Abstract:

In order to better solve the online estimation problem of electric vehicle power battery health state （SOH）， reduce redundant samples in real-vehicle data collection， improve feature loss caused by unstable operating conditions， and enhance the accuracy of SOH estimation of real-vehicle batteries， a SOH estimation method based on incremental capacity analysis （ICA） method to extract features and dynamic time regularization （DTW） to optimize feature samples is proposed. Firstly， incremental capacity analysis is applied to extract the battery IC curve from the real battery charging cycle data， and shape features such as curve peak height are used as health factors. Then， dynamic time regularization is used as the similarity criterion to calculate the similarity of the battery charge cycle samples based on the IC curve shape， and the charge cycle data similar to the baseline charge cycle is retained to optimize the training samples. Finally， the fully connected neural network （MLP） model is used for SOH estimation. Comparative tests are conducted with real vehicle running battery data， and the results show that the method can significantly improve the training sample quality and enhance the battery SOH estimation accuracy.

Key words: lithium-ion battery, state-of-health estimation, capacity estimation, similar samples