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.

The explicit nonlinear model predictive control (eNMPC) approach is a challenging yet encouraging prospective to design model-based and real-time controllers for complicated automotive applications. The precision of the nonlinear control-oriented model may contribute to the superior performance of the controller but adversely affect the real-time capability and even the convergence of optimization solution involved in such an approach. To address this challenge, control-relevant parameter estimation (CRPE) is used to ameliorate the capability of the nonlinear control-oriented model to exploit the cardinal dynamics of a complex plug-in hybrid powertrain. As such, an eNMPC energy management system is developed to be implemented in to an experimental hardware with fettered memory and computational capabilities. Hardware-in-the-Loop (HIL) simulation, which is conducted on a dSPACE MicroAutoBox unit along with sophisticated high-fidelity Autonomie model shows a superior performance of the proposed control scheme for various driving scenarios while maintaining real-time capability.

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.