State of charge estimation for lithium-ion batteries: An adaptive approach

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• 作者：Huazhen Fang ; Yebin Wang ; Zafer Sahinoglu ; Toshihiro Wada ; Satoshi Hara
• 关键词：State of charge ; Li+ battery ; State and parameter estimation ; Adaptive estimation ; Iterated extended Kalman filter
• 刊名：Control Engineering Practice
• 出版年：April, 2014
• 年：2014
• 卷：25
• 期：Complete
• 页码：45-54
• 全文大小：1168 K

文摘

State of charge (SoC) estimation is of key importance in the design of battery management systems. An adaptive SoC estimator, which is named AdaptSoC, is developed in this paper. It is able to estimate the SoC in real time when the model parameters are unknown, via joint state (SoC) and parameter estimation. The AdaptSoC algorithm is designed on the basis of three procedures. First, a reduced-complexity battery model in state-space form is developed from the well-known single particle model (SPM). Then a joint local observability/identifiability analysis of the SoC and the unknown model parameters is performed. Finally, the SoC is estimated simultaneously with the parameters using the iterated extended Kalman filter (IEKF). Simulation and experimental results exhibit the effectiveness of the AdaptSoC.