A state-of-the-art review on battery cell balancing strategies

Abstract In the modern sustainable economy, batteries and their management systems are both important and critical, governing the safety, performance, and reliable operation of energy storage systems. With increasing demand for renewable energy integration, Electric Vehicles (EV), and grid stability, Battery Managment System (BMS) has become crucial in optimizing battery performance, prolonging battery lifespan, and minimizing environmental impact. Furthermore, cell balancing is one of the essential features among BMS key functionalities. It balances charge flow to the different cells in a battery pack to prevent overcharge or deep discharge to avoid deterioration or failure. Efficient cell balancing improves the energy efficiency, preserves battery health, and contributes to the sustainability objectives of electrification. Despite the important role of cell balancing, there are in a few publications that overviewed this technology, and these publications have not entirely considered balancing different aspects. To this end, the proposed review paper completely overviewed cell balancing concepts, and its different equalization topologies. Next, different aspects, and control strategies using Artificial Intelligence (AI), Machine Learning (ML), and Digital Twin (DT) concepts are classified, taken into consideration some of their regular models (such as Reinforcement Learning—RL), and also next-generation models (such as Quantum Neural Networks-QNN) described. Finally, in order to fully maintain the importance of BMS, and cell balancing, batteries' different standards, financed projects, as well as future research topics have been provided for further developments.