SOC estimation of lithium-ion batteries for electric vehicles based on multimode ensemble SVR

The state of charge (SOC) of a battery plays an important role in the battery management system (BMS) of electric vehicles (EVs), since this provides the available runtime for users. However, since driving conditions are various, the monitored battery data (voltage, current, etc.) are also different. If mixed data are used to build an SOC estimation model, the accuracy of the model is low. On the other hand, using only one kind of data set, results in an intelligent model with poor stability and generalization. To resolve these problems, a novel multimode ensemble support vector regression (ME-SVR) method is proposed to estimate SOC. In this method, considering the characters of battery data, the original data set is divided into multiple data subsets by a clustering algorithm. Then, an SVR estimation model is established for each data subset. Finally, the estimation results of multiple SVRs are integrated and the output is obtained according to the weighted average idea of ensemble learning. The experimental results under different driving conditions reveal that this novel algorithm can significantly improve SOC estimation accuracy and enhance the stability and generalization of the model.

Automated summary

This article introduces a novel multimode ensemble support vector regression (ME-SVR) method, which divides the original data set into multiple subsets using a clustering algorithm, establishes an SVR estimation model for each subset, integrates the results of multiple SVRs, and significantly improves SOC estimation accuracy and enhances the stability of the model.