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On the path to decarbonizing road transport, electric commercial vehicles will play a significant role. The first applications were directed to the smaller trucks for distribution traffic with relatively moderate driving and range requirements, but meanwhile, the first generation of a complete portfolio of truck sizes is developed and available on the market. In these early applications, many compromises were accepted to overcome component availability, but meanwhile, the supply chain can address the specific needs of electric trucks. With that, the optimization towards higher usability and lower costs can be moved to the next level. Especially for long-haul trucks, efficiency is a driving factor for the total costs of ownership. Besides the propulsion system, all other systems must be optimized for higher efficiency. This includes thermal management since the thermal management components consume energy and have a direct impact on the driving range.

Heavy-duty vehicles equipped with polymer electrolyte membrane fuel cells (PEM-FC) are an environmentally friendly alternative to vehicles powered by internal combustion engines. A major challenge for heavy-duty fuel cell vehicles is the potential cooling deficit under high load conditions at high ambient temperatures. To solve this problem, a spray cooling system can be utilized, in which liquid water is sprayed on the main cooler at the front end of the vehicle. The evaporation of the sprayed liquid water results in an increased cooling power. In this paper, the recovery of liquid water within the cathode loop of a mobile PEM-FC system is presented and discussed. For this purpose, three different topologies of the cathode subsystem of the PEM-FC are investigated for recovering liquid water directly from the fuel cell exhaust gas. To obtain liquid water, vapor in the exhaust gas is cooled below the saturation temperature in an additional heat exchanger.

Understanding left-turn vehicle-pedestrian accident mechanisms is critical for developing accident-prevention systems. This study aims to clarify the features of driver behavior focusing on drivers’ gaze, vehicle speed, and time to collision (TTC) during left turns at intersections on left-hand traffic roads. Herein, experiments with a sedan and light-duty truck (< 7.5 tons GVW) are conducted under four conditions: no pedestrian dummy (No-P), near-side pedestrian dummy (Near-P), far-side pedestrian dummy (Far-P) and near-and-far side pedestrian dummies (NF-P). For NF-P, sedans have a significantly shorter gaze time for left-side mirrors compared with light-duty trucks. The light-duty truck’s average speed at the initial line to the intersection (L1) and pedestrian crossing line (L0) is significantly lower than the sedan’s under No-P, Near-P, and NF-P conditions, without any significant difference between any two conditions.

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

Planning for charging in transport missions is vital when commercial long-haul vehicles are to be electrified. In this planning, accurate range prediction is essential so the trucks reach their destinations as planned. The rolling resistance significantly influences truck energy consumption, often considered a simple constant or a function of vehicle speed only. This is, however, a gross simplification, especially as the tire temperature has a significant impact. At 80 km/h, a cold tire can have three times higher rolling resistance than a warm tire. A temperature-dependent rolling resistance model is proposed. The model is based on thermal networks for the temperature at four places around the tire. The model is tuned and validated using rolling resistance, tire shoulder, and tire apex temperature measurements with a truck in a climate wind tunnel with ambient temperatures ranging from -30 to 25 °C at an 80 km/h constant speed.

In 2022 in the UK, the transport sector was the largest single contributing sector to greenhouse gas emissions, responsible 34% of all territorial carbon dioxide emissions [1]. In the UK there is growing uptake in zero emission powertrain technologies, with the most promising variants based on battery electric or hydrogen fuel cell electric configurations. Given the limited number of fuel cell electric buses currently in operation in Europe, vehicle models and simulations are one of the few methods available to estimate energy consumption and provide the necessary increased confidence in operating range. This paper investigates the impact of route characteristics, thermal demand and coefficient of performance of different heat source configurations on the operational energy consumption of fuel cell electric buses. Using a MATLAB/Simulink model, the total energy demand of a vehicle operating in different route/elevation profiles is considered.

Considering the current trend towards the electrification of commercial vehicles, the development of Beam eAxle solutions has become necessary. The utilization of an electric drive unit in heavy-duty solid axle-based commercial vehicles presents unique and demanding challenges. These include the necessity for elevated peak and continuous torque while meeting packaging constraints, structural integrity requirements, and extended service life. One such solution was developed by BorgWarner to address these challenges. This paper offers a comprehensive overview of the design and development process undertaken for this Dual Motor Beam eAxle system. This includes the initial comparison of various eAxle solutions, the specifications of components selected for this design, and the initial results from dyno and vehicle development.

In order to study the influence of engine silicone oil fan clutch on the performances of engine cooling system under different control strategies, a model of engine cooling system for light truck is established. The working characteristics of the silicone oil clutch and the measured performance parameters of the cooling system components are taken into account in our proposed model. Modeling methods for different silicone oil fan control strategies are also given. Using the established model, the performance parameters under different vehicle speeds, such as coolant temperature of engine outlet and power consumption of cooling fan, are calculated and analyzed. The in-suite measurement of the engine cooling system is carried out to get the temperatures of engine coolant inlet and outlet from engine ECU. The model is validated by the comparison between the calculation and the measured results.

By installing an automated mechanical transmission (AMT) on heavy-duty vehicles and developing a reasonable shift strategy, it can reduce driver fatigue and eliminate technical differences among drivers, improving vehicle performance. However, after detaching from the experience of good drivers, the current shifting strategy is limited to the vehicle state at the current moment, and cannot make predictive judgment of the road environment ahead, and problems such as cyclic shifting will occur due to insufficient power when driving on the ramp. To improve the adaptability of heavy-duty truck shift strategy to dynamic driving environments, this paper first analyzes the shortcomings of existing traditional heavy-duty truck shift strategies on slopes, and develops a comprehensive performance shift strategy incorporating slope factors. Based on this, forward-looking information is introduced to propose a predictive intelligent shift strategy that balances power and economy.

To better understand the technical challenges of commercial vehicle electrification, BorgWarner converted a production Internal Combustion Engine (ICE) medium duty truck into a fully electrified vehicle. The resulting vehicle includes a newly developed dual-motor rear Beam eAxle driven by a pair of high-performance silicon carbide (SiC) inverters, an 800V battery system, and a new thermal management system customized for the electric vehicle. This paper will detail the conversion process along with the key components involved in the build. The resulting performance of the fully electrified commercial vehicle will be presented in comparison to the original production vehicle. The primary aim is to outline what is entailed in an electric vehicle conversion and to share the learnings gained throughout this build and development process.

Internal combustion engines are becoming ever more efficient as mankind seeks to mitigate the effects of climate change while still maintaining the benefits that a mechanized society has brought to the global economy. As peak values, mass production spark-ignition engines can now achieve approximately 40% brake thermal efficiency and heavy-duty truck compression-ignition engines can approach 50%. While commendable, the unfortunate truth is that the remainder gets emitted as waste heat and is sent to the atmosphere to no useful purpose. Clearly, if one could recover some of this waste heat for beneficial use then this is likely to become important as new means of mitigating fossil CO2 emissions are demanded. A previous study by the authors has identified that the closed Joule cycle (or complications of it beginning to approximate the closed Ericsson cycle) could reasonably be developed to provide a practical means of recovering exhaust heat when applied to a large ship engine.

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.

Determining occupant kinematics in a vehicle crash is essential when understanding injury mechanisms and assessing restraint performance. Identifying contact marks is key to the process. This study was conducted to assess the ability to photodocument the various fluids on different vehicle interior component types and colors with and without the use of ultraviolet (UV) lights. Biological (blood, saliva, sweat and skin), consumable and chemical fluids were applied to vehicle interior components, such as seatbelt webbing, seat and airbag fabrics, roof liner and leather steering wheel. The samples were photodocumented with natural light and UV light (365 nm) exposure immediately after surface application and again 14 days later. The review of the photos indicated that fabric type and color were important factors. The fluids deposits were better visualized on non-porous than porous materials. For example, blood was better documented on curtain airbags than side or driver airbags.

Methanol is one of the most promising fuels for the decarbonization of the off-road and transportation sectors. Although methanol is typically seen as an alternative fuel for spark ignition engines, mixing-controlled compression ignition (MCCI) combustion is typically preferred in most off-road and medium-and heavy-duty applications due to its high reliability, durability and high-efficiency. In this paper, the potential of using ignition enhancers to enable methanol MCCI combustion was investigated. Methanol was blended with 2-ethylhexyl nitrate (EHN) and experiments were performed in a single-cylinder production-like diesel research engine, which has a displacement volume of 0.83 L and compression ratio of 16:1. The effect of EHN has been evaluated with three different levels (3%vol, 5%vol, and 7%vol) under low- and part-load conditions. The injection timing has been swept to find the stable injection window for each EHN level and load.

A major concern for a high-power density, heavy-duty engine is the durability of its components, which are subjected to high thermal loads from combustion. The thermal loads from combustion are unsteady and exhibit strong spatial gradients. Experimental techniques to characterize these thermal loads at high load conditions on a moving component such as the piston are challenging and expensive due to mechanical limitations. High performance computing has improved the capability of numerical techniques to predict these thermal loads with considerable accuracy. High-fidelity simulation techniques such as three-dimensional computational fluid dynamics and finite element thermal analysis were coupled offline and iterated by exchanging boundary conditions to predict the crank angle-resolved convective heat flux and surface temperature distribution on the piston of a heavy-duty diesel engine.

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).

The transition towards electrification in commercial vehicles has received more attention in recent years. This paper details the conversion of a production Medium-Duty class-5 commercial truck, originally equipped with a gasoline engine and 10-speed automatic transmission, into a battery electric vehicle (BEV). The conversion process involved the removal of the internal combustion engine, transmission, and differential unit, followed by the integration of an ePropulsion system, including a newly developed dual-motor beam axle that propels the rear wheels. Other systems added include an 800V/99 kWh battery pack, advanced silicon carbide (SiC) inverters, an upgraded thermal management system, and a DC fast charging system. A key part of the work was the development of the propulsion system controls, which prioritized drivability, NVH suppression, and energy optimization.

Reconstruction of inline crashes between vehicles with a low closing speed, so-called “low speed” crashes, continues to be a class of vehicle collisions that reconstructionists require specific methods to handle. In general, these collisions tend to be difficult to reconstruct due primarily to the lack of, or limited amount of, physical evidence available after the crash. Traditional reconstruction methods such as impulse-momentum (non-residual damage based) and CRASH3 (residual damage based) both are formulated without considering tire forces of the vehicles. These forces can be important in this class of collisions. Additionally, the CRASH3 method depends on the use of stiffness coefficients for the vehicles obtained from high-speed crash tests. The question of the applicability of these (high-speed) stiffness coefficients to collisions producing significantly less deformation than experimental crashes on which they are generated, raises questions of the applicability.

For commercial vehicles, reliability is key since the vehicle is typically linked to the daily earnings of the owner. To ensure continuous vehicle operation, early diagnostics of critical issues and proactive maintenance are important. However, an electric vehicle is a complex and dynamic system consisting of numerous components interacting with each other and with external environments such as road conditions, traffic, weather, and driving behavior. Thus, vehicle operation and performance are highly contextual and for identifying an abnormal operation (diagnostics) the solution must consider the conditions under which it is driven. To address this, the paper proposes an AI-based digital twin of an electric three-wheeler vehicle. TabNet a deep-learning based model is used to learn and generate near-ideal vehicle behavior. The focus of the paper is motor subsystem. The model is trained using appx 200 vehicles first 1500 km driven data.

Commercial vehicle are exposed to harsh environment conditions like dust, mud, wind, rain, extreme sun and winter throughout. Apart from white goods and other conventional loading these vehicles also used in applications which involve Handling of Dirty Loads, Construction Raw materials, Mining Industry etc. which leads to fast deterioration of Interiors. Also, in most cases drivers are not the owners. Hence due to high cost of Cleaning at dealerships and low Product maintenance awareness amongst Commercial Vehicle Users, on Road Washing & Cleaning by riverside is common practice which leads to early deterioration of Interior trims. This paper deals with the retention of newness of soft trim parts such as headliner, wall trims and carpets. Causes of product deterioration and attributes which influence newness like product appeal, NVH, perceived quality, environmental impact, geometry retention over time etc. have been discussed in detail.