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This study introduces the Enhanced Truck Dispersion Framework, an innovative approach to modeling emissions from idling and moving trucks in urban environments. The framework integrates the Behavior, Energy, Autonomy, and Mobility (BEAM) model for the regional transportation model; the Emission Factors (EMFAC) model and the Motor Vehicle Emission Simulator (MOVES) for emission calculations; and the Research LINE (RLINE) source model with a grid-based point-source model for dispersion analysis. Field data collected at a distribution center in Ontario, California, U.S., is used to calibrate and validate the model, particularly for idling trucks. The study simulates traffic patterns in Inland Southern California, focusing on moving vehicles and idling trucks and based on real-world data. Results reveal significant pollutant concentrations near major highways and warehouse districts, highlighting the impact of both moving and idling truck emissions on urban air quality. This comprehensive approach provides valuable insights for policymakers and urban planners in developing targeted strategies to mitigate the environmental and health impacts of truck emissions in urban areas. 

This paper presents an integrated approach to optimizing microgrid management and electric truck logistics for transportation research. The experiment involves a 100 kW solar photovoltaic system, a 500 kWh battery energy storage system, the electric demand of a commercial building, and a heavy-duty vehicle charging system. The study aims to demonstrate how synchronized optimization of a microgrid control algorithm and a truck route algorithm can reduce overall system costs. The microgrid management system is designed to meet the constraints and requirements of a commercial electric truck charging scheduler. This integrated approach is an improvement over previous systems as it uses the scheduler’s outputs—such as time frames and energy requirements—as constraints for microgrid management. The truck scheduling algorithm iteratively learned to optimize charging times, ensuring that charging occurs during low-cost periods or when renewable energy is available. The electric vehicle scheduler adjusted truck arrival times based on the availability of clean energy sources, creating a feedback loop that continuously improves cost efficiency. Results indicated significant cost savings, with electric utility costs for electric vehicle (EV) charging being only 0% to 20% of the original value while the transportation system is only 23% to 64%, compared with the baseline scenario without the co-optimization framework. These findings suggest that the proposed integrated approach can effectively reduce costs and improve the efficiency of microgrid and electric truck operations. Uncoordinated charging schedules leads to a higher power demand than a well-organized battery electric truck (BET) dispatching strategy. Optimizing truck charging times and energy needs based on microgrid conditions can significantly reduce electricity and transportation costs. 

The internal combustion engine remains a critical propulsion system, especially in heavy-duty freight transportation. However, the traditional internal combustion engine is also a major contributor to anthropogenic greenhouse gas emissions and criteria pollutants. The use of low-carbon fuels such as renewable natural gas (RNG) and hydrogen can improve the sustainability of the transportation sector immediately. The purpose of this study was to investigate the feasibility of using low concentrations of hydrogen blends in a production heavy-duty natural gas highway engine. This study was designed to test the hypothesis that low concentrations of hydrogen can be injected into the pipeline and used in existing transportation engines without affecting engine efficiency and criteria emissions. Engine performance was characterized by in-cylinder pressure measurements, while pre- and post-catalyst emissions measurements were performed across all fuels over different steady-state conditions. Results showed a 4 % increase in the peak cylinder pressure for the 5 % hydrogen blend compared to the baseline RNG high load steady-state conditions along with start of combustion and combustion phasing occurring about one-half crank angle degree earlier. All experiments showed a relatively stable combustion as measured by the coefficient of variability of peak cylinder pressure and the indicated mean effective pressure. Engine-out and tailpipe nitrogen oxide (NOx) emissions experienced increases with the 5 % hydrogen blend. Carbon monoxide and methane emissions increased for the 1 % and 3 % hydrogen blends, likely due to incomplete oxidation at lower combustion temperatures, as indicated by the lower cylinder pressures for these fuels. 

This research investigates how blending ethanol with gasoline influences both gaseous and particulate emissions, as well as the toxicological characteristics of particulates emitted from a plug-in hybrid electric vehicle adapted to run on fuel mixtures containing up to 85% ethanol by volume. Testing was conducted on E10, E30, and E83 fuels, while the vehicle was exercised on a chassis dynamometer over three repetitions of the Federal Test Procedure and US06 cycles. Results showed important reductions in nitrogen oxide emissions for E30 and E83 for both cycles, along with reductions in particulate matter mass, black carbon, and solid particle number. Total hydrocarbon emissions demonstrated increases with E30 and E83 and tracked well with increases in benzene, toluene, ethylbenzene, and xylene isomers. Formaldehyde and acetaldehyde emissions trended in sympathy with higher-ethanol blending. The use of E30 and E83 blends produced more reactive emissions, which subsequently adversely affected the ozone-forming potential for these fuels compared to E10. The toxicological properties exhibited mixed results, with the higher-ethanol blends showing reduced oxidative stress compared to E10, while E83 induced a higher cytotoxic response relative to E30 and E10 fuels. 

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO2-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NOx, VOCs, SOx, PM2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. 

The electrification of truck fleets presents a promising pathway toward reducing emissions in the freight transportation sector. While the feasibility of operating battery electric trucks (BETs) in drayage application has been demonstrated, the impact of charging infrastructure strategies in support of such operation is not well understood. This study evaluates the use of home base, opportunity, and network charging strategies for drayage BETs with a particular focus on the I-710, a major freight corridor in Southern California. Using second-by-second real-world operation data of five typical drayage trucks, we identified 1,403 trips and 338 tours, and simulated them being performed by BETs with different specifications using different charging strategies. Our findings show that BETs with large battery packs would be able to complete nearly all the tours while only relying on home base charging. However, this would come at the cost of increased vehicle weight, which reduces payload capacity. In contrast, BETs with smaller battery packs, which offer a larger payload capacity but have a more limited range, would not be able to complete a number of tours if utilizing home base charging alone. However, incorporating realistic opportunity charging at frequently visited stops (e.g., warehouses) and taking advantage of strategically placed network charging stations can help overcome the range limitation and enable the same level of tour completion as utilizing BETs with larger battery packs. 

Path planning algorithms must balance computational efficiency and path optimality, particularly in real-time applications. Rapidly-exploring Random Tree (RRT) and its variants provide fast execution but often generate suboptimal paths, while grid-based algorithms like A* and hybrid A* yield optimal paths at a high computational cost. This paper presents a novel path-planning approach that integrates heuristic functions from A* and hybrid A* into the RRT* framework. By refining node cost evaluation, our method enhances both path quality and computational efficiency. Experimental evaluations and ablation studies demonstrate its generalizability across multiple RRT variants. Furthermore, we explore the impact of different sampling strategies in conjunction with our modified cost function to optimize key metrics such as path length, execution time, and smoothness. As a practical application, we implement this enhanced algorithm in an automated parking system, a key feature in Connected and Automated Vehicles (CAVs). Quantitative results show that ARRT* reduces path length by up to 17% compared to standard RRT*, while also improving computational efficiency and minimizing steering variations, making it more suitable for real-world autonomous navigation in constrained environments. 

With the rapid growth in e-commerce in the last few decades, the amount of truck traffic, especially in urban areas, has increased substantially. The emergence of connected and automated vehicle technology has enabled the development of various advanced applications aimed at improving the safety, efficiency, and sustainability of existing transportation systems to support the increased traffic. As an example, freight signal priority (FSP) and Connected Eco-Driving (CED) applications have been proposed to reduce vehicle stops and delays at signalized intersections, thereby enabling more efficient travel on urban corridors. However, the majority of FSP and CED studies to date were conducted in simulation due to the limited availability of connected vehicles and infrastructure in the real world. In this study, we implemented these two applications on an adaptive coordinated signalized corridor in Long Beach, California, and evaluated their efficacy with a connected truck. The field trials demonstrated that both FSP and CED effectively smoothed the trajectories of the connected truck, leading to significant reductions in fuel consumption and travel time. These findings confirm the potential of these two connected vehicle applications for enhancing the efficiency and sustainability of urban freight operations.