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In the past decade, the transportation industry has witnessed a rapid transition from conventional fossil fuels to electric power. This shift has spurred diverse electrification initiatives spanning various vehicle categories, including E-cycles, 2-wheelers, 3-wheelers, cars, and commercial vehicles. Central to these road transport vehicles are essential components such as battery systems, electric motors, and field-oriented controllers. These controllers’ interface with the vehicle control unit, optimizing motor performance across diverse operational conditions. The reliability of the core motor and controller system is of paramount importance, ensuring seamless operation throughout its life. Notably, certain applications, like 2-wheeler, demand customized designs with compact configurations to save space and eliminate excessive wiring. This necessitates heightened reliability due to limited serviceability within these confined designs.

With the shift towards electric vehicles in the present world scenario, pick-up trucks play a significant role in providing much commercial and private transportation of goods. Focusing on the application of food delivery by electric pick-up trucks, by using solar powered thermoelectric generators, the food temperature and texture can be maintained that increases customer satisfaction. The power generated by the solar panels can be utilized for this purpose without increasing the load on the battery. Also, the additional solar energy that can be used to charge the battery is a way to increase efficiency and decrease the dependency on non-renewable sources. In this basic research, the electric powertrain mathematical model is developed in MATLAB and Simulink. Using the solar panel model the size of the solar panel needed in order to acquire the power needed to run the thermoelectric generator and charge the battery is calculated.

The brake systems are given top priority by automotive OEMs in the development of medium and heavy commercial trucks and buses, which can carry increased loads. When trucks and buses are travelling at high speeds or crossing downhill, during braking operations, the friction faces (brake drum and liner) experience a significant rise in temperature due to the conversion of kinetic energy into heat energy within seconds. This lowers the friction coefficient at the interface, resulting in distortions, thermal cracks, hub grease burning, and overheating. Drum brake system designs must be improved and optimized to dissipate more heat from the brake drum assembly and prevent brake failure. Nowadays advance transient numerical simulations assist in the design, development and optimization of the brake system to visualize 3D flow physics and temperature variations throughout the brake duty cycles. In the current study, different Cases of drum brakes to improve cooling efficiency are evaluated.

An accurate estimate of vehicle speed is essential for optimal anti-lock braking system (ABS) calculations. Currently, most vehicles including heavy-duty class 8 trucks mainly rely on wheel speed sensors (WSS) to estimate velocity. However, as soon as braking is applied, WSS become inaccurate for determining the velocity due to the longitudinal slip developed in the tires. Using the inertial measurement unit (IMU) to estimate vehicle speed allows for its use in conjunction with the WSS to accurately calculate the slip ratio at each tire. These slip ratio values can then be used as the main control variable in the ABS algorithm to utilize the grip available more fully at each tire, to improve stopping distance and controllability. A steady state braking analysis model is developed and validated against Federal Motor Vehicle Safety Standards (FMVSS) 121 60-0 mph stopping distance data for a loaded class 8 tractor semi-trailer combination.

The design of brake system specification is an iterative process, where repeated calculations need to be performed to achieve the target brake performance and finalize the system parameters. Hence there is a need for a methodology to arrive at a set of optimized brake system parameters which can in turn reduce the brake system definition time. In this paper, the brake system dynamics of a passenger/commercial vehicle is mathematically modelled (divided into two parts i) foundation brake model ii) brake apply system model) and uses genetic algorithm to optimize the system parameters. The objective function maximizes the vehicle deceleration and arrives at the target brake feel which includes brake pedal effort and brake pedal travel. When compared to conventional method of brake design which uses iterative approach, this method has the capability to arrive at the optimized results at a faster rate.

During validation of a new brake lining on a light duty truck application, the brake rotor exhibited high lateral runout on the friction surfaces. As the engineering team investigated the issue more carefully, they noticed the rotor lateral runout was also changing from revolution to revolution. The team ran testing on multiple light pickup vehicles and found differences in the amount of rotor runout variation. The rotor lateral runout and runout variation can cause vibration and pulsation of the passenger seat and the steering wheel. To identify the root cause of the high level of rotor lateral runout and runout variation, measurement data was collected and analyzed from the vehicle level test. During further analysis, some of the runout variation corresponded to a wheel bearing internal frequency. The bearing internal geometry was studied to confirm what factors affected the runout variation.

Electrification of off-road vehicle powertrains can increase mobility, improve energy efficiency, and enable new utility by providing high amounts of electrical power for auxiliary devices. These vehicles often operate in extreme temperature conditions at low ground speeds and high power levels while also having significant cooling airpath restrictions. The restrictions are a consequence of having grilles and/or louvers in the airpath to prevent damage from the operating environment. Moreover, the maximum operating temperatures for high voltage electrical components, like batteries, motors, and power-electronics, can be significantly lower than those of the internal combustion engine. Rejecting heat at a lower temperature gradient requires higher flow rates of air for effective heat exchange to the operating environment at extreme temperature conditions.

This study investigates the effect of exhaust rebreathe (RB) on the low-load regime of a Gasoline Compression Ignition (GCI) heavy-duty engine. For this engine, a custom-designed cam profile with a second exhaust event occurring during the intake stroke was tested under different experimental load and speed conditions. First, the study focuses on the of rebreathe on combustion and gas exchange processes in the low load range of 240-300 kPa BMEP at three key speeds: 820, 1200, and 1600 rpm. Then, a general analysis of the thermal management of this technology is assessed in the low-load map, evaluating the impact on turbine outlet temperature and after-treatment performance related to the conversion rates for NOx and total hydrocarbons (THC). The detailed analysis revealed an increase of around 9% in the trapped residuals for the RB operation, translating to an in-cylinder temperature increase and raising the exhaust temperature up to 50°C.

The objective of the project was to compare the fuel consumption of a prototype hybrid electric CNG truck with that of two trucks: a CNG truck and a diesel truck for the similar market and operating conditions. The tests were conducted on a test route representative of the conditions encountered by these vehicles in normal driving operations. The test route length was 276 km with a maximum altitude difference of 374 m. The test route had four sections, including a hilly section with a length of 88 km. The result of the comparison between the two CNG trucks was expressed as fuel savings of CNG in percentage. The fuel consumption of the diesel truck was accurately measured using the gravimetric method. The hybrid electric CNG truck showed average fuel savings of 3.6% and demonstrated up to 7.7% in savings for the entire trip compared to the CNG truck.

To meet emission targets, alternative fuels are increasingly vital in reducing exhaust emissions. One effective and cost-efficient method to achieve sustainable emission reductions is through hydrogen (H2) operated modern combustion engines. In line with the energy transformation, similar to Germany’s “Energiewende”, industries need CO2-neutral solutions, with special importance in the mobility sector. Hydrogen, as a carbon-free fuel, presents a viable alternative to conventional options and has been subject to extensive scientific research. The development of hydrogen combustion engines is still in the conceptualization phase. This study focuses on a direct-injection (DI) combustion engine from a commercial vehicle converted from diesel DI-compression ignition (CI) to hydrogen DI-spark ignition (SI) operation. The main objectives were to validate simulation models using 1D and 3D simulation software and to conduct a comprehensive analysis of engine operation.

It is a kind of range-extended electric vehicle (REEV) using a lithium-titanate battery but can use as a hybrid electric vehicle (HEV). The proposal aims to use a battery of just the right size as the power core of the system to help the engine maintain maximum efficiency. The performance of the lithium-titanate battery is between battery and capacitor. It also has anti-overcharge, anti-over-discharge, and high-temperature stability, suitable for use in vehicles. The engine only works when the SOC is low, independent of the power requirement. By increasing the battery to the extent that it can supply the motor, the system directly suppresses the influence of power on the engine speed, and the engine runs completely at the optimal BSFC. The lithium-titanate battery has a rate of up to 5C, which directly shortens the working time of the engine. The battery will not be as large as an EV, and the vehicle still has a certain amount of pure electric mileage.

Estimated engine torque is an important parameter used by automotive systems for automated transmission and clutch control. Heavy-duty engine and transmission manufacturers widely use SAE J -1939 based ECU torque calculation based on mass air/fuel flow steady state maps created during calibration of the engine for this purpose. As an alternative, to enhance the accuracy of this important control variable, a virtual flywheel torque sensor (VFTS) was developed. It measures the engine torque based on the harmonics of the instantaneous flywheel speed signal. Initial dynamometer testing showed the VFTS estimated torque values exhibited a maximum inaccuracy of 12% of the actual measured torque over the range of conditions tested. In this paper we report the results of on road truck testing of the VFTS. A loaded heavy truck with a gross vehicle weight rating of 80,000 pounds was used.

This study intends to improve the design of front axles for heavy commercial vehicles, with a major goal of reducing weight while maintaining mechanical strength. The front axle is critical in supporting the weight of the vehicle and facilitating steering while effectively absorbing shocks generated by differences in road surfaces. To achieve these requirements, a front axle beam that minimizes weight, fuel consumption, and stress on the load-carrying member must be designed. In this work, finite element analysis (FEA) techniques are used using CATIA software to assess the structural and mechanical attributes of several front axle designs. The purpose is to pick the best front axle shape depending on specific load situations and driving torque needs. The influence of alternative component shapes on stress and strain distribution is evaluated using surface changes and ANSYS Workbench numerical simulation software.

The simultaneous testing of energy consumption and emissions of heavy-duty hybrid Dump Truck was carried out based on the chassis dynamometer. The effects of different working conditions on energy consumption and emissions were analyzed. A Parallel hybrid dump truck with diesel in phase VI of China was selected to test the energy consumption and emissions by using engine mode and hybrid mode respectively under the conditions of CHTC-D and C-WTVC. The results show that compared with pure engine mode, CHTC-D condition of heavy hybrid dump truck has higher energy saving potential than C-WTVC condition, and the energy saving rates are 23.8 % and 18.3 % respectively; For the hybrid mode, the specific emissions of CO2 under C-WTVC and CHTC-D conditions were 1071 g/km and 1010 g/km, respectively.

The Chinese government and industries have proposed strategic plans and policies for automotive renewable-energy transformation in response to China’s commitments to peak the national carbon emissions before 2030 and to achieve carbon neutrality by 2060. We thus analyze the evolution of carbon emissions from the vehicle fleet in China with our data-driven models based on these plans. Our results indicate that the vehicle life-cycle carbon emissions are appreciable, accounting for 8.9% of the national total and 11.3% of energy combustion in 2020. Commercial vehicles are the primary source of automotive carbon emissions, accounting for about 60% of the vehicle energy cycle. Among these, heavy-duty trucks are the most important, producing 38.99% of the total carbon emissions in the vehicle operation stage in 2020 and 52.18% in 2035.

Research on the energy consumption and emission of heavy-duty hybrid electric buses was carried out based on the chassis dynamometer. The effects of different working conditions on the driving range, electric power, energy consumption and emissions were analyzed. The testing process included three phases, the pure electric driving range phase, energy adjustment phase and electrical energy balance phase. The testing cycle includes CHTC-B (China Heavy-duty commercial vehicle Test Cycle for Bus) and CCBC (Chinese typical City Bus Cycle). The energy consumption and emissions were measured simultaneously during the test. The results show that the driving range of CHTC-B method is less than that of constant velocity method and the power consumption of CHTC-B method is higher than that of constant velocity method. The energy consumption of CHTC-B is 20% higher than that of CCBC.

Since there are no typical working conditions in the field of engineering vehicles, this paper implements the construction of driving conditions based on Markov stochastic model and energy management strategy of a multi-mode hybrid mine dump truck based on dynamic programming algorithm under this background. Firstly, on the basis of existing vehicle driving data, we calculate the velocity transfer probability matrix with the Markov stochastic model, and then construct the vehicle driving condition after predicting the velocity. The constructed working conditions are applied to the energy management strategy of dynamic programming algorithm to calculate the energy consumption of the hybrid mining truck. It is proved that this construction method can be applied to the hybrid electric mine dump truck, and the DP based energy management strategy has lower energy consumption.

The fuel economy and emission of the hybrid vehicle depend largely on the selected engine. And the dedicated hybrid engine (DHE) can be controlled to operate in the optimal operating range because DHE can be decoupled from the vehicle transmission system. The main purpose of this paper is to improve the thermal efficiency of the diesel engine under common operating conditions combined with high compression ratio (CR) and early or late intake valve closing (IVC) angle. According to the vehicle road spectrum data, the optimal operating range of the engine is determined to be 1200-1400 rpm and 70%-90% load. Then CR and IVC angle are optimized by using the calibrated one-dimensional thermodynamic model of the engine under limited peak combustion pressure (Pmax). The results show that the adjustment of IVC angle and CR can control the thermal state at the end of compression stroke. The combination of CR and IVC angle can achieve the optimal fuel consumption improvement.

To many, a digital twin offers “functionality,” or the ability to virtually rerun events that have happened on the real system and the ability to simulate future performance. However, this requires models based on the physics of the system to be built into the digital twin, links to data from sensors on the real live system, and sophisticated algorithms incorporating artificial intelligence (AI) and machine learning (ML). All of this can be used for integrated vehicle health management (IVHM) decisions, such as determining future failure, root cause analysis, and optimized energy performance. All of these can be used to make decisions to optimize the operation of an aircraft—these may even extend into safety-based decisions.

The Homogeneous Charge Compression Ignition (HCCI) combustion eliminates the issues of higher particulate matter and nitrogen oxides emissions that prevail in the traditional compression ignition (CI) combustion mode. The complete replacement of traditional fuels with renewable fuels for internal combustion engines is challenging because significant infrastructure changes in the production and delivery systems are required to ensure renewable fuel availability and economic feasibility. Thus, the use of renewable acetone blended with traditional gasoline has been proposed in the present study to smoothen the transition from the traditional CI to the HCCI engines. HCCI experiments were performed in a light-duty diesel engine at 1500 rpm rated speed. By varying the volumetric proportion of the acetone in the gasoline from 20% to 40%, the HCCI engine load range from 20%-60% was achieved, significantly higher than the limited diesel HCCI load range of 20%-38%.