Battery health analysis of electric vehicle based on EL-SVR

Lithium-ion battery has become an indispensable energy storage component in our life because of its environmental protection and high energy characteristics. The battery SOH is the decisive factor to ensure its stability. For the sake to improve the accuracy of EV battery SOH prediction. Firstly, data structuring, PCA dimension reduction and data standardization were used to transform downloaded data into data that could be trained with high accuracy model. After that, the characteristic factors related to battery capacity were extracted from the battery charging data and correlation analysis was carried out. According to the method of Pearson coefficient, the features with strong correlation were left and then imported into the sample data. The factor parameters of SVR and other models were optimized by grid search algorithm, and the final prediction model was established. Lithium-ion battery has become an indispensable energy storage component in our life because of its environmental protection and high energy characteristics. The battery SOH is the decisive factor to ensure its stability.