Abstract
State of health (SOH) estimation is one of the essential functions of an electric vehicle battery management system. An accurate estimation result is conducive to extending the life of lithium-ion batteries and ensuring vehicles' safe and reliable operation. Based on data-driven SOH estimation of the lithium-ion battery, a cross-entropy feature selection method is proposed in this work. Firstly, 51 features are extracted based on the partial charging voltage and incremental capacity curves, and each feature is evaluated by the Pearson correlation coefficient, the Kendall correlation coefficient, and cross-entropy, respectively. Then, features with an absolute value greater than 0.9 are picked out. Finally, selected feature sets are compared via the back propagation neural network, Gaussian process regression, and random forest. Experimental results show that the feature selection method based on cross-entropy achieves better SOH estimation accuracy.
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