Mitigating CO₂ emissions from long-haul commercial trucking is a major challenge that must be addressed to achieve substantial reductions in greenhouse gas (GHG) emissions from the transportation sector. Extensive recent research and development programs have shown how significant near-term reductions in GHGs from commercial vehicles can be achieved by combining technological advances. This paper reviews progress in technology for engine efficiency improvements, vehicle resistance and drag reductions, and the introduction of hybrid electric powertrains in long-haul trucks. The results of vehicle demonstration projects by major vehicle manufacturers have shown peak brake thermal efficiency of 55% in heavy-duty diesel engines and have demonstrated freight efficiency improvements of 150% relative to a 2009 baseline in North America. These improvements have been achieved by combining multiple incremental improvements in both engine and vehicle technologies. Powertrain electrification through hybridization has been shown to offer some potential reductions in fuel consumption. These potential benefits depend on the vehicle use, the details of the powertrain design, and the duty cycle. To date, most papers have focused on standard drive cycles, leaving a research gap in how hybrid electric powertrains would be designed to minimize fuel consumption over real-world drive cycles, which are essential for a reliable powertrain design. The results of this paper suggest that there is no “one-size-fits-all” solution to reduce the GHGs in long-haul trucking, and a combination of technologies is required to provide an optimum solution for each application.

Air pollution is a major concern, particularly in developing countries. Road transport and mobile sources are considered the root causes of air pollutants. With the implementation of zero-carbon and zero-energy concepts at the district scale, cities can make great strides towards sustainable development. Urban planning schemes are moving from mere building solutions to the larger positive energy district (PED) scale. Alongside other technology systems in PEDs, increased uptake of electro-mobility solutions can play an important role in CO₂ mitigation at the district level. This paper aims to quantify the exhaust emissions of six conventional and two fully hybrid vehicles using a portable emission measurement system (PEMS) in real driving conditions. The fuel consumption and exhaust pollutants of the conventional and hybrid vehicles were compared in four different urban and highway driving routes during autumn 2019 in Iran. The results showed that hybrid vehicles presented lower fuel consumption and produced relatively lower exhaust emissions. The conventional group’s fuel consumption (CO₂ emissions) was 11%, 41% higher than that of the hybrids. In addition, the hybrid vehicles showed much better fuel economy in urban routes, which is beneficial for PEDs. Micro-trip analysis showed that although conventional vehicles emitted more CO₂ at lower speeds, the hybrids showed a lower amount of CO₂. Moreover, in conventional vehicles, NO_x emissions showed an increasing trend with vehicle speed, while no decisive trend was found for NO_x emissions versus vehicle speed in hybrid vehicles.

Decarbonizing long-haul goods transportation poses a substantial challenge. High-efficiency natural gas (NG) engines, which retain the efficiency of a diesel engine but reduce the carbon content of the fuel, offer substantial potential for near-term greenhouse gas (GHG) reductions. A fast-running model that can predict engine performance, GHG and air pollutant emissions is critical to assessing this approach for different applications and vehicle drivetrain configurations. This paper presents the development, validation and application of an engine system model that adapts GT-SUITE™’s phenomenological DI-Pulse predictive model to predict the performance and emissions of a 6-cylinder NG engine using a high pressure direct-injection combustion process. The model includes the engine air exchange system, enabling the prediction of the engine and in-cylinder conditions and overall performance over transient drive cycles. The engine model with a fixed set of calibration parameters captures the complex high-pressure direct injection combustion process and generates time-resolved parameters that are fed into a coupled machine learning model to predict emissions, including nitrogen oxide (NOx) and methane (CH ₄ ) emissions. While the 1-D model’s predictions for CH ₄ were not accurate, coupling the 1-D engine model with a machine learning model has been shown to substantially improve the estimation of CH ₄ emissions and allow accurate prediction of engine total GHG emissions over different duty cycles. The model has been validated using transient engine dynamometer data and is then applied to assess performance and emissions over several regulatory and real-world long-haul drive cycles. The model showed an average error of less than 5% in steady operation. Cumulative errors of NOx and CH ₄ emissions in studied cycles were also less than 10%. The results showed that CH ₄ share in total GHG emissions ranges from 0.2% to 1.4% over various drive cycles. By predicting engine performance and emissions, the developed combined model has considerable potential for use in engine evaluation studies, especially when combined with new technologies across different duty cycles.

Integrating powertrain electrification with efficient engines using low‐carbon fuels like natural gas (NG) improves energy conversion efficiency and mitigates greenhouse gas (GHG) emissions in heavy‐duty (HD) trucks. High‐pressure direct injection (HPDI) NG engines, using less than 5% diesel (energy basis) as pilot fuel, offer diesel‐like efficiency with 15–25% lower GHGs. This work evaluates integrating an HPDI NG engine with a hybrid electric powertrain to reduce net energy consumption and GHG emissions from regional‐haul trucks. The HPDI NG engine is developed and validated using experimental test‐cell data, matching the rated power of the diesel engine in the conventional baseline model. Two hybrid electric configurations: a pretransmission parallel (P) hybrid and a complex power‐split hybrid (PSH), are innovatively designed to meet the high‐torque, transient demands of HD trucks. A multistage optimal component‐sizing framework is implemented to minimize energy consumption while maintaining baseline performance. Hybridizing the HPDI NG engines improves net powertrain energy efficiency by 5.2% for the PSH and 4.0% for the P hybrid design across a real‐world highway cycle at full cargo load. Tank‐to‐wheel CO and CH emissions, along with component‐level energy losses and efficiencies, are compared between hybridized and conventional configurations across urban and highway driving under varying cargo loads.