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An adaptive boosting charging strategy optimization based on thermoelectric-aging model, surrogates and multi-objective optimization

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  • Su, Shaosen
  • Li, Wei
  • Garg, Akhil
  • Gao, Liang

Abstract

This paper presents an adaptive boosting charging strategy incorporating the capacity estimation method based on the operation parameters, which is used for the state estimation and the adaptive adjustment of the charging strategy during the charging/discharging cycling process. Firstly, a coupling thermoelectric-aging battery model involving second-order resistor-capacity equivalent circuit model, two-state thermal model, semi-empirical aging models is set up for the simulation purpose. Secondly, numerical surrogate models describing the charging time and capacity loss in different aging states and charging strategies and a capacity estimation model are generated based on the coupling battery model, design of experiment method and artificial intelligence method. Subsequently, a multi-objective optimization framework based on surrogate models and the second non-dominated sorting genetic algorithm is used to solve the optimization problem of the boosting charging strategy to balance the charging time and capacity attenuation under different aging states. Finally, the simulation of the processes of capacity estimation and charging parameter optimal selection is carried on, and the simulation and comparison of the charging/discharging cycles applying the proposed and standard constant current constant voltage charging strategy are conducted. The results show that the proposed strategy can restrict the capacity loss at 4.44% which is 4.2% higher than the capacity loss by applying standard CCCV, the average charging time decreases from 3792.3 s to 2881.4 s. The average temperature increment is increased significantly, while it is still in the ideal operating temperature, and the average charging time can be significantly decreased. Based on this work, further researches can be conducted to develop the actual boosting charging management systems including aging state estimation and adaptive charging parameter selection.

Suggested Citation

  • Su, Shaosen & Li, Wei & Garg, Akhil & Gao, Liang, 2022. "An adaptive boosting charging strategy optimization based on thermoelectric-aging model, surrogates and multi-objective optimization," Applied Energy, Elsevier, vol. 312(C).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922002422
    DOI: 10.1016/j.apenergy.2022.118795
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    1. Ecker, Madeleine & Shafiei Sabet, Pouyan & Sauer, Dirk Uwe, 2017. "Influence of operational condition on lithium plating for commercial lithium-ion batteries – Electrochemical experiments and post-mortem-analysis," Applied Energy, Elsevier, vol. 206(C), pages 934-946.
    2. Hu, Chao & Jain, Gaurav & Zhang, Puqiang & Schmidt, Craig & Gomadam, Parthasarathy & Gorka, Tom, 2014. "Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery," Applied Energy, Elsevier, vol. 129(C), pages 49-55.
    3. Jiang, Bo & Dai, Haifeng & Wei, Xuezhe, 2020. "Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition," Applied Energy, Elsevier, vol. 269(C).
    4. Suri, Girish & Onori, Simona, 2016. "A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries," Energy, Elsevier, vol. 96(C), pages 644-653.
    5. Li, Yunjian & Li, Kuining & Xie, Yi & Liu, Jiangyan & Fu, Chunyun & Liu, Bin, 2020. "Optimized charging of lithium-ion battery for electric vehicles: Adaptive multistage constant current–constant voltage charging strategy," Renewable Energy, Elsevier, vol. 146(C), pages 2688-2699.
    6. Taesic Kim & Darshan Makwana & Amit Adhikaree & Jitendra Shamjibhai Vagdoda & Young Lee, 2018. "Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems," Energies, MDPI, vol. 11(1), pages 1-15, January.
    7. Zheng, Yuejiu & Wang, Jingjing & Qin, Chao & Lu, Languang & Han, Xuebing & Ouyang, Minggao, 2019. "A novel capacity estimation method based on charging curve sections for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 185(C), pages 361-371.
    8. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    9. Patil, Meru A. & Tagade, Piyush & Hariharan, Krishnan S. & Kolake, Subramanya M. & Song, Taewon & Yeo, Taejung & Doo, Seokgwang, 2015. "A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation," Applied Energy, Elsevier, vol. 159(C), pages 285-297.
    10. Pan, Rui & Yang, Duo & Wang, Yujie & Chen, Zonghai, 2020. "Health degradation assessment of proton exchange membrane fuel cell based on an analytical equivalent circuit model," Energy, Elsevier, vol. 207(C).
    Full references (including those not matched with items on IDEAS)

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    2. Zhang, Xiaoxi & Pan, Yongjun & Xiong, Yue & Zhang, Yongzhi & Tang, Mao & Dai, Wei & Liu, Binghe & Hou, Liang, 2024. "Deep learning-based vibration stress and fatigue-life prediction of a battery-pack system," Applied Energy, Elsevier, vol. 357(C).
    3. Lv, Haichao & Kang, Lixia & Liu, Yongzhong, 2023. "Analysis of strategies to maximize the cycle life of lithium-ion batteries based on aging trajectory prediction," Energy, Elsevier, vol. 275(C).
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    5. Fan, Zhaohui & Fu, Yijie & Liang, Hong & Gao, Renjing & Liu, Shutian, 2023. "A module-level charging optimization method of lithium-ion battery considering temperature gradient effect of liquid cooling and charging time," Energy, Elsevier, vol. 265(C).

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