Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range

Da Li, Lu Liu, Chuanxu Yue, Xiaojin Gao and Yunhai Zhu ()
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Da Li: Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
Lu Liu: Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
Chuanxu Yue: Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
Xiaojin Gao: Science and Technology Service Platform, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
Yunhai Zhu: Science and Technology Service Platform, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China

Energies, 2025, vol. 18, issue 7, 1-19

Abstract: The state of charge ( SOC ) of lithium-ion batteries is essential for their proper functioning and serves as the basis for estimating other parameters within the battery management system. To enhance the accuracy of SOC estimation in lithium-ion batteries, we propose a joint estimation method that integrates lithium-ion battery parameter identification and SOC assessment using cat swarm optimization dual Kalman filtering (CSO–DKF), which accounts for variable-temperature conditions. We adopt a second-order equivalent circuit model, utilizing the Kalman filtering (KF) algorithm as a parameter filter for dynamic parameter identification, while the extended Kalman filtering (EKF) algorithm acts as a state filter for real-time SOC estimation. These two filters operate alternately throughout the iterative process. Additionally, the cat swarm optimization (CSO) algorithm optimizes the noise covariance matrices of both filters, thereby enhancing the precision of parameter identification and SOC estimation. To support this algorithm, we establish an environmental temperature battery database and incorporate temperature variables to achieve accurate SOC estimation under variable-temperature conditions. The results indicate that creating a database that accommodates temperature variations and optimizing dual Kalman filtering through the cat swarm optimization algorithm significantly improves SOC estimation accuracy.

Keywords: variable temperature; environmental temperature battery database; cat swarm optimization algorithm; dual Kalman filtering; SOC estimation (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: 2025
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