optimization

Researchers at the University of Southern California developed a truck routing model that minimizes fuel consumption and reduces emissions while explicitly accounting for parking availability and hours-of-service constraints. The researchers used the model to test various scenarios that reflect the practical constraints faced by drivers.

Unrestricted charging of electric vehicles (EVs) can result in violations of the grid’s operating limits, and/or equipment damage or failure. To address this, the researchers propose a two-stage smart charging algorithm for EVs in single-family residences.

This paper describes the development and demonstration of a smart charging system for a plug-in electric vehicle (EV) that can help manage grid impacts.

Researchers employ a least-cost infrastructure planning framework to analyze the impacts of demand, supply, and government policy uncertainties for future hydrogen supply chains. Long-term investment strategies substantially reduce system costs in all our scenarios

The transportation sector is responsible for a significant part of the U.S.’s greenhouse emissions, with a considerable amount being generated by medium-and heavy-duty trucks. However, when it comes to the trucking industry, ‘green’ routing studies do not consider other critical practical factors, like working hours regulations and parking availability. This study addresses a variant of the shortest path and truck driver scheduling problem under parking availability constraints which focuses on optimizing fuel consumption and emissions by controlling the truck's travel speed and accounting for time-dependent traffic conditions.

The report involves a discussion of smart charging for electric vehicles, a comprehensive literature review on the subject, development of models for analyzing EV charging and grid impact, and algorithms for solving the smart charging problem.