Optimal Parameter Estimation for an Enhanced Self-Correcting Battery Model Based on Real Electric Vehicle Drive Cycle Profile
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Abstract
Lithium-ion batteries (LiBs) are the dominant energy storage solution for electric vehicles (EVs) due to their outstanding performance. However, accurately determining the ungiven parameters of LiB models from measured voltage and current data poses a highly nonlinear and multimodal optimization challenge. Precise estimation is critical for effective simulation, control, and evaluation of energy storage systems. Traditional methods often fail due to the problem's complexity, while metaheuristic algorithms (MAs) provide better solutions but require improvements to mitigate local optima entrapment and slow convergence. Recently, advanced MAs have been introduced to enhance these aspects, yet their application in battery parameter estimation remains underexplored. This paper evaluates four recently developed MAs—PID Search Algorithm (PSA), Spider Wasp Optimization (SWO), Triangulation Topology Aggregation Optimizer (TTAO), and Black Kite Algorithm (BKA)—for estimating parameters of an Enhanced Self-Correcting Battery Model. Using Urban Dynamometer Driving Schedule (UDDS) drive cycle data, these algorithms are assessed based on best fitness, average fitness, worst fitness, standard deviation (StD), average efficiency (Avg), and convergence speed. Results reveal that BKA is the most efficient and effective method among the tested approaches, whereas SWO performs poorly for this specific task.
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Abdel-Basset, M., Mohamed, R., Jameel, M., & Abouhawwash, M. (2023). Spider wasp optimizer: a novel meta-heuristic optimization algorithm. Artificial Intelligence Review, 56(10), 11675–11738. doi: 10.1007/s10462-023-10446-y.
Bensidhoum, T., Lekouaghet, B., & Touil, S. A. (2024). Photovoltaic Model Parameters Estimation Via the Fully Informed Search Algorithm. International Journal of Robotics and Control Systems, 4(2), 929–940. doi: 10.31763/ijrcs.v4i2.1391.
Chu, Y., Cui, N., & Liu, K. (2025). Nonlinear modeling and SOC estimation of lithium-ion batteries based on block-oriented structures. Energy, 315, 134273. doi: 10.1016/j.energy.2024.134273.
Fahmy, H. M., Alqahtani, A. H., & Hasanien, H. M. (2024). Precise modeling of lithium-ion battery in industrial applications using Walrus optimization algorithm. Energy, 294. doi: 10.1016/j.energy.2024.130859.
Gao, Y. (2023). PID-based search algorithm: A novel metaheuristic algorithm based on PID algorithm. Expert Systems with Applications, 232, 120886. doi: 10.1016/j.eswa.2023.120886.
Hamza, A., Lekouaghet, B., & Himeur, Y. (2023). Hybrid Whale-Mud-Ring Optimization for Precise Color Skin Cancer Image Segmentation. 2023 6th International Conference on Signal Processing and Information Security (ICSPIS), Dubai, United Arab Emirates, 87–92. doi: 10.1109/ICSPIS60075.2023.10343708.
Joyce, T., & Herrmann, J. M. (2018). A Review of No Free Lunch Theorems, and Their Implications for Metaheuristic Optimisation BT - Nature-Inspired Algorithms and Applied Optimization. In: Yang, XS. (eds) Nature-Inspired Algorithms and Applied Optimization. Studies in Computational Intelligence, 744. Springer, Cham. doi: 10.1007/978-3-319-67669-2_2.
Lekouaghet, B., Haddad, M., & Hamouda, N. (2024a). Exploring Recent Metaphor-Based Algorithms for Optimizing TIG Welding Parameters. 2024 2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC), Setif, Algeria, 1-5, doi:10.1109/ICEEAC61226.2024.10576297,1–5.
Lekouaghet, B., Khelifa, M. A., & Boukabou, A. (2024b). Precise parameter estimation of PEM fuel cell via weighted mean of vectors optimizer. Journal of Computational Electronics, 23(5), 1–10. doi: 10.1007/S10825-024-02204-2/METRICS.
Lekouaghet, B., Merrouche, W., Bouguenna, E., & Himeur, Y. (2023). Identifying the Unknown Parameters of Equivalent Circuit Model for Li-Ion Battery Using Rao-1 Algorithm. Engineering Proceedings, 56(1), 228. doi: 10.3390/ASEC2023-15343.
Lekouaghet, B., Merrouche, W., Bouguenna, E., Taghezouit, B., & Benghanem, M. (2024c). Advanced parameter estimation for lithium-ion battery model using the information sharing group teaching optimization algorithm. Journal of Power Sources, 624, 235615. doi: 10.1016/j.jpowsour.2024.235615.
Liu, K., Peng, Q., Liu, Y., Cui, N., & Zhang, C. (2024). Explainable neural network for sensitivity analysis of lithium-ion battery smart production. IEEE/CAA Journal of Automatica Sinica, 11(9), 1944–1953. doi: 10.1109/JAS.2024.124539.
Lucaferri, V., Quercio, M., Laudani, A., & Riganti Fulginei, F. (2023). A Review on Battery Model-Based and Data-Driven Methods for Battery Management Systems. Energies. doi: 10.3390/en16237807.
Merrouche, W., Lekouaghet, B., & Bouguenna, E. (2023a). Artificial Search Algorithm for Parameters Optimization of Li-Ion Battery Electrical Model. 2023 International Conference on Decision Aid Sciences and Applications, DASA 2023, 17–22. doi: 10.1109/DASA59624.2023.10286632.
Merrouche, W., Lekouaghet, B., & Bouguenna, E. (2023b). Honey Badger Algorithm for Estimating the Parameters of Li-ion Battery 3rd Order ECM. NEIS 2023; Conference on Sustainable Energy Supply and Energy Storage Systems, 163–169.
Mohamed, M. A. A., Yu, T. F., Ramsden, G., Marco, J., & Grandjean, T. (2025). Advancements in parameter estimation techniques for 1RC and 2RC equivalent circuit models of lithium-ion batteries: A comprehensive review. Journal of Energy Storage, 113, 115581. doi: 10.1016/j.est.2025.115581.
Plett, G.L. (2016). Battery Management Systems. Volume II: Equivalent-Circuit Methods. In Artech House, Boston, London. Artech. ISBN: 9781630810276.
Shaheen, A. M., Hamida, M. A., El-Sehiemy, R. A., & Elattar, E. E. (2021). Optimal parameter identification of linear and non-linear models for Li-Ion Battery Cells. Energy Reports, 7, 7170–7185. doi: 10.1016/j.egyr.2021.10.086.
Wang, J., Wang, Wc., Hu, Xx., Qiu, L. & Zang, Hf. (2024). Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems. Artif Intell Review. 57, 98 (2024). https://doi.org/10.1007/s10462-024-10723-4.
Wang, S., Zhang, S., Wen, S., & Fernandez, C. (2024). An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter. Journal of Power Sources, 624, 235594. doi: 10.1016/j.jpowsour.2024.235594.
Zhao, S., Zhang, T., Cai, L., & Yang, R. (2024). Triangulation topology aggregation optimizer: A novel mathematics-based meta-heuristic algorithm for continuous optimization and engineering applications. Expert Systems with Applications, 238, 121744. doi: 10.1016/j.eswa.2023.121744.