Article: Precise state-of-charge estimation for LIBs: a cutting-edge nonlinear model approach with enhanced robustness and reliability Journal: International Journal of Critical Infrastructures (IJCIS) 2025 Vol.21 No.11 pp.1 - 29 Abstract: Precise state-of-charge (SoC) prediction is essential for optimising the performance, safety, and longevity of lithium-ion batteries (LIBs) in battery management systems (BMS). However, traditional prediction tactics, including Kalman filters and sliding mode observers (SMOs), struggle with sensor noise, model uncertainties, and external disturbances, leading to inaccuracies in real-world applications. This study proposes a nonlinear battery framework integrated with a Luenberger observer enhanced by H-infinity (H∞) optimisation to boost SoC prediction accuracy and robustness. The H∞ framework effectively mitigates disturbances, while sensor fault prediction enhances reliability under varying operational conditions. The recommended tactic is computationally efficient and suitable for real-time SoC prediction. Empirical outcomes validate the superior accuracy and stability of the recommended approach, achieving prediction errors that are up to 3.8% lower than those of conventional SMOs. The findings demonstrate potential for next-generation BMS applications, particularly in electric vehicles (EVs) and energy storage systems. Future work will focus on adaptive parameter prediction techniques to boost performance under real-world battery ageing conditions. Inderscience Publishers - linking academia, business and industry through research

Title: Precise state-of-charge estimation for LIBs: a cutting-edge nonlinear model approach with enhanced robustness and reliability

Authors: Xiaowei Zhang

Addresses: College of Intelligent Manufacturing, Yancheng Polytechnic College, Yancheng City, Jiangsu Province, 224005, China

Abstract: Precise state-of-charge (SoC) prediction is essential for optimising the performance, safety, and longevity of lithium-ion batteries (LIBs) in battery management systems (BMS). However, traditional prediction tactics, including Kalman filters and sliding mode observers (SMOs), struggle with sensor noise, model uncertainties, and external disturbances, leading to inaccuracies in real-world applications. This study proposes a nonlinear battery framework integrated with a Luenberger observer enhanced by H-infinity (H∞) optimisation to boost SoC prediction accuracy and robustness. The H∞ framework effectively mitigates disturbances, while sensor fault prediction enhances reliability under varying operational conditions. The recommended tactic is computationally efficient and suitable for real-time SoC prediction. Empirical outcomes validate the superior accuracy and stability of the recommended approach, achieving prediction errors that are up to 3.8% lower than those of conventional SMOs. The findings demonstrate potential for next-generation BMS applications, particularly in electric vehicles (EVs) and energy storage systems. Future work will focus on adaptive parameter prediction techniques to boost performance under real-world battery ageing conditions.

Keywords: lithium-ion battery; LIB; state-of-charge; SoC; Luenberger estimator; H-infinity theory; battery reliability; energy storage systems; battery management systems; BMS; electric vehicles.

DOI: 10.1504/IJCIS.2025.149029

International Journal of Critical Infrastructures, 2025 Vol.21 No.11, pp.1 - 29

Received: 20 Mar 2025
Accepted: 17 Jun 2025
Published online: 09 Oct 2025 *

Title: Precise state-of-charge estimation for LIBs: a cutting-edge nonlinear model approach with enhanced robustness and reliability

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