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Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter

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  • Linghu, Jinqing
  • Kang, Longyun
  • Liu, Ming
  • Luo, Xuan
  • Feng, Yuanbin
  • Lu, Chusheng

Abstract

Accurate estimation for state-of-charge of the battery is very important for energy storage systems in electric vehicles and smart grids. To improve the accuracy and reliability of state-of-charge estimation, accurate model equations and a set of robust algorithm are necessary. Different from the commonly used method, this paper adopts a polynomial based on Gaussian function to build up the open circuit voltage function, and proposes an adaptive fifth-degree cubature Kalman filter algorithm to estimate the battery state-of-charge. Two typical driving cycles, including the dynamic stress test and the Worldwide harmonized Light Vehicles Test Cycle are applied to evaluate the performance of the proposed estimator. The results indicate that compared with the unscented Kalman filter and the adaptive cubature Kalman filter, the adaptive fifth-degree cubature Kalman filter can achieve higher state-of-charge estimation accuracy and better overcome the impact of large measurement error and initial error.

Suggested Citation

  • Linghu, Jinqing & Kang, Longyun & Liu, Ming & Luo, Xuan & Feng, Yuanbin & Lu, Chusheng, 2019. "Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219318997
    DOI: 10.1016/j.energy.2019.116204
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    5. 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).
    6. Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
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    9. 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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    11. Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
    12. 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).
    13. 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).
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