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Advanced parameter estimation for lithium-ion battery model using the information sharing group teaching optimization algorithm
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DOI：10.1016/j.jpowsour.2024.235615.png)
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Abstract
En 中文
Accurate battery modeling is crucial for optimizing the performance and safety of Lithium-ion batteries (LiBs), particularly in applications such as electric vehicles and smart grids. This paper introduces the Information Sharing Group Teaching Optimization Algorithm (ISGTOA), a novel human-based metaheuristic algorithm designed to estimate the 21 parameters of third-order Equivalent Circuit Model (ECM) for LiBs. Unlike existing methods, ISGTOA effectively manages the increased complexity of a model through advanced group teaching strategies and sophisticated information-sharing mechanisms inspired by classroom dynamics. We validated the effectiveness of ISGTOA using two distinct datasets-High Dynamic Profile (HDP) and Urban Dynamometer Driving Schedule (UDDS)-across various LiB chemistries (NCA and NMC) and temperature conditions (-5 degrees C, 25 degrees C and 45 degrees C). The results demonstrate that ISGTOA achieves superior parameter estimation accuracy and convergence speed compared to established and recent optimization methods such as TLBO, TLSBO, AISA, MTBO, and DTBO. Specifically, ISGTOA attained a minimum RMSE of 8.357 mV with rapid convergence and high stability in the HDP dataset and minimized RMSE to 10 mV within ten iterations at both 25 degrees C and 45 degrees C in the UDDS dataset. Additionally, ISGTOA exhibited high efficiency (up to 97.75 %) under varying thermal environments. This work not only introduces a novel algorithm for complex battery modeling but also sets the stage for future advancements in real-time battery management systems, emphasizing the importance of robust, versatile, and efficient parameter estimation techniques.
Keywords:
Group teaching optimization algorithm
Information sharing
Li-ion battery
Equivalent circuit model
Urban dynamometer driving schedule
Parameter identification
Journal
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7.9
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3.7W
Citations:
15.0W