Applications of artificial intelligence and cell balancing techniques for battery management system (BMS) in electric vehicles: A comprehensive review

Electric Vehicles are considered to be dominant in the land transport system in the near future in context to decarbonisation and sustainability. Compared to conventional vehicles, electric and hybrid ones can reduce pollutants significantly. In present researches, there are many different chemistries of battery available for EV's application but the technology which has been adopted for an effective solution for energy storage is mostly based on Li-ion. But Li-ion battery packs are highly acknowledged in EV industry as a result of their outstanding specific energy density, lifecycle, nominal voltage, low cost& & performance. Monitoring on state estimation during charging and discharging becomes important so that battery pack can be utilized in better and effective manner for optimum output. Li-ion battery packs represent the soul of the majority of electric vehicles. So, its proper safe operating region must be clearly specified for desired output. To tackle these concerns, Battery Management System is such an important embedded mechanism to enhance the effectiveness of performance of the battery pack which includes precise monitoring, supervision of charging-discharging phenomenon, cell balancing, thermal management, safety of battery pack. The various intelligent strategies and cell balancing strategies used for the battery management system in EVs have been analysed i.e., review assesses experimental, model-based, and data-driven approaches. The detailed analysis has been incorporated in this review for intelligent algorithms i.e. FLC, SVM, PSO, ANN, and GA for battery SOC estimation in terms of their types, features, accuracy, key advantages, and key limitations.