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.

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.

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.