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Comparison of SOC Estimation between the Integer-Order Model and Fractional-Order Model Under Different Operating Conditions

Author

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  • Guoqing Jin

    (Chongqing Changan Automobile Co., Ltd., Chongqing 400023, China)

  • Lan Li

    (State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China)

  • Yidan Xu

    (State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China)

  • Minghui Hu

    (State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China)

  • Chunyun Fu

    (State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China)

  • Datong Qin

    (State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China)

Abstract

Accurate estimation of the state of charge (SOC) is an important criterion to prevent the batteries from being over-charged or over-discharged, and this assures an electric vehicle’s safety and reliability. To investigate the effect of different operating conditions on the SOC estimation results, a dual-polarization model (DPM) and a fractional-order model (FOM) are established in this study, taking into account the prediction accuracy and structural complexity of a battery model. Based on these two battery equivalent circuit models (ECMs), a hybrid Kalman filter (HKF) algorithm is adopted to estimate the SOC of the battery; the algorithm comprehensively utilizes the ampere-hour (Ah) integration method, the Kalman filter (KF) algorithm, and the extended Kalman filter (EKF) algorithm. The SOC estimation results of the DPM and FOM, under the dynamic stress test (DST), federal urban driving schedule (FUDS), and hybrid pulse power characteristic (HPPC) cycle conditions, are compared and analyzed through six sets of experiments. Simulation results show that the SOC estimation accuracy of both the models is high and that the errors are within the range of ±0.06. Under any operating conditions, the SOC estimation error, based on the FOM, is always lower than the SOC estimation error of the DPM, but the adaptability of the FOM is not as high as that of the DPM.

Suggested Citation

  • Guoqing Jin & Lan Li & Yidan Xu & Minghui Hu & Chunyun Fu & Datong Qin, 2020. "Comparison of SOC Estimation between the Integer-Order Model and Fractional-Order Model Under Different Operating Conditions," Energies, MDPI, vol. 13(7), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1785-:d:342591
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    References listed on IDEAS

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    Cited by:

    1. Xin Zhang & Jiawei Hou & Zekun Wang & Yueqiu Jiang, 2022. "Joint SOH-SOC Estimation Model for Lithium-Ion Batteries Based on GWO-BP Neural Network," Energies, MDPI, vol. 16(1), pages 1-17, December.
    2. Carlos Augusto Berlitz & Andrea Pietrelli & Fabien Mieyeville & Gaël Pillonnet & Bruno Allard, 2023. "Microbial Fuel Cell as Battery Range Extender for Frugal IoT," Energies, MDPI, vol. 16(18), pages 1-15, September.
    3. Neha Bhushan & Saad Mekhilef & Kok Soon Tey & Mohamed Shaaban & Mehdi Seyedmahmoudian & Alex Stojcevski, 2022. "Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies," Sustainability, MDPI, vol. 14(23), pages 1-31, November.

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