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Parameter Identification of Electrochemical Model for Vehicular Lithium-Ion Battery Based on Particle Swarm Optimization

Author

Listed:
  • Xiao Yang

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China)

  • Long Chen

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
    Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, Jiangsu, China)

  • Xing Xu

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
    Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, Jiangsu, China)

  • Wei Wang

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China)

  • Qiling Xu

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China)

  • Yuzhen Lin

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China)

  • Zhiguang Zhou

    (New Energy Development Department Powertrain Technology Center, Chery Automobile Co., Ltd., Wuhu 241009, Anhui, China)

Abstract

The dynamic characteristics of power batteries directly affect the performance of electric vehicles, and the mathematical model is the basis for the design of a battery management system (BMS).Based on the electrode-averaged model of a lithium-ion battery, in view of the solid phase lithium-ion diffusion equation, the electrochemical model is simplified through the finite difference method. By analyzing the characteristics of the model and the type of parameters, the solid state diffusion kinetics are separated, and then the cascade parameter identifications are implemented with Particle Swarm Optimization. Eventually, the validity of the electrochemical model and the accuracy of model parameters are verified through 0.2–2 C multi-rates battery discharge tests of cell and road simulation tests of a micro pure electric vehicle under New European Driving Cycle (NEDC) conditions. The results show that the estimated parameters can guarantee the output accuracy. In the test of cell, the voltage deviation of discharge is generally less than 0.1 V except the end; in road simulation test, the output is close to the actual value at low speed with the error around ±0.03 V, and at high speed around ±0.08 V.

Suggested Citation

  • Xiao Yang & Long Chen & Xing Xu & Wei Wang & Qiling Xu & Yuzhen Lin & Zhiguang Zhou, 2017. "Parameter Identification of Electrochemical Model for Vehicular Lithium-Ion Battery Based on Particle Swarm Optimization," Energies, MDPI, vol. 10(11), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1811-:d:118252
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    References listed on IDEAS

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    1. Zuchang Gao & Cheng Siong Chin & Joel Hay King Chiew & Junbo Jia & Caizhi Zhang, 2017. "Design and Implementation of a Smart Lithium-Ion Battery System with Real-Time Fault Diagnosis Capability for Electric Vehicles," Energies, MDPI, vol. 10(10), pages 1-15, September.
    2. Zuchang Gao & Cheng Siong Chin & Wai Lok Woo & Junbo Jia, 2017. "Integrated Equivalent Circuit and Thermal Model for Simulation of Temperature-Dependent LiFePO 4 Battery in Actual Embedded Application," Energies, MDPI, vol. 10(1), pages 1-22, January.
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    Citations

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    Cited by:

    1. Gabriele Sordi & Claudio Rabissi & Andrea Casalegno, 2023. "Reliable Thermal-Physical Modeling of Lithium-Ion Batteries: Consistency between High-Frequency Impedance and Ion Transport," Energies, MDPI, vol. 16(12), pages 1-17, June.
    2. 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).
    3. Banguero, Edison & Correcher, Antonio & Pérez-Navarro, Ángel & García, Emilio & Aristizabal, Andrés, 2020. "Diagnosis of a battery energy storage system based on principal component analysis," Renewable Energy, Elsevier, vol. 146(C), pages 2438-2449.
    4. An, Qing & Peng, Jian, 2023. "Parameter identification of lithium battery pack based on novel cooperatively coevolving differential evolution algorithm," Renewable Energy, Elsevier, vol. 216(C).
    5. Mina Naguib & Aashit Rathore & Nathan Emery & Shiva Ghasemi & Ryan Ahmed, 2023. "Robust Electro-Thermal Modeling of Lithium-Ion Batteries for Electrified Vehicles Applications," Energies, MDPI, vol. 16(16), pages 1-20, August.
    6. Zhongbao Wei & Feng Leng & Zhongjie He & Wenyu Zhang & Kaiyuan Li, 2018. "Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method," Energies, MDPI, vol. 11(7), pages 1-16, July.
    7. Tang, Ruoli & Zhang, Shangyu & Zhang, Shihan & Zhang, Yan & Lai, Jingang, 2023. "Parameter identification for lithium batteries: Model variable-coupling analysis and a novel cooperatively coevolving identification algorithm," Energy, Elsevier, vol. 263(PB).

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