1. School of Automation Wuhan University of Technology Wuhan 430070 China; 2. School of Automotive Engineering Wuhan University of Technology Wuhan 430070 China
Abstract：The inaccurate battery models and particle degeneration problems often result in estimation errors or even divergence over time using the traditional unscented Kalman filter(UKF) and particle filter (PF) algorithms to estimate the state of charge(SOC) of power battery. In this study, an innovation method based on the unscented particle filter(UPF) is presented to suppress the particle degeneracy and noise interference. The unscented Kalman algorithm is used to calculate the mean and covariance for each particle and solve the problem of particle degeneration in particle filter technology. Through the lithium-ion battery charge-discharge test, the equivalent model is identified, and finally the algorithm is tested and verified under the pulse charge-discharge and UDDS dynamic conditions. The results show that the UPF method based on the two RC equivalent circuit model can improve the real-time performance and the precision of SOC estimation, and the estimation accuracy is less than 2%, the convergence rate is less than 250 s.
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2018, Vol. 33 
