Abstract
The integration of quantum-inspired techniques into optimization algorithms allows for enhanced exploration of complex parameter spaces, enabling efficient convergence toward optimal solutions. This article presents the application of Gaussian Quantum-behaved Particle Swarm Optimization (GQPSO) strategy, tailored for optimizing the parameters of the Lithium Battery Extended Thevenin model. Inspired by the principles of swarm intelligence and quantum mechanics, the GQPSO leverages Gaussian distributions to enhance the exploration of parameter spaces, capitalizing on the advantages offered by quantum-inspired optimization techniques. Through tests on high-accuracy data, the effectiveness of the GQPSO strategy is demonstrated. The optimized Lithium Battery Extended Thevenin model showcases remarkable accuracy, with an RMSE of 2.06116086e−02 and a standard deviation of 1.27510624e−02. The GQPSO strategy’s exceptional performance showcases the advantages of combining quantum mechanics’ principles and Gaussian distributions with swarm intelligence for accurate and efficient lithium battery modeling, paving the way for improved energy storage system applications.
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References
von Bülow, F., Meisen, T.: A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions. J. Energy Storage 57, 105978 (2023). https://doi.org/10.1016/J.EST.2022.105978
Elmahallawy, M., Elfouly, T., Alouani, A., Massoud, A.M.: A comprehensive review of lithium-ion batteries modeling, and state of health and remaining useful lifetime prediction. IEEE Access 10, 119040–119070 (2022). https://doi.org/10.1109/ACCESS.2022.3221137
Merrouche, W., et al.: Improved model and simulation tool for dynamic SOH estimation and life prediction of batteries used in PV systems. Simul. Model. Pract. Theory 119, 102590 (2022). https://doi.org/10.1016/j.simpat.2022.102590
Kadam, P.P., Kadam, S.: Quantum optimization techniques and it’s comparison with classical optimization. In: Senjyu, T., So-In, C., Joshi, A. (eds.) SMART 2023. LNCS, vol. 645, pp. 639–647. Springer, Singapore (2023). https://doi.org/10.1007/978-981-99-0769-4_55
Hakemi, S., Houshmand, M., KheirKhah, E., Hosseini, S.A.: A review of recent advances in quantum-inspired metaheuristics (2022). https://doi.org/10.1007/s12065-022-00783-2
Gharehchopogh, F.S.: Quantum-inspired metaheuristic algorithms: comprehensive survey and classification. Artif. Intell. Rev. (2023). https://doi.org/10.1007/s10462-022-10280-8
Reddy, K., Saha, A.K.: A review of swarm-based metaheuristic optimization techniques and their application to doubly fed induction generator (2022). https://doi.org/10.1016/j.heliyon.2022.e10956
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks, pp. 1942–1948. IEEE (1995). https://doi.org/10.1109/ICNN.1995.488968
Gad, A.G.: Particle swarm optimization algorithm and its applications: a systematic review. Arch. Comput. Methods Eng. (2022). https://doi.org/10.1007/s11831-021-09694-4
Yang, S., Wang, M., Jiao, L.: A quantum particle swarm optimization. In: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), pp. 320–324. IEEE (2004). https://doi.org/10.1109/CEC.2004.1330874
Zhang, Y., Lyden, S., De La Barra, B.A.L., Haque, M.E.: Optimization of Tremblay’s battery model parameters for plug-in hybrid electric vehicle applications. In: 2017 Australasian Universities Power Engineering Conference, AUPEC 2017 (2018). https://doi.org/10.1109/AUPEC.2017.8282405
Solomon, O.O., Zheng, W., Chen, J., Qiao, Z.: State of charge estimation of Lithium-ion battery using an improved fractional-order extended Kalman filter. J. Energy Storage (2022). https://doi.org/10.1016/j.est.2022.104007
Service, T.C.: A No Free Lunch theorem for multi-objective optimization. Inf. Process. Lett. 110, 917–923 (2010). https://doi.org/10.1016/j.ipl.2010.07.026
Joyce, T., Herrmann, J.M.: A review of no free lunch theorems, and their implications for metaheuristic optimisation. In: Yang, X.-S. (ed.) Nature-Inspired Algorithms and Applied Optimization. SCI, vol. 744, pp. 27–51. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67669-2_2
Haddad, S., Lekouaghet, B., Benghanem, M., Soukkou, A., Rabhi, A.: Parameter estimation of solar modules operating under outdoor operational conditions using artificial hummingbird algorithm. IEEE Access (2022). https://doi.org/10.1109/ACCESS.2022.3174222
Lekouaghet, B., Khelifa, M.A., Boukabou, A.: Adolescent identity search algorithm for parameter extraction in photovoltaic solar cells and modules. J. Comput. Electron. (2022). https://doi.org/10.1007/s10825-022-01881-1
dos Santos Coelho, L.: Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst. Appl. 37, 1676–1683 (2010). https://doi.org/10.1016/j.eswa.2009.06.044
dos Santos Coelho, L.: A quantum particle swarm optimizer with chaotic mutation operator. Chaos Solitons Fractals (2008). https://doi.org/10.1016/j.chaos.2006.10.028
Plett, G.L.: Battery Management Systems: Equivalent-Circuit Methods (2016)
Plett, G.L.: Battery Management Systems, Volume I: Battery Modeling. Artech (2015)
Merrouche, W., Lekouaghet, B., Bouguenna, E., Himeur, Y.: Parameter estimation of ECM model for Li-ion battery using the weighted mean of vectors algorithm. J. Energy Storage 76, 109891 (2024). https://doi.org/10.1016/j.est.2023.109891
Merrouche, W., Lekouaghet, B., Bouguenna, E.: Artificial search algorithm for parameters optimization of Li-ion battery electrical model. In: 2023 International Conference on Decision Aid Sciences and Applications (DASA), pp. 17–22. IEEE (2023). https://doi.org/10.1109/DASA59624.2023.10286632
Lekouaghet, B., Merrouche, W., Bouguenna, E., Himeur, Y.: Identifying the unknown parameters of ECM model for Li-ion battery using Rao-1 algorithm. In: The 4th International Electronic Conference on Applied Sciences session Energy, Environmental and Earth Science. MDPI (2023). https://doi.org/10.3390/ASEC2023-15343
Merrouche, W., Gaci, I., Ould-Amrouche, S., Boubezari, A.: PWM buck converter used in PV controller. In: Proceedings of 2019 7th International Renewable and Sustainable Energy Conference, IRSEC 2019. pp. 1–6. IEEE (2019). https://doi.org/10.1109/IRSEC48032.2019.9078250
Li, W., Rentemeister, M., Badeda, J., Jöst, D., Schulte, D., Sauer, D.U.: Digital twin for battery systems: cloud battery management system with online state-of-charge and state-of-health estimation. J. Energy Storage. 30, 101557 (2020). https://doi.org/10.1016/j.est.2020.101557
Li, W., Chen, J., Quade, K., Luder, D., Gong, J., Sauer, D.U.: Battery degradation diagnosis with field data, impedance-based modeling and artificial intelligence. Energy Storage Mater. 53, 391–403 (2022). https://doi.org/10.1016/j.ensm.2022.08.021
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Merrouche, W., Lekouaghet, B., Bouguenna, E. (2024). Gaussian Quantum-Behaved PSO Strategy for Lithium Battery Model Optimization. In: Drias, H., Yalaoui, F. (eds) Quantum Computing: Applications and Challenges. QSAC 2023. Information Systems Engineering and Management, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-031-59318-5_9
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