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Butler-Volmer equation-based model and its implementation on state of power prediction of high-power lithium titanate batteries considering temperature effects

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  • Jiang, Jiuchun
  • Liu, Sijia
  • Ma, Zeyu
  • Wang, Le Yi
  • Wu, Ke

Abstract

This paper provides a further step towards popularizing the proposed Butler-Volmer (BV) equation-based model and its implementation on state of power (SOP) prediction at various temperatures, which is based on the relationship between state of charge and state of useful charge. The actual 10 s SOP of battery is obtained using the constant current pulse when the restriction of voltage is exactly managed. The COMPLEX method is taken to determine the coefficients of the simplified form of BV equation, enabling online estimation of battery states. Robustness analysis of the proposed model and algorithm on SOP prediction over a large temperature range is analyzed and verified, showing their reliability and accuracy in estimating the terminal voltage and predicting power capability.

Suggested Citation

  • Jiang, Jiuchun & Liu, Sijia & Ma, Zeyu & Wang, Le Yi & Wu, Ke, 2016. "Butler-Volmer equation-based model and its implementation on state of power prediction of high-power lithium titanate batteries considering temperature effects," Energy, Elsevier, vol. 117(P1), pages 58-72.
    • Handle: RePEc:eee:energy:v:117:y:2016:i:p1:p:58-72
    DOI: 10.1016/j.energy.2016.10.087
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    1. J.-M. Tarascon & M. Armand, 2001. "Issues and challenges facing rechargeable lithium batteries," Nature, Nature, vol. 414(6861), pages 359-367, November.
    2. Omar, Noshin & Monem, Mohamed Abdel & Firouz, Yousef & Salminen, Justin & Smekens, Jelle & Hegazy, Omar & Gaulous, Hamid & Mulder, Grietus & Van den Bossche, Peter & Coosemans, Thierry & Van Mierlo, J, 2014. "Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model," Applied Energy, Elsevier, vol. 113(C), pages 1575-1585.
    3. He, HongWen & Zhang, YongZhi & Xiong, Rui & Wang, Chun, 2015. "A novel Gaussian model based battery state estimation approach: State-of-Energy," Applied Energy, Elsevier, vol. 151(C), pages 41-48.
    4. Xiong, Rui & Sun, Fengchun & He, Hongwen & Nguyen, Trong Duy, 2013. "A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles," Energy, Elsevier, vol. 63(C), pages 295-308.
    5. Diouf, Boucar & Pode, Ramchandra, 2015. "Potential of lithium-ion batteries in renewable energy," Renewable Energy, Elsevier, vol. 76(C), pages 375-380.
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    Cited by:

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    3. 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.
    4. Xiaopeng Tang & Ke Yao & Boyang Liu & Wengui Hu & Furong Gao, 2018. "Long-Term Battery Voltage, Power, and Surface Temperature Prediction Using a Model-Based Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-16, January.
    5. 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.
    6. 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.
    7. Shun Xiang & Guangdi Hu & Ruisen Huang & Feng Guo & Pengkai Zhou, 2018. "Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints," Energies, MDPI, vol. 11(2), pages 1-20, January.
    8. Berrueta, Alberto & Urtasun, Andoni & Ursúa, Alfredo & Sanchis, Pablo, 2018. "A comprehensive model for lithium-ion batteries: From the physical principles to an electrical model," Energy, Elsevier, vol. 144(C), pages 286-300.

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