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.
Highlights
Electric vehicles (EVs) play a very important role to minimize the local concentrations of toxins in urban and rural areas
Based on the study and analysis done so far, the key problems identified in the conventional passive cell balancing system are as follows: In the passive balancing system heat dissipation from the balancing resistor leads to thermal challenges Passive balancing is preferably done during charging and it may not be a feasible option under fast charging scenario due to high balancing time
IV RESULTS & DISCUSSION In this unique Machine learning statistical based approach, the comparison and bench marking of the results are done with different machine learning (ML) models
Summary
Electric vehicles (EVs) play a very important role to minimize the local concentrations of toxins in urban and rural areas. The growing dependence on EVs necessitates high voltage, high efficiency and long life-span battery systems, which needs optimized battery monitoring algorithms [2]. To balance the cell voltages the passive system uses balancing resistors to dissipate extra charge from the overcharged cells [10] whereas, the active system transfers extra charge from highly charged cells to low charged cells trying to preserve energy in the battery pack. This requires more expensive and complex hardware solutions [11].
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