Abstract:

Based on the complex operation data of real-vehicle batteries， the IC peak features are extracted as effective features of battery charging segments using incremental capacity analysis method in this paper， and the IC peak features are processed using t-SNE nonlinear dimensionality reduction method to eliminate the redundancy of multidimensional features and solve the problem that it is difficult to extract reliable features from real-vehicle data. In addition， a support vector regression model is constructed to estimate the battery health status. The results show that the use of incremental capacity curve peak features can effectively characterize the recession changes of the battery health state. The smoothing and noise reduction methods for real vehicle data can improve the quality of training data better. The SVR model based on t-SNE dimensionality reduction features improves the estimation accuracy of battery health state and ensures accurate estimation on a limited sample data set.

Key words: power battery, SOH estimation, nonlinear dimensionality reduction, machine learning