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A novel resistor-inductor network-based equivalent circuit model of lithium-ion batteries under constant-voltage charging condition

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  • Yang, Jufeng
  • Cai, Yingfeng
  • Pan, Chaofeng
  • Mi, Chris

Abstract

A constant-current constant-voltage (CCCV) charge protocol is commonly used for lithium-ion batteries. The dynamic characteristic of the constant-voltage (CV) charging current is discovered to be related to battery aging. In order to quantitatively describe the load current during the CV charging period, an equivalent circuit model (ECM) based on the resistor-inductor (RL) network is proposed in this paper. Motivated by the current expression derived based on the conventional resistor–capacitor (RC) network-based ECM, an RL network-based ECM is developed to characterize the CV charging current. Then, the parallel-connected RL networks are employed to improve the model fidelity. The test data of four lithium iron phosphate (LiFePO4) batteries in different aging states are employed to validate the proposed model. Comparative results show that the proposed 2nd-order ECM is the best choice, considering both the model accuracy and complexity. In addition, a simplified 2nd-order model is proposed, achieving a satisfactory accuracy with only three model parameters to be identified. Therefore, this model can be easily implemented in the battery management system (BMS).

Suggested Citation

  • Yang, Jufeng & Cai, Yingfeng & Pan, Chaofeng & Mi, Chris, 2019. "A novel resistor-inductor network-based equivalent circuit model of lithium-ion batteries under constant-voltage charging condition," Applied Energy, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:appene:v:254:y:2019:i:c:s0306261919314138
    DOI: 10.1016/j.apenergy.2019.113726
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    References listed on IDEAS

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    1. Wang, Yujie & Liu, Chang & Pan, Rui & Chen, Zonghai, 2017. "Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator," Energy, Elsevier, vol. 121(C), pages 739-750.
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    Cited by:

    1. Mehta, Rohit & Gupta, Amit, 2024. "Mathematical modelling of electrochemical, thermal and degradation processes in lithium-ion cells—A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    2. Yang, Jufeng & Li, Xin & Sun, Xiaodong & Cai, Yingfeng & Mi, Chris, 2023. "An efficient and robust method for lithium-ion battery capacity estimation using constant-voltage charging time," Energy, Elsevier, vol. 263(PB).
    3. Yao, Jiachi & Chang, Zhonghao & Han, Te & Tian, Jingpeng, 2024. "Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems," Energy, Elsevier, vol. 294(C).

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