Artificial neural network for predictions of vehicle drivable range and period

An artificial neural network for predicting the drivable range and period for vehicles under remaining fuel is presented in this paper. The driver's driving behaviors, vehicle condition and traffic route are all taking into consideration. The presented method could learn from the statuses of vehicle such as vehicle speed and engine speed and traffic route to make a prediction of vehicle drivable range and period under remaining fuel. The experimental results are presented for to guarantee the artificial neural network could estimate the vehicle drivable range and period accurately.

I. INTRODUCTION

To boost the efficiency of the fuel combustion for vehicle driving is more and more important issue in last decade. There are some models of vehicle equipped with residual range estimation. But no vehicle equipped with residual period estimation for vehicle driving. As we know, in traffic jam situation, we would like to know the residual period rather than to know residual range of the vehicle under remaining fuel. Especially when the vehicle stuck in the snow road and you need to keep the warm temperature for surviving by heat from combustion engine. We need to know the residual period for engine running under remaining fuel to extend survive period for rescue. In this paper, we focus on the residual range estimation and residual period estimation for the vehicle driving under remaining fuel. An artificial neural network is used for to predict the residual range and residual period of the vehicle.