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An improved state of charge estimation method based on cubature Kalman filter for lithium-ion batteries

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  • Peng, Jiankun
  • Luo, Jiayi
  • He, Hongwen
  • Lu, Bing

Abstract

In this paper, an improved state of charge (SOC) estimation method of Lithium-Ion battery is developed based on a cubature Kalman filter (CKF) supported by experimental data. Firstly, a first-order RC model and corresponding fractional order model are established to evaluate the estimation accuracy of different models. Secondly, model parameters are identified through a custom Hybrid Pulse Power Characteristic (HPPC) experiment based on the Sequential Quadratic Programming (SQR) method. Then, a CKF algorithm is used to estimate the battery SOC under different battery models with no prior knowledge of initial SOC. The results show that the proposed CKF method has a better estimate robustness rather than Extended Kalman filter (EKF) and the fractional order model can achieve higher accuracy while it consumes more computing resources compared with equivalent circuit models. SOC estimation error of CKF algorithms is less than 3%. Thirdly, a battery management unit in the loop approach is applied to verify the accuracy of estimation. Last but not least, in order to reduce the estimation error due to battery degradation and battery model errors, a fuzzy controller is constructed to modified the gain coefficient of Kalman. The proposed improved method can minimize the estimation error of SOC by 2%.

Suggested Citation

  • Peng, Jiankun & Luo, Jiayi & He, Hongwen & Lu, Bing, 2019. "An improved state of charge estimation method based on cubature Kalman filter for lithium-ion batteries," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:30
    DOI: 10.1016/j.apenergy.2019.113520
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    Cited by:

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    10. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).
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    13. Ingvild B. Espedal & Asanthi Jinasena & Odne S. Burheim & Jacob J. Lamb, 2021. "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies, MDPI, vol. 14(11), pages 1-24, June.
    14. He, Lin & Wang, Yangyang & Wei, Yujiang & Wang, Mingwei & Hu, Xiaosong & Shi, Qin, 2022. "An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery," Energy, Elsevier, vol. 244(PA).
    15. Shen, Jiangwei & Ma, Wensai & Xiong, Jian & Shu, Xing & Zhang, Yuanjian & Chen, Zheng & Liu, Yonggang, 2022. "Alternative combined co-estimation of state of charge and capacity for lithium-ion batteries in wide temperature scope," Energy, Elsevier, vol. 244(PB).
    16. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    17. Xinfeng Zhang & Xiangjun Li & Kaikai Yang & Zhongyi Wang, 2023. "Lithium-Ion Battery Modeling and State of Charge Prediction Based on Fractional-Order Calculus," Mathematics, MDPI, vol. 11(15), pages 1-15, August.
    18. Li, Xiaoyu & Huang, Zhijia & Tian, Jindong & Tian, Yong, 2021. "State-of-charge estimation tolerant of battery aging based on a physics-based model and an adaptive cubature Kalman filter," Energy, Elsevier, vol. 220(C).
    19. 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.
    20. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    21. Song, Yuchen & Liu, Datong & Liao, Haitao & Peng, Yu, 2020. "A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 261(C).
    22. Xingtao Liu & Chaoyi Zheng & Ji Wu & Jinhao Meng & Daniel-Ioan Stroe & Jiajia Chen, 2020. "An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries," Energies, MDPI, vol. 13(2), pages 1-16, January.

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