#7643. A comparative study of different online model parameters identification methods for lithium-ion battery

October 2026publication date
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Engineering (all);
Materials Science (all);
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Abstract:
Precise states estimation for the lithium-ion battery is one of the fundamental tasks in the battery management system (BMS), where building an accurate battery model is the first step in model-based estimation algorithms. To date, although the comparative studies on different battery models have been performed intensively, little attention is paid to the comparison among different online parameters identification methods regarding model accuracy, robustness ability, adaptability to the different battery operating conditions and computation cost. In this paper, based on the Thevenin model, the three most widely used online parameters identification methods, including extended Kalman filter (EKF), particle swarm optimization (PSO), and recursive least square (RLS), are evaluated comprehensively under static and dynamic tests.
Keywords:
comprehensive performance; lithium-ion battery; online model parameters identification methods; state-of-charge; Thevenin model

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