A Model-Based Approach for Predicting the Remaining Driving Range in Electric Vehicles



Javier A. Oliva Christoph Weihrauch Torsten Bertram


The limited driving range has been pointed out as one of the main technical factors affecting the acceptance of electric vehicles. Offering the driver accurate information about the remaining driving range (RDR) reduces the range anxiety and increases the acceptance of the driver. The integration of electric vehicles into future transportation systems demands advanced driving assistance systems that offer reliable information regarding the RDR. Unfortunately, the RDR is, due to many sources of uncertainty, difficult to predict. The driving style, the road conditions or the traffic situation are some of these uncertain factors. A model-based approach for predicting the RDR by combining unscented filtering and Markov chains is introduced in this paper. Detailed models are implemented for representing the electric vehicle and its energy storage system. The RDR prediction is validated by a set of simulation based experiments for different driving scenarios. Whereas traditional approaches consider the RDR as a deterministic quantity, to our knowledge, this approach is the first to represent the RDR by a probability density function. We aim to provide initial steps towards a solution for generating reliable information regarding the RDR which can be used by driving assistance systems in electric vehicles.

How to Cite

A. Oliva, J. ., Weihrauch, C. ., & Bertram, T. . (2013). A Model-Based Approach for Predicting the Remaining Driving Range in Electric Vehicles. Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2282
Abstract 62 | PDF Downloads 36



model based prognostics, range prediction, electric vehicles, markov chains

Andre, M. (2004). The ARTEMIS European driving cycles for measuring car pollutant emissions. In Science of the total environment.

Bowman, A. W., & Azzalini, A. (Eds.). (1997). Applied smoothing techniques for data analysis. New York: Oxford University Press Inc.

Conradi, P., & Hanssen, S. (2011). Dynamic cruising range prediction for electric vehicles. In Advanced microsystems for automotive applications 2011 (p. 269-277). Springer-Verlag Berlin Heidelberg.

Daigle, M., & Goebel, K. (2010). Improving computational efficiency of prediciton in model-based prognostics using the unscented transform. In Annual conference of the prognostics and health management society 2010.

Daigle, M., & Goebel, K. (2011). A model-based prognostics approach applied to pneumatic valves. In International journal of prognostics and health management.

Daigle, M., Saxena, A., & Goebel, K. (2012). An efficient deterministic approach to model-based prediction un- certainty estimation. In Annual conference of the prognostics and health management society 2012.

Franke, T., Neumann, I., Bu ̈hler, F., Cocron, P., & Krems, J. (2012). Experiencing range in an electric vehicle - understanding psychological barriers. In Applied psychology: An international review (Vol. 61, p. 368-391).

Guzzella, L., & Sciarretta, A. (Eds.). (2005). Vehicle propulsion systems: Introduction to modeling and optimization. Springer Verlag, Heildelberg.

Hayes, J., Oliveira, R. de, Vaughan, S., & Egan, M. (2011). Simplified electric vehicle power train models and range estimation. In Vehicle power and propulsion conference (VPPC), 2011 IEEE (p. 1-5).

Hoaglin, D. C., Moesteller, F., & Tukey, J. C. (Eds.). (1983). Understanding robust and exploratory data analysis. Wiley.

Jongerden, M., & Haverkort, B. (2009). Which battery model to use? In Software, IET (Vol. 15, p. 445-457).

Julier, S., & Uhlmann, J. (2004). Unscented filtering and nonlinear estimation. In Proceedings of the IEEE.

Kim, E., Lee, J. L., & Shin, K. G. (2013). Real-time pre- diction of battery power requirements for electric vehicles. In ACM/IEEE 4th international conference on cyber-physical systems (ICCPS 13).

Lee, T., & Filipi, Z. (2011). Representative real-world driv- ing cycles in Midwestern US. In Les rencontres scien- tifiques d’lFP energies nouvelles - RHEVE 2011.

Lee, T. C., Judge, G. G., & Zellner, A. (Eds.). (1970). Esti- mating the parameters of the markov probability model from aggregate time series data. North-Holland, 2nd edition.

Manwell, J. F., & McGowan, J. G. (1994). Extension fo the kinetic battery model for wind-hybrid power systems. In Proceedings of EWEC.

Rigatos, G. (2009). Particle filtering for state estimation in nonlinear industrial systems. In Instrumentation and measurement, IEEE transactions on.

Saexena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2009). On applying the prognostics performance metrics. In Annual conference of the prognostics and health management society 2009.

Yu, H., Tseng, F., & McGee, R. (2012). Driving pattern identification for EV range estimation. In Electric vehicle conference (IEVC), 2012 IEEE international.
Technical Papers