Road Load Model Analysis for Eco-Routing Navigation Systems in Electric Vehicles

This paper presents a road load estimation model to be employed in eco-routing navigation systems of electric vehicles. The proposed method analyses the energy consumption over a specified driving route on the basis of road surface profile estimation. This involves road elevation information of the route gathered through available digital elevation mapping sources. A piecewise linear approximation technique is adopted to estimate the road model for a dedicated drive trip. The work adds a simple and inexpensive feature that can assist in the development of an eco-routing navigation system for proper prediction of the least energy consumption route available for the vehicle. The power consumption is evaluated which accounts for the three dimensional vehicle dynamics over a route having inherent dips and crests. A detailed analysis of driving power requirement is presented. The necessary simulations are conducted in Matlab- Simulink environment and results are discussed accordingly. The presented results demonstrate the effectiveness of the proposed model in order to enhance the applicability of the eco-routing navigation system.

I. Introduction

The ever increasing fuel crisis, thriving environmental consciousness and adverse effects of climate change have opened avenues for development of environment friendly vehicles which can minimize the carbon footprint [1]. Therefore the development of pollution-free vehicle technology has taken on an accelerated pace to fulfill these requirements worldwide. Electrification of the power train in electric vehicle (EV) leads to new variances and possibilities and allows research in several new directions. However one of the major factors that act as barrier to acceptance of EVs is the range anxiety [2], [3]. Accurate range prediction and reduction of range anxiety are critical issues that need to be tended for widespread use of EVs. This requires a critical analysis of EV power consumption which is possible by used of road surface estimation model.