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Accurate and Efficient Estimation of Lithium-Ion Battery State of Charge with Alternate Adaptive Extended Kalman Filter and Ampere-Hour Counting Methods

Author

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  • Zhongxiao Liu

    (State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China)

  • Zhe Li

    (State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China
    Beijing Co-Innovation Center for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Jianbo Zhang

    (State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China
    Beijing Co-Innovation Center for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Laisuo Su

    (Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA)

  • Hao Ge

    (State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China)

Abstract

State of charge (SOC) estimation is a key issue in battery management systems. The challenge lies in balancing the trade-off between accuracy and computation cost. To this end, we propose an alternate method by combining the ampere-hour integral (AHI) method which has low computation cost, and the adaptive extended Kalman filter (AEKF) method, which has high accuracy. The technical viability of this alternate method is verified on a LiMnO 2 -LiNiO 2 battery module with a nominal capacity of 130 Ah under the New European Driving Cycle (NEDC) condition. Drifts in current and voltage measurement are considered. The experimental results show that the absolute SOC error using the AHI method monotonously increases from 0% to 7.2% with the computation time of 10 s while the calculation time is obtained on a ThinkPad E450 PC with an Intel Core i7-5500U CPU @2.40 GHz and 16.0 GB RAM. The absolute SOC error of the AEKF method maintains within 3.5% with the computation time of 49 s. Therefore, the alternate method almost maintains the same SOC accuracy compared to the AEKF method which reduces the maximum absolute SOC error by 50% compared to the AHI method. Therefore, the alternate method almost has the same computation time compared with the AHI method which reduces the computation time by nearly 75% compared to the AEKF method.

Suggested Citation

  • Zhongxiao Liu & Zhe Li & Jianbo Zhang & Laisuo Su & Hao Ge, 2019. "Accurate and Efficient Estimation of Lithium-Ion Battery State of Charge with Alternate Adaptive Extended Kalman Filter and Ampere-Hour Counting Methods," Energies, MDPI, vol. 12(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:757-:d:208746
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    References listed on IDEAS

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    2. Nima Lotfi & Poria Fajri & Samuel Novosad & Jack Savage & Robert G. Landers & Mehdi Ferdowsi, 2013. "Development of an Experimental Testbed for Research in Lithium-Ion Battery Management Systems," Energies, MDPI, vol. 6(10), pages 1-28, October.
    3. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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    Cited by:

    1. Chaoran Li & Fei Xiao & Yaxiang Fan, 2019. "An Approach to State of Charge Estimation of Lithium-Ion Batteries Based on Recurrent Neural Networks with Gated Recurrent Unit," Energies, MDPI, vol. 12(9), pages 1-22, April.
    2. Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun & Sang-Joon Lee, 2022. "Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach," Mathematics, MDPI, vol. 10(6), pages 1-17, March.
    3. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
    4. Claudio Rossi & Carlo Falcomer & Luca Biondani & Davide Pontara, 2022. "Genetically Optimized Extended Kalman Filter for State of Health Estimation Based on Li-Ion Batteries Parameters," Energies, MDPI, vol. 15(9), pages 1-18, May.

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