Proton-exchange-membrane fuel cells (PEMFCs) are clean and sustainable mobile power sources for transportation. Recently, their deployment in heavy-duty vehicles (HDVs) has attracted growing interest owing to their high energy scalability and lower infrastructure requirements. However, to meet the stringent requirements for efficiency and long-term durability for HDV applications, PEMFCs typically employ a relatively high platinum group metal (PGM) loading (>0.2 mgPGM/cm2). This elevated PGM loading significantly increases the stack and system costs, surpassing the U.S. Department of Energy (DOE) target of $60/kW for commercial viability. Reducing PGM loading while maintaining performance and durability remains a central challenge for HDV fuel cells. Here we exploit metal oxide-Pt interactions and utilize the strong CeOx-Pt interaction to design a CeOx@Pt catalyst structure with exceptional durability. At a low total PGM loading (0.1 mgPGM/cm2), the CeOx@Pt/C catalyst demonstrates high fuel cell performance (8.8 kW/gPGM) and stability (power retention >90%) after the challenging HDV durability testing (90,000 accelerated-stress-test cycles). With the CeOx@Pt/C catalyst, we showcase over 70% reduction in Pt cost from the M2FCT target (to $9/kW), highlighting its promising potential for enabling stable and cost-effective fuel cell systems for heavy-duty applications.

This paper investigates the influence of rolling resistance on fuel consumption in Class 8 heavy-duty vehicles, with a focus on a modeling approach through variations in the rolling resistance coefficient (Crr) across different driving scenarios. Leveraging TruckSim’s multibody modeling approach for vehicle dynamics and MATLAB/Simulink co-simulation capability, the study provides insights into how tire rolling resistance affects energy efficiency under varying conditions while enabling controlled, repeatable comparisons across various scenarios. Results show that across the evaluated scenarios, increases in Crr impact the vehicle’s speed, fuel consumption, engine torque, and crankshaft spin. Specifically, increasing Crr from 0.004 to 0.013 was found to lead up to 68% higher fuel consumption in high demand scenarios. These findings aim to guide efforts to optimize tire design and vehicle performance that help achieve improved fuel efficiency.

Physics-guided cross-scale temporal attention network for modeling and prediction of multi-axle heavy-duty vehicle dynamics

Performance-driven parametric analysis and optimization of hydrogen refueling systems for heavy-duty vehicles

Multi-motor electric heavy-duty vehicles face significant energy efficiency challenges due to the complex coordination of discrete gear selection and continuous energy flow allocation in the powertrains. This study aims to address this coordination dilemma by proposing a multi-agent deep reinforcement learning energy management strategy (EMS). The framework employs collaborative control across three agents to simultaneously optimize middle axle/rear axle gear shifts (DQN) and power distribution (DDPG), effectively handling the hybrid action space. A specialized rule is integrated to accelerate convergence and enhance real-cycle adaptability. Simulation results on CHTC-TT and CHTC-HT cycles show the proposed strategy achieves only 3.14% and 4.65% higher energy consumption, respectively, compared to a rule-optimized benchmark. This validates its practicality and robustness for real-world electric heavy-duty transportation applications.

Heavy-duty vehicle (HDV) fuel cells require oxygen reduction reaction (ORR) catalysts with exceptional activity and durability under stringent operating conditions. However, conventional solid-solution Pt-based ORR catalysts often fail to simultaneously meet the requirements of high-power density and long-term stability, due to their inherent activity-stability trade-offs and mass-transport limitations at working conditions of fuel cells. Herein, we report a universal ligand-tuned co-reduction strategy to efficiently load Pt-based intermetallic compound (IMC) nanoparticles with high density (>50 wt% metal content and ∼3 nm particle size) and a high degree of ordering into hollow mesoporous carbon (HMC) by narrowing the reduction-potential gap between Pt and other transition metal precursors to achieve stable high-power HDV fuel cells. The dense and ordered IMCs ensure the efficient and stable ORR, while the mass-transport-favorable HMC promotes oxygen transport to the active sites. This design greatly enhances HDV fuel cell ORR activity, stability, and mass-transport efficiency. Under the HDV-relevant conditions (250 kPaabs and cathode loading of 0.15 mgPt cm-2), the optimized H-L10-PtCo/HMC catalyst achieves an exceptional current density of 1.73 A cm-2 at 0.7 V and retains 85% after 90,000 accelerated durability cycles, significantly exceeding the U.S. Department of Energy targets. The findings establish H-L10-PtCo/HMC as a next-generation cathode electrocatalyst for HDV applications.

With increasingly stringent international limits on diesel particulate matter emissions, Continuous-Regeneration Particulate Filters (CRPFs) have been widely applied in heavy-duty vehicle (HDV) exhaust systems. However, their impacts on the complete gaseous pollutant profile remain insufficiently characterized. This study investigated the effects of three CRPF configurations on gaseous emissions from a China III diesel engine under the World Harmonized Transient Cycle (WHTC). Regulated pollutants (CO, total hydrocarbons (THC), NOx, and CO2) and unregulated pollutants (benzene series compounds and aldehydes) were measured before and after CRPF installation. The results demonstrated that CRPFs achieved high reduction efficiencies for CO (98.5–99.9%) and THC (77.4–99.9%) through catalytic oxidation, while showing negligible effects on NOx (0.2–3.0% reduction) and slight increases in CO2 (0.07–0.55%). For unregulated pollutants, aldehydes were effectively reduced (formaldehyde: 84.1–100.0%; acetaldehyde: 47.4–100.0%), whereas benzene series compounds exhibited variable responses, with some species showing increased emissions. These findings reveal complex pollutant transformation mechanisms within CRPF systems and provide references for optimizing aftertreatment configurations to meet China VI and subsequent emission standards, thereby contributing to the mitigation of air pollution, the protection of public health, and the promotion of sustainable societal development.

Data collected by Eurostat indicate that road transport of large loads is dominated by tractor units with semi-trailers. Furthermore, road transport accounts for a significant and growing participation in greenhouse gas emissions. In Poland, there were 567,800 tractor units in 2023. Their high intensity of use and energy consumption have made them the focus of numerous studies aimed at reducing fuel consumption and exhaust emissions. Analysing energy consumption and identifying reduction strategies leads to lower operating costs for transport companies, yielding both social and economic benefits. The aim of the research was to conduct a detailed analysis of the trip profiles (velocity, routes) of actual transport tasks and to identify opportunities for reducing energy consumption in regional heavy-duty transport. Based on the research and calculations performed, a practical method for reducing energy consumption and exhaust emissions was established. The proposed approach allows for a reduction in energy consumption by up to 13.75%, fuel consumption by up to 12.16%, and exhaust emissions by up to 12.11–12.16% (depending on the component) in a short period of time and without additional investment.

Current onboard hydrogen storage systems are volumetrically inefficient and represent a major constraint on the driving range of heavy-duty fuel cell vehicles. This work presents a conceptual model of an internally reinforced Type I rectangular-shaped pressure vessel as a solution to enhance the volumetric efficiency of hydrogen storage in heavy-duty vehicles. The pressure vessel’s geometry incorporates an internal reinforcing structure to ensure both the structural integrity of the vessel and compliance with the standards for onboard hydrogen storage. Initially, an analytical approach was employed to determine the base parameters of the wall and the internal structure of the reinforced pressure vessel. Finite element analysis was then conducted to validate the analytical solutions and assess the structural integrity of the pressure vessel under design pressure conditions. This was followed by a parametric optimisation study in which the design parameters were systematically varied to identify an optimal pressure vessel design. The 35 MPa reinforced titanium pressure vessel offers 29% more volumetric capacity than the conventional Type IV storage system. The gravimetric capacity of the titanium pressure vessel is low, 2.9 wt%; despite this, the mass of the vessel is applicable in HDVs. This design increases hydrogen storage capacity, offering a range increase of approximately 29% for the same design space.

Battery electric trucks (BETs) are a promising option to reduce emissions from heavy-duty vehicles. However, the transition to BETs will cause an additional demand for electricity. Future charging strategies will influence the future peak load as well as the operational and technical feasibility of BETs. We simulated 2410 representative single-day German truck driving profiles with three different charging strategies: (1) as slow as possible, (2) as fast as possible, and (3) slowly at depots and as fast as possible at public locations. Assuming a 33% electrification rate by 2030 and near-complete fleet conversion by 2045, we scaled our results to the German truck fleet. We found that charging as fast as possible leads to additional peak loads up to 6 GW in 2030 and 18 GW in 2045, while the other charging strategies reduce additional peak loads to 3 GW in 2030 and 8 GW in 2045. Therefore, implementing wise charging strategies will reduce future peak load.

The present study evaluates the effect of real-world operational factors and driving behaviors that significantly contribute to CO2 emissions and total energy consumption of the port-based heavy-duty vehicles (HDVs). Interpretable machine learning techniques are applied within an eXplainable Artificial Intelligence (XAI) framework to assess the impact of input variables on prediction accuracy. The inherent simplifications in these approaches often limit their ability to capture the complex, nonlinear characteristics of vehicular emission determinants, particularly under dynamic, micro-operational conditions associated with real-world settings. XGBoost showed higher predictive accuracy over conventional regression and other ensemble methods, with up to 46% improvement in R 2 and over 80% reduction in estimation errors. To address the black-box nature associated with the model, this study adopts XAI techniques, with SHapley Additive exPlanations (SHAP) employed to quantify feature contributions and enhance the interpretability. The results show that real-world CO2 emission levels remain substantially high under dynamic operational conditions, emphasizing the need for improved transit and freight management strategies to mitigate vehicular emissions. This further reinforces the importance of regulatory frameworks that incorporate CO2 emission and fuel-efficiency standards alongside conventional pollutant limits. Such progressive targets are intended to curb the climate impact, stimulate technological innovation, and support long-term low-carbon transition goals.

As a core component of transportation infrastructure, the service status of road engineering has a direct impact on traffic safety and operational efficiency. This paper focuses on the frequent damage problems in road engineering applications. Through literature research and data analysis, it systematically sorts out three major types of common diseases: structural damage, functional damage, and auxiliary facility damage. The research indicates that overloaded traffic, construc-tion quality defects, environmental degradation, and delayed maintenance are the primary factors contributing to these issues. Among them, the incidence of dis-eases such as cracks, ruts, and uneven settlement accounts for more than 80%. For every 5% increase in the proportion of heavy-duty vehicles, the service life of the pavement can be shortened by 12%-18%. Damage problems not only cause annual economic losses of hundreds of billions of yuan but also directly or indirectly induce 17.3% of road traffic accidents. By analyzing the formation mecha-nisms and influence laws of various types of damage, this paper provides theoretical support for optimizing road engineering design, construction control, and the formulation of maintenance strategies, which is of great practical significance for extending road service life and enhancing infrastructure resilience.

This study presents a cradle-to-grave lifecycle analysis of energy use and greenhouse gas (GHG) emissions for U.S. medium- and heavy-duty vehicles across current (2021) and future (2035) technologies using the Greenhouse gas, Regulated Emissions, and Energy use in Technologies (GREET) model with industry-vetted assumptions. Results vary across vehicle classes but point to common trends: today, battery electric vehicles (BEVs) offer significant (10-60%) GHG emissions reduction compared to diesel internal combustion engine vehicles and are the lowest emissions option per ton-mile of cargo movement, followed by hydrogen fuel cell electric vehicles (FCEVs) (5-50% emissions reduction). Emissions savings depend largely on the duty cycle and fuel economy of the vehicle type. Future vehicle technology advancements result in comparable emission reductions associated with BEVs and hydrogen FCEVs. Weight-limited BEV trucks see less per-ton-mile emissions reduction due to the impact of battery weight on increased vehicle weight and reduced payload capacity. By 2035, improvements in vehicle efficiency can reduce emissions across all powertrains. However, very low levels of emissions require switching vehicles' use-phase fuel/energy to low-carbon fuels and electricity. Renewable diesel, e-fuels, hydrogen produced from natural gas with carbon capture and storage or renewables, and use of low-carbon electricity can all achieve over 70% reduction in GHG emissions from the current day diesel-based internal combustion engine vehicle.

<div>This study presents a structured approach to the aerodynamic evaluation of commercial heavy-duty vehicles by categorizing the underlying flow physics into three primary phenomena: pressure-induced separation, geometry-induced separation, and flow diffusion. Furthermore, the study gives insights into the benefits of Detached Eddy Simulations (DES) over traditional Reynolds-Averaged Navier–Stokes (RANS) approaches by analyzing the flow behavior in cases that correspond to these phenomena. Fundamental insights on pressure and geometry-induced separation were developed through simulations of flow over a sphere and a rectangular cylinder at a Reynolds number of 2.8 × 10<sup>6</sup>. Additionally, flow diffusion was investigated using a coaxial jet interacting with surrounding fluid at a Reynolds number of 2.1 × 10<sup>4</sup>. These cases were analyzed using three turbulence modeling techniques: <i>k</i>-<i>ε</i>, <i>k</i>-<i>ω</i> SST, and DES.</div> <div>To demonstrate the practical relevance of these phenomena, a comprehensive aerodynamic performance study was conducted on a commercial heavy-duty truck. This final analysis integrates all three flow behaviors, showcasing their combined impact on vehicle aerodynamics. The study emphasizes the effectiveness of the DES approach in capturing complex flow structures with enhanced accuracy. Furthermore, this study provides meshing guidelines for near-wall and wake dominant regions, to be implemented in DES-based simulations. The findings aim to support future research by offering a robust framework for applying advanced turbulence models in real-world aerodynamic evaluations.</div>

<div>This study investigates noise, vibration, and harshness (NVH) characteristics of hydraulic steering systems in medium- and heavy-duty commercial vehicles due to hydraulic system design. Utilizing on-vehicle and lab environment testing, primarily a pressure sweep test and speed sweep test, to identify sources of NVH. Testing demonstrated a significant impact to perceptible noise and vibration through changes to system and component design. NVH mitigation is accomplished by reducing pressure pulsations, cavitation, and turbulence within the fluid by changing hydraulic plumbing diameter. Reduction in sound pressure level (SPL) averaged 30% with peak reduction of 75%. While optimizing hose diameter is an effective method for controlling NVH in commercial vehicle hydraulic steering systems, additional studies should be conducted in optimizing plumbing materials and routing.</div>

Aiming at the challenge of simultaneously optimizing ride comfort and wheel grounding performance for mining dump trucks under severe road conditions, this paper proposes a hydro-pneumatic suspension parameter design method based on an improved multi-objective particle swarm optimization (IMOPSO) algorithm. First, a dynamic model of the hydro-pneumatic suspension is established, incorporating the coupled nonlinear characteristics of the valve system and the gas chamber. The accuracy of the model is verified through bench tests. Subsequently, the influence of key parameters, including the damping orifice diameter, check valve seat hole diameter, and initial gas charging height, on the vertical dynamic performance of the vehicle, is systematically analyzed. On this basis, a multi-objective optimization model is constructed with the objective of minimizing the root mean square (RMS) values of both the sprung mass acceleration and the dynamic tire load. To enhance the global search capability and convergence performance of the MOPSO algorithm, adaptive inertia weighting, dynamic flight parameter update, and an enhanced mutation strategy are introduced. Simulation results demonstrate that the optimized suspension achieves significant improvements under various road conditions. On class-C roads, the RMS values of the sprung mass acceleration (SMA) and the dynamic tire load (DTL) are reduced by 37.6% and 15.8%, respectively, while the suspension rattle space (SRS) decreases by 10.2%. Under transient bump roads, the peak-to-peak (Pk-Pk) values of the same two indicators drop by 38.9% and 44.9%, respectively. Furthermore, compared to the NSGA-II algorithm, the proposed method demonstrates superior performance in terms of convergence stability and overall performance balance. These results indicate that the proposed design effectively balances ride comfort, wheel grounding performance, and driving safety. This study provides a theoretical foundation and an engineering-feasible method for the performance balancing and parameter co-design of suspension systems in heavy-duty engineering vehicles.

Optimization of fuel cell heavy-duty commercial vehicles sizing and energy management based on an offline-online framework

Comprehensive and spatially detailed passenger vehicle and truck traffic volume data for the United States estimated by machine learning.

Experimental research and analysis of electrophoretic quality on heavy-duty vehicle frames

Assessment of phase change materials for thermal energy storage in battery systems for heavy-duty vehicle applications