energy

Researchers developed tools to generate matrix subsets for specific applications to speed up the MOtor Vehicle Emission Simulator processes.

Researchers present a simulation-based quasi-statistical approach to estimate electric vehicle energy consumption under various on-road vehicle operating conditions.

This paper presents a stochastic multi-agent optimization model that supports energy infrastructure planning under uncertainty.

This brief summarizes research findings from the University of California, Riverside, on a prediction-based, adaptive connected eco-driving strategy to account for real-world uncertainties.

This study explores how has EV adoption increased over the past years and predicts how will the EV market continue to expand and penetrate in the future years using a case study with regional granular travel survey data.

In this paper, researchers propose a generic framework of real-time energy management for plug-in hybrid electric vehicles, where an Estimation Distribution Algorithm (EDA) is used for on-line (i.e., real-time) optimization of the power-split strategy.

Researchers reviewed the existing literature on autonomous vehicles (AVs) in shared and private ownership scenarios and assessed the benefits inherent to AVs (regardless of ownership model) as well as of the benefits and challenges of AV-sharing in rural areas relative to urban areas.