Guidelines for the Characterization of the Internal Impedance of Lithium-Ion Batteries in PHM Algorithms
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Abstract
This article aims to describe the most important aspects to consider when using the concept of internal impedance in algorithms that focus on characterizing the degradation of lithium-ion (Li-ion) batteries. The first part of the article provides a literature review that will help the reader understand the concept of electrochemical impedance spectroscopy (EIS) and how Li-ion batteries can be represented through electrochemical or empirical models, in order to interpret the outcome of typical discharge and/or degradation tests on Li-ion batteries. The second part of the manuscript shows the obtained results of an accelerated degradation experiment performed under controlled conditions on a Li-ion cell. Results show that changes observed on the EIS test can be linked to battery degradation. This knowledge may be of great value when implementing algorithms aimed to predict the End-of-Life (EoL) of the battery in terms of temperature, voltage, and discharge current measurements. The purpose of this article is to introduce the reader to several types of Li-ion battery models, and show how the internal impedance of a Li-ion battery is a dynamic parameter that depends on different factors; and then, illustrate how the EIS can be used to obtain an equivalent circuit model and how the different electronic components vary with the use given to the battery.
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Electrochemical Impedance Spectroscopy, lithium ion battery, Equivalent circuit model
Daigle, M., & Kulkarni, C. (2013). Electrochemistry-based Battery Modeling for Prognostics. Annual Conference of the Prognostics and Health Management Society, 1–13.
Do, D. V., Forgez, C., Benkara, K. E. K., & Friedrich, G. (2009). Impedance observer for a Li-ion battery using Kalman filter. IEEE Transactions on Vehicular Technology, 58(8), 3930–3937.
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.
Mauracher, P., & Karden, E. (1997). Dynamic modelling of lead/acid batteries using impedance spectroscopy for parameter identification. Journal of Power Sources, 67(1–2), 69–84.
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
Olivares, B. E., Cerda Muñoz, M. A., Orchard, M. E., & Silva, J. F. (2013). Particle-filtering-based prognosis framework for energy storage devices with a statistical characterization of state-of-health regeneration phenomena. IEEE Transactions on Instrumentation and Measurement, 62(2), 364–376. https://doi.org/10.1109/TIM.2012.2215142
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
Tröltzsch, U., Kanoun, O., & Tränkler, H.-R. (2006). Characterizing aging effects of lithium ion batteries by impedance spectroscopy. Electrochimica Acta, 51(8), 1664–1672.
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.org/10.1016/j.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