State-Of-Charge Estimation for Lithium-Ion Battery Using Improved DUKF Based on State-Parameter Separation

Chuan-Xiang Yu, Yan-Min Xie, Zhao-Yu Sang, Shi-Ya Yang and Rui Huang
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Chuan-Xiang Yu: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China
Yan-Min Xie: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China
Zhao-Yu Sang: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China
Shi-Ya Yang: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China
Rui Huang: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China

Energies, 2019, vol. 12, issue 21, 1-19

Abstract: State-of-charge estimation and on-line model modification of lithium-ion batteries are more urgently required because of the great impact of the model accuracy on the algorithm performance. This study aims to propose an improved DUKF based on the state-parameter separation. Its characteristics include: (1) State-Of-Charge (SoC) is treated as the only state variable to eliminate the strong correlation between state and parameters. (2) Two filters are ranked to run the parameter modification only when the state estimation has converged. First, the double polarization (DP) model of battery is established, and the parameters of the model are identified at both the pulse discharge and long discharge recovery under Hybrid Pulse Power Characterization (HPPC) test. Second, the implementation of the proposed algorithm is described. Third, combined with the identification results, the study elaborates that it is unreliable to use the predicted voltage error of closed-loop algorithm as the criterion to measure the accuracy of the model, while the output voltage obtained by the open-loop model with dynamic parameters can reflect the real situation. Finally, comparative experiments are designed under HPPC and DST conditions. Results show that the proposed state-parameter separated IAUKF-UKF has higher SoC estimation accuracy and better stability than traditional DUKF.

Keywords: lithium-ion batteries; SoC estimation; state-parameter separation; improved dual unscented Kalman filter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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