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Proportional integral derivative (PID) control technique is a famous and cost-effective control strategy, in real implementation, applied in various engineering applications. Also, the ant colony optimization (ACO) algorithm is extensively applied in various industrial problems. This paper addresses the usage of the ACO algorithm to tune the PID controller gains for a semi-active heavy vehicle suspension system integrated with cabin and seat. The magnetorheological (MR) damper is used in main suspension as a semi-active device to enhance the ride comfort and vehicle stability. The proposed semi-active suspension consists of a system controller that calculate the desired damping force using a PID controller tuned using ACO, and a continuous state damper controller that predict the input voltage that is required to track the desired damping force.

This study focuses on the operation of trolley-assisted mining truck, which leverage overhead lines for uphill propulsion, substantially reducing fuel consumption and carbon emissions. The pantograph mounted at the truck body's front exhibits complex vibrational behavior due to the subgrade stiffness and the nonlinearities of the hydro-pneumatic suspension. Vertical dynamic model of the mining truck is constructed which considering the road conditions and suspension characteristics to illustrate the pantograph's contact force. The vibration characteristic of pantograph base is analyzed which using the spatial transformation relationship between the truck's center mass of gravity and the base of pantograph. The stiffness of pantograph is designed based on a pantograph-catenary system model considering different road conditions. The real mining truck is modeled in the Trucksim software to obtain the vibration of pantograph base.

Lightweight design is a key factor in general engineering design practice, however, it often conflicts with fatigue durability. This paper presents a way for improving the effectiveness of fatigue performance dominated optimization, demonstrated through a case study on suspension brackets for heavy-duty vehicles. This case study is based on random load data collected from fatigue durability tests in proving grounds, and fatigue failures of the heavy-duty vehicle suspension brackets were observed and recorded during the tests. Multi-objective fatigue optimization was introduced by employing multiaxial time-domain fatigue analysis under random loads combined with the non-dominated sorting genetic algorithm II with archives.

Multi-motor powertrain topologies are playing an increasingly important role in the development of heavy duty battery electric trucks due to the changing driving requirements of these vehicles. The use of multiple motors and/or transmissions in a powertrain provides additional degrees of freedom for the energy management. The energy management system (EMS) consist of the gear selection strategy and torque split between the drive motors. The aim of the EMS is thereby to achieve high energy efficiency in motor and regenerative operation, while reducing the number of gear changes to ensure driving comfort. Ongoing research focuses on the energy management system of hybrid electric trucks, where the aim is to optimize the torque split between the combustion engine and the electric motor. In this paper, the EMS for an electric truck is described as a mixed-integer nonlinear control problem. This type of optimal control problem is notoriously difficult to solve.

This paper presents an optimal control co-design framework of a parallel electric-hydraulic hybrid powertrain specifically tailored for heavy-duty vehicles. A pure electric powertrain, comprising a rechargeable lithium-ion battery, a highly efficient electric motor, and a single or double-speed gearbox, has garnered significant attention in the automotive sector due to the increasing demand for clean and efficient mobility. However, the state-of-the-art has demonstrated limited capabilities and has struggled to meet the design requirements of heavy-duty vehicles with high power demands, such as a class 8 semi-trailer truck. This is especially evident in terms of a driving range on one battery charge, battery charging time, and load-carrying capacity. These challenges primarily stem from the low power density of lithium-ion batteries and the low energy conversion efficiency of electric motors at low speeds.

To improve the braking energy recovery rate of pure electric garbage removal vehicles and ensure the braking effect of garbage removal vehicles, a strategy using particle swarm algorithm to optimize the regenerative braking fuzzy control of garbage removal vehicles is proposed. A multi-section front and rear wheel braking force distribution curve is designed considering the braking effect and braking energy recovery. A hierarchical regenerative braking fuzzy control strategy is established based on the braking force and braking intensity required by the vehicle. The first layer is based on the braking force required by the vehicle, based on the front and rear axle braking force distribution plan, and uses fuzzy controllers.

Internal combustion (IC) engines still power most of the vehicles on road and will likely to remain so in the near future, especially for heavy duty applications in which electrification is typically more challenging. Therefore, continued improvements on IC engines in terms of efficiency and longevity are necessary for a more sustainable transportation sector. Two important design objectives for heavy duty engines with wet liners are to reduce friction loss and to lower the risks of cavitation damages, both of which can be greatly influenced by the piston-liner clearance and the design of the piston skirt. However, engine design optimization is difficult due to the nonlinear interactions between the key design variables and the design objectives, as well as the multi-physics and multi-scale nature of the mechanisms that are relevant to the design objectives.

The thermal behavior of the electric axle is an essential indicator which requires certain attention during the development process. Due to the complexity of heat generation mechanism and heat transfer boundary conditions, it is difficult to accurately predict the axle’s temperature, especially in real driving conditions. In this paper, a comprehensive 1D model is developed to simulate its heat transfer process effectively and accurately. The heat transfer model is developed based on the thermal network method, and the electric axle is divided into thermal mass according to its heat transfer characteristics. The heat generation model, which accounts for meshing loss, bearing loss, churning loss, and windage loss, exchanges heat flux and oil temperature information with the heat transfer model to take into account the effect of lubricating oil temperature on power loss.

As we move toward electrification in future mobility, weight and cost reduction continue to be priorities in vehicle development. This has led to continued interest in advanced molding processes and holistic design to enable polymer materials for demanding structural applications such as pickup truck beds. In addition to performance, it is necessary to continue to improve styling, functionality, quality, and sustainability to exceed customer expectations in a competitive market. To support development of a lightweight truck bed design, a cross-functional team objectively explored the latest materials and manufacturing technologies relevant to this application. In Phase 1 of this work, the team considered a variety of alternatives for each functional area of the bed, including thermoplastic and thermoset materials with a range of processing technologies.

For the design optimization of the electric bus body frame orienting frontal crash, considering the uncertainties that may affect the crashworthiness performance, a robust optimization scheme considering tolerance design is proposed, which maps the acceptable variations in objectives and feasibility into the parameter space, allowing for the analysis of robustness. Two contribution analysis methods, namely the entropy weight and TOPSIS method, along with the grey correlation calculations method, are adopted to screen all the design variables. Fifteen shape design variables with a relatively high impact are chosen for design optimization.

This paper presents a comprehensive analysis of emerging powertrain technologies for a wide spectrum of vehicles, ranging from light-duty passenger vehicles to medium and heavy-duty trucks. The study focuses on the anticipated evolution of these technologies over the coming decades, assessing their potential benefits and impact on sustainability. The analysis encompasses simulations across a wide range of vehicle classes, including compact, midsize, small SUVs, midsize SUVs, and pickups, as well as various truck types, such as class 4 step vans, class 6 box trucks, and class 8 regional and long-haul trucks. It evaluates key performance metrics, including fuel consumption, estimated purchase price, and total cost of ownership, for these vehicles equipped with advanced powertrain technologies such as mild hybrid, full hybrid, plug-in hybrid, battery electric, and fuel cell powertrains.

Threaded joints are considered the most basic of components. Although in use for over a century, significant problems still exist with their usage. Wheel bolt loosening in overloaded segments such as HD tippers and high-speed intercity buses poses a safety challenge for drivers, passengers, and pedestrians. Wheel nut loosening is a notable cause of service, fretting, and cracks in the mating components; contributing a significant chunk of warranty cost to the company. The need of the hour is to reinforce these joints while keeping resources at bay. This paper establishes a methodology for the evaluation and design of a safe wheel bolt joint interface including key parameters such as embedding, axial forces, and shear forces. It is necessary to obtain the minimum preload requirement for a wheel bolt joint to hold the clamped surfaces intact, which if not maintained otherwise would cause relative movement, play, shear load onto the bolt, and eventually failure.

Off-road diesel engines remain one of the most significant contributors to the overall oxides of nitrogen (NOX) inventory and the California Air Resources Board (CARB) has indicated that reductions of up to 90% from current standards may be necessary to achieve its air quality goals. In recognition of this, CARB has funded a program aimed at demonstrating emission control technologies for off-road engines. This program builds on previous efforts to demonstrate Low NOX technologies for on-road engines. The objective was to demonstrate technologies to reduce tailpipe NOX and particulate matter (PM) emissions by 90 and 75%, respectively, from the current Tier 4 Final standards. In addition, the emission reductions were to be achieved while also demonstrating a 5 to 8.6% carbon dioxide (CO2) reduction and remaining Greenhouse Gas (GHG) neutral with respect to nitrous oxide (N2O) and methane (CH4).

Toyota has developed a new 2.4L L4 turbo (2.4L-T) engine with 8AT and 1-motor hybrid electric powertrains for midsize pickup trucks. The aim of these powertrains is to fulfill both strict fuel economy and emission regulations toward “Carbon Neutrality”, while exceeding customer expectations. The new 2.4L L4 turbocharged gasoline engine complies with severe Tier3 Bin30/LEVIII SULEV30 emission regulations for body-on-frame midsize pickup trucks improving both thermal efficiency and maximum torque. This engine is matched with a newly developed 8-speed automatic transmission with wide range and close step gear ratios and extended lock-up range to fulfill three trade-off performances: powerful driving, NVH and fuel economy. In addition, a 1-motor hybrid electric version is developed with a motor generator and disconnect clutch between the engine and transmission.

Compared to passenger cars, commercial vehicles have relatively high fuel and energy consumption, relatively high average annual driving mileage, and a wide range of use. Therefore, energy-saving management of commercial vehicles is crucial. For multi-axle distributed pure electric drive commercial vehicles, a dynamic allocation control strategy for driving torque based on energy consumption optimization is proposed. First, the basic idea of the driving torque distribution control strategy was analyzed and a relevant mathematical model was established. Then, the offline optimization of the distribution coefficients of the driving torque for each axle was carried out through a genetic algorithm, and the entire vehicle driving force distribution strategy using the distribution coefficients as an online lookup table was determined.

The horizontal exhaust in trucks is preferred over the vertical exhaust stack since the cost of production is lower than that of the vertical stack. The horizontal exhaust also comes with lower fuel costs since the overall drag coefficient is lower than that of the vertical stack. However, since a horizontal exhaust exits into the underbody, it is essential to minimize the exit temperatures of the exhaust to keep component temperatures within design limits. In this study, a shape optimization is executed for the exhaust tip geometry to reduce exhaust exit temperatures while maintaining exhaust pressure by employing a computational fluid dynamics (CFD) workflow, using the geometry and morphing tool PowerDELTA®, coupled simulation approach between PowerFLOW® the flow solver and PowerTHERM® the thermal solver and Isight® for executing the optimization objectives. A good correlation is observed with experimental data for the baseline truck design.

Efficient transportation for carrying heavy loads is a common challenge across various applications, from supermarkets to industrial purposes. Conventional trolleys often fall short when loaded with heavy cargo, resulting in increased exertion and diminished productivity. Moreover, these challenges can adversely affect posture and lumbar spine health, especially for elder people and persons with cervical problems. There is a need for more user-friendly, ergonomic, and space-efficient solutions. This project addresses these challenges through an innovative design that encompasses various aspects of trolley functionality, including the study of comfort, wheel selection, and material considerations, drawing from ergonomic research. Multiple methods are employed to optimize the trolley’s dimensions to improve its overall performance. The trolley’s design features a collapsible basket for the transport of smaller-sized items and a base frame for larger goods and luggage.

In farm tractors, the available drawbar power, and Power Take-Off (PTO) power are generally lower than the engine power due to parasitic losses. These losses are caused by engine-driven auxiliary loads such as cooling fans, hydraulic pumps for power steering, alternators, etc. Minimizing these parasitic losses can increase the available drawbar power and PTO power, resulting in direct fuel savings by reducing fuel consumption. The continuous increase in fuel costs and the environmental impact of emitted gases from burned fuel into the atmosphere have necessitated the replacement of hydraulic power steering and mechanical fans with Electric Power Steering (EPS) and electric fans, respectively, to improve efficiency. The existing battery has been replaced with a higher capacity battery to provide power to the electric fan, electric power steering, and other electrical components.

Tractor is primarily used for Haulage and agricultural applications due to this high tractive effort. A tractor usage has been increased in recent times for its wide range of implement applications. Considering environmental factors and sustainability, restrictions are set on the Tractor emissions. This brings new challenge in the Tractor industry to reduce the carbon footprint. Conventional casting process involves preparation of die & mold, material removal and machining in the final stage to get the desired final product. Alternatively Additive Manufacturing Process (AMP) helps in creation of lighter and stronger parts by adding material layer by layer. By saving the material, weight of the overall Tractor is reduced which helps in reducing carbon footprint. But the disadvantage of this process is the limited availability and high cost of AMP material and lack of infrastructure/skill set for operation handling.

In order to meet future emission targets and to achieve better fuel efficiency, closed loop air mass control strategies have become essential across all vehicle segments. Closed loop airmass control mandates measuring fresh air mass entering the engine combustion chamber. However, in Naturally Aspirated (NA) engines, while measuring airmass using conventional air mass sensors (AMS), heavy pulsations in the Air-intake results in errors which would impact closed loop airmass control and lead to inconsistencies in emissions. To address this issue, we studied different approaches using AMS with Resonator, differential pressure sensor across the intake air filter and Lambda based airmass control. Based on this empirical study we found that modelling air mass with differential pressure sensor (Delta-P) using Bernoulli’s principle (Flow rate ∝ √Differential pressure) results in higher accuracies compared to conventional methods.