Data-Driven Methods for Battery SOH Estimation: Survey and a Critical Analysis

State-of-health (SOH) estimation is a critical factor in ensuring the efficiency, reliability, and safety of lithium-ion batteries (LIBs) in electric vehicles (EVs). However, due to the complexity of electrochemical processes in batteries and the dynamics of working conditions, it is challenging to estimate SOH accurately, especially in real-world EV application scenarios. Thus, various data-driven methods with robust and adaptive features for SOH estimation have been widely proposed in the current literature. However, there is a lack of a comprehensive investigation and performance comparison of those methods, which makes them hard to be adopted in practice. Hence, in this paper, we have studied current major data-driven methods with real-world EV battery data to evaluate the performance. Besides, we summarize each method's advantages and limitations with the consideration of the critical features required to achieve accurate SOH estimation in real-world applications. Hopefully, this paper provides a practical insight into the related fields.

Automated summary

Accurate estimation of state-of-health (SOH) is crucial for the efficiency and safety of lithium-ion batteries in electric vehicles. Data-driven methods show potential for accurate SOH estimation, but there is a lack of comprehensive research and performance comparison among these methods.