A comparative study of fault diagnostic methods for lithium-ion batteries based on a standardized fault feature comparison method

Lithium-ion batteries are widely used in cleaner productions such as electric vehicles and energy storage systems, but the frequent occurrence of battery safety accidents also restrict their large-scale promotion and application. Fault diagnosis is critical for the safe operations of lithium-ion batteries, which has become an important function for battery management systems. Many types of fault diagnosis methods have been proposed for lithium batteries, however, the differences in parameter dimension and diagnosis strategy among diagnosis methods make it difficult to compare, resulting in the inability to choose the appropriate method in real applications. This paper proposes a standardized fault feature comparison method to quantitatively study the sensitivity and robustness of different fault diagnostic methods for lithium-ion batteries under different failure degrees, ambient temperatures, state of charges, and aging levels. The diagnostic methods based on battery model, sample entropy and correlation coefficient are constructed. Different degrees of short-circuit experiments under dynamic current conditions are used to prove the effectiveness of three diagnostic methods. The standardized fault feature comparison method is utilized to dequantize and standardize the fault signals, and then the diagnostic performance of three diagnostic methods is intuitively compared. Furthermore, the comparative conclusions of sensitivity and robustness of different methods are obtained, which prove that the selection of parameters and diagnostic thresholds in battery fault diagnostic methods needs to fully consider environmental conditions and battery status. Some methods need to clarify the usable range or make improvements under low temperature or large inconsistencies. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Lithium-ion batteries are widely used in cleaner productions such as electric vehicles and energy storage systems, but the frequent occurrence of battery safety accidents also restrict their large-scale promotion and application. This study proposes a standardized fault feature comparison method to quantitatively study the sensitivity and robustness of different fault diagnostic methods for lithium-ion batteries under various conditions. The results highlight the importance of considering environmental conditions and battery status when choosing parameters and diagnostic thresholds in battery fault diagnostic methods.