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Temperature dependent power capability estimation of lithium-ion batteries for hybrid electric vehicles

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  • Zheng, Fangdan
  • Jiang, Jiuchun
  • Sun, Bingxiang
  • Zhang, Weige
  • Pecht, Michael

Abstract

The power capability of lithium-ion batteries affects the safety and reliability of hybrid electric vehicles and the estimate of power by battery management systems provides operating information for drivers. In this paper, lithium ion manganese oxide batteries are studied to illustrate the temperature dependency of power capability and an operating map of power capability is presented. Both parametric and non-parametric models are established in conditions of temperature, state of charge, and cell resistance to estimate the power capability. Six cells were tested and used for model development, training, and validation. Three samples underwent hybrid pulse power characterization tests at varied temperatures and were used for model parameter identification and model training. The other three were used for model validation. By comparison, the mean absolute error of the parametric model is about 29 W, and that of the non-parametric model is around 20 W. The mean relative errors of two models are 0.076 and 0.397, respectively. The parametric model has a higher accuracy in low temperature and state of charge conditions, while the non-parametric model has better estimation result in high temperature and state of charge conditions. Thus, two models can be utilized together to achieve a higher accuracy of power capability estimation.

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  • Zheng, Fangdan & Jiang, Jiuchun & Sun, Bingxiang & Zhang, Weige & Pecht, Michael, 2016. "Temperature dependent power capability estimation of lithium-ion batteries for hybrid electric vehicles," Energy, Elsevier, vol. 113(C), pages 64-75.
    • Handle: RePEc:eee:energy:v:113:y:2016:i:c:p:64-75
    DOI: 10.1016/j.energy.2016.06.010
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    13. Xu, Le & Zhao, Yan & Lian, Jiabiao & Xu, Yuanguo & Bao, Jian & Qiu, Jingxia & Xu, Li & Xu, Hui & Hua, Mingqing & Li, Huaming, 2017. "Morphology controlled preparation of ZnCo2O4 nanostructures for asymmetric supercapacitor with ultrahigh energy density," Energy, Elsevier, vol. 123(C), pages 296-304.
    14. Ma, Yan & Ding, Hao & Liu, Yongqin & Gao, Jinwu, 2022. "Battery thermal management of intelligent-connected electric vehicles at low temperature based on NMPC," Energy, Elsevier, vol. 244(PA).
    15. Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & Lu, Dylan Dah-Chuan & He, Tingting, 2018. "Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter," Energy, Elsevier, vol. 158(C), pages 1028-1037.
    16. 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.
    17. James Jeffs & Andrew McGordon & Alessandro Picarelli & Simon Robinson & Yashraj Tripathy & Widanalage Dhammika Widanage, 2018. "Complex Heat Pump Operational Mode Identification and Comparison for Use in Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-24, August.
    18. 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.
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    20. Mamun, A. & Sivasubramaniam, A. & Fathy, H.K., 2018. "Collective learning of lithium-ion aging model parameters for battery health-conscious demand response in datacenters," Energy, Elsevier, vol. 154(C), pages 80-95.
    21. Hatherall, Ollie & Barai, Anup & Niri, Mona Faraji & Wang, Zeyuan & Marco, James, 2024. "Novel battery power capability assessment for improved eVTOL aircraft landing," Applied Energy, Elsevier, vol. 361(C).
    22. Xingtao Liu & Chaoyi Zheng & Ji Wu & Jinhao Meng & Daniel-Ioan Stroe & Jiajia Chen, 2020. "An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries," Energies, MDPI, vol. 13(2), pages 1-16, January.

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