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Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

Author

Listed:
  • Zhongyue Zou

    (School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China)

  • Jun Xu

    (School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China)

  • Chris Mi

    (Department of Electrical and Computer Engineering, University of Michigan, Dearborn, MI 48128, USA)

  • Binggang Cao

    (School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China)

  • Zheng Chen

    (Department of Electrical and Computer Engineering, University of Michigan, Dearborn, MI 48128, USA)

Abstract

Four model-based State of Charge (SOC) estimation methods for lithium-ion (Li-ion) batteries are studied and evaluated in this paper. Different from existing literatures, this work evaluates different aspects of the SOC estimation, such as the estimation error distribution, the estimation rise time, the estimation time consumption, etc. The equivalent model of the battery is introduced and the state function of the model is deduced. The four model-based SOC estimation methods are analyzed first. Simulations and experiments are then established to evaluate the four methods. The urban dynamometer driving schedule (UDDS) current profiles are applied to simulate the drive situations of an electrified vehicle, and a genetic algorithm is utilized to identify the model parameters to find the optimal parameters of the model of the Li-ion battery. The simulations with and without disturbance are carried out and the results are analyzed. A battery test workbench is established and a Li-ion battery is applied to test the hardware in a loop experiment. Experimental results are plotted and analyzed according to the four aspects to evaluate the four model-based SOC estimation methods.

Suggested Citation

  • Zhongyue Zou & Jun Xu & Chris Mi & Binggang Cao & Zheng Chen, 2014. "Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries," Energies, MDPI, vol. 7(8), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:8:p:5065-5082:d:39022
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    References listed on IDEAS

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    3. Yunfeng Jiang & Xin Zhao & Amir Valibeygi & Raymond A. De Callafon, 2016. "Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery," Energies, MDPI, vol. 9(8), pages 1-17, July.
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    5. Woo-Yong Kim & Pyeong-Yeon Lee & Jonghoon Kim & Kyung-Soo Kim, 2019. "A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles," Energies, MDPI, vol. 12(17), pages 1-20, September.
    6. Saeed Mian Qaisar, 2020. "Event-Driven Coulomb Counting for Effective Online Approximation of Li-Ion Battery State of Charge," Energies, MDPI, vol. 13(21), pages 1-20, October.
    7. Turksoy, Arzu & Teke, Ahmet & Alkaya, Alkan, 2020. "A comprehensive overview of the dc-dc converter-based battery charge balancing methods in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    8. Renxin Xiao & Jiangwei Shen & Xiaoyu Li & Wensheng Yan & Erdong Pan & Zheng Chen, 2016. "Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods," Energies, MDPI, vol. 9(3), pages 1-15, March.
    9. Xu, Jun & Wang, Haitao & Shi, Hu & Mei, Xuesong, 2020. "Multi-scale short circuit resistance estimation method for series connected battery strings," Energy, Elsevier, vol. 202(C).
    10. Ines Baccouche & Sabeur Jemmali & Bilal Manai & Noshin Omar & Najoua Essoukri Ben Amara, 2017. "Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(6), pages 1-22, May.
    11. Qingxia Yang & Jun Xu & Binggang Cao & Xiuqing Li, 2017. "A simplified fractional order impedance model and parameter identification method for lithium-ion batteries," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-13, February.
    12. Angela Amato & Matteo Bilardo & Enrico Fabrizio & Valentina Serra & Filippo Spertino, 2021. "Energy Evaluation of a PV-Based Test Facility for Assessing Future Self-Sufficient Buildings," Energies, MDPI, vol. 14(2), pages 1-23, January.
    13. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    14. Teng Liu & Yuan Zou & Dexing Liu & Fengchun Sun, 2015. "Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 8(7), pages 1-18, July.
    15. Xiao Wang & Jun Xu & Yunfei Zhao, 2018. "Wavelet Based Denoising for the Estimation of the State of Charge for Lithium-Ion Batteries," Energies, MDPI, vol. 11(5), pages 1-13, May.
    16. Jun Xu & Binggang Cao & Junping Wang, 2016. "A Novel Method to Balance and Reconfigure Series-Connected Battery Strings," Energies, MDPI, vol. 9(10), pages 1-13, September.
    17. Haipeng Li & Jiayi Wang & Yan Zhao & Taizhe Tan, 2018. "Synthesis of the ZnO@ZnS Nanorod for Lithium-Ion Batteries," Energies, MDPI, vol. 11(8), pages 1-8, August.
    18. Jufeng Yang & Bing Xia & Yunlong Shang & Wenxin Huang & Chris Mi, 2016. "Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications," Energies, MDPI, vol. 10(1), pages 1-20, December.
    19. Saeed Sepasi & Leon R. Roose & Marc M. Matsuura, 2015. "Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation," Energies, MDPI, vol. 8(6), pages 1-17, June.
    20. Bizhong Xia & Guanghao Chen & Jie Zhou & Yadi Yang & Rui Huang & Wei Wang & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2019. "Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization," Energies, MDPI, vol. 12(15), pages 1-20, July.
    21. Yin Hua & Min Xu & Mian Li & Chengbin Ma & Chen Zhao, 2015. "Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles," Energies, MDPI, vol. 8(5), pages 1-22, April.

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