This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Battery Parameter Estimation from Recorded Fleet Data
ISSN: 0148-7191, e-ISSN: 2688-3627
Published October 17, 2016 by SAE International in United States
Annotation ability available
Existing battery parameter model structures are evaluated by estimating model parameters on real driving data applying standard system identification methods. Models are then evaluated on the test data in terms of goodness of fit and RMSE in voltage predictions. This is different from previous battery model evaluations where a common approach is to train parameters using standardized tests, e.g. hybrid pulse-power capability (HPPC), with predetermined charge and discharge sequences. Equivalent linear circuit models of different complexity were tested and evaluated in order to identify parameter dependencies at different state of charge levels and temperatures. Models are then used to create voltage output given a current, state of charge and temperature. The average accuracy of modelling the DC bus voltage provides a model goodness of fit average higher than 90% for a single RC circuit model. Both single RC equivalent circuit model and R-equivalent circuit model produce goodness of fit at average 75 % or higher. The dual RC equivalent circuit model experienced larger errors in voltage estimations compared to single R and RC equivalent circuit models.
CitationArvidsson, R. and McKelvey, T., "Battery Parameter Estimation from Recorded Fleet Data," SAE Technical Paper 2016-01-2360, 2016, https://doi.org/10.4271/2016-01-2360.
- E. E. Agency, “Monitoring CO2 emissions from new passenger cars in the EU: summary of data for 2011,” 2012.
- A. R. F. McKinsey & Company, “Electric vehicles in Europe : gearing up for a new phase,” Amsterdam, 2014.
- Marano V., Onori S., Guezennec Y., Rizzoni G., and Madella N., “Lithium-ion batteries life estimation for plug-in hybrid electric vehicles,” 2009 IEEE Veh. Power Propuls. Conf., pp. 536-543, 2009.
- Tang X., Mao X., Lin J., and Koch B., “Li-ion battery parameter estimation for state of charge,” Am. Control Conf. (ACC), 2011, pp. 941 - 946, 2011.
- Birkl C. R. and Howey D. a, “Model identification and parameter estimation for LiFePO 4 batteries,” IET Hybrid Electr. Veh. Conf. 2013, HEVC 2013, pp. 1-6, 2013.
- Lin X., Perez H. E., Mohan S., Siegel J. B., Stefanopoulou A. G., Ding Y., and Castanier M. P., “A lumped-parameter electro-thermal model for cylindrical batteries,” J. Power Sources, vol. 257, pp. 1-11, 2014.
- Dawoud B., Amer E., and Gross D., “Experimental investigation of an adsorptive thermal energy storage,” Int. J. energy Res., vol. 31, no. August 2007, pp. 135-147, 2007.
- Uddin, Kotub, Perera, Surak, Widanage, Widanalage Dhammika and Marco, James (2015), “Characterising Li-ion battery degradation through the identification of perturbations in electrochemical battery models, ” Electric Vehicle Symposium 28 (EVS28), Goyang, Korea, 3-5 May 2015
- Ljung L., System Identification: Theory for User, vol. 11, no. 3. 1987.