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Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network

Author

Listed:
  • Van Quan Dao

    (Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea)

  • Minh-Chau Dinh

    (Institute of Mechatronics, Changwon National University, Changwon 51140, Korea)

  • Chang Soon Kim

    (Institute of Mechatronics, Changwon National University, Changwon 51140, Korea)

  • Minwon Park

    (Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea)

  • Chil-Hoon Doh

    (Distributed Power System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Jeong Hyo Bae

    (Distributed Power System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Myung-Kwan Lee

    (Battery Solution Co., Ltd., Jeonnam 58324, Korea)

  • Jianyong Liu

    (IES Co., Ltd., Busan 46744, Korea)

  • Zhiguo Bai

    (IES Co., Ltd., Busan 46744, Korea)

Abstract

Currently, Lithium-ion batteries (LiB) are widely applied in energy storage devices in smart grids and electric vehicles. The state of charge (SOC) is an indication of the available battery capacity, and is one of the most important factors that should be monitored to optimize LiB’s performance and improve its lifetime. However, because the SOC relies on many nonlinear factors, it is difficult to estimate accurately. This paper presented the design of an effective SOC estimation method for a LiB pack Battery Management System (BMS) based on Kalman Filter (K F ) and Artificial Neural Network (ANN). First, considering the configuration and specifications of the BMS and LiB pack, an ANN was constructed for the SOC estimation, and then the ANN was trained and tested using the Google TensorFlow open-source library. An SOC estimation model based on the extended KF (EKF) and a Thevenin battery model was developed. Then, we proposed a combined mode EKF-ANN that integrates the estimation of the EKF into the ANN. Both methods were evaluated through experiments conducted on a real LiB pack. As a result, the ANN and KF methods showed maximum errors of 2.6% and 2.8%, but the EKF-ANN method showed better performance with less than 1% error.

Suggested Citation

  • Van Quan Dao & Minh-Chau Dinh & Chang Soon Kim & Minwon Park & Chil-Hoon Doh & Jeong Hyo Bae & Myung-Kwan Lee & Jianyong Liu & Zhiguo Bai, 2021. "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network," Energies, MDPI, vol. 14(9), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2634-:d:548793
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    References listed on IDEAS

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    Cited by:

    1. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    2. Sumukh Surya & Akash Samanta & Vinicius Marcis & Sheldon Williamson, 2022. "Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison," Energies, MDPI, vol. 15(2), pages 1-21, January.
    3. Mazin Mohammed Mogadem & Yan Li, 2021. "Memristive Equivalent Circuit Model for Battery," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
    4. Miquel Martí-Florences & Andreu Cecilia & Ramon Costa-Castelló, 2023. "Modelling and Estimation in Lithium-Ion Batteries: A Literature Review," Energies, MDPI, vol. 16(19), pages 1-36, September.
    5. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
    6. Qi Wang & Tian Gao & Xingcan Li, 2022. "SOC Estimation of Lithium-Ion Battery Based on Equivalent Circuit Model with Variable Parameters," Energies, MDPI, vol. 15(16), pages 1-15, August.

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