Lithium-ion Battery State of Health Estimation Using Empirical Mode Decomposition Sample Entropy and Support Vector Machine

Accurate knowledge of the state of health (SOH) is important for battery management system to maintain safe operation of batteries and extend their lifetime. In order to improve the accuracy in estimating the SOH of a battery, a new method based on empirical mode decomposition sample entropy (EMDSE) and support vector machine (SVM) is proposed in this paper. Compared with the tradition SE-based method, EMD is used to filter the noise of the original signal. Then the EMDSE as a new feature is used for the SVM training, and the potential relationship between the SOH and the EMDSE is established. Finally, the effectiveness of the proposed method is verified by experimental results.