Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

• Published in: IEEE access Vol. 9; pp. 132846 - 132861
• Main Authors: Duraisamy, Thiruvonasundari, Kaliyaperumal, Deepa
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Back propagation networks; Balancing; Batteries; battery management system; Control algorithms; Control theory; Electric vehicle; Electric vehicles; Error analysis; Integrated circuit modeling; Machine learning; Machine learning algorithms; Mean square errors; Neural networks; Optimization; passive cell balancing; Performance indices; Power management; Resistors; Root-mean-square errors; Service life; State of charge; Thermodynamic properties
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3115255

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.