Recent Advancements in Cell Balancing Techniques of BMS for EVs: A Critical Review

Abstract

Recently, a severe danger has evolved regarding the explosion of Electric Vehicle (EV) batteries due to their thermal issues. A proficient system is employed for managing the operations of the battery, which is the Battery Management System (BMS). A vital role of the BMS is Cell Balancing (CB). This work emphasized reviewing the practical and recent advancements in CB techniques of BMS for EVs. The latest developments in the design and operation of BMS implementing Artificial Intelligence (AI), Machine Learning (ML), and Artificial Neural Network (ANN)-based CB techniques are also explored and analyzed here. CB phenomenon is largely grouped as the Active Balancing (AB) and Passive Balancing (PB) methods. The different AB and PB techniques are elaborately illustrated with appropriate descriptions, circuit diagrams, model equations, and tables. The pros, cons, and practical applications of each technique are highlighted through the recent case studies. The current during the balancing gets decreased from 1.56 A–0.87 A–0.2 A by the customary PB technique, the Proportional Integral (PI)-controller, and the ANN-based BMS sequentially. As a result, the dissipated heat is reduced from 2.02 KJ–0.19 KJ–0.01 KJ, and the rise in temperature gets reduced from 2.35 °C–1.03 °C–0.1 °C. This implies that the ANN-based BMS provides economical designing, superior performance, and enhanced efficiency than the PI-based and customary PB techniques. Therefore, this latest technology can satisfactorily increase the battery lifecycle and driving range of EVs.