AI-Enhanced Battery Management Systems for Electric Vehicles: Advancing Safety, Performance, and Longevity

Abstract

Electric vehicles (EVs) are essential to lowering carbon emissions and solving global environmental issues. The battery powers EVs, making its management crucial to safety and performance. As a self-check system, a Battery Management System (BMS) ensures operating dependability and eliminates catastrophic failures. As batteries age, internal resistance increases and capacity decreases, hence a BMS monitors battery health and performance in real time. EV energy storage systems (ESSs) need a complex BMS algorithm to maintain efficiency. Using battery efficiency calculations that account for charging time, current, and capacity, this approach should reliably forecast the battery's SoC and SoH. As batteries age, internal resistance increases, reducing constant current (CC) charging time. By analyzing these changes, the SoH can be predicted more precisely. Conventional methods for estimating SoC and enhancing BMS performance, such as deep neural networks, are used to minimize error rates. However, as the battery ages, AI approaches have gained prominence for their ability to provide precise diagnostics, fault analysis, and thermal management. These AI-driven techniques significantly enhance safety and reliability during charging and discharging cycles. To further ensure safety, a fault diagnosis algorithm is integrated into the BMS. This algorithm proactively addresses potential issues, thus maintaining the efficiency and safety of the battery. The effectiveness of the proposed BMS algorithms are demonstrated through its successful application in an ESS, validating its capability to manage the battery’s state, enhance performance, and ensure operational sustainability in EVs.