Adaptive Driving Situation Characterization for Predicting the Driving Load of Electric Vehicles in Uncertain Environments



Javier A. Oliva Torsten Bertram


Battery powered electric vehicles (EVs) have emerged as a promising solution for reducing the consumption of fossil fuels in modern transportation systems. Unfortunately the battery pack has a low energy storage capacity, which causes the driving range of the EV to become very limited. It is therefore essential to properly characterize the different driving situations of the vehicle in order to better predict the driving load along the road ahead and to better estimate the remaining driving range (RDR). However, this prediction cannot be achieved straightforward due to sources of uncertainty introduced by the randomness of the driving environment. In this paper a novel approach for characterizing driving situations and for predicting the driving load of an EV is presented. The prediction of the driving load occurs in a model-based fashion, where the model input variables are modeled as discretetime Markov processes. An approach for estimating the transition
probabilities between Markov states in the presence of sparse driving data is introduced. Furthermore, to capture the changes in the driving environment a Bayes-based methodology for recursively updating the established transition probabilities is presented. The validity of the proposed approach is illustrated through simulation and by a series of experimental case studies.

How to Cite

Oliva, J. A., & Bertram, T. (2014). Adaptive Driving Situation Characterization for Predicting the Driving Load of Electric Vehicles in Uncertain Environments. PHM Society European Conference, 2(1).
Abstract 63 | PDF Downloads 21



model based prognostics, Uncertainty Quantification, loading, Markov chain, Bayesian updating

Abourizk, S. M., Halpin, D. W., & Wilson, J. R. (1994). Fitting Beta Distributions Based on Sample Data. Journal of Construction Engineering and Management, 120(2), 288-305.
Bertuccelli, L., & How, J. (2008). Estimation of nonstationary markov chain transition models. In Decision and Control (CDC), IEEE conference on (p. 55-60).
Guzzella, L., & Sciarretta, A. (Eds.). (2005). Vehicle propulsion systems: Introduction to modeling and optimization. Springer Verlag, Heildelberg.
Johannesson, L. (2005). Development of a time invariant stochastic model of a transport mission (Tech. Rep.).
Johannesson, L., Asbogard, M., & Egardt, B. (2007). Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming. In Intelligent transportation systems, IEEE transactions on (Vol. 8, p. 71-83).
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 driving cycles in Midwestern US. In Les rencontres scientifiques d’lFP energies nouvelles - RHEVE 2011.
Lee, T. C., Judge, G. G., & Zellner, A. (Eds.). (1970). Estimating the parameters of the markov probability model from aggregate time series data. North-Holland, 2nd edition.
Oliva, J. A., Weihrauch, C., & Bertram, T. (2013). A modelbased approach for predicting the remaining driving range in electric vehicles. In Annual conference of the prognostics and health management society 2013. (p. 438-448).
Strelioff, C. C., Crutchfield, J. P., & H¨ubler, A. W. (2007). Inferring Markov Chains: Bayesian estimation, model comparison, entropy rate, and out-of-class modeling. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 76(1).
Wang, Z., Xu, G., Li,W., & Xu, Y. (2007). Driving load forecasting using cascade neural networks. In Advances in neural networks ISNN 2007 (Vol. 4493, p. 988-997). Springer Berlin Heidelberg.
Yang, J., Huang, X., Tan, Y., & He, X. (2008). Forecast of driving load of hybrid electric vehicles by using discrete cosine transform and Support Vector Machine. In Neural networks, IEEE International Joint Conference on (p. 2227-2234).
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