Review of Optimization‐Based Sensor Fault Detection for Lithium‐Ion Batteries in Electric Vehicles

The electrical vehicle (EV) market is evolving rapidly with continuous advancements in charging infrastructure, battery technology, and a range of capabilities. Lithium-ion batteries are considered the mainstream energy storage systems for EVs because of their long life span, better power concentration, and high energy concentration. However, the safety issues of batteries are the main obstacles restricting the rapid development of EVs. Owing to the reduced resistance to abuse, operating conditions, and outside factors, several faults occur in battery technology, resulting in quicker battery depreciation and accidents such as explosions, fires, and runaway reactions of heat. Thus, fault diagnosis in batteries is important for improving safety. Faults in charge storage systems are categorized as actuator, sensor, and battery faults. Hence, different approaches used in diagnosing battery faults, particularly lithium-ion batteries, were reviewed in this study. Furthermore, a comparative analysis of traditional fault detection techniques and meta-heuristic-based fault detection methods is performed. In addition, a critical analysis was performed to summarize the focus of existing works in this area. This would help future researchers solve the predominant challenges in this area, thereby attempting to bring innovations for effective fault detection.