A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle
Tóm tắt
Từ khóa
Tài liệu tham khảo
Yu, C., Ji, G., Zhang, C., et al.: Cost-efficient thermal management for a 48 V Li-ion battery in a mild hybrid electric vehicle. Automot. Innov. 1(4), 320–330 (2018)
Zhao, B., Lv, C., Hofman, T.: Driving-cycle-aware energy management of hybrid electric vehicles using a three-dimensional markov chain model. Automot. Innov. 2, 146–156 (2019)
Huang, Y., Khazeraee, M., Wang, H., et al.: Design of a regenerative auxiliary power system for service vehicles. Automot. Innov. 1(1), 62–69 (2018)
Choi, S.S., Lim, H.S.: Factors that affect cycle-life and possible degradation mechanisms of a Li-ion cell based on LiCoO2. J. Power Sources 111(1), 130–136 (2002)
Lu, L., Han, X., Li, J., et al.: A review on the key issues for lithium-ion battery management in electric vehicles. J. Power Sources 226, 272–288 (2013)
Xiong, R., Tian, J., Mu, H., et al.: A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries. Appl. Energy 207, 372–383 (2017)
Xiong, R., Li, L., Tian, J.: Towards a smarter battery management system: a critical review on battery state of health monitoring methods. J. Power Sources 405, 18–29 (2018)
Berecibar, M., Gandiaga, I., Villarreal, I., et al.: Critical review of state of health estimation methods of Li-ion batteries for real applications. Renew. Sustain. Energy Rev. 56, 572–587 (2016)
Bloom, I., Cole, B.W., Sohn, J.J., et al.: An accelerated calendar and cycle life study of Li-ion cells. J. Power Sources 101(2), 238–247 (2001)
Wang, J., Liu, P., Hicks-Garner, J., et al.: Cycle-life model for graphite-LiFePO4 cells. J. Power Sources 196(8), 3942–3948 (2011)
Jin, X., Vora, A., Hoshing, V., et al.: Physically-based reduced-order capacity loss model for graphite anodes in Li-ion battery cells. J. Power Sources 342, 750–761 (2017)
Xiong, R., Sun, F., Chen, Z., et al.: A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles. Appl. Energy 113, 463–476 (2014)
Jin, G., Matthews, D.E., Zhou, Z.: A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft. Reliab. Eng. Syst. Saf. 113, 7–20 (2013)
Wang, Y., Yang, D., Zhang, X., et al.: Probability based remaining capacity estimation using data-driven and neural network model. J. Power Sources 315, 199–208 (2016)
Zheng, Y., Qin, C., Lai, X., et al.: A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model. Appl. Energy 251, 113327 (2019)
Han, X., Ouyang, M., Lu, L., et al.: A comparative study of commercial lithium ion battery cycle life in electric vehicle: capacity loss estimation. J. Power Sources 268, 658–669 (2014)
Birkl, C.R., Roberts, M.R., McTurk, E., et al.: Degradation diagnostics for lithium ion cells. J. Power Sources 341, 373–386 (2017)
Vetter, J., Novák, P., Wagner, M.R., et al.: Ageing mechanisms in lithium-ion batteries. J. Power Sources 147(1–2), 269–281 (2005)
Dubarry, M., Truchot, C., Liaw, B.Y.: Synthesize battery degradation modes via a diagnostic and prognostic model. J. Power Sources 219, 204–216 (2012)
Han, X., Ouyang, M., Lu, L., et al.: A comparative study of commercial lithium ion battery cycle life in electrical vehicle: aging mechanism identification. J. Power Sources 251, 38–54 (2014)
Han, X., Ouyang, M., Lu, L., et al.: Cycle life of commercial lithium-ion batteries with lithium titanium oxide anodes in electric vehicles. Energies 7(8), 4895–4909 (2014)
Zheng, Y., Wang, J., Qin, C., et al.: A novel capacity estimation method based on charging curve sections for lithium-ion batteries in electric vehicles. Energy 185, 361–371 (2019)
Hu, X., Li, S.E., Jia, Z., et al.: Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles. Energy 64, 953–960 (2014)
Mai, L., Yan, M., Zhao, Y.: Track batteries degrading in real time. Nature 546(7659), 469–470 (2017)
Dubarry, M., Truchot, C., Cugnet, M., et al.: Evaluation of commercial lithium-ion cells based on composite positive electrode for plug-in hybrid electric vehicle applications. Part I: initial characterizations. J. Power Sources 196(23), 10328–10335 (2011)
He, Y., Shen, J.F., Shen, J.N., et al.: Embedding monotonicity in the construction of polynomial open-circuit voltage model for lithium-ion batteries: a semi-infinite programming formulation approach. Ind. Eng. Chem. Res. 54(12), 3167–3174 (2015)
Weng, C., Sun, J., Peng, H.: A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring. J. Power Sources 258, 228–237 (2014)
Sun, F., Xiong, R.: A novel dual-scale cell state-of-charge estimation approach for series-connected battery pack used in electric vehicles. J. Power Sources 274, 582–594 (2015)
Li, Y., Abdel-Monem, M., Gopalakrishnan, R., et al.: A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter. J. Power Sources 373, 40–53 (2018)
Pastor-Fernández, C., Uddin, K., Chouchelamane, G.H., et al.: A comparison between electrochemical impedance spectroscopy and incremental capacity-differential voltage as Li-ion diagnostic techniques to identify and quantify the effects of degradation modes within battery management systems. J. Power Sources 360, 301–318 (2017)
Dubarry, M., Liaw, B.Y.: Identify capacity fading mechanism in a commercial LiFePO4 cell. J. Power Sources 194(1), 541–549 (2009)
Weng, C., Cui, Y., Sun, J., et al.: On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression. J. Power Sources 235, 36–44 (2013)
Safari, M., Delacourt, C.: Aging of a commercial graphite/LiFePO4 cell. J. Electrochem. Soc. 158(10), A1123–A1135 (2011)
Bloom, I., Jansen, A.N., Abraham, D.P., et al.: Differential voltage analyses of high-power, lithium-ion cells: 1. Technique and application. J. Power Sources 139(1–2), 295–303 (2005)
Bloom, I., Walker, L.K., Basco, J.K., et al.: Differential voltage analyses of high-power lithium-ion cells. 4. Cells containing NMC. J. Power Sources 195(3), 877–882 (2010)
Honkura, K., Takahashi, K., Horiba, T.: Capacity-fading prediction of lithium-ion batteries based on discharge curves analysis. J. Power Sources 196(23), 10141–10147 (2011)
Dahn, H.M., Smith, A.J., Burns, J.C., et al.: User-friendly differential voltage analysis freeware for the analysis of degradation mechanisms in Li-ion batteries. J. Electrochem. Soc. 159(9), A1405–A1409 (2012)
Feng, X., Li, J., Ouyang, M., et al.: Using probability density function to evaluate the state of health of lithium-ion batteries. J. Power Sources 232, 209–218 (2013)
Zheng, Y., Ouyang, M., Han, X., et al.: Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles. J. Power Sources 377, 161–188 (2018)
Prada, E., Di Domenico, D., Creff, Y., et al.: Simplified electrochemical and thermal model of LiFePO4-Graphite Li-ion batteries for fast charge applications. J. Electrochem. Soc. 159(9), A1508–A1519 (2012)
Kassem, M., Bernard, J., Revel, R., et al.: Calendar aging of a graphite/LiFePO4 cell. J. Power Sources 208, 296–305 (2012)
Weng, C., Feng, X., Sun, J., et al.: State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking. Appl. Energy 180, 360–368 (2016)
Chan, C.C., Lo, E.W.C., Weixiang, S.: The available capacity computation model based on artificial neural network for lead–acid batteries in electric vehicles. J. Power Sources 87(1–2), 201–204 (2000)
Weigert, T., Tian, Q., Lian, K.: State-of-charge prediction of batteries and battery–supercapacitor hybrids using artificial neural networks. J. Power Sources 196(8), 4061–4066 (2011)
You, G., Park, S., Oh, D.: Real-time state-of-health estimation for electric vehicle batteries: a data-driven approach. Appl. Energy 176, 92–103 (2016)
Bai, G., Wang, P., Hu, C., et al.: A generic model-free approach for lithium-ion battery health management. Appl. Energy 135, 247–260 (2014)
Kang, L., Zhao, X., Ma, J.: A new neural network model for the state-of-charge estimation in the battery degradation process. Appl. Energy 121, 20–27 (2014)
Wu, J., Zhang, C., Chen, Z.: An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Appl. Energy 173, 134–140 (2016)
Kassem, M., Delacourt, C.: Postmortem analysis of calendar-aged graphite/LiFePO4 cells. J. Power Sources 235, 159–171 (2013)