SOC Estimation of Li-ion Battery Based on Gaussian mixture regression

doi:10.19799/j.cnki.2095-4239.2019.0231

Abstract

Abstract: The accuracy estimation of State of Charge (SOC) of power battery is the key evidence endurance mileage and the basic premise for the energy management for electric vehicles. To lower the battery system due to nonlinearity, unstable and other factors on battery SOC estimation. Since the state data of a lithium battery processes the characteristics of nonlinearity, unstable and outside interference. A method for SOC prediction based on Gaussian mixture regression (GMR) is proposed to solve the problem that the state date embedding abnormal value and noise of traditional Gaussian procession model. The K-means clustering algorithm and EM algorithm are used respectively to optimize Gaussian mixture model (GMM) hyper-parameters.The GMR is used to predict the output of SOC prediction. The experimental results verify and the comparative analysis of GMR and Gaussian process regression (GPR) verify that GMR algorithm has high prediction accuracy and effectiveness in the SOC estimation.

Key words: power battery, state of charge, Gaussian process regression, Gaussian mixture regression

WEI Meng, LI Jiabo, YE Min, GAO Kangping, XU Xinxinxin. SOC Estimation of Li-ion Battery Based on Gaussian mixture regression[J]. Energy Storage Science and Technology, doi: 10.19799/j.cnki.2095-4239.2019.0231.

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