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Model prediction-based battery-powered heating method for series-connected lithium-ion battery pack working at extremely cold temperatures

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  • Huang, Deyang
  • Chen, Ziqiang
  • Zhou, Shiyao

Abstract

The degraded performance of lithium-ion batteries at low temperatures is a key obstacle to the development of battery energy storage system applied in extremely cold environment. Therefore, this paper proposes a heating method based on model prediction to support the low-temperature operation of battery pack without additional power sources. Battery pack model is developed based on Thevenin equivalent circuit model. A co-estimator is established to update model parameters and state-of-charge online using adaptive recursive least squares and extended Kalman filter. The permissible discharging current of pack is predicted based on multiple constraints to prevent over-discharge. Then, the battery-powered heating structure, control circuit, and heating strategy are designed. The strategy contains a preheating process for cold-start and a holding process for stabilizing cell temperature. The method is verified experimentally through systematic battery-in-the-loop tests at the environmental temperature of – 40 °C. Results show that the method can uniformly preheat all in-pack cells from − 40 °C to − 20 °C in 330 s consuming 4.7% of nominal capacity. In holding process, it is energy-efficient to raise cell temperature continuously and then maintain at 5 °C, which makes 68.3% of nominal capacity available when loading a modified federal urban driving schedule.

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  • Huang, Deyang & Chen, Ziqiang & Zhou, Shiyao, 2021. "Model prediction-based battery-powered heating method for series-connected lithium-ion battery pack working at extremely cold temperatures," Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220323434
    DOI: 10.1016/j.energy.2020.119236
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    References listed on IDEAS

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    Cited by:

    1. Wu, Tingting & Wang, Changhong & Hu, Yanxin & Liang, Zhixuan & Fan, Changxiang, 2023. "Research on electrochemical characteristics and heat generating properties of power battery based on multi-time scales," Energy, Elsevier, vol. 265(C).
    2. Guo, Ruohan & Shen, Weixiang, 2022. "A data-model fusion method for online state of power estimation of lithium-ion batteries at high discharge rate in electric vehicles," Energy, Elsevier, vol. 254(PA).
    3. Ren, Ruyang & Zhao, Yaohua & Diao, Yanhua & Liang, Lin, 2022. "Experimental study on preheating thermal management system for lithium-ion battery based on U-shaped micro heat pipe array," Energy, Elsevier, vol. 253(C).
    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. 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).
    6. Xu, Bowen & Lu, Xinjiang & Bai, Yunxu & Xu, Jie, 2024. "A convolution and memory network-based spatiotemporal model for thermal dynamics of multiple heat sources and its application in serial-connected lithium batteries," Energy, Elsevier, vol. 293(C).
    7. He, Xitian & Sun, Bingxiang & Zhang, Weige & Su, Xiaojia & Ma, Shichang & Li, Hao & Ruan, Haijun, 2023. "Inconsistency modeling of lithium-ion battery pack based on variational auto-encoder considering multi-parameter correlation," Energy, Elsevier, vol. 277(C).

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