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Model Predictive Control-Based Fast Charging for Vehicular Batteries

Author

Listed:
  • Jingyu Yan

    (Shenzhen Institutes of Advanced Technology, The Chinese Academy of Science, Shenzhen 518055, China
    Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China)

  • Guoqing Xu

    (Shenzhen Institutes of Advanced Technology, The Chinese Academy of Science, Shenzhen 518055, China
    Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China
    Department of Electrical Engineering, Tongji University, Shanghai 200092, China)

  • Huihuan Qian

    (Shenzhen Institutes of Advanced Technology, The Chinese Academy of Science, Shenzhen 518055, China
    Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China)

  • Yangsheng Xu

    (Shenzhen Institutes of Advanced Technology, The Chinese Academy of Science, Shenzhen 518055, China
    Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China)

  • Zhibin Song

    (Shenzhen Institutes of Advanced Technology, The Chinese Academy of Science, Shenzhen 518055, China)

Abstract

Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method.

Suggested Citation

  • Jingyu Yan & Guoqing Xu & Huihuan Qian & Yangsheng Xu & Zhibin Song, 2011. "Model Predictive Control-Based Fast Charging for Vehicular Batteries," Energies, MDPI, vol. 4(8), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:4:y:2011:i:8:p:1178-1196:d:13603
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    Citations

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    Cited by:

    1. Guan-Jhu Chen & Yi-Hua Liu & Yu-Shan Cheng & Hung-Yu Pai, 2021. "A Novel Optimal Charging Algorithm for Lithium-Ion Batteries Based on Model Predictive Control," Energies, MDPI, vol. 14(8), pages 1-18, April.
    2. Wang, Yujie & Zhou, Caijie & Chen, Zonghai, 2022. "Optimization of battery charging strategy based on nonlinear model predictive control," Energy, Elsevier, vol. 241(C).
    3. Cheng-Shan Wang & Wei Li & Zhun Meng & Yi-Feng Wang & Jie-Gui Zhou, 2015. "Three-Phase High-Power and Zero-Current-Switching OBC for Plug-In Electric Vehicles," Energies, MDPI, vol. 8(7), pages 1-33, June.
    4. Edison Banguero & Antonio Correcher & Ángel Pérez-Navarro & Francisco Morant & Andrés Aristizabal, 2018. "A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems," Energies, MDPI, vol. 11(4), pages 1-15, April.
    5. Shyang-Chyuan Fang & Bwo-Ren Ke & Chen-Yuan Chung, 2017. "Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System," Energies, MDPI, vol. 10(7), pages 1-20, June.
    6. Chun-Liang Liu & Yi-Shun Chiu & Yi-Hua Liu & Yeh-Hsiang Ho & Shu-Syuan Huang, 2013. "Optimization of a Fuzzy-Logic-Control-Based Five-Stage Battery Charger Using a Fuzzy-Based Taguchi Method," Energies, MDPI, vol. 6(7), pages 1-20, July.
    7. Abu Eldahab, Yasser E. & Saad, Naggar H. & Zekry, Abdalhalim, 2016. "Enhancing the design of battery charging controllers for photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 646-655.
    8. Zhang, Caiping & Jiang, Jiuchun & Gao, Yang & Zhang, Weige & Liu, Qiujiang & Hu, Xiaosong, 2017. "Charging optimization in lithium-ion batteries based on temperature rise and charge time," Applied Energy, Elsevier, vol. 194(C), pages 569-577.
    9. Kujundžić, Goran & Ileš, Šandor & Matuško, Jadranko & Vašak, Mario, 2017. "Optimal charging of valve-regulated lead-acid batteries based on model predictive control," Applied Energy, Elsevier, vol. 187(C), pages 189-202.

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