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Enhancing battery management for HEVs and EVs: A hybrid approach for parameter identification and voltage estimation in lithium-ion battery models

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  • Khosravi, Nima
  • Dowlatabadi, Masrour
  • Abdelghany, Muhammad Bakr
  • Tostado-Véliz, Marcos
  • Jurado, Francisco

Abstract

In recent years, batteries have evolved increasingly overall in numerous applications. Among batteries, LIBs are the most advantageous technology because of their raised power and energy densities. This study proposes a hybrid method, combining a war strategy optimization (WSO) algorithm and a hierarchical deep learning neural network (HDLNN) named WSO-HDLNN, to identify the parameters of lithium-ion batteries (LIBs) used in hybrid and electric vehicles (HEVs and EVs). The hybrid approach utilizes the WSO technique to generate parameters and predicts the components using the HDLNN approach. The proposed method significantly reduces the estimated voltage and measured voltage error while effectively identifying the battery parameters. The MATLAB/SIMULINK platform is employed for implementation and comparison with other existing methods such as differential evolution (DE), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO). Simulation results demonstrate the efficiency of the proposed WSO-HDLNN strategy in reducing battery voltage errors by accurately identifying parameters and improving voltage estimation accuracy. Further, notable novelty in this work is the integration of the WSO algorithm with the HDLNN in the WSO-HDLNN protocol for LIB parameter identification. This fusion is distinct as it synergizes the strengths of optimization and deep learning, enhancing efficiency and accuracy in LIB parameter estimation. The WSO algorithm introduces a novel war strategy element, leading to faster convergence to optimal solutions, significantly reducing computational time. Moreover, the WSO-HDLNN approach showcases robustness in handling noisy data, a unique feature ensuring accurate parameter estimates amidst real-world uncertainties, setting it apart from conventional LIB modeling methods.

Suggested Citation

  • Khosravi, Nima & Dowlatabadi, Masrour & Abdelghany, Muhammad Bakr & Tostado-Véliz, Marcos & Jurado, Francisco, 2024. "Enhancing battery management for HEVs and EVs: A hybrid approach for parameter identification and voltage estimation in lithium-ion battery models," Applied Energy, Elsevier, vol. 356(C).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s0306261923017282
    DOI: 10.1016/j.apenergy.2023.122364
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    References listed on IDEAS

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    1. Li, Weihan & Cao, Decheng & Jöst, Dominik & Ringbeck, Florian & Kuipers, Matthias & Frie, Fabian & Sauer, Dirk Uwe, 2020. "Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries," Applied Energy, Elsevier, vol. 269(C).
    2. Ruan, Haokai & Wei, Zhongbao & Shang, Wentao & Wang, Xuechao & He, Hongwen, 2023. "Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging," Applied Energy, Elsevier, vol. 336(C).
    3. Li, Weihan & Cui, Han & Nemeth, Thomas & Jansen, Jonathan & Ünlübayir, Cem & Wei, Zhongbao & Feng, Xuning & Han, Xuebing & Ouyang, Minggao & Dai, Haifeng & Wei, Xuezhe & Sauer, Dirk Uwe, 2021. "Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning," Applied Energy, Elsevier, vol. 293(C).
    4. Alkhulaifi, Yousif M. & Qasem, Naef A.A. & Zubair, Syed M., 2022. "Exergoeconomic assessment of the ejector-based battery thermal management system for electric and hybrid-electric vehicles," Energy, Elsevier, vol. 245(C).
    5. Tu, Hao & Moura, Scott & Wang, Yebin & Fang, Huazhen, 2023. "Integrating physics-based modeling with machine learning for lithium-ion batteries," Applied Energy, Elsevier, vol. 329(C).
    6. Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wei, Xuezhe & Shang, Wenlong & Dai, Haifeng, 2022. "A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 322(C).
    7. Khosravi, Nima & Baghbanzadeh, Rasoul & Oubelaid, Adel & Tostado-Véliz, Marcos & Bajaj, Mohit & Hekss, Zineb & Echalih, Salwa & Belkhier, Youcef & Houran, Mohamad Abou & Aboras, Kareem M., 2023. "A novel control approach to improve the stability of hybrid AC/DC microgrids," Applied Energy, Elsevier, vol. 344(C).
    8. Tian, Yong & Lai, Rucong & Li, Xiaoyu & Xiang, Lijuan & Tian, Jindong, 2020. "A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter," Applied Energy, Elsevier, vol. 265(C).
    9. Xie, Yanxin & Wang, Shunli & Zhang, Gexiang & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2023. "Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 336(C).
    10. Wang, Shunli & Takyi-Aninakwa, Paul & Jin, Siyu & Yu, Chunmei & Fernandez, Carlos & Stroe, Daniel-Ioan, 2022. "An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation," Energy, Elsevier, vol. 254(PA).
    11. Zhang, Chu & Hu, Haowen & Ji, Jie & Liu, Kang & Xia, Xin & Nazir, Muhammad Shahzad & Peng, Tian, 2023. "An evolutionary stacked generalization model based on deep learning and improved grasshopper optimization algorithm for predicting the remaining useful life of PEMFC," Applied Energy, Elsevier, vol. 330(PA).
    12. Khosravi, N. & Abdolmohammadi, H.R. & Bagheri, S. & Miveh, M.R., 2021. "Improvement of harmonic conditions in the AC/DC microgrids with the presence of filter compensation modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    13. Lai, Qingzhi & Ahn, Hyoung Jun & Kim, YoungJin & Kim, You Na & Lin, Xinfan, 2021. "New data optimization framework for parameter estimation under uncertainties with application to lithium-ion battery," Applied Energy, Elsevier, vol. 295(C).
    14. Tang, Ruoli & Zhang, Shihan & Zhang, Shangyu & Lai, Jingang & Zhang, Yan, 2023. "Semi-online parameter identification methodology for maritime power lithium batteries," Applied Energy, Elsevier, vol. 339(C).
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    2. Fahmy, Hend M. & Alqahtani, Ayedh H. & Hasanien, Hany M., 2024. "Precise modeling of lithium-ion battery in industrial applications using Walrus optimization algorithm," Energy, Elsevier, vol. 294(C).

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