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A new approach to consider the influence of aging state on Lithium-ion battery state of power estimation for hybrid electric vehicle

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  • Esfandyari, M.J.
  • Esfahanian, V.
  • Hairi Yazdi, M.R.
  • Nehzati, H.
  • Shekoofa, O.

Abstract

Battery State of Power (SoP) estimation is one of the most crucial tasks of the battery management system in electric and hybrid electric vehicles. The inevitable error in estimates of battery State of Charge (SoC) and State of Health (SoH) is a cause of inaccuracies towards estimating the SoP for an aged battery. To overcome this, the present study aims to propose a new approach for predicting an aged cell SoP in which no a priori knowledge of battery SoH is required and the estimation method is robust to inaccuracies of SoC estimates. Accordingly, a combined reference mode of constant-current and constant-voltage is utilized to estimate fresh cell SoP which is then adapted to various aging states using a model-less control system. The control system, which belongs to a class of fuzzy logic-based controllers, benefits form a closed-loop framework leading to a more reliable and accurate SoP estimate. For verification, an experimental setup comprised of fresh and aged LiFePO4 cell samples is designed and the extracted data are utilized in a Model-in-the-Loop simulation for a hybrid electric vehicle. The results demonstrate the improved accuracy and robustness of SoP estimation while achieving a guaranteed safe operation of battery.

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  • Esfandyari, M.J. & Esfahanian, V. & Hairi Yazdi, M.R. & Nehzati, H. & Shekoofa, O., 2019. "A new approach to consider the influence of aging state on Lithium-ion battery state of power estimation for hybrid electric vehicle," Energy, Elsevier, vol. 176(C), pages 505-520.
    • Handle: RePEc:eee:energy:v:176:y:2019:i:c:p:505-520
    DOI: 10.1016/j.energy.2019.03.176
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    3. Mei Zhang & Wanli Chen & Jun Yin & Tao Feng, 2022. "Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost," Energies, MDPI, vol. 15(16), pages 1-18, August.
    4. 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.
    5. Nguyễn, Bảo-Huy & Vo-Duy, Thanh & Henggeler Antunes, Carlos & Trovão, João Pedro F., 2021. "Multi-objective benchmark for energy management of dual-source electric vehicles: An optimal control approach," Energy, Elsevier, vol. 223(C).
    6. Mihai Machedon-Pisu & Paul Nicolae Borza, 2019. "Are Personal Electric Vehicles Sustainable? A Hybrid E-Bike Case Study," Sustainability, MDPI, vol. 12(1), pages 1-24, December.
    7. Song, Ziyou & Hou, Jun & Li, Xuefeng & Wu, Xiaogang & Hu, Xiaosong & Hofmann, Heath & Sun, Jing, 2020. "The sequential algorithm for combined state of charge and state of health estimation of lithium-ion battery based on active current injection," Energy, Elsevier, vol. 193(C).
    8. Abraham Alem Kebede & Md Sazzad Hosen & Theodoros Kalogiannis & Henok Ayele Behabtu & Towfik Jemal & Joeri Van Mierlo & Thierry Coosemans & Maitane Berecibar, 2022. "Model Development for State-of-Power Estimation of Large-Capacity Nickel-Manganese-Cobalt Oxide-Based Lithium-Ion Cell Validated Using a Real-Life Profile," Energies, MDPI, vol. 15(18), pages 1-15, September.
    9. Shanshan Guo & Zhiqiang Han & Jun Wei & Shenggang Guo & Liang Ma, 2022. "A Novel DC-AC Fast Charging Technology for Lithium-Ion Power Battery at Low-Temperatures," Sustainability, MDPI, vol. 14(11), pages 1-10, May.

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