Tracking the variation in battery dynamics as a function of health is presently attracting attention in academia and industry due to the increased usage of expensive batteries in dynamic systems such as aircraft and electric cars. The online adaptation of battery models to account for age-dependent changes in dynamics is necessary to maintain accurate estimates of the remaining system operations that can be supported under battery power. A novel method for the adaptation of parameters in an electrochemical model of a lithium- ion battery is presented here. An unscented Kalman filtering algorithm is shown to enable the production of internal battery state estimates and age-dependent electrochemical model parameter estimates using only battery current and voltage data collected over randomized discharge profiles. The use of only data collected over randomized discharge profiles distinguishes this work from other works that make use of reference discharge cycles to judge battery health. The experimental results presented here compare online model estimates produced by the proposed algorithm to offline model estimates obtained by periodically taking batteries offline to run reference discharge cycles.
How to Cite
model adaptation, lithium ion battery, electrochemical modeling, unscented Kalman filtering, battery aging
Transactions on Signal Processing, 50(2), 174–188.
Bole, B., Teubert, C., Chi, Q. C., Edward, H., Vazquez, S., Goebel, K., & Vachtsevanos, G. (2013). SIL/HIL replication of electric aircraft powertrain dynamics and inner-loop control for V&V of system health management routines. In Annual conference of the prognostics and health management society.
Broussely, M., Biensan, P., Bonhommeb, F., Blanchard, P., Herreyre, S., Nechev, K., & Staniewicz, R. (2005). Main aging mechanisms in li ion batteries. Journal of Power Sources, 146(1-2), 90-96.
Dai, H., Wei, X., & Sun, Z. (2006). Online soc estimation of high-power Lithium-Ion batteries used on HEVs. In IEEE international conference on vehicular electronics and safety.
Daigle, M., & Kulkarni, C. (2013, October). Electrochemistry-based battery modeling for prog- nostics. In Annual conference of the prognostics and health management society 2013 (p. 249-261).
Daigle, M., & Kulkarni, C. (2014). A battery health monitoring framework for planetary rovers. In Proceedings of the IEEE aerospace conference.
Daigle, M., Roychoudhury, I., Narasimhan, S., Saha, S., Saha, B., & Goebel, K. (2011, September). Investigating the effect of damage progression model choice on prognostics performance. In Proceedings of the annual conference of the prognostics and health management society 2011 (p. 323-333).
Daigle, M., Saxena, A., & Goebel, K. (2012, Septem- ber). An efficient deterministic approach to model- based prediction uncertainty estimation. In Annual conference of the prognostics and health management society (p. 326-335).
Julier, S. J., & Uhlmann, J. K. (1997). A new extension of the Kalman filter to nonlinear systems. In Proceedings of the 11th international symposium on aerospace/defense sensing, simulation and controls (pp. 182–193).
Julier, S. J., & Uhlmann, J. K. (2004, Mar). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401–422.
Karthikeyan, D. K., Sikha, G., & White, R. E. (2008). Ther- modynamic model development for lithium intercalation electrodes. Journal of Power Sources, 185(2), 1398–1407.
Ning, G., & Popov, B. N. (2004). Cycle life modeling of lithium-ion batteries. Journal of The Electrochemical Society, 151(10), A1584–A1591.
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.
Oliva, J., Weihrauch, C., & Bertram, T. (2013, October). A model-based approach for predicting the remaining driving range in electric vehicles. In Annual conference of the prognostics and health management society 2013 (p. 438-448).
Park, M., Zhang, X., Chung, M., Less, G. B., & Sastry, A. M. (2010). A review of conduction phenomena in li-ion batteries. Journal of Power Sources, 195(24), 7904– 7929.
Rahn, C. D., & Wang, C.-Y. (2013). Battery systems engineering. Wiley.
Ramadesigan, V., Northrop, P. W., De, S., Santhanagopalan, S., Braatz, R. D., & Subramanian, V. (2012). Modeling and simulation of lithium-ion batteries from a systems engineering perspective. Journal of The Electrochemical Society, 159(3), R31–R45.
Saha, B., & Goebel, K. (2009, September). Modeling Li-ion battery capacity depletion in a particle filtering framework. In Proceedings of the annual conference of the prognostics and health management society 2009.
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009, February). Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Transactions on Instrumentation and Measurement, 58(2), 291–296.
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