A Fusion Method to Estimate the State-of-Health of Lithium-ion Batteries

Abstract

Accurate state-of-health (SOH) estimation for Lithiumion batteries (LIBs) is vital for the battery management systems (BMS). This paper puts forward a fusion method to estimate battery SOH, which incorporates the incremental capacity analysis (ICA) with the long short-term memory (LSTM) network. First, a revised Lorentzian function-based voltage-capacity (VC) model is adopted to capture the IC curve. By leveraging merely data logged during the constant current (CC) charging stage, battery degradation information contained in the IC curve is concretized as the parameters of the VC model by simple curve fitting. These parameters with specific physical meanings are deemed as features that characterize battery health status. Correlation analysis is then performed for these features, and features of interest (FOIs) are selected as inputs of the LSTM. The LSTM model can learn the long-term dependencies of battery degradation, and thus improve the robustness of the prediction model against noise. Finally, four battery aging datasets with different chemistries are employed for model validation, and results reveal that the proposed method can achieve accurate SOH estimation results, with the maximum mean absolute errors limited within 2%.