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A Lossy Counting-Based State of Charge Estimation Method and Its Application to Electric Vehicles

Author

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  • Hong Zhang

    (School of Mechanical and Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Li Zhao

    (School of Mechanical and Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100192, China)

  • Yong Chen

    (Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100192, China
    Mechanical Electical Engineering School, Beijing Information Science & Technology University, Beijing 100192, China)

Abstract

Estimating the residual capacity or state-of-charge (SoC) of commercial batteries on-line without destroying them or interrupting the power supply, is quite a challenging task for electric vehicle (EV) designers. Many Coulomb counting-based methods have been used to calculate the remaining capacity in EV batteries or other portable devices. The main disadvantages of these methods are the cumulative error and the time-varying Coulombic efficiency, which are greatly influenced by the operating state (SoC, temperature and current). To deal with this problem, we propose a lossy counting-based Coulomb counting method for estimating the available capacity or SoC. The initial capacity of the tested battery is obtained from the open circuit voltage (OCV). The charging/discharging efficiencies, used for compensating the Coulombic losses, are calculated by the lossy counting-based method. The measurement drift, resulting from the current sensor, is amended with the distorted Coulombic efficiency matrix. Simulations and experimental results show that the proposed method is both effective and convenient.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:12:p:12395-13828:d:59976
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    References listed on IDEAS

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    Cited by:

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    2. Li Zhao & Kun Li & Wu Zhao & Han-Chen Ke & Zhen Wang, 2022. "A Sticky Sampling and Markov State Transition Matrix Based Driving Cycle Construction Method for EV," Energies, MDPI, vol. 15(3), pages 1-19, January.
    3. Yang Guo & Ziguang Lu, 2022. "A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias," Energies, MDPI, vol. 15(4), pages 1-18, February.
    4. Bizhong Xia & Zhen Sun & Ruifeng Zhang & Zizhou Lao, 2017. "A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(4), pages 1-15, April.

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