Recent developments in lithium-ion technology have enabled a revolution in the automotive industry. Fully electric vehicles (EVs) operate under distinctly variable conditions, requiring high-voltage battery packs to meet their torque/power demands. Our goal is to provide a simulation engine which, for a given battery pack size, determines when recharging or battery pack replacement are needed. To that end, we study both the State-of-Charge (SOC) and the State-of-Health (SOH) indicators, using discrete state space models for both. Predictions are based on a probabilistic characterization of EV usage profiles, which in turn are a function of generic user-input, such as mission maps, vehicle mechanical characteristics,
driving schedules, and battery pack configuration. State space models benefit from the incorporation of metamodels for the ohmic internal resistance and the Coulomb efficiency of the pack. Both meta-models i) effectively introduce additional phenomenology –such as dependency on
the magnitude of discharged current and depth of discharge (DoD)–, and ii) provide a link between SOC/SOH and how each discharge cycle affects the health status of the battery pack as a whole. The approach for the simulation engine presented here is stochastic in nature, meaning that prognostics for the SOC and SOH are generated in a particle filter-based scheme. Thus risk and confidence intervals can be obtained for the end-of-discharge and end-of-life respectively
How to Cite
Usage Profiles, Simulation Engine, Model-based Prognostics, Particle Filtering, Electric Vehicles, Lithium-Ion Batteries, Battery-autonomy, Remaining Useful Life, SOC, SOH
Transactions on Signal Processing, 50, 174–188.
Bard, A., & Faulkner, L. (2001). Electrochemical methods fundamentals and applications. John Wiley and Sons.
Bond, T., Burns, J., Stevens, D., Dahn, H., & Dahn, J. (2013). Improving precision and accuracy in coulombic efficiency measurements of li-ion batteries. J. Electrochem. Soc, 160, 521-527.
Bose, B. (2010). Global warming: Energy, environmental pollution, and the impact of power electronics. IEEE Industrial Electronics Magazine, 4, 6-17.
Bowman, A., & Azzalini, A. (1997). Applied smoothing techniques for data analysis. Oxford University Press Inc.
Charkhgard, M., & Farrokhi, M. (2010). State-of-charge estimation for lithium-ion batteries using neural networks and ekf. IEEE Transactions on Industrial Electronics, 57, 4178-4187.
Creal, D. (2012). A Survey of Sequential Monte Carlo Methods for Economics and Finance. Econometric Reviews, 31, 245–296.
Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing, 10, 197–208.
Doucet, A., & Johansen, A. (2008). A tutorial on particle filtering and smoothing: Fifteen years later. Handbook of nonlinear filtering, 12, 656–704.
Espinoza, P. (2017). A simulation engine for ion-lithium battery packs in electric vehicles based on energetic autonomy and remaining useful life criteria (Unpublished master’s thesis). University of Chile.
Friedman, L. W. (1996). The simulation metamodel. Kluwer Academic Publishers.
Gregory, P. (2005). Bayesian logical data analysis for the physical sciences. Cambridge University Press.
Han, H., Xu, H., Yuan, Z., & Shen, Y. (2014). A new soh prediction model for lithium-ion battery for electric vehicles. In 2014 17th international conference on electrical machines and systems (p. 997-1002).
Ikezoe, M., Hirata, N., Amemiya, C., & Miyamoto, T. (2012). Development of high capacity lithium-ion battery for nissan leaf (Tech. Rep.). SAE Technical Paper.
Kalman, R. (1960). A new approach to linear filtering and prediction problems. ASME. J. Basic Eng., 82, 35-45.
Kim, N., Rousseau, A., & Rask, E. (2016). Parameter estimation for a lithium-ion battery from chassis dynamometer tests. IEEE Transactions on Vehicular Technology, 65, 4393-4400.
Liu, J., & West, M. (2001). Sequential monte carlo methods in practice. In (p. 197-223). Springer.
Macharis, C., Lebeau, P., Mierlo, J. V., & Lebeau, K. (2013). Electric versus conventional vehicles for logistics: A total cost of ownership. In Electric vehicle symposium and exhibition (p. 1-10).
Miles,M. (2001). Recent advances in lithium battery technology. In Gallium arsenide integrated circuit symposium (p. 219-222).
Musso, C., Oudjane, N., & Gland, F. L. (2001). Sequential monte carlo methods in practice. In (p. 247-260). Springer.
Navarrete, H. (2014). Caracterización estadística del perfil de uso de baterías para el pronóstico del estadode- carga (Unpublished master’s thesis). University of Chile.
Olivares, B., Cerda, M., Orchard, M., & Silva, J. (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, 364–376.
Orchard, M., Tang, L., Goebel, K., & Vachtsevanos, G. (2009). A novel rspf approach to prediction of high-risk low-probability failure events. In Annual conference of the prognostics and health management society.
Orchard, M., Tang, M., Saha, B., Goebel, K., & Vachtsevanos, G. (2010). Risk-sensitive particle-filteringbased prognosis framework for estimation of remaining useful life in energy storage devices. Studies in Informatics and Control, 19, 209-218.
Orchard, M., Tobar, F., & Vachtsevanos, G. (2009). Outer feedback correction loops in particle filtering-based prognostic algorithms: Statistical performance comparison. Studies in Informatics and Control, 18, 295-304.
Orchard, M., & Vachtsevanos, G. (2009). A particle-filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, 31, 221–246.
Pattipati, B., Sankavaram, C., & Pattipati, K. (2011). System identification and estimation framework for pivotal automotive battery management system characteristics. IEEE Transactions on Systems, Man, and Cybernetics Society, 41, 869-884.
Penna, J., Nascimento, C., & Rodrigues, L. (2012). Health monitoring and remaining useful life estimation of lithium-ion aeronautical batteries. In Aerospace conference, 2012 ieee (p. 1-12).
Pola, D., Navarrete, H., Orchard, M., Rabié, R., Cerda, M., Olivares, B., . . . Pérez, A. (2015). Particle-filteringbased discharge time prognosis for lithium-ion batteries with a statistical characterization of use profiles. IEEE Transactions on Reliability, 64, 710-720.
Rahmoun, A., Biechl, H., & Rosin, A. (2012). Soc estimation for li-ion batteries based on equivalent circuit diagrams and the application of a kalman filter. In Electric power quality and supply reliability conference (p. 1-4).
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, 291-296.
Salkind, A., Fennie, C., Singh, P., Atwater, T., & Reisner, D. (1999). Determination of state-of-charge and state-ofhealth of batteries by fuzzy logic methodology. Journal of Power Sources, 80, 293 - 300.
Snihir, I., Rey,W., Verbitskiy, E., Belfadhel-Ayeb, A., & Notten, P. (2006). Battery open-circuit voltage estimation by a method of statistical analysis. Journal of Power Sources, 159, 1484–1487.
Urbain, M., Rael, S., Davat, B., & Desprez, P. (2008). Energetical modelling of lithium-ion battery discharge and relaxation. In Ieee power electronics specialists conference (p. 3628-3634).
Wansart, J., & Schneider, E. (2010). Modeling market development of electric vehicles. In Annual ieee systems conference (p. 371-376).
Weng, C., Sun, J., & Peng, H. (2013). An Open-Circuit- Voltage Model of Lithium-Ion Batteries for Effective Incremental Capacity Analysis. In Dynamic systems and control conference.
Wishart, J., Carlson, R., Chambon, P., & Gray, T. (2013). The electric drive advanced battery project: Development and utilization of an on-road energy storage system testbed. In Sae world congress (p. 1-30).
Xiong, R., He, H., Sun, F., & Zhao, K. (2013). Evaluation on state of charge estimation of batteries with adaptive extended kalman filter by experiment approach. IEEE Transactions on Vehicular Technology, 62, 108-117.
Yang, H., Gao, Y., Farley, K., Jerue, M., Perry, J., & Tse, Z. (2015). Ev usage and city planning of charging station installations. In Wireless power transfer conference (p. 1-4).
Young, K., Wang, C., Wang, L., & Strunz, K. (2013). Electric vehicle integration into modern power networks. Springer Science.
Zeff, S. (2016). My electric journey with a nissan leaf. IEEE Consumer Electronics Magazine, 5, 79-80.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.