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Study on the Optimal Charging Strategy for Lithium-Ion Batteries Used in Electric Vehicles

Author

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  • Shuo Zhang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China)

  • Chengning Zhang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China)

  • Rui Xiong

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China)

  • Wei Zhou

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China)

Abstract

The charging method of lithium-ion batteries used in electric vehicles (EVs) significantly affects its commercial application. This paper aims to make three contributions to the existing literature. (1) In order to achieve an efficient charging strategy for lithium-ion batteries with shorter charging time and lower charring loss, the trade-off problem between charging loss and charging time has been analyzed in details through the dynamic programing (DP) optimization algorithm; (2) To reduce the computation time consumed during the optimization process, we have proposed a database based optimization approach. After off-line calculation, the simulation results can be applied to on-line charge; (3) The novel database-based DP method is proposed and the simulation results illustrate that this method can effectively find the suboptimal charging strategies under a certain balance between the charging loss and charging time.

Suggested Citation

  • Shuo Zhang & Chengning Zhang & Rui Xiong & Wei Zhou, 2014. "Study on the Optimal Charging Strategy for Lithium-Ion Batteries Used in Electric Vehicles," Energies, MDPI, vol. 7(10), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:10:p:6783-6797:d:41415
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    References listed on IDEAS

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    1. Xiong, Rui & Sun, Fengchun & Gong, Xianzhi & Gao, Chenchen, 2014. "A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 1421-1433.
    2. Xiong, Rui & Sun, Fengchun & He, Hongwen & Nguyen, Trong Duy, 2013. "A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles," Energy, Elsevier, vol. 63(C), pages 295-308.
    3. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
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    Citations

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    Cited by:

    1. Fangrong Xue & Zhi Ling & Yubing Yang & Xingpo Miao, 2017. "Design and Implementation of Novel Smart Battery Management System for FPGA Based Portable Electronic Devices," Energies, MDPI, vol. 10(3), pages 1-14, February.
    2. Xiaogang Wu & Wenwen Shi & Jiuyu Du, 2017. "Multi-Objective Optimal Charging Method for Lithium-Ion Batteries," Energies, MDPI, vol. 10(9), pages 1-18, August.
    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. György Károlyi & Anna I. Pózna & Katalin M. Hangos & Attila Magyar, 2022. "An Optimized Fuzzy Controlled Charging System for Lithium-Ion Batteries Using a Genetic Algorithm," Energies, MDPI, vol. 15(2), pages 1-23, January.
    5. Omer Faruk Goksu & Ahmet Yigit Arabul & Revna Acar Vural, 2020. "Low Voltage Battery Management System with Internal Adaptive Charger and Fuzzy Logic Controller," Energies, MDPI, vol. 13(9), pages 1-15, May.
    6. Yusuf A. Sha’aban & Augustine Ikpehai & Bamidele Adebisi & Khaled M. Rabie, 2017. "Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control," Energies, MDPI, vol. 10(10), pages 1-20, September.
    7. Yunna Wu & Meng Yang & Haobo Zhang & Kaifeng Chen & Yang Wang, 2016. "Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method," Energies, MDPI, vol. 9(3), pages 1-20, March.
    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. Lei Zhao & Haoyu Li & Yuan Liu & Zhenwei Li, 2015. "High Efficiency Variable-Frequency Full-Bridge Converter with a Load Adaptive Control Method Based on the Loss Model," Energies, MDPI, vol. 8(4), pages 1-27, April.
    10. Muhammad Umair Ali & Sarvar Hussain Nengroo & Muhamad Adil Khan & Kamran Zeb & Muhammad Ahmad Kamran & Hee-Je Kim, 2018. "A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control," Energies, MDPI, vol. 11(5), pages 1-15, May.
    11. Lixing Chen & Zhong Chen & Xueliang Huang & Long Jin, 2016. "A Study on Price-Based Charging Strategy for Electric Vehicles on Expressways," Energies, MDPI, vol. 9(5), pages 1-18, May.

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