LITHIUM-ION BATTERY STATE ESTIMATION METHOD BASED ON SUKF-EKF FUSION

Wang Qiwen; Sheng Qiang; He Yucai

doi:10.19912/j.0254-0096.tynxb.2025-0830

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 11-21. DOI: 10.19912/j.0254-0096.tynxb.2025-0830

LITHIUM-ION BATTERY STATE ESTIMATION METHOD BASED ON SUKF-EKF FUSION

Wang Qiwen¹, Sheng Qiang¹, He Yucai²

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Abstract

To address the insufficient accuracy and poor adaptability to parameter variations of Extended Kalman Filter （EKF） in lithium-ion battery state estimation, this paper proposes a method utilizing EKF for online battery parameter identification and updating, while integrating Sliding Mode Observer （SMO） and Unscented Kalman Filter （UKF） for lithium battery state estimation （SUKF）. The method establishes a dual-layer temporal scale collaborative estimation architecture, employing EKF for online battery parameter identification and State of Health （SOH） estimation, while combining SMO’s robustness with UKF’s nonlinear processing capabilities to achieve high-precision State of Charge （SOC） estimation. Verification through three representative testing environments—highway fuel economy test conditions, New European driving cycle, and urban dynamometer driving schedule—demonstrates that the fusion algorithm achieves average SOC estimation errors of 0.13%, 0.25%, and 0.14% respectively, with maximum errors constrained within 0.46%, representing over 85% improvement in accuracy compared to traditional EKF algorithms. SOH estimation under varying complexity conditions exhibits average errors ranging from 0.022% to 0.16%, with maximum errors not exceeding 0.53%. Under 5% noise disturbance conditions, the algorithm demonstrates robust estimation accuracy and convergence performance. As operational complexity increases, the performance advantages of the fusion algorithm become more pronounced, significantly enhancing the accuracy and robustness of battery state estimation, thus providing an effective solution for power battery management systems.

Key words

lithium-ion battery / state estimation / sliding mode observer / unscented Kalman filter / state of charge / state of health

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Wang Qiwen, Sheng Qiang, He Yucai. LITHIUM-ION BATTERY STATE ESTIMATION METHOD BASED ON SUKF-EKF FUSION[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 11-21 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0830

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