A Practical Guide for the Characterization of Lithium-Ion Battery Internal Impedances in PHM Algorithms
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Published
Jul 14, 2017
Aramis Pérez
Matías Benavides
Heraldo Rozas
Sebastián Seria
Marcos Orchard
Abstract
This article aims at describing the most important aspects to be considered when using the concept of battery internal impedances in algorithms that focus on characterizing the State-of-Health (SOH) degradation of lithium-ion (Li-ion) batteries. The first part provides a brief literature review that will help the reader to interpret the outcome of typical Li-ion discharge and/or degradation tests. The second part of the paper shows preliminary results for accelerated degradation experiments performed on a Li-ion cell under controlled conditions. Results show changes on electrochemical impedance spectroscopy test that can be linked to battery degradation. This knowledge may be of great value when implementing algorithms aimed at predicting the battery End-of-Life (EoL) in terms of temperature, voltage, and discharge current measurements.
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Keywords
PHM
References
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Vetter, J., Novák, P., Wagner, M. R., Veit, C., Möller, K. C., Besenhard, J. O., Hammouche, A. (2005). Ageing mechanisms in lithium-ion batteries. Journal of Power Sources, 147(1–2), 269–281. https://doi.org10.1016j.jpowsour.2005.01.006
Wang, H., He, L., Sun, J., Liu, S., & Wu, F. (2011). Study on correlation with SOH and EIS model of Li-ion battery. In Proceedings of the 6th International Forum on Strategic Technology, IFOST 2011 (Vol. 1, pp. 261–264). IEEE. https://doi.org/10.1109/IFOST.2011.6021018
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1060–1065). EDA Consortium. Retrieved from http://dl.acm.org/citation.cfm?id=2492970
Zou, Y., Hu, X., Ma, H., & Li, S. E. (2014). Combined SOC and SOH estimation over lithium-ion battery cell cycle lifespan for Electric vehicles. Journal of Power Sources, 273(January), 793–803. https://doi.org/10.1016/j.jpowsour.2014.09.146
Dai, H., Wei, X., & Sun, Z. (2009). A new SOH prediction concept for the power lithium-ion battery used on HEVs. In 5th IEEE Vehicle Power and Propulsion Conference, VPPC ’09 (pp. 1649–1653). IEEE. https://doi.org/10.1109/VPPC.2009.5289654
Daigle, M., & Kulkarni, C. (2013). Electrochemistry-based Battery Modeling for Prognostics. Annual Conference of the Prognostics and Health Management Society, 1–13.
Koch, R., & Kuhn, R. (2014). Electrochemical Impedance Spectroscopy for Online Battery Monitoring - Power Electronics Control. In Power Electronics and Applications (EPE’14-ECCE Europe), 2014 16th European Conference on (pp. 1–10). IEEE.
Linden, D. (1984). Handbook of batteries and fuel cells. New York, McGraw-Hill Book Co., 1984, 1075 P. No Individual Items Are Abstracted in This Volume., 1.
Ning, G., Haran, B., & Popov, B. N. (2003). Capacity fade study of lithium-ion batteries cycled at high discharge rates. Journal of Power Sources, 117(1–2), 160–169. https://doi.org/10.1016/S0378-7753(03)00029-6
Ning, G., & Popov, B. N. (2004). Cycle Life Modeling of Lithium-Ion Batteries. Journal of The Electrochemical Society, 151(10), A1584. https://doi.org/10.1149/1.1787631
Ning, G., White, R. E., & Popov, B. N. (2006). A generalized cycle life model of rechargeable Li-ion batteries. Electrochimica Acta, 51(10), 2012–2022. https://doi.org/10.1016/j.electacta.2005.06.033
Ramadass, P., Haran, B., White, R., & Popov, B. N. (2003). Mathematical modeling of the capacity fade of Li-ion cells. Journal of Power Sources, 123(2), 230–240. https://doi.org/10.1016/S0378-7753(03)00531-7
Rong, P., & Pedram, M. (2003). An analytical model for predicting the remaining battery capacity of lithium-ion batteries. Proceedings -Design, Automation and Test in Europe, DATE, 14(5), 1148–1149. https://doi.org/10.1109/DATE.2003.1253775
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009). Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework. IEEE Transactions on Instrumentation and Measurement, 58(2), 291–296. https://doi.org/10.1109/TIM.2008.2005965
Santhanagopalan, S., Zhang, Q., Kumaresan, K., & White, R. E. (2008). Parameter Estimation and Life Modeling of Lithium-Ion Cells. Journal of The Electrochemical Society, 155(4), A345. https://doi.org/10.1149/1.2839630
Vetter, J., Novák, P., Wagner, M. R., Veit, C., Möller, K. C., Besenhard, J. O., Hammouche, A. (2005). Ageing mechanisms in lithium-ion batteries. Journal of Power Sources, 147(1–2), 269–281. https://doi.org10.1016j.jpowsour.2005.01.006
Wang, H., He, L., Sun, J., Liu, S., & Wu, F. (2011). Study on correlation with SOH and EIS model of Li-ion battery. In Proceedings of the 6th International Forum on Strategic Technology, IFOST 2011 (Vol. 1, pp. 261–264). IEEE. https://doi.org/10.1109/IFOST.2011.6021018
Xie, Q., Lin, X., Wang, Y., & Pedram, M. (2012). State of health aware charge management in hybrid electrical energy storage systems. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2012 (pp.
1060–1065). EDA Consortium. Retrieved from http://dl.acm.org/citation.cfm?id=2492970
Zou, Y., Hu, X., Ma, H., & Li, S. E. (2014). Combined SOC and SOH estimation over lithium-ion battery cell cycle lifespan for Electric vehicles. Journal of Power Sources, 273(January), 793–803. https://doi.org/10.1016/j.jpowsour.2014.09.146
Section
Regular Session Papers