A Review of Current Approaches to Fault Diagnosis in Lithium-Ion Batteries

Abstract

In the age of rapid digital transformation, electric vehicles have become more and more common, and have gradually displaced the status of diesel. It is essential for people to pay more attention to the safety of lithium batteries. To illustrate, a lithium-ion battery is prone to error under long-term charging or discharging and high temperature. If the fault diagnosis is not at the right time, it might lead to the declining performance of the lithium-ion battery and shorten the servicing life. This study aims to discuss the technologies for detecting the error in lithium batteries, which are categorized into three forms: Model-based method, Signal processing-based method and Machine learning method. Meanwhile, these technologies have been widely applied in the area of electric vehicle and battery management systems, detecting the state of batteries in real-time. Overall, this study considers that there are no any fault diagnosis methods that are suitable for all the application scenarios, and researchers should choose the approach according to their needs. The future development trend is the combination of various fault diagnosis techniques, improving the accuracy, reliability and performance.