Energy Consumption Evaluation Based on a Personalized Driver-Vehicle Model

Abstract

A new approach to evaluate personalized energy consumption is presented in this paper. The method consists of identifying driver–vehicle dynamics using the probability weighted autoregressive model, which is one of the multi-mode ARX models, and then of reproducing the driver–vehicle behavior in a vehicle-following task. The energy consumption of the vehicle is estimated from the velocity profile calculated by using the driver–vehicle model. In this paper, driving simulator and real-world driving data were recorded to identify the driver–vehicle model in various situations. As a result, real-world energy consumption could be reproduced in a variety of situations with an average error of 1.9% and a standard deviation within 1.5%. Several promising applications of the energy consumption evaluation are introduced in this paper, such as an online energy consumption prediction, a powertrain choice-assistance system for car buyers, and a solution to estimate the macroscopic energy consumption of aggregated vehicles in a traffic flow.