A5002817385- Electric Vehicles and Infrastructure
- Vehicle emissions and performance
- Transportation Planning and Optimization
- Energy, Environment, and Transportation Policies
- Transportation and Mobility Innovations
- Advanced Battery Technologies Research
- Smart Grid Energy Management
- Multi-Criteria Decision Making
- Data Management and Algorithms
- Topic Modeling
- Air Quality Monitoring and Forecasting
- Air Quality and Health Impacts
- Natural Language Processing Techniques
- Traffic Prediction and Management Techniques
- Electric and Hybrid Vehicle Technologies
- Energy Harvesting in Wireless Networks
- Strategic Planning and Analysis
- Advanced Sensor and Control Systems
- Life Cycle Costing Analysis
- Microgrid Control and Optimization
- Sharing Economy and Platforms
- Global Healthcare and Medical Tourism
- Hybrid Renewable Energy Systems
- Manufacturing Process and Optimization
- Cognitive Science and Mapping
National Transportation Research Center
2025
Oak Ridge National Laboratory
2024-2025
Government of the United States of America
2024
University of South Carolina
2020-2023
Central South University
2017-2018
Stanford University
2018
Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on algebraic derivative estimation, we present novel method to forecast real time. In contrast mainstream machine-learning-based methods, proposed does not require access historical data. It eliminates time-consuming computationally expensive offline training. Consequently, its prediction performance is constrained by quantity quality of training Moreover, can...
<div class="section abstract"><div class="htmlview paragraph">In 2022, the U.S. transportation sector was largest source of greenhouse gas emissions in country, with combination passenger and commercial vehicles contributing 80% these emissions. As adoption electric continues to climb, sights are being set on electrification heavy-duty vehicle (HDCV) fleets. The sustainability shifts relies part addition significant renewable energy generation resources both bolster grid face...
<div class="section abstract"><div class="htmlview paragraph">The transportation sector is responsible for a significant portion of greenhouse gas emissions. Within the sector, truck freight third associated Alternative powertrains are seen as viable approach to significantly reduce these Prior making large-scale transition, it important consider following questions: will power grid support transition alternative powertrains?; truly carbon emissions?; and impose an unnecessary...
<div class="section abstract"><div class="htmlview paragraph">Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles infrastructure constraints. Hydrogen fuel cell medium- heavy-duty (MHDVs) offer a viable alternative, aligning with decarbonization goals Department Energy entities. Historically, alternative fuels like compressed natural gas liquefied propane have faced slow adoption barriers availability. To...
Light commercial vehicles (LCVs) account for about 10–15% of road traffic in Europe. There have only been few investigations on their on-road emission performance. Here, remote sensing vehicle measurements from 18 locations across four European countries are combined a comprehensive analysis NOx and smoke rates diesel LCV the past two decades. This allows differentiating performance by standards, model years, curb weights, engine loads, manufacturers, age, temperature, as well measurement...
Medical tourism has developed rapidly worldwide, especially in Asia, and one of the most important problems facing patient-tourists is selection optimum destination. In this paper, we present a novel multiple criteria group decision making (MCGDM) methodology to evaluate rank medical destinations vague based on information. A systematic assessment model was constructed by investigating MCGDM with neutrosophic fuzzy preference relations (NFPRs). We began defining NFPRs which allow lacking...
Public-transit systems face a number of operational challenges: (a) changing ridership patterns requiring optimization fixed line services, (b) optimizing vehicle-to-trip assignments to reduce maintenance and operation codes, (c) ensuring equitable fair coverage areas with low ridership. Optimizing these objectives presents hard computational problem due the size complexity decision space. State-of-the-art methods formulate problems as variants vehicle routing use data-driven heuristics for...
Vehicle automation requires new onboard sensors, communication equipment, and/or data processing units, and may encourage modifications to existing components (such as the steering wheel). These changes impact vehicle’s mass, auxiliary load, coefficient of drag, frontal area, which then change vehicle performance. This paper uses powertrain simulation model FASTSim quantify autonomy-related design on a fuel consumption. Levels 0, 2, 5 autonomous vehicles are modeled for two battery-electric...
Conditional story generation and contextual text continuation have become increasingly popular topics in NLP community. Existing models are often prone to output paragraphs of texts that gradually diverge from the given prompt. Although generated may a reasonable perplexity diversity, it could easily be identified by human as gibberish. The goal our project is improve coherence consistency across sentences language-generation model. We aim solve this issue first training sentence pair...
The electrification of heavy-duty commercial vehicles (HDCVs) is pivotal in reducing greenhouse gas emissions and urban air pollution; however, this transition poses significant challenges for the existing electric grid, which not designed to meet high electricity demands HDCVs. This can lead a less effective reduction freight transportation's carbon intensity despite efforts. Deploying renewable energy sources, such as photovoltaics, alongside storage solutions, essential address these...
In this study, we extend the scope of established Market Acceptance Advanced Automotive Technologies (MA3T) model, originally developed for light-duty vehicles (LDVs), to realm Medium and Heavy-Duty Vehicles (MHDVs) by creating Truck Choice model. This model is designed simulate selection advanced vehicle technologies various segments MHDV fleets. It takes into account future projections in powertrain technology advancements, energy prices, fleet operation characteristics, policy impacts,...