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A LiFePO4 battery pack capacity estimation approach considering in-parallel cell safety in electric vehicles

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  • Wang, Limei
  • Cheng, Yong
  • Zhao, Xiuliang

Abstract

In electric vehicles (EVs), several battery cells are connected in parallel to establish a battery module. The safety of the battery module is influenced by inconsistent battery cell performance which causes uneven currents flowing through internal in-parallel battery cells. A battery cell model is developed based on the Matlab–Simscape platform and validated by tests. The battery cell model is used to construct simulation models for analyzing the effect of battery cell inconsistency on the performance of an in-parallel battery module. Simulation results indicate that the state-of-charge (SOC) of a battery module cannot characterize the SOC of ALL the internal battery cells in the battery module. When the battery management system (BMS) controls the end-of-charge (EOC) time according to the SOC of a battery module, some internal battery cells are over-charged. To guarantee the safety of ALL battery cells through the whole battery life, a safety EOC voltage of the battery module should be set according to the number of battery cells in the battery module and the applied charge current. Simulations reveal that the SOC of the “normal battery module” is related to its charge voltage when aged battery module is charged to the EOC voltage. Then, a function describing their relationship is established. Both the capacity and the charge voltage shift are estimated by comparing the measured voltage-to-capacity curve with the standard one provided by the manufactory. A battery pack capacity estimation method is proposed according to the SOC and the capacity of the “normal battery module”. Experimental results show that battery pack capacity estimation difference between the proposed method and the standard current integration method is to within 0.35%.

Suggested Citation

  • Wang, Limei & Cheng, Yong & Zhao, Xiuliang, 2015. "A LiFePO4 battery pack capacity estimation approach considering in-parallel cell safety in electric vehicles," Applied Energy, Elsevier, vol. 142(C), pages 293-302.
    • Handle: RePEc:eee:appene:v:142:y:2015:i:c:p:293-302
    DOI: 10.1016/j.apenergy.2014.12.081
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    References listed on IDEAS

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    4. Yang, Jufeng & Xia, Bing & Huang, Wenxin & Fu, Yuhong & Mi, Chris, 2018. "Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis," Applied Energy, Elsevier, vol. 212(C), pages 1589-1600.
    5. Avvari, G.V. & Pattipati, B. & Balasingam, B. & Pattipati, K.R. & Bar-Shalom, Y., 2015. "Experimental set-up and procedures to test and validate battery fuel gauge algorithms," Applied Energy, Elsevier, vol. 160(C), pages 404-418.
    6. Wang, Shunli & Shang, Liping & Li, Zhanfeng & Deng, Hu & Li, Jianchao, 2016. "Online dynamic equalization adjustment of high-power lithium-ion battery packs based on the state of balance estimation," Applied Energy, Elsevier, vol. 166(C), pages 44-58.
    7. Hosseinzadeh, Elham & Arias, Sebastian & Krishna, Muthu & Worwood, Daniel & Barai, Anup & Widanalage, Dhammika & Marco, James, 2021. "Quantifying cell-to-cell variations of a parallel battery module for different pack configurations," Applied Energy, Elsevier, vol. 282(PA).
    8. Chuanxue Song & Yulong Shao & Shixin Song & Cheng Chang & Fang Zhou & Silun Peng & Feng Xiao, 2017. "Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control," Energies, MDPI, vol. 10(3), pages 1-13, March.
    9. Chuanxue Song & Yulong Shao & Shixin Song & Silun Peng & Fang Zhou & Cheng Chang & Da Wang, 2017. "Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering," Energies, MDPI, vol. 10(5), pages 1-13, May.
    10. Gong, Dongliang & Gao, Ying & Kou, Yalin & Wang, Yurang, 2022. "State of health estimation for lithium-ion battery based on energy features," Energy, Elsevier, vol. 257(C).
    11. Chang, Long & Ma, Chen & Zhang, Chenghui & Duan, Bin & Cui, Naxin & Li, Changlong, 2023. "Correlations of lithium-ion battery parameter variations and connected configurations on pack statistics," Applied Energy, Elsevier, vol. 329(C).
    12. Liu, Xinhua & Ai, Weilong & Naylor Marlow, Max & Patel, Yatish & Wu, Billy, 2019. "The effect of cell-to-cell variations and thermal gradients on the performance and degradation of lithium-ion battery packs," Applied Energy, Elsevier, vol. 248(C), pages 489-499.
    13. Zheng Chen & Ningyuan Guo & Xiaoyu Li & Jiangwei Shen & Renxin Xiao & Siqi Li, 2017. "Battery Pack Grouping and Capacity Improvement for Electric Vehicles Based on a Genetic Algorithm," Energies, MDPI, vol. 10(4), pages 1-15, March.
    14. Wang, Limei & Pan, Chaofeng & Liu, Liang & Cheng, Yong & Zhao, Xiuliang, 2016. "On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis," Applied Energy, Elsevier, vol. 168(C), pages 465-472.
    15. Tian, Jiaqiang & Wang, Yujie & Liu, Chang & Chen, Zonghai, 2020. "Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles," Energy, Elsevier, vol. 194(C).
    16. Hua Zhang & Lei Pei & Jinlei Sun & Kai Song & Rengui Lu & Yongping Zhao & Chunbo Zhu & Tiansi Wang, 2016. "Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group," Energies, MDPI, vol. 9(5), pages 1-18, May.

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