Online Estimation of Open Circuit Voltage Based on Extended Kalman Filter with Self-Evaluation Criterion

Xin Qiao, Zhixue Wang, Enguang Hou, Guangmin Liu and Yinghao Cai
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Xin Qiao: School of Rail Transportation, Shandong Jiaotong University, Jinan 250357, China
Zhixue Wang: School of Rail Transportation, Shandong Jiaotong University, Jinan 250357, China
Enguang Hou: School of Rail Transportation, Shandong Jiaotong University, Jinan 250357, China
Guangmin Liu: School of Rail Transportation, Shandong Jiaotong University, Jinan 250357, China
Yinghao Cai: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Energies, 2022, vol. 15, issue 12, 1-22

Abstract: Open circuit voltage (OCV) is crucial for battery degradation analysis. However, high-precision OCV is usually obtained offline. To this end, this paper proposes a novel self-evaluation criterion based on the capacity difference of State of Charge (SoC) unit interval. The criterion is integrated into extended Kalman filter (EKF) for joint estimations of OCV and SoC. The proposed method is evaluated in a typical application scenario, energy storage system (ESS), using a LiFePO 4 (LFP) battery. Extensive experimental results show that a more accurate OCV and incremental capacity and differential voltage (IC-DV) can be achieved online with the proposed method. Our method also greatly improves the accuracy of SoC estimation at each SoC point where the maximum estimation error of SoC is less than 0.3%.

Keywords: lithium-ion battery; open circuit voltage; incremental capacity and differential voltage curves; extended Kalman filter; self-evaluation criterion (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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