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A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles

Author

Listed:
  • Jianping Gao

    (College of Vehicle and Transportation Engineering, Henan University of Science and Technology, Luoyang 471023, China)

  • Yongzhi Zhang

    (National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Hongwen He

    (National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Accurate state of charge (SoC) estimation of batteries plays an important role in promoting the commercialization of electric vehicles. The main work to be done in accurately determining battery SoC can be summarized in three parts. (1) In view of the model-based SoC estimation flow diagram, the n -order resistance-capacitance (RC) battery model is proposed and expected to accurately simulate the battery’s major time-variable, nonlinear characteristics. Then, the mathematical equations for model parameter identification and SoC estimation of this model are constructed. (2) The Akaike information criterion is used to determine an optimal tradeoff between battery model complexity and prediction precision for the n -order RC battery model. Results from a comparative analysis show that the first-order RC battery model is thought to be the best based on the Akaike information criterion (AIC) values. (3) The real-time joint estimator for the model parameter and SoC is constructed, and the application based on two battery types indicates that the proposed SoC estimator is a closed-loop identification system where the model parameter identification and SoC estimation are corrected mutually, adaptively and simultaneously according to the observer values. The maximum SoC estimation error is less than 1% for both battery types, even against the inaccurate initial SoC.

Suggested Citation

  • Jianping Gao & Yongzhi Zhang & Hongwen He, 2015. "A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 8(8), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:8:p:8594-8612:d:54102
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    References listed on IDEAS

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    1. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    2. Fei Feng & Rengui Lu & Guo Wei & Chunbo Zhu, 2015. "Online Estimation of Model Parameters and State of Charge of LiFePO 4 Batteries Using a Novel Open-Circuit Voltage at Various Ambient Temperatures," Energies, MDPI, vol. 8(4), pages 1-27, April.
    3. Noshin Omar & Mohamed Daowd & Peter van den Bossche & Omar Hegazy & Jelle Smekens & Thierry Coosemans & Joeri van Mierlo, 2012. "Rechargeable Energy Storage Systems for Plug-in Hybrid Electric Vehicles—Assessment of Electrical Characteristics," Energies, MDPI, vol. 5(8), pages 1-37, August.
    4. Hongwen He & Hongzhou Qin & Xiaokun Sun & Yuanpeng Shui, 2013. "Comparison Study on the Battery SoC Estimation with EKF and UKF Algorithms," Energies, MDPI, vol. 6(10), pages 1-13, September.
    5. He, Hongwen & Zhang, Xiaowei & Xiong, Rui & Xu, Yongli & Guo, Hongqiang, 2012. "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 39(1), pages 310-318.
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    Citations

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    Cited by:

    1. Lan-Rong Dung & Hsiang-Fu Yuan & Jieh-Hwang Yen & Chien-Hua She & Ming-Han Lee, 2016. "A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions," Energies, MDPI, vol. 9(1), pages 1-21, January.
    2. Dai, Haifeng & Xu, Tianjiao & Zhu, Letao & Wei, Xuezhe & Sun, Zechang, 2016. "Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales," Applied Energy, Elsevier, vol. 184(C), pages 119-131.
    3. 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.
    4. Lin, Cheng & Yu, Quanqing & Xiong, Rui & Wang, Le Yi, 2017. "A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 205(C), pages 892-902.
    5. Yunna Wu & Meng Yang & Haobo Zhang & Kaifeng Chen & Yang Wang, 2016. "Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method," Energies, MDPI, vol. 9(3), pages 1-20, March.
    6. Zheng, Linfeng & Zhang, Lei & Zhu, Jianguo & Wang, Guoxiu & Jiang, Jiuchun, 2016. "Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model," Applied Energy, Elsevier, vol. 180(C), pages 424-434.
    7. Hong Zhang & Li Zhao & Yong Chen, 2015. "A Lossy Counting-Based State of Charge Estimation Method and Its Application to Electric Vehicles," Energies, MDPI, vol. 8(12), pages 1-18, December.

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