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A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries

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  • Ye, Min
  • Guo, Hui
  • Xiong, Rui
  • Yu, Quanqing

Abstract

Obtaining an estimation of the parameters and state of charge (SoC) of a lithium-ion battery is crucial for an electric vehicle. The parameters of a battery model are usually different throughout the battery lifetime. To obtain an accurate SoC and parameters and reduce the computational cost, a double-scale dual adaptive particle filter for online parameters and SoC estimation of lithium-ion batteries is proposed. First, the lithium-ion battery is modeled using the Thevenin model. Second, a double-scale dual particle filter is proposed and applied to the battery parameter and SoC estimation. To improve the accuracy and convergence ability to the initial environmental offset, a double-scale dual adaptive particle filter is proposed. Finally, the effectiveness and applicability of the two algorithms are verified by Lithium Nickel Manganese Cobalt Oxide (NMC) batteries of different ages.

Suggested Citation

  • Ye, Min & Guo, Hui & Xiong, Rui & Yu, Quanqing, 2018. "A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries," Energy, Elsevier, vol. 144(C), pages 789-799.
    • Handle: RePEc:eee:energy:v:144:y:2018:i:c:p:789-799
    DOI: 10.1016/j.energy.2017.12.061
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    References listed on IDEAS

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    1. Dai, Haifeng & Guo, Pingjing & Wei, Xuezhe & Sun, Zechang & Wang, Jiayuan, 2015. "ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries," Energy, Elsevier, vol. 80(C), pages 350-360.
    2. 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.
    3. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    4. Sun, Fengchun & Hu, Xiaosong & Zou, Yuan & Li, Siguang, 2011. "Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 36(5), pages 3531-3540.
    5. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    6. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    7. Cuma, Mehmet Ugras & Koroglu, Tahsin, 2015. "A comprehensive review on estimation strategies used in hybrid and battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 517-531.
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    Cited by:

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    4. Liu, Guoan & Xu, Cheng & Li, Haomiao & Jiang, Kai & Wang, Kangli, 2019. "State of charge and online model parameters co-estimation for liquid metal batteries," Applied Energy, Elsevier, vol. 250(C), pages 677-684.
    5. Lv, Jie & Lin, Shili & Song, Wenji & Chen, Mingbiao & Feng, Ziping & Li, Yongliang & Ding, Yulong, 2019. "Performance of LiFePO4 batteries in parallel based on connection topology," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    6. Jiang, Yan & Meng, Xin, 2023. "A battery capacity estimation method based on the equivalent circuit model and quantile regression using vehicle real-world operation data," Energy, Elsevier, vol. 284(C).
    7. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    8. Yang, Xiaolong & Chen, Yongji & Li, Bin & Luo, Dong, 2020. "Battery states online estimation based on exponential decay particle swarm optimization and proportional-integral observer with a hybrid battery model," Energy, Elsevier, vol. 191(C).
    9. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
    10. Jiang, Bo & Tao, Siyi & Wang, Xueyuan & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique," Energy, Elsevier, vol. 278(PA).
    11. Guo, Feng & Hu, Guangdi & Xiang, Shun & Zhou, Pengkai & Hong, Ru & Xiong, Neng, 2019. "A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters," Energy, Elsevier, vol. 178(C), pages 79-88.
    12. Qinghe Liu & Shouzhi Liu & Haiwei Liu & Hao Qi & Conggan Ma & Lijun Zhao, 2019. "Evaluation of LFP Battery SOC Estimation Using Auxiliary Particle Filter," Energies, MDPI, vol. 12(11), pages 1-13, May.
    13. Hongya Zhang & Hao Chen & Haisheng Fang, 2022. "Cooling Optimization Strategy for a 6s4p Lithium-Ion Battery Pack Based on Triple-Step Nonlinear Method," Energies, MDPI, vol. 16(1), pages 1-31, December.
    14. Wang, Shun-Li & Fernandez, Carlos & Zou, Chuan-Yun & Yu, Chun-Mei & Chen, Lei & Zhang, Li, 2019. "A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction," Energy, Elsevier, vol. 171(C), pages 444-455.
    15. Victor Pizarro-Carmona & Marcelo Cortés-Carmona & Rodrigo Palma-Behnke & Williams Calderón-Muñoz & Marcos E. Orchard & Pablo A. Estévez, 2019. "An Optimized Impedance Model for the Estimation of the State-of-Charge of a Li-Ion Cell: The Case of a LiFePO 4 (ANR26650)," Energies, MDPI, vol. 12(4), pages 1-16, February.
    16. Ma, Yan & Mou, Hongyuan & Zhao, Haiyan, 2020. "Cooling optimization strategy for lithium-ion batteries based on triple-step nonlinear method," Energy, Elsevier, vol. 201(C).
    17. Zheng, Linfeng & Zhu, Jianguo & Lu, Dylan Dah-Chuan & Wang, Guoxiu & He, Tingting, 2018. "Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries," Energy, Elsevier, vol. 150(C), pages 759-769.
    18. Liang Zhang & Shunli Wang & Daniel-Ioan Stroe & Chuanyun Zou & Carlos Fernandez & Chunmei Yu, 2020. "An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries," Energies, MDPI, vol. 13(8), pages 1-12, April.
    19. Fahmy, Hend M. & Alqahtani, Ayedh H. & Hasanien, Hany M., 2024. "Precise modeling of lithium-ion battery in industrial applications using Walrus optimization algorithm," Energy, Elsevier, vol. 294(C).

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