Estimation of state-of-power capability for lithium-ion battery considering limiting conditions recalibration

References

• Changhe, L., H. Minghui, J. Guoqing, X. Yidan, and Z. Jun. 2021. State of power estimation of lithium-ion battery based on fractional-order equivalent circuit model. Journal of Energy Storage, 41:102954. https://doi.org/https://doi.org/10.1016/j.est.2021.102954.
   Google Scholar
• Guoliang, W., C. Zhu, and C. C. Chan. 2010. Comparison of the first order and the second order equivalent circuit model applied in state of charge estimation for battery used in electric vehicles. Journal of Asian Electric Vehicles, 8 (1): 1357–1362.
   Google Scholar
• He, H., R. Xiong, and J. Fan. 2011. Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach. Energies 4 (4):582–98. doi:https://doi.org/10.3390/en4040582.
   Web of Science ®Google Scholar
• He, H., R. Xiong, X. Zhang, Fan, J., et al. 2011. State-of-charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved Thevenin model, vehicular technology, IEEE transactions on vehicular technology. 60 (4):1461–1469. doi: https://doi.org/10.2337/db10-0845.
   Google Scholar
• Hu, C., B. D. Youn, and J. Chung. 2012. A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation. Applied Energy 92 (none):695–703. doi:https://doi.org/10.1016/j.apenergy.2011.08.002.
   Google Scholar
• Jiang, C., H. Li, Luo, Y., Xie, B., He, X., et al. 2013. Experimental study on the influence of temperature and discharge rate on battery performance. Science Technology and Engineering, 13 (9):2496–2502.
   Google Scholar
• Jin, X., Q. Gu, Pan, Y., Hua, Y., et al. 2019. Online state of power estimation methods for lithium-ion batteries in EV. Chinese Journal of Power Sources 43 (9):1448–1452.
   Google Scholar
• Lee, S., J. Kim, J. Lee, Cho, B.H, et al. 2008. State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge. Journal of Power Sources 185 (2):1368–1376. doi:https://doi.org/10.1016/j.jpowsour.2008.08.103.
   Google Scholar
• Liu, X., Y. He, Zeng, G., Zheng, X., et al. 2016. State-of-power estimation for Li-ion battery considering the effect of temperature. Journal of Electrotechnical Technology 31 (13):155–163.
   Google Scholar
• Ng, K. S., Y. F. Huang, C. S. Moo, Hsieh, Y.C., et al. An enhanced Coulomb counting method for estimating state-of-charge and state-of-health of lead-acid batteries (INTELEC 2009 - 31st International Telecommunications Energy Conference). doi:https://doi.org/10.1109/INTLEC.2009.5351796 . 2009. Scopus; 1–5.
   Google Scholar
• Peng, L., J. Peng, H. Jichao, and W. Zhenpo. 2020. Battery voltage and state of power prediction based on an improved novel polarization voltage model. Energy Reports, 6: 2299–2308.
   Google Scholar
• Piao, C.-H., W.-L. Fu, G.-H. Lei, and C.-D. Cho. 2010. Online parameter estimation of the Ni-MH batteries based on statistical methods. Energies 3 (2):206–15. doi:https://doi.org/10.3390/en3020206.
   Google Scholar
• Ruisen, H. 2017. A joint online estimation algorithm of state of charge, state of health and state of power for lithium batteries. Southwest Jiaotong University.
   Google Scholar
• Sun, B., K. Gao, J. Jiang, Luo, M., He, T., Zheng, F., Guo, h., et al. 2015. Research on discharge peak power prediction of battery based on ANFIS and subtraction clustering. Journal of Electronic Technology 30 (4):271–279.
   Google Scholar
• Sun, F., R. Xiong, and H. Hongwen. 2014. Estimation of state-of-charge and state-of-power capability of lithium-ion battery considering varying health conditions. Journal of Power Sources 259:166–76. doi:https://doi.org/10.1016/j.jpowsour.2014.02.095.
   Google Scholar
• Sun, F., X. Meng, Lin, C., Wang, Z., et al. 2010. Dynamic stress test profile of power battery for electric vehicle transactions of Beijing institute of technology. Beijing li gong da xue xue bao. 30 (3):296–300.
   Google Scholar
• Tang, Z., C. Tan, Chen, Y., Cui, Y., Xue, J., et al. 2006. Research on high rate discharge for lithium-ion battery. Chinese Journal of Power Sources, 13 (5):383–387.
   Google Scholar
• Wang, L. 2016. The Estimation of State of Charge and Power Prediction of Lithium-ion Battery Used on H∞ Filter. Tianjin University.
   Google Scholar
• Wang, Y., J. Tian, Z. Sun, L. Wang, R. Xu, M. Li, and Z. Chen. 2020. A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renewable and Sustainable Energy Reviews 131: doi: https://doi.org/10.1016/j.rser.2020.110029.
   Google Scholar
• Xiong, R., F. Sun, H. Hongwen, and T. D. Nguyen. 2013. A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles. Energy 63:295–308.
   Web of Science ®Google Scholar
• Xiong, R., H. He, F. Sun, and K. Zhao. 2012. Online estimation of peak power capability of Li-ion batteries in electric vehicles by a hardware-in-loop approach. Energies 5 (5):1455–69. doi:https://doi.org/10.3390/en5051455.
   Google Scholar
• Zhang, D., Y. Ma, and Q. Bo. 2012. Estimation of state of charge of Li-ion battery based on adaptive Kalman filtering. Automotive Technology 8 (8):42–45.
   Google Scholar
• Zhang, J., W. Tong, Sun, Y., et al. 2017. Summarize of lithium battery status of health estimation method. Journal of Power Supply, 15 (2):128–134.
   Google Scholar
• Zhengyu, L., and A. Z. Yang. 2013. Estimation for SOC of lithium battery based on QPSO-BP neural network. Journal of Electronic Measurement and Instrument 27 (3):224–28.
   Google Scholar