Systematic review of the impacts of electric vehicles on evolving transportation systems

Abstract

Electric vehicles (EVs) promise significant advancements, including high energy efficiency and the facilitation of grid-stabilizing technologies such as vehicle-to-grid. However, their increased adoption introduces challenges such as elevated congestion, compromised safety, and grid instability. These challenges stem from differences in acceleration and deceleration patterns between EVs and internal combustion engine vehicles (ICEVs), mismatches between charging station demand and grid supply, and potential cyberattacks on the communications of EVs with charging stations and local grids. To address these issues, novel mathematical and machine-learning models have been developed. These models incorporate both simulated and real-world traffic flow data, charging station distribution and utilization data, and in-vehicle energy management and driver assistance data. The outcomes include optimally planned routes for EVs to destinations and charging stations, stabilized power distribution systems during peak hours, enhanced security in EV-station-grid communication, more energy-efficient storage systems, and reduced range anxiety for EV drivers. This paper systematically reviews the emerging impacts of EVs on evolving transportation systems, highlighting the latest developments in these areas and identifying potential directions for future research. By reviewing these specific challenges and solutions, this paper aims to contribute to the development of more efficient and sustainable electrified transportation systems.

Graphical abstract

Keywords

Electric vehicle / Traffic congestion / Charging infrastructure / Traffic flow model / Machine learning