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An operation efficacy-oriented predictive control management for power-redistributable lithium-ion battery pack

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  • Zhong, Hao
  • Lei, Fei
  • Zhu, Wenhao
  • Zhang, Zhe

Abstract

The improvement of the operation efficacy of lithium-ion batteries is a critical issue for developing charge/discharge management systems in electric vehicles. An operation efficacy-oriented predictive control management strategy is proposed to explore the maximum operation efficacy of battery cells based on a power-redistributable battery pack consisting of multiple individual controllable battery power units. Battery electrothermal dynamics are described by a control-oriented energy-temperature model considering energy efficiency. The proposed strategy reformulation as a standard quadratic programming problem generates an optimal charge/discharge protocol by maximizing energy efficiency and minimizing aging rates. A traditional battery pack with passive rule-based balancing management is quantitatively compared by employing a high-fidelity battery pack model as the research object. The results show that after 30 cycles of fully charging/discharging, the operation efficacy of batteries can be improved to a large extent, for example, an apparent increase in the average energy efficiency from 91.18% to 97.80% and a non-negligible reduction in the capacity aging rate from 0.021% to 0.009%. Finally, three other tests, including 200 cycles of fully charging/discharging, a 50-cell module test, and an automotive driving cycle test, are simulated and discussed, demonstrating a higher operation efficacy for managing more batteries or handling dynamic driving conditions.

Suggested Citation

  • Zhong, Hao & Lei, Fei & Zhu, Wenhao & Zhang, Zhe, 2022. "An operation efficacy-oriented predictive control management for power-redistributable lithium-ion battery pack," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s036054422200754x
    DOI: 10.1016/j.energy.2022.123851
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    1. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    2. Eddahech, Akram & Briat, Olivier & Vinassa, Jean-Michel, 2013. "Thermal characterization of a high-power lithium-ion battery: Potentiometric and calorimetric measurement of entropy changes," Energy, Elsevier, vol. 61(C), pages 432-439.
    3. Lai, Xin & Huang, Yunfeng & Gu, Huanghui & Han, Xuebing & Feng, Xuning & Dai, Haifeng & Zheng, Yuejiu & Ouyang, Minggao, 2022. "Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects," Energy, Elsevier, vol. 238(PA).
    4. Huang, Deyang & Chen, Ziqiang & Zhou, Shiyao, 2022. "Self-powered heating strategy for lithium-ion battery pack applied in extremely cold climates," Energy, Elsevier, vol. 239(PB).
    5. Diao, Weiping & Xue, Nan & Bhattacharjee, Vikram & Jiang, Jiuchun & Karabasoglu, Orkun & Pecht, Michael, 2018. "Active battery cell equalization based on residual available energy maximization," Applied Energy, Elsevier, vol. 210(C), pages 690-698.
    6. Zou, Changfu & Hu, Xiaosong & Wei, Zhongbao & Tang, Xiaolin, 2017. "Electrothermal dynamics-conscious lithium-ion battery cell-level charging management via state-monitored predictive control," Energy, Elsevier, vol. 141(C), pages 250-259.
    7. Liao, Peng & Tang, Tie-Qiao & Liu, Ronghui & Huang, Hai-Jun, 2021. "An eco-driving strategy for electric vehicle based on the powertrain," Applied Energy, Elsevier, vol. 302(C).
    8. Hu, Xiaosong & Jiang, Haifu & Feng, Fei & Liu, Bo, 2020. "An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management," Applied Energy, Elsevier, vol. 257(C).
    9. Bouchhima, Nejmeddine & Schnierle, Marc & Schulte, Sascha & Birke, Kai Peter, 2017. "Optimal energy management strategy for self-reconfigurable batteries," Energy, Elsevier, vol. 122(C), pages 560-569.
    10. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
    11. E, Jiaqiang & Zhang, Bin & Zeng, Yan & Wen, Ming & Wei, Kexiang & Huang, Zhonghua & Chen, Jingwei & Zhu, Hao & Deng, Yuanwang, 2022. "Effects analysis on active equalization control of lithium-ion batteries based on intelligent estimation of the state-of-charge," Energy, Elsevier, vol. 238(PB).
    12. Xiong, Rui & Pan, Yue & Shen, Weixiang & Li, Hailong & Sun, Fengchun, 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    13. Li, Xiaoyu & Xu, Jianhua & Hong, Jianxun & Tian, Jindong & Tian, Yong, 2021. "State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy," Energy, Elsevier, vol. 214(C).
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    1. Liu, Haoran & Yu, Jiaqi & Wang, Ruzhu, 2022. "Model predictive control of portable electronic devices under skin temperature constraints," Energy, Elsevier, vol. 260(C).

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