基于无迹粒子滤波的车载锂离子电池状态估计

doi:10.19595/j.cnki.1000-6753.tces.171195

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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.

Key words： State of charge lithium-ion battery unscented Kalman filter particle filter unscented particle filter

Received: 18 August 2017 Published: 14 September 2018

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TM911

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	Articles by authors
	Xie Changjun
	Fei Yalong
	Zeng Chunnian
	Fang Wei

Cite this article:

Xie Changjun,Fei Yalong,Zeng Chunnian等. State-of-Charge Estimation of Lithium-Ion Battery Using Unscented Particle Filter in Vehicle[J]. Transactions of China Electrotechnical Society, 2018, 33(17): 3958-3964.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.171195 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I17/3958

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