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State-of-charge estimation tolerant of battery aging based on a physics-based model and an adaptive cubature Kalman filter

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  • Li, Xiaoyu
  • Huang, Zhijia
  • Tian, Jindong
  • Tian, Yong

Abstract

Battery performance declines with aging. This phenomenon makes it difficult to estimate the state-of-charge (SOC) of the battery. Because physics-based battery models (PBMs) can predict the performance decline caused by battery aging with high accuracy and robustness, a high-fidelity and reduced-order PBM is developed for battery SOC estimation according to the requirements of electric vehicle applications. The key model parameters are calibrated primarily according to low C-rate battery charging data. Based on the relationship between the lithium insertion ratios of the electrodes and the battery SOC, an SOC observer is designed. An adaptive cubature Kalman filter (ACKF) is combined with the reduced-order PBM to achieve adaptive tracking of the battery SOC. Three battery cells with different aging states are tested to verify the effectiveness of the proposed method. In addition, cycle aging experiments are conducted on a fresh battery for more than 1300 cycles. The experimental results reveal that the maximum error of SOC estimation is within 1.6% and the root mean square error is within 0.4% for both fresh and aged batteries.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:220:y:2021:i:c:s0360544221000165
    DOI: 10.1016/j.energy.2021.119767
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    6. John H. T. Luong & Cang Tran & Di Ton-That, 2022. "A Paradox over Electric Vehicles, Mining of Lithium for Car Batteries," Energies, MDPI, vol. 15(21), pages 1-25, October.
    7. 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.
    8. Cristobal Morales & Augusto Lismayes & Hector Chavez & Harold R. Chamorro & Lorenzo Reyes-Chamorro, 2021. "The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards," Energies, MDPI, vol. 14(21), pages 1-13, November.
    9. Li, Renzheng & Wang, Hui & Dai, Haifeng & Hong, Jichao & Tong, Guangyao & Chen, Xinbo, 2022. "Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network," Energy, Elsevier, vol. 250(C).
    10. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    11. Yong Tian & Qianyuan Dong & Jindong Tian & Xiaoyu Li, 2023. "Capacity Estimation of Lithium-Ion Batteries Based on Multiple Small Voltage Sections and BP Neural Networks," Energies, MDPI, vol. 16(2), pages 1-18, January.
    12. Park, Jinhyeong & Kim, Kunwoo & Park, Seongyun & Baek, Jongbok & Kim, Jonghoon, 2021. "Complementary cooperative SOC/capacity estimator based on the discrete variational derivative combined with the DEKF for electric power applications," Energy, Elsevier, vol. 232(C).
    13. Wu, Chunling & Hu, Wenbo & Meng, Jinhao & Xu, Xianfeng & Huang, Xinrong & Cai, Lei, 2023. "State-of-charge estimation of lithium-ion batteries based on MCC-AEKF in non-Gaussian noise environment," Energy, Elsevier, vol. 274(C).

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