학술논문

Numerical Energy Analysis of In-Wheel Motor Driven Autonomous Electric Vehicles
Document Type
Periodical
Source
IEEE Transactions on Transportation Electrification IEEE Trans. Transp. Electrific. Transportation Electrification, IEEE Transactions on. 9(3):3662-3676 Sep, 2023
Subject
Transportation
Aerospace
Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Electric vehicles
Torque
Energy efficiency
Energy consumption
Autonomous vehicles
Greenhouse gases
Predictive models
Autonomous electric vehicles (AEVs)
energy efficiency
greenhouse gas (GHG) emissions
in-wheel motor (IWM)
predictive modeling
Language
ISSN
2332-7782
2372-2088
Abstract
Autonomous electric vehicles (EVs) are being widely studied nowadays as the future technology of ground transportation, while their conventional powertrain systems limit their energy efficiencies and may hinder their broad applications in the future. Here, we report a study on the energy consumption, efficiency improvement, and greenhouse gas (GHG) emissions of a mid-size autonomous EV (AEV) driven by in-wheel motors (IWMs), through the development of a numerical energy model, validated and implemented in a case study. The energy analysis was conducted under three driving conditions: flat road, upslope, and downslope driving, considering autonomous driving patterns, motor efficiency optimization, and regenerative braking. The case study based on the baseline EV driving data in West Los Angeles showed that an IWM-driven AEV can save up to 17.5% of energy during slope driving. In addition, it can reduce around 5.5% of GHG emissions annually in each state in the United States. Using the efficiency maps of a commercial IWM, the energy model and validated results in this study are in line with actual situations and can be used to support the future development of energy-efficient AEVs and sustainable energy transitions in ground transportation.