life, the cell modules need to age uniformly. This aging process is determined by the amount of energy stored in a cell module called state of charge. In order to maintain this state of charge uniformly across cell modules, a cell balancer is needed. This paper proposes a novel active cell balancer that supports bidirectional cell balancing operation with very high efficiency. The active cell balancer is designed using MATLAB/Simulink for a commercially available 14S10P 48V/32Ah battery pack. An overall efficiency of 96.8% is achieved during the cell balancing operation.
2 citations
23 Sep 2022
TL;DR: In this article , a Proportional Integral Controller (PIC) was implemented in a MATLAB Simulink environment and the results obtained show remarkable improvement in the SoC and voltage levels of the batteries after the balancing operation compared to the previous works.
Abstract: To save mankind from the impending disaster of global warming, the transition of transport away from fossil fuels is crucial. Experimentations are going on for the development of some appropriate substitutes for fossil fuel-based vehicles, which has led to the emergence of Electric Vehicles (EVs), From the literature, it is found that the Battery Management System (BMS) is of vital concern to the researchers since the battery is one of the primary components of EV. BMS comprises the cell balancing phenomenon for balancing the battery's State of Charge (SoC) and thereby voltage levels. There are mainly two cell balancing methods, namely active and passive cell balancing methods, of which the passive method has proved to be more efficient for low-power system applications. Work has been done on conventional logic-based Passive Cell Balancing (PCB). This work focuses on introducing and implementing a Proportional Integral controller in PCB. It is executed in a MATLAB Simulink environment and the results obtained show remarkable improvement in the SoC and voltage levels of the batteries after the balancing operation compared to the previous works. Also, the balancing time required has reduced drastically, proving it to be a very effective technique for cell balancing in EVs.
1 citations
TL;DR: A customized passive battery management system, which offers a selection of different operating configurations regarding the connection of external sources and loads, has been developed and the unique capabilities of the device are explained.
Abstract: A customized passive battery management system (BMS), which offers a selection of different operating configurations regarding the connection of external sources and loads, has been developed. The device supports balance, charge, de-balance, discharge and permanent storage battery processes. The control unit is run by its own written algorithm (code). Suggestions for potential hardware and software changes that can be made to expand the capabilities of the device are listed. The device is tested in five different operating configurations and the output data (battery-cell voltages and balancing currents) are plotted in characteristic diagrams. The output data is analyzed and the unique capabilities of the device are explained. The detailed PCB design, code, and output measurement data files are included within the paper.
1 citations
References
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TL;DR: The review presents the key feedback factors that are indispensable for accurate estimation of battery SoC, and presents the possible recommendations for the development of next generation of smart SoC estimation and battery management systems for electric vehicles and battery energy storage system.
Abstract: Battery technology is the bottleneck of the electric vehicles (EVs). It is important, both in theory and practical application, to do research on the modeling and state estimation of batteries, which is essential to optimizing energy management, extending the life cycle, reducing cost, and safeguarding the safe application of batteries in EVs. However, the batteries, with strong time-variables and nonlinear characteristics, are further influenced by such random factors such as driving loads, operational conditions, in the application of EVs. The real-time, accurate estimation of their state is challenging. The classification of the estimation methodologies for estimating state-of-charge (SoC) of battery focusing with the estimation method/algorithm, advantages, drawbacks, and estimation error are systematically and separately discussed. Especially for the battery packs existing of the inevitable inconsistency in cell capacity, resistance and voltage, the advanced characterizing monomer selection, and bias correction-based method has been described and discussed. The review also presents the key feedback factors that are indispensable for accurate estimation of battery SoC, it will be helpful for ensuring the SoC estimation accuracy. It will be very helpful for choosing an appropriate method to develop a reliable and safe battery management system and energy management strategy of the EVs. Finally, the paper also highlights a number of key factors and challenges, and presents the possible recommendations for the development of next generation of smart SoC estimation and battery management systems for electric vehicles and battery energy storage system.
544 citations
TL;DR: This review will hopefully lead to increasing efforts toward the development of an advanced Li-ion battery in terms of economics, longevity, specific power, energy density, safety, and performance in vehicle applications.
Abstract: A variety of rechargeable batteries are now available in world markets for powering electric vehicles (EVs). The lithium-ion (Li-ion) battery is considered the best among all battery types and cells because of its superior characteristics and performance. The positive environmental impacts and recycling potential of lithium batteries have influenced the development of new research for improving Li-ion battery technologies. However, the cost reduction, safe operation, and mitigation of negative ecological impacts are now a common concern for advancement. This paper provides a comprehensive study on the state of the art of Li-ion batteries including the fundamentals, structures, and overall performance evaluations of different types of lithium batteries. A study on a battery management system for Li-ion battery storage in EV applications is demonstrated, which includes a cell condition monitoring, charge, and discharge control, states estimation, protection and equalization, temperature control and heat management, battery fault diagnosis, and assessment aimed at enhancing the overall performance of the system. It is observed that the Li-ion batteries are becoming very popular in vehicle applications due to price reductions and lightweight with high power density. However, the management of the charging and discharging processes, CO2 and greenhouse gases emissions, health effects, and recycling and refurbishing processes have still not been resolved satisfactorily. Consequently, this review focuses on the many factors, challenges, and problems and provides recommendations for sustainable battery manufacturing for future EVs. This review will hopefully lead to increasing efforts toward the development of an advanced Li-ion battery in terms of economics, longevity, specific power, energy density, safety, and performance in vehicle applications.
469 citations
TL;DR: The results indicate that the fuzzy logic energy-management system using the BWS was effective in ensuring that the engine operates in the vicinity of its maximum fuel efficiency region while preventing the battery from over-discharging.
Abstract: Fuzzy logic is used to define a new quantity called the battery working state (BWS), which is based on both battery terminal voltage and state of charge (SOC), to overcome the problem of battery over-discharge and associated damage resulting from inaccurate estimates of the SOC. The BWS is used by a fuzzy logic energy-management system of a plug-in series hybrid electric vehicle (HEV) to make a decision on the power split between the battery and the engine, based on the BWS and vehicle power demand, while controlling the engine to work in its fuel economic region. The fuzzy logic management system was tested in real time using an HEV simulation test bench with a real battery in the loop. Simulation results are presented to demonstrate the performance of the proposed fuzzy logic energy-management system under different driving conditions and battery SOCs. The results indicate that the fuzzy logic energy-management system using the BWS was effective in ensuring that the engine operates in the vicinity of its maximum fuel efficiency region while preventing the battery from over-discharging.
289 citations
TL;DR: An effective health indicator to indicate lithium-ion battery state of health and moving-window-based method to predict battery remaining useful life and capacity estimation results show that the capacity estimation errors were within 1.5%.
Abstract: This paper developed an effective health indicator to indicate lithium-ion battery state of health and moving-window-based method to predict battery remaining useful life. The health indicator was extracted based on the partial charge voltage curve of cells. Battery remaining useful life was predicted using a linear aging model constructed based on the capacity data within a moving window, combined with Monte Carlo simulation to generate prediction uncertainties. Both the developed capacity estimation and remaining useful life prediction methods were implemented based on a real battery management system used in electric vehicles. Experimental data for cells tested at different current rates, including 1 and 2 C, and different temperatures, including 25 and 40 °C, were collected and used. The implementation results show that the capacity estimation errors were within 1.5%. During the last 20% of battery lifetime, the root-mean-square errors of remaining useful life predictions were within 20 cycles, and the 95% confidence intervals mainly cover about 20 cycles.
242 citations
Journal Article•
TL;DR: In this paper, the authors present a method to determine the state of charge (SOC) of lithium-ion batteries on the basis of two different equivalent circuit diagrams and an extended Kalman filter (EKF).
Abstract: This paper presents the fundamentals of a method how to determine the state of charge (SOC) of lithium-ion batteries on the basis of two different equivalent circuit diagrams and an extended Kalman filter (EKF). It describes how to identify the parameters of these circuits by characteristic measurements. The comparison between measurement and computation results shows a good accordance. In the first step the dependency of these parameters on the temperature and on the battery age is neglected. Streszczenie. W artykule przedstawiono podstawy metody pozwalającej określic stan naladowania (SOC) akumulatorow litowo-jonowych na podstawie dwoch roznych schematow zastepczych i rozszerzonego filtru Kalmana (EKF). Opisano, jak zidentyfikowac parametry akumulatorow na podstawie pomiarow ich charakterystyk. Porownanie wynikow pomiarow z wynikami symulacji wykazuje zgodnośc. W pierwszym etapie pominieto zaleznośc parametrow akumulatorow od temperatury i od czasu uzytkowania.( Modelowanie akumulatorow litowo-jonowych z wykorzystaniem schematow zastepczych)
97 citations