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Handling stability is one of the important characteristics to evaluate the vehicle performance. This paper presents the modeling, simulation, optimization of handling for a heavy truck. A detailed heavy truck model was built by ADAMS, which consists of the frame, suspension system, steering system, wheel, the cab, etc. The simulation results fit the experiment data very well, which shows that the dynamic model can accurately predict the performance of the vehicle by modeling and simulation. Handling simulation was carried out to show how the parameters influence the performance of the handling. The optimization was carried out based on the simulation results. The experiment data shows that the handling of the heavy truck such as on center handing, steering returnability, steering-effort has been significantly improved.

With the development of highway transportation and automobile industry technology, highway truck overload phenomenon occurs frequently, which poses a danger to road safety and personnel life safety. So it is very important to identify the overload phenomenon. Traditionally, static detection is adopted for overload identification, which has low efficiency. Aiming at this phenomenon, a dynamic overload identification method is proposed. Firstly, the coupled road excitation model of vehicle speed and speed bump is established, and then the 4-DOF vehicle model of half car is established. At the same time, considering that the double input vibration of the front and rear wheels will be coupled when vehicle passes through the speed bump, the model is decoupled. Then, the vertical trajectory of the body in the front axle position is obtained by Carsim software simulation.

The displacement of the shaft head fails to be accurately measured while the three-axle heavy-duty truck is driving on the reinforced pavement. In order to obtain accurate fatigue load spectrum of the suspension bracket, the acceleration signals of the shaft heads of the suspension obtained by the reinforced pavement test measurement are virtually iterated as responses. A more accurate model of the rigid-flexible coupled multi-body dynamics (MBD) of the whole vehicle is established by introducing a flexible frame based on the comprehensive modal theory. Furthermore, the vertical displacements of the shaft heads are obtained by the reverse solution of the virtual iterative method with well-pleasing precision. The accuracy of the virtual iteration is verified by comparing the simulation results with the vertical acceleration of the shaft head under the reinforced pavement in the time domain and damage domain.

Significant effort has been put toward developing future-generation biofuels aimed at either spark-ignition or compression-ignition engines. Butyl-Acetate (BA), C6H12O2, is one such fuel that may be viable as a soot reduction drop-in blend candidate without significant impact on performance or efficiency. Though BA does have a low CN (≈ 20) and heating value (27 MJ/kg), it offers promise as a drop in blend-candidate with pump diesel due to its improved cold weather performance, high flash point, and potential for high volume renewable production capacity. This work investigated the impacts of 5% by volume blend of BA and standard pump diesel (DF2) on overall performance and with a particular focus on soot behavior. Tests were completed at 13 operating points spanning the operating map including full power. Results show a significant reduction in soot without significant impact on NOx emissions and minimal impact on thermal efficiency.

Increasing concerns due to global warming have led to stringent regulation of greenhouse gas (GHG) emissions from diesel engines. Specifically, for GHG phase-2 regulation (2027), more than 4% improvement is needed when compared to phase-1 regulation (2017) in the light heavy-duty (LHD) diesel engine category. At the same time, California Air Resources Board (CARB) and Environmental Protection Agency (EPA) have proposed the new Low NOx standards that require up to 90% reduction in tailpipe (TP) NOx emissions in comparison to the current TP NOx standards that were implemented in 2010. In addition, CARB and EPA have proposed new certification requirements – Low Load Cycle (LLC) and revised heavy-duty in-use testing (HDIUT) based on the moving average window (MAW) method that would require rigorous thermal management. Hence, strategies for simultaneous reduction in GHG and TP NOx emissions are required to meet future regulations.

Internal combustion engines will continue to be the leading power-train in the heavy-duty, on-highway sector as technologies like hydrogen, fuel cells, and electrification face challenges. Natural gas (NG) engines offer several advantages over diesel engines including near zero particle matter (PM) emissions, lower NOx emissions, lower capital and operating costs, availability of vast domestic NG resources, and lower CO2 emissions being the cleanest burning of all hydrocarbons (HC). The main limitation of this type of engine is the lower efficiency compared to diesel counterparts. Addressing the limitations (knock and misfire) for achieving diesel-like efficiencies is key to accomplishing widespread adoption, especially for the US market. With the aim to achieve high brake thermal efficiency (BTE), three (3) computational fluid dynamics (CFD) optimized pistons with three different compression ratios (CR) have been tested.

Eco-driving algorithms enabled by Vehicle to Everything (V2X) communications in Connected and Automated Vehicles (CAVs) can improve fuel economy by generating an energy-efficient velocity trajectory for vehicles to follow in real time. Southwest Research Institute (SwRI) demonstrated a 7% reduction in energy consumption for fully loaded class 8 trucks using SwRI’s eco-driving algorithms. However, the impact of these schemes on vehicle emissions is not well understood. This paper details the effort of using data from SwRI’s on-road vehicle tests to measure and evaluate how eco-driving could impact emissions. Two engine and aftertreatment configurations were evaluated: a production system that meets current NOX standards and a system with advanced aftertreatment and engine technologies designed to meet low NOX 2031+ emissions standards.

In recent years, one of the keys to achieving energy conservation and emission reduction and practicing sustainable development strategies is the wide-area access of large-scale electric vehicles. The charging behavior of large-scale electric vehicles has brought great challenges to the load management and adjustment capacity determination of the power system. Therefore, the prediction of adjustable ability of electric vehicle aggregator based on deep-belief-network is proposed in this paper. First of all, this paper selects the indicators related to the load of the electric bus station: including the arrival time, departure time, and daily mileage of the electric vehicle, from which the SOC variation trend and accurate charging demand of the single electric vehicle are obtained.

In a military off-road vehicle, generally designed to operate in an aggressive operating environment, the typical comfort requirements for trucks and passenger cars are revised for robustness, safety and security. An example is the cabin space optimisation to provide easy access to many types of equipment required on-board. In this field, racks hung to the cabin chassis are generally used to support several electronic systems, like radios. The dynamic loads on a rack can reach high values in the operative conditions of a military vehicle. Rack failures should be prevented for the safety of driver, crew and load and the successful execution of a mission. Therefore, dynamic and durability tests of these components, including the fixtures to the vehicle, are required.

To decarbonize heavy-duty vehicles solely through electrification with batteries is challenging as large batteries are required for a meaningful range, severely impacting payload. Employment of hybrid electric powertrains where fuel cells are integrated with batteries can deliver increased range and payload. However, the energy balance between the fuel cell and the battery needs to be analyzed to optimize the sizing of the powertrain components. This study has performed a multi-objective optimization using genetic algorithm to obtain the optimum range and hydrogen consumption for a DAF 44 tons heavy-duty truck. The proposed truck powertrain has been numerically modelled in AVL CRUISE M software. The electric drive from Involution Technologies Ltd and Bramble Energy Ltd’s printed circuit board fuel cell (PCBFC) are used in the model.

Drive cycles have been the official way to create standardized comparisons of fuel economy and emission levels between vehicles. Since the 1970s these have evolved to be more representative of real-world driving, with today’s standard being the World Harmonized Light Vehicle Testing Procedure. The performance of battery electric vehicles which consist of electric drives, battery, regenerative braking and their management systems may differ when compared to that of vehicles powered by conventional internal combustion engines. However, drive cycles used for evaluating the performance of vehicles, were originally developed for conventional powered vehicles. Moreover, the kinematic parameters that can distinguish the real-world performance of the differently powered vehicles are not fully known. This work aims to investigate the difference between vehicles powered by pure internal combustion engine, electric hybrid and pure electric drive.

For the shifting process of the centralized central drive four motor-4AMT(4MC-4AMT) propulsion system applied on the pure electric heavy truck, there were several methodologies to fulfill it, such as torque control method, speed control method, free-speed hybrid control method and torque-speed hybrid control method, where the torque control method and torque-speed hybrid control method were applied on 4MC-4AMT. Through the simulation and test, it was found that the shifting process of torque-speed hybrid control method was much quicker than torque control one and shift smoothness was also acceptable with the same gear selection and shifting actuators.

Considering the change of vehicle future power demand in the process of energy distribution can improve the fuel saving effect of hybrid system. However, current studies are mostly based on historical information to predict the future power demand, where it is difficult to guarantee the accuracy of prediction. To tackle this problem, this paper combines hybrid energy management with predictive cruise control, proposing a hierarchical control strategy of predictive energy management (PEM) that includes two layers of algorithms for speed planning and energy distribution. In the interest of decreasing the energy consumed by power components and ensuring transportation timeliness, the upper-level introduces a predictive cruise control algorithm while considering vehicle weight and road slope, planning the future vehicle speed during long-distance driving.

With the growing prosperity of the long-distance freight and urban logistics industry, the demand for cargo trucks is gradually increasing. The connecting bracket is the critical connecting part of the truck chassis, which bears the load transmitted by the road excitation and reduces the damage to the frame caused by the load. However, the occurrence of rough road conditions is inevitable in heavy-duty transportation. In this paper, road durability tests and fatigue life analysis are carried out on the original structure to ensure the safety of the vehicle. Based on the known boundary and load constraints, a lightweight and high-performance structure is obtained through size optimization, as the original structure cannot meet the performance requirements. Firstly, the road test was conducted on the truck where the original bracket structure is located.

Road anomalies pose significant challenges for on-road safety, ride comfort, and fuel economy. The recent advancement of Connected Vehicle technology has made it feasible to overcome this challenge by sharing the detected road hazards information with other vehicles and entities. However, localization accuracies of the detected road hazards are often very low due to noisy detection results and limited GPS sensor performances. In this paper, a cloud based data management system with in-vehicle and on-cloud data processing modules is presented for road hazards detection and localization. Stereo camera and a consumer-grade GPS sensor on a testing vehicle are used to detect road anomaly information, e.g., type, size, and location, where a novel in-vehicle data processing module is implemented based on Kalman Filter and Phase Adjustment.

Platooning vehicles present novel pathways to saving fuel during transportation. With the rise of autonomous solutions, platooning becomes an increasingly apparent sector requiring the application of this new technology. Platooning vehicles travel together intending to reduce aerodynamic resistance during operation. Drafting allows following vehicles to increase fuel economy and save money on refueling, whether that be at the pump or at a charging station. However, autonomous solutions are still in infancy, and controller evaluation is an exciting challenge proposed to researchers. This work brings forth a new application of an emissions quantification metric called vehicle-specific power (VSP). Rather than utilize its emissions investigative benefits, the present work applies VSP to heterogeneous Class 8 Heavy-Duty truck platoons as a means of evaluating the efficacy of Cooperative Adaptive Cruise Control (CACC).

PVC (polyvinylchloride) synthetic leather or called leatherette is being widely used for automotive interior applications for seat cover, gear boot, gap hider, steering wheel and roof liner due to their leather like feel and texture, flexibility, sewability, affordability, and wide design freedom. However, the leatherette construction such as top coating, backing fabric and fabric weaving pattern plays a critical role in the finished leatherette performance for the specific application. This study provides the influence of different coating material and different backing fabric in squeak behavior of gear boot PVC leatherette. The squeak behavior was studied by stick slip test as per automotive engineering requirements, and the response of these coating and fabric surface was measured in the form of Risk Priority Number (RPN).

Multi-purpose agricultural tractors are vehicles that are usually used in rough paths and on off-road situations characterized by strong slope variations. The main feature of this kind of vehicles is the stability in working conditions to avoid overturning while it is on duty. This characteristic is given by the interaction between the suspension system and the vehicle frame. Due to the limited size of this kind of vehicle, the stability feature could be given by chassis deformation or using a two-piece frame connected by a spherical joint. This paper presents the validation of a numerical lumped-parameters model able to reproduce the vertical dynamics of a multi-purpose tractor featured by a yielding chassis. The unknown model parameters have been estimated firstly with static tests to study the vertical tire and suspension stiffnesses. The dynamic tests using a four-post-test rig have been performed to tune the unknown dynamic parameters.

‘Active safety systems’ are actively being developed to prevent collisions. The integration of ‘active safety systems’ and traditional ‘passive safety systems’ such as seatbelt and airbags is an important issue. The ‘Integrated safety’ performance is that comprehensively controls the performance of ‘active’ and ‘passive’ safety systems to reduce occupant injuries. To develop ‘integrated safety’ performance, it is important to develop crash scenarios for autonomous vehicles. This study is about the development of ‘Estimation Tool of Occupant Injury Risk’ for deriving risk integrated safety scenarios focused on occupant injury. The results of random traffic simulation using ‘Virtual Prototype’ were used to select parameters, and ‘MADYMO Equivalent Simplified Vehicle Crash Analysis Model’ was used to derive F-D characteristics for each vehicle collision condition.

Platooning is a promising technology which can mitigate greenhouse gas impacts and reduce transportation energy consumption. Platooning is a coordinated driving strategy where trucks align themselves in order to realize aerodynamic benefits to reduce required motive force. The aerodynamic benefit is seen as either a “pull” effect experienced by the following vehicles or a “push” effect experienced by the leader. The energy savings magnitude increases nonlinearly as headway (following distance) is reduced [1]. In efforts to maximize energy savings, cooperative adaptive cruise control (CACC) is utilized to maintain relatively short headways. However, when platooning is attempted in the real world, small transient accelerations caused by imperfect control result in observed energy savings being less than expected values. This study analyzes the performance of a recently developed nonlinear model predictive control (NMPC) platooning strategy over challenging terrain.