Energy Storage Science and Technology ›› 2020, Vol. 9 ›› Issue (3): 958-963.doi: 10.19799/j.cnki.2095-4239.2019.0231

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SOC estimation of Li-ion battery based on gaussian mixture regression

WEI Meng(), LI Jiabo, YE Min, GAO Kangping, XU Xinxin   

  1. Highway Maintenance Equipment National Engineering Laboratory, Chang'an University, Xi'an 710064, Shaanxi, China
  • Received:2019-10-14 Revised:2019-11-27 Online:2020-05-05 Published:2020-05-11

Abstract:

The state of charge (SOC) of a power battery must be accurately estimated as it determines the endurance mileage and is the basic premise for the energy management of electric vehicles. However, SOC estimation of battery systems is degraded by nonlinearity, instability, and other factors. Accordingly, the characteristic state data of a lithium battery contain nonlinearities, fluctuations, and external interference. This study proposes an SOC prediction method based on Gaussian mixture regression (GMR), which resolves the problems of abnormal values embedded in the state data and noise in the traditional Gaussian procession model (GPM). The hyper-parameters of the Gaussian mixture model are sequentially optimized by k-means clustering and an EM algorithm. The GMR predicts the SOC output. In an experimental validation and a comparative analysis of GMR and GPM, the GMR algorithm achieved superior prediction accuracy and effectiveness in SOC estimation.

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

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