Electric vehicle state of charge estimation: Nonlinear correlation and fuzzy support vector machine

• We propose a measure of nonlinear correlation.
• We propose a reliability identification method for multidimensional samples.
• We propose an error interval for SVM regression.
• The daily use of electric vehicle is simulated in a filed test.

The aim of this study is to estimate the state of charge (SOC) of the lithium iron phosphate (LiFePO4) battery pack by applying machine learning strategy. To reduce the noise sensitive issue of common machine learning strategies, a kind of SOC estimation method based on fuzzy least square support vector machine is proposed. By applying fuzzy inference and nonlinear correlation measurement, the effects of the samples with low confidence can be reduced. Further, a new approach for determining the error interval of regression results is proposed to avoid the control system malfunction. Tests are carried out on modified COMS electric vehicles, with two battery packs each consists of 24 50 Ah LiFePO4 batteries. The effectiveness of the method is proven by the test and the comparison with other popular methods.