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Hybrid unscented particle filter based state-of-charge determination for lead-acid batteries

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  • Shen, Yanqing

Abstract

Accurate prediction of cell SOC (state of charge) is important for the safety and functional capabilities of the battery energy storage application system. This paper presents a hybrid UPF (unscented particle filter) based SOC determination combined model for batteries. To simulate the entire dynamic electrical characteristics of batteries, a novel combined state space model, which takes current as a control input and let SOC and two constructed parameters as state variables, is advanced to represent cell behavior. Besides that, an improved UPF method is used to evaluate cell SOC. Taking lead-acid batteries for example, we apply the established model for test. Results show that the evolved combined state space cell model simulates battery dynamics robustly with high accuracy and the prediction value based on the improved UPF method converges to the real SOC very quickly within the error of±2%.

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  • Shen, Yanqing, 2014. "Hybrid unscented particle filter based state-of-charge determination for lead-acid batteries," Energy, Elsevier, vol. 74(C), pages 795-803.
    • Handle: RePEc:eee:energy:v:74:y:2014:i:c:p:795-803
    DOI: 10.1016/j.energy.2014.07.051
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    References listed on IDEAS

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    Cited by:

    1. Huang, Deyang & Chen, Ziqiang & Zheng, Changwen & Li, Haibin, 2019. "A model-based state-of-charge estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature," Energy, Elsevier, vol. 185(C), pages 847-861.
    2. 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.
    3. Bizhong Xia & Zhen Sun & Ruifeng Zhang & Deyu Cui & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2017. "A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries," Energies, MDPI, vol. 10(8), pages 1-14, August.
    4. Cheng, Yujie & Lu, Chen & Li, Tieying & Tao, Laifa, 2015. "Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach," Energy, Elsevier, vol. 90(P2), pages 1983-1993.
    5. Rodrigues, E.M.G. & Osório, G.J. & Godina, R. & Bizuayehu, A.W. & Lujano-Rojas, J.M. & Matias, J.C.O. & Catalão, J.P.S., 2015. "Modelling and sizing of NaS (sodium sulfur) battery energy storage system for extending wind power performance in Crete Island," Energy, Elsevier, vol. 90(P2), pages 1606-1617.
    6. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
    7. Olabi, A.G. & Wilberforce, Tabbi & Sayed, Enas Taha & Abo-Khalil, Ahmed G. & Maghrabie, Hussein M. & Elsaid, Khaled & Abdelkareem, Mohammad Ali, 2022. "Battery energy storage systems and SWOT (strengths, weakness, opportunities, and threats) analysis of batteries in power transmission," Energy, Elsevier, vol. 254(PA).
    8. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2017. "A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique," Energy, Elsevier, vol. 141(C), pages 1402-1415.
    9. 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).
    10. 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.
    11. Shen, Yanqing, 2018. "Improved chaos genetic algorithm based state of charge determination for lithium batteries in electric vehicles," Energy, Elsevier, vol. 152(C), pages 576-585.

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