<div>The excitation forces of the tamper pairs in the vibrational screed system not only affect the road density but also affect the road surface quality. Thus, to enhance the performance of the asphalt paver machine, an experimental study of an asphalt paver machine is carried out to evaluate in detail the effect of the excitation frequencies of the tamper pairs and vibrator screed on the density and quality of the road surface. From the actual structure of the vibrational screed system of the asphalt paver machine used in the experiment, its mathematical model is then built to calculate the vibration equations. The fuzzy controller is then applied to control the deflection angles between the tamper pairs to enhance the working performance of the vibrational screed system. The study result shows that both the excitation frequencies of the tamper pairs (<i>f<sub>tp</sub> </i>) and vibrator screed (<i>f<sub>vs</sub> </i>) greatly affect the density and quality of the road surface. To increase the compression density of the road, the excitation of <i>f<sub>tp</sub> </i> from 15 to 17 Hz combined with the excitation of <i>f<sub>vs</sub> </i> should be applied. Conversely, the excitation of <i>f<sub>tp</sub> </i> from 5 to 11 Hz should be used while the excitation of <i>f<sub>vs</sub> </i> should be ignored to optimize the quality of the road surface. By controlling the deflection angles in the tamper pairs, the working efficiency of the asphalt paver machine is significantly improved under all different working conditions compared to that without the control.</div>

<div>Analyzing and accurately estimating the energy consumption of battery electric buses (BEBs) is essential as it directly impacts battery aging. As fleet electrification of transit agencies (TAs) is on the rise, they must take into account battery aging, since the battery accounts for nearly a quarter of the total bus cost. Understanding the strain placed on batteries during day-to-day operations will allow TAs to implement best-use practices, continue successful fleet electrification, and prolong battery life. The main objective of this research is to estimate and analyze the energy consumption of BEBs based on ambient conditions, geographical location, and driver behavior. This article presents a model for estimating the battery energy consumption of BEBs, which is validated using the data on federal transit bus performance tests performed by Penn State University and experimental aggregated trip data provided by the Central Ohio Transit Authority (COTA). The developed simulator aims to realistically estimate the actual BEB energy consumption, including factors that are difficult to account for, such as the weather conditions, driver behavior, and uncertain passenger load along a route. The results of the model are compared to results from Penn State University, COTA aggregated trip data, and other methodologies for energy consumption estimation. Finally, the impact of seasonal weather variations and driver aggressiveness on the energy consumption is assessed through simulation analyses.</div>

<div>Assessing the effect of road grade on the performance evaluation and testing of heavy-duty vehicles (HDVs) requires the efficient construction of a high-quality multi-parameter driving cycle of HDVs. However, existing pure random heuristic methods fail to preserve the driving characteristics of the original driving cycles, resulting in poor-quality outputs. In addition, the randomness inherent in multiple heuristic approaches limits the search efficiency. To address these issues, this study proposes a novel Monte Carlo tree search heuristic method (MCTSHM) for efficiently constructing multi-parameter driving cycles of HDVs. First, a satisfactory criterion model was used to design the objective function for the multi-parameter driving cycle, ensuring the evaluation indices satisfy given constraints. Next, heuristics were designed to maintain the dynamic transition characteristics of driving cycles. An improved Monte Carlo tree search was conducted to efficiently select heuristics more suited to the current driving cycle. Finally, the expected driving cycle was returned by combining heuristics with the Monte Carlo tree search using a hyper-heuristic architecture. The experimental results were analyzed using driving data collected from HDVs. The relative deviation of all characteristic indices between the generated and original cycles remained within 10% of the set threshold, indicating high similarity to original database. In comparison with a purely random selection of heuristics, the MCTSHM improved the construction efficiency of multi-parameter driving cycles by 38% under the set conditions.</div>

<div>Power steering pumps are the heart of any hydraulic power steering system. They provide the heavy lifting power required in the form of high-pressure fluid flow that is utilized in powered steering gears or steering racks to assist drivers in vehicle maneuvers, specifically in low-speed situations. Failure of the power steering pump will inevitably increase work needed from the driver to steer a vehicle and decrease the driver comfort at the same time. This article covers investigations into a customer return issue, affecting more than 20% of pumps, for one particular failure mode, pump input shaft seal leakage, and how the failure is not caused by failure at the input shaft nor by failure of the input shaft seal. It was found that internal damage to the pump rotating assembly allows high-pressure oil to overcome the input shaft seal sealing effect. The cause of the failure was determined to be rooted in the manufacturing process, which was re-ordered to reduce the failure rate to an acceptable value (<1%).</div>

<div>In this article, the hybrid drive is discussed of the combination of conventional tractors with electrified trailers, usually referred to as E-trailer. We demonstrate that this approach offers the possibility of achieving fuel savings exceeding 20%. For regional trips, about half of this reduction is achieved without offline charging, i.e., without applying electric energy from the E-trailer battery. For motorway dominant trips, more use is required of the battery energy.</div> <div>A new control strategy is proposed, validated through simulations, in which only three control parameters are required, which can be tuned effectively to achieve maximum fuel reduction under certain trip and loading conditions. This control strategy adjusts the E-trailer torque request, based on the requested power for the tractor diesel engine, being estimated through a smart kingpin sensor. It ensures that the E-trailer supports the tractor propulsion when significant power is required, and recovers energy when the demand for power is low. The control parameters consist of the maximum torque request for the E-trailer during support, the maximum negative torque request during regeneration, and the transition power between regeneration and support.</div> <div>Semitrailers are generally not linked to a specific tractor. The control strategy is unique in that it does not need access to the tractor data network, thus achieving optimum interchangeability. The sensitivity with respect to driving resistance parameters appears to be low and may be counteracted by tuning the control parameters. More care is needed for the assessment of the trailer mass and trailer center of gravity.</div> <div>Finally, the total fuel reduction is discussed in comparison to the charging costs for the E-trailer battery (cost–benefit analysis), for realistic cost levels for fuel and kWh.</div>

<div>Rollover protective structures (ROPS) that absorb energy during vehicle rollovers play a crucial role in providing integrated passive safety for operators restrained by seat belts. These protective structures, integrated into the vehicle frame, are designed to absorb high-impact energy and deform in a controlled manner without intruding into the occupant’s safe zone. This research focuses on the detailed analytical design procedure and performance evaluation criteria of the two-post open ROPS used on motor graders against lateral loads. An experimental test on a standard tubular square hollow section (SHS) column subjected to lateral load has demonstrated a significant correlation between the post-yield behavior of plastic hinge development and energy absorption, compared with results from various formulations adopted in finite element analysis (FEA). To reduce design iteration time and the cost of physical destructive testing, the complete equipment experimental setup is virtually simulated, building upon a thorough understanding of plastic hinge formation on columns under large deflections. This simulation provides comprehensive insights into the structural elasto-plastic response and employs the nonlinear implicit and explicit schemes of FEA to accurately predict energy absorption and force vs deflection behavior. The study follows the guidance outlined in ISO 3471: 2008 standard specifications, validating key structural performance parameters through virtual CAE simulation to ensure alignment with the standard’s force and energy requirements.</div> <div>The research emphasizes the control of merging empirical and analytical methods with advanced CAE tools, allowing engineers to design and evaluate ROPS with superior energy absorption and minimal deflection. By adopting this holistic approach, designers can significantly enhance ROPS structural integrity, ensuring improved safety and protection for operators in the demanding conditions of off-highway vehicles.</div>

<div>The United States Environmental Protection Agency (US EPA) Greenhouse Gas (GHG) Phase 3 regulation targets a substantial reduction in GHG emissions across model year (MY) 2027–2032 class 2b-8 vehicles. This article explores the implementation of alternative fuels, such as compressed natural gas (CNG) and liquefied petroleum gas (LPG), along with powertrain hybridization as viable pathways for achieving these stringent standards in a cost-effective manner. A detailed analysis is performed on a Class-7 medium–heavy-duty (MHD) truck configuration, featuring an inline 4-cylinder 5.2-L spark-ignited (SI) engine, modeled with both CNG and LPG fuels. The vehicle’s powertrain is simulated to evaluate GHG emissions and fuel efficiency. The study further examines the impact of low rolling resistance (LRR) tires and varying tire rolling resistance coefficients (C<sub>rr</sub>) on vehicle performance. For further lowering the GHG emissions, a hybrid powertrain sizing study was performed. The simulation results indicate that hybrid powertrain configurations, when combined with LRR tires, can achieve significant CO<sub>2</sub> emission reductions, meeting and exceeding the US EPA Phase 3 GHG targets. The powertrain with the CNG engine equipped with fuel-saving technologies such as neutral-idle, engine start–stop, and automatic engine shutdown can comply with MY 2032 standards while running 7.7 N/kN C<sub>rr</sub> tires. The hybrid powertrain with the LPG engine and 5.6 N/kN C<sub>rr</sub> tires reaches compliance with MY 2032 fleet average standards while maintaining minimal payload penalties. This research provides critical insights into the feasibility of leveraging alternative fuels and hybrid technologies to meet upcoming GHG regulations, presenting a viable pathway for manufacturers to reduce operational costs while achieving environmental compliance.</div>

<div>In the heavy-duty commercial trucks sector, selecting the most energy-efficient vehicle can enable great reductions of the fleet operating costs associated with energy consumption and emissions. Customization and selection of the vehicle design among all possible options, also known as “vehicle specification,” can be formulated as a design space exploration problem where the objective is to find the optimal vehicle configuration in terms of minimum energy consumption for an intended application. A vehicle configuration includes both vehicle characteristics and powertrain components. The design space is the set of all possible vehicle configurations that can be obtained by combining the different powertrain components and vehicle characteristics. This work considers Class 8 heavy-duty trucks (gross combined weight up to 36,000 kg). The driving characteristics, such as the desired speed profile and the road elevation along the route, define the intended application. The objective of the optimization is to minimize the energy consumption. Therefore, a means to evaluate the energy consumption of the vehicles over the desired route is required. This work leverages real-world truck data and additional simulated data to train machine learning models (neural network and random forest). The models can capture the relationship between vehicle specification and driving characteristics with the vehicle’s energy consumption, with an <i>R</i><sup>2</sup> correlation of 0.9986 for conventional trucks and <i>R</i><sup>2</sup> of 0.9998 for electric trucks. The result of this work is the development of a framework that allows to explore a large design space and find the most efficient vehicle configuration for a specific route.</div>

<div>Heavy-duty trucks idling during the hotel period consume millions of gallons of diesel/fuel a year, negatively impacting the economy and environment. To avoid engine idling during the hotel period, the heating, ventilation, and air-conditioning (HVAC) and auxiliary loads are supplied by a 48 V onboard battery pack. The onboard battery pack is charged during the drive phase of a composite drive cycle, which comprises both drive and hotel phases, using the transmission-mounted electric machine (EM) and battery system. This is accomplished by recapturing energy from the wheels and supplementing it with energy from the engine when wheel energy alone is insufficient to achieve the desired battery state of charge (SOC). This onboard battery pack is charged using the transmission-mounted EM and battery system during the drive phase of a composite drive cycle (i.e., drive phase and hotel phase). This is achieved by recapturing wheel energy and energy from the engine when the wheel energy is insufficient to achieve the desired SOC during the drive phase. In the authors’ previous work, a dynamic programming (DP)–based framework is developed that employs a multi-objective cost function to minimize fuel consumption and maximize the regeneration to achieve the benchmark results for the SOC trajectories.</div> <div>This article discusses the real-time implementable control strategies for the heavy-duty truck’s hybrid powertrain, including the mode switch and EM torque for charging. The mode switch is a rule-based control strategy that responds to the wheel torque demand, while the EM torque’s control can have several approaches, such as rule-based, optimal charging strategies that are inspired by equivalent cost minimization strategy (ECMS), or adaptive strategy that updates the equivalent factor according to the battery SOC state. This work presents and studies the different choices to control the EM torque and their impact on vehicle performance and energy consumption. The complete cycle results are compared with the benchmark results, and the energy analysis is accomplished to validate the efficacy of the proposed real-time implementable optimal control strategies (i.e., rule-based and adaptive ECMS). The adaptive optimal control strategy is the potential candidate to be implemented on a real heavy-duty vehicle for optimal management of hotel loads, as it produces the SOC trajectory closer to the benchmark results within the error of ±1.25% while costing minimal fuel consumption. The fuel saving of 2.96% is achieved when compared to conventional heavy-duty trucks for each day of a typical highway trip and hotel phase for each heavy-duty truck, which is 18.2% higher than the rule-based control strategy.</div>

<div>This article aims to analyze and evaluate the roll safety thresholds (RSTs) and roll safety zones of tractor semi-trailer vehicles during turning maneuvers, using the roll safety factor (RSF) and yaw rate of the vehicle bodies. To achieve this, a full dynamics model is established using the multibody system method. This model is then used to survey and evaluate the vehicle’s motion state, using ramp steer maneuver (RSM) steering rules. In each survey case, the maximum values of RSF and yaw rate of vehicle bodies are synthesized in 3D data, with an initial velocity range of 40 km/h to 80 km/h and a magnitude of steering wheel angle range of 12.5° to 300°. These 3D data are used to determine the proposed values of RSF, which can be used as examples to set the threshold values of the yaw rate of vehicle bodies and roll safety zones. At a velocity of 60 km/h, the dynamic rollover threshold for proposed roll safety factor (RSF<sub>prop</sub>) is equal to 1, with corresponding values of 15.718°/s and 14.962°/s. Similarly, the warning threshold for RSF<sub>prop</sub> is equal to 0.6, with values of 9.514°/s and 9.404°/s, and for RSF<sub>prop</sub> equal to 0.7, the values are 10.705°/s and 10.625°/s. The control threshold for a vehicle velocity of 60 km/h and RSF<sub>prop</sub> equal to 0.9 is calculated as 13.588°/s and 13.339°/s. These results can be used as a basis for developing early warning and control systems for various vehicle operating modes.</div>