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A novel active equalization method for lithium-ion batteries in electric vehicles

Author

Listed:
  • Wang, Yujie
  • Zhang, Chenbin
  • Chen, Zonghai
  • Xie, Jing
  • Zhang, Xu

Abstract

Cell inconsistency is inevitable due to manufacturing constraint. Therefore, cell equalization is essentially required. In this paper, we propose a novel active equalization method based on the remaining capacity of cells which is feasible for lithium-ion battery packs in electric vehicles (EVs). The cell models are established based on a combined electrochemical model of lithium-ion batteries. The remaining capacity and state-of-charge (SOC) of cells are observed at the beginning of equalization. The particle filter (PF) method is employed to estimate the cell SOCs during equalization in order to eliminate the drift noise of the current sensor. The first high-SOC cell discharge (FHCD) and first low-SOC cell charge (FLCC) equalization algorithms are proposed and compared with 1% and 3% SOC bounds, respectively. The validation experiment results have shown that the proposed algorithm is suitable for equalization of lithium-ion batteries in EVs.

Suggested Citation

  • Wang, Yujie & Zhang, Chenbin & Chen, Zonghai & Xie, Jing & Zhang, Xu, 2015. "A novel active equalization method for lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 145(C), pages 36-42.
    • Handle: RePEc:eee:appene:v:145:y:2015:i:c:p:36-42
    DOI: 10.1016/j.apenergy.2015.01.127
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    References listed on IDEAS

    as
    1. He, Yao & Liu, XingTao & Zhang, ChenBin & Chen, ZongHai, 2013. "A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries," Applied Energy, Elsevier, vol. 101(C), pages 808-814.
    2. Zhong, Liang & Zhang, Chenbin & He, Yao & Chen, Zonghai, 2014. "A method for the estimation of the battery pack state of charge based on in-pack cells uniformity analysis," Applied Energy, Elsevier, vol. 113(C), pages 558-564.
    3. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy," Applied Energy, Elsevier, vol. 137(C), pages 427-434.
    4. Dong, Guangzhong & Zhang, Xu & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state of energy estimation of lithium-ion batteries based on neural network model," Energy, Elsevier, vol. 90(P1), pages 879-888.
    5. Liu, Xingtao & Chen, Zonghai & Zhang, Chenbin & Wu, Ji, 2014. "A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation," Applied Energy, Elsevier, vol. 123(C), pages 263-272.
    6. Zheng, Yuejiu & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Han, Xuebing & Xu, Liangfei & Ma, Hongbin & Dollmeyer, Thomas A. & Freyermuth, Vincent, 2013. "Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model," Applied Energy, Elsevier, vol. 111(C), pages 571-580.
    7. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2014. "A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries," Applied Energy, Elsevier, vol. 135(C), pages 81-87.
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