Model-Based Remaining Driving Range Prediction in Electric Vehicles by using Particle Filtering and Markov Chains

Abstract

The remaining driving range (RDR) has been identified as one of the main obstacles for the success of electric vehicles. Offering the driver accurate information about the RDR reduces the range anxiety and increases the acceptance of electric vehicles. The RDR is a random variable that depends not only on deterministic factors like the vehicle’s weight or the battery’s capacity, but on stochastic factors such as the driving style or the traffic situation. A reliable RDR prediction algorithm must account the inherent uncertainty given by these factors. This paper introduces a model-based approach for predicting the RDR by combining a particle filter with Markov chains. The predicted RDR is represented as a probability distribution which is approximated by a set of weighted particles. Detailed models of the battery, the electric powertrain and the vehicle dynamics are implemented in order to test the prediction algorithm. The prediction is illustrated by means of simulation based experiments for different driving situations and an established prognostic metric is used to evaluate its accuracy. The presented approach aims 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.