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Battery thermal management of intelligent-connected electric vehicles at low temperature based on NMPC

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  • Ma, Yan
  • Ding, Hao
  • Liu, Yongqin
  • Gao, Jinwu

Abstract

Electric vehicles running at low temperature causes range anxiety and safety hazards because of the reduction of available battery capacity and battery degradation caused by lithium plating. An optimization strategy for low temperature heating of intelligent-connected electric vehicle battery pack is proposed in this paper. Based on the Bernardi's theory, a control-oriented model of the battery pack heating system is established, which considers the effect of low temperature discharge on battery aging. A hybrid heating method combining heat pump air conditioning and electric heater is adopted to increase the heating rate and reduce energy consumption. Aiming at the problem that the battery heating process is affected by the time-varying parameters of the battery and the running state of the electric vehicle leading to the nonlinearity of the system, a nonlinear model predictive control (NMPC) heating optimization strategy is proposed. And a multi-objective optimization function constrained by many variables such as compressor speed is established to adjust battery temperature and energy consumption. Moreover, at each sampling point in the prediction time domain of NMPC, the future vehicle speed prediction information obtained based on vehicle-to-cloud communication is introduced into the heating process as interference. The simulation results show that compared with using electric heater alone, the heating time of the method proposed in this paper is shortened by 29%, and the energy consumption is reduced by 45%.

Suggested Citation

  • Ma, Yan & Ding, Hao & Liu, Yongqin & Gao, Jinwu, 2022. "Battery thermal management of intelligent-connected electric vehicles at low temperature based on NMPC," Energy, Elsevier, vol. 244(PA).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pa:s0360544221028206
    DOI: 10.1016/j.energy.2021.122571
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Li, Kai & Chen, Hong & Hou, Shengyan & Eriksson, Lars & Gao, Jinwu, 2023. "A novel engine and battery coupled thermal management strategy for connected HEVs based on switched model predictive control under low temperature," Energy, Elsevier, vol. 278(C).
    2. Zhao, Yihang & Dan, Dan & Zheng, Siyu & Wei, Mingshan & Xie, Yi, 2023. "A two-stage eco-cooling control strategy for electric vehicle thermal management system considering multi-source information fusion," Energy, Elsevier, vol. 267(C).
    3. Karimshoushtari, Milad & Kordestani, Mojtaba & Shojaei, Sina & Dönmez, Bilge Kağan & Rashid, Muzamil & Weslati, Feisel & Bouyoucef, Kamal, 2023. "On the applicability of advanced model-based strategies to control of electrified vehicle thermal systems," Energy, Elsevier, vol. 283(C).
    4. Ma, Yan & Ma, Qian & Liu, Yongqin & Gao, Jinwu & Chen, Hong, 2024. "Two-level optimization strategy for vehicle speed and battery thermal management in connected and automated EVs," Applied Energy, Elsevier, vol. 361(C).
    5. Olis, Walker & Rosewater, David & Nguyen, Tu & Byrne, Raymond H., 2023. "Impact of heating and cooling loads on battery energy storage system sizing in extreme cold climates," Energy, Elsevier, vol. 278(PB).
    6. Dan Dan & Yihang Zhao & Mingshan Wei & Xuehui Wang, 2023. "Review of Thermal Management Technology for Electric Vehicles," Energies, MDPI, vol. 16(12), pages 1-38, June.
    7. Sun, Xilei & Zhou, Feng & Fu, Jianqin & Liu, Jingping, 2024. "Experiment and simulation study on energy flow characteristics of a battery electric vehicle throughout the entire driving range in low-temperature conditions," Energy, Elsevier, vol. 292(C).
    8. Themistoklis Stamadianos & Nikolaos A. Kyriakakis & Magdalene Marinaki & Yannis Marinakis, 2023. "Routing Problems with Electric and Autonomous Vehicles: Review and Potential for Future Research," SN Operations Research Forum, Springer, vol. 4(2), pages 1-34, June.

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