State of Health Estimation of Lithium-Ion Batteries based on the CC-CV Charging Curve and Neural Network

References

• M. Sandhu, and T. Thakur, “Modified cascaded H-bridge multilevel inverter for hybrid renewable energy applications,” IETE J. Res., pp. 1–13, Jul. 2020. DOI:10.1080/03772063.2020.1784802.
   Web of Science ®Google Scholar
• K. Liu, C. Zou, K. Li, and T. Wik, “Charging pattern optimization for lithium-ion batteries with an electrothermal-aging model,” IEEE Trans. Ind. Infs., Vol. 14, no. 12, pp. 5463–5474, Aug. 2018.
   Web of Science ®Google Scholar
• H. Chaoui, N. Golbon, I. Hmouz, R. Souissi, and S. Tahar, “Lyapunov-based adaptive state of charge and state of health estimation for lithium-ion batteries,” IEEE Trans. Ind. Electron, Vol. 62, no. 3, pp. 1610–1618, Mar. 2015.
   Web of Science ®Google Scholar
• S.-C. Huang, K.-H. Tseng, J.-W. Liang, C.-L. Chang, and M. G. Pecht, “An online SOC and SOH estimation model for lithium-ion batteries,” Energies, Vol. 10, no. 4, pp. 512, Apr. 2017.
   Web of Science ®Google Scholar
• D. Liu, H. Wang, Y. Peng, W. Xie, and H. Liao, “Satellite lithium-ion battery remaining cycle life prediction with novel indirect health indicator extraction,” Energies, Vol. 6, no. 8, pp. 3654–3668, Jul. 2013.
   Web of Science ®Google Scholar
• Z. Guo, X. Qiu, G. Hou, B. Y. Liaw, and C. Zhang, “State of health estimation for lithium ion batteries based on charging curves,” J. Power Sources, Vol. 249, pp. 457–462, Mar. 2014.
   Web of Science ®Google Scholar
• H. Tian, P. Qin, K. Li, and Z. Zhao, “A review of the state of health for lithium-ion batteries: research status and suggestions,” J. Cleaner Prod., Vol. 261, pp. 120813, Jul. 2020.
   Web of Science ®Google Scholar
• C. Lin, A. Tang, and W. Wang, “A review of SOH estimation methods in lithium-ion batteries for electric vehicle applications,” Energy Procedia, Vol. 75, pp. 1920–1925, Aug. 2015.
   Google Scholar
• X. Tang, C. Zou, K. Yao, G. Chen, B. Liu, Z. He, and F. Gao, “A fast estimation algorithm for lithium-ion battery state of health,” J. Power Sources, Vol. 396, pp. 453–458, Aug. 2018.
   Web of Science ®Google Scholar
• C. Zhang, F. Yan, C. Du, J. Kang, and R. F. Turkson, “Evaluating the degradation mechanism and state of health of LiFePO4 lithium-ion batteries in real-world plug-in hybrid electric vehicles application for different ageing paths,” Energies, Vol. 10, no. 1, pp. 110, Jan. 2017.
   Web of Science ®Google Scholar
• Y. Li, M. Abdel-Monem, R. Gopalakrishnan, M. Berecibar, E. Nanini-Maury, N. Omar, P. van den Bossche, and J. Van Mierlo, “A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter,” J. Power Sources, Vol. 373, pp. 40–53, Jan. 2018.
   Web of Science ®Google Scholar
• H. Rahimi-Eichi, U. Ojha, F. Baronti, and M.-Y. Chow, “Battery management system: an overview of its application in the smart grid and electric vehicles,” IEEE Ind. Electron. Mag., Vol. 7, no. 2, pp. 4–16, Jun. 2013.
   Web of Science ®Google Scholar
• H. Dai, G. Zhao, M. Lin, J. Wu, and G. Zheng, “A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain,” IEEE Trans. Ind. Electron, Vol. 66, no. 10, pp. 7706–7716, Nov. 2018.
   Web of Science ®Google Scholar
• C. Weng, J. Sun, and H. Peng, “A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring,” J. Power Sources, Vol. 258, pp. 228–237, Jul. 2014.
   Web of Science ®Google Scholar
• K. S. Ng, C.-S. Moo, Y.-P. Chen, and Y.-C. Hsieh, “Enhanced Coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries,” Appl. Energy, Vol. 86, no. 9, pp. 1506–1511, Sep. 2009.
   Web of Science ®Google Scholar
• T.-H. Wu, and C.-S. Moo, “State-of-charge estimation with state-of-health calibration for lithium-ion batteries,” Energies, Vol. 10, no. 7, pp. 987, Jul. 2017.
   Web of Science ®Google Scholar
• S. Zhang, X. Guo, X. Dou, and X. Zhang, “A rapid online calculation method for state of health of lithium-ion battery based on Coulomb counting method and differential voltage analysis,” J. Power Sources, Vol. 479, pp. 228740, Dec. 2020.
   Web of Science ®Google Scholar
• K. Tsang, and W. Chan, “State of health detection for lithium-ion batteries in photovoltaic system,” Energy Convers. Manag, Vol. 65, pp. 7–12, Jan. 2013.
   Web of Science ®Google Scholar
• J. Yu, B. Mo, D. Tang, J. Yang, J. Wan, and J. Liu, “Indirect state-of-health estimation for lithium-ion batteries under randomized use,” Energies, Vol. 10, no. 12, pp. 2012, Dec. 2017.
   Web of Science ®Google Scholar
• P. Singh, C. Chen, C. M. Tan, and S.-C. Huang, “Semi-empirical capacity fading model for SOH estimation of li-ion batteries,” Appl. Sci., Vol. 9, no. 15, pp. 3012, Jul. 2019.
   Google Scholar
• X. Bian, L. Liu, J. Yan, Z. Zou, and R. Zhao, “An open circuit voltage-based model for state-of-health estimation of lithium-ion batteries: model development and validation,” J. Power Sources, Vol. 448, pp. 227401, Feb. 2020.
   Web of Science ®Google Scholar
• X. Tang, K. Liu, X. Wang, F. Gao, J. Macro, and W. D. Widanage, “Model migration neural network for predicting battery aging trajectories,” IEEE Trans. Transp. Electrif., Vol. 6, no. 2, pp. 363–374, Mar. 2020.
   Web of Science ®Google Scholar
• H.-T. Lin, T.-J. Liang, and S.-M. Chen, “Estimation of battery state of health using probabilistic neural network,” IEEE. Trans. Industr. Inform., Vol. 9, no. 2, pp. 679–685, Oct. 2012.
   Web of Science ®Google Scholar
• Y. Wu, Q. Xue, J. Shen, Z. Lei, Z. Chen, and Y. Liu, “State of health estimation for lithium-ion batteries based on healthy features and long short-term memory,” IEEE. Access., Vol. 8, pp. 28533–28547, Feb. 2020.
   Web of Science ®Google Scholar
• H. Chaoui, and C. C. Ibe-Ekeocha, “State of charge and state of health estimation for lithium batteries using recurrent neural networks,” IEEE Trans. Veh. Technol, Vol. 66, no. 10, pp. 8773–8783, Jun. 2017.
   Web of Science ®Google Scholar
• Y. Li, et al., “Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review,” Renew. Sust. Energ. Rev, Vol. 113, pp. 109254, Oct. 2019.
   Web of Science ®Google Scholar
• D. Liu, J. Zhou, H. Liao, Y. Peng, and X. Peng, “A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics,” IEEE Trans. Syst. Man Cybern.: Syst, Vol. 45, no. 6, pp. 915–928, Jan. 2015.
   Web of Science ®Google Scholar
• B. Gou, Y. Xu, and X. Feng, “State-of-health estimation and remaining-useful-life prediction for lithium-ion battery using a hybrid data-driven method,” IEEE Trans. Veh. Technol, Vol. 69, no. 10, pp. 10854–10867, Aug. 2020.
   Web of Science ®Google Scholar
• X. Shu, G. Li, J. Shen, Z. Lei, Z. Chen, and Y. Liu, “A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization,” Energy, Vol. 204, pp. 117957, Aug. 2020.
   Web of Science ®Google Scholar
• P. Venugopal, “State-of-Health estimation of li-ion batteries in electric vehicle using IndRNN under variable load condition,” Energies, Vol. 12, no. 22, pp. 4338, Jan. 2019.
   Web of Science ®Google Scholar
• D. Yang, Y. Wang, R. Pan, R. Chen, and Z. Chen, “A neural network based state-of-health estimation of lithium-ion battery in electric vehicles,” Energy Procedia, Vol. 105, pp. 2059–2064, 2017.
   Google Scholar
• Z. Wang, S. Zeng, J. Guo, and T. Qin, “State of health estimation of lithium-ion batteries based on the constant voltage charging curve,” Energy, Vol. 167, pp. 661–669, Jan. 2019.
   Web of Science ®Google Scholar
• X. Li, C. Yuan, X. Li, and Z. Wang, “State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression,” Energy, Vol. 190, pp. 116467, Jan. 2020.
   Web of Science ®Google Scholar
• H. Yu, and B.M. Wilamowski, “Levenberg-marquardt training,” in Industrial electronics handbook, Vol. 5, no. 12. Boca Raton, FL: CRC Press, Feb. 2011, pp. 1.
   Google Scholar