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.

A Hybrid Method to Calculate the Real Driving Range of Electric Vehicles on Intercity Routes

“…Analyzing Equation (20), we observe that most of the involved terms correspond to set-up values that remain constant over the entire route, including the fully charged and fully discharged battery voltages, the nominal capacity of the battery, the mass of vehicle, and the drag coefficient. As for the rolling coefficient, it can also be considered constant if the type of pavement of the route does not change very much.…”

“…Electric vehicle driving range can be obtained either by applying a simulation process based on the use of the associated algorithms [18][19][20][21][22], or from a self-learning process applying real data from previous experiences [23][24][25][26][27][28][29]. This second method, although more accurate, requires a precise knowledge of the driving conditions such as the route characteristics and style of driving, which are not always are available.…”

A multi-mode electric vehicle range estimator based on driving pattern recognition

“…Markov model is utilised in a study by Oliva et al. 29,30 to predict the driving speed profile in the near future. Various sources of uncertainty such as driving pattern and measurement noise have been considered in those studies.…”

“…Their RDR estimation algorithm consists of two steps. Firstly, the battery state is estimated by a particle filter 29 or Kalman filter. 30 Secondly, the future driving profile is predicted using Markov chain.…”

“…Firstly, the battery state is estimated by a particle filter 29 or Kalman filter. 30 Secondly, the future driving profile is predicted using Markov chain. Then according to the distribution of the propagated particles, the RDR has been estimated as a probability distribution.…”