Search Results

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.

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.

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.

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.

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.

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 automotive world is moving towards electric powertrain systems. The electric powertrain systems have emerged as a promising alternative to the conventional powertrain system. The performance of electric vehicle is highly dependent on operating temperature of electric and electronic components of the vehicle. All power electronics and electric components in EV generate heat during operation and it must be removed to prevent overheating of components. Higher temperatures raise safety concerns whereas lower temperatures deteriorate the performance of power electronics & electric components. These power electronics & electrical components perform efficiently and safely if operated within certain temperature range. This paper presents an advanced efficient cost-effective thermal technique for small commercial electric vehicle (SCEV) to improve the performance & life of major electric components.

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.

Abstract Due to the incoming phase out of fossil fuels from the market in order to reduce the carbon footprint of the automotive sector, hydrogen-fueled engines are candidate mid-term solution. Thanks to its properties, hydrogen promotes flames that poorly suffer from the quenching effects toward the engine walls. Thus, emphasis must be posed on the heat-up of the oil layer that wets the cylinder liner in hydrogen-fueled engines. It is known that motor oils are complex mixtures of a number of mainly heavy hydrocarbons (HCs); however, their composition is not known a priori. Simulation tools that can support the early development steps of those engines must be provided with oil composition and properties at operation-like conditions. The authors propose a statistical inference-based optimization approach for identifying oil surrogate multicomponent mixtures. The algorithm is implemented in Python and relies on the Bayesian optimization technique.

Global climate change is a major concern worldwide and most refrigerants which are being used today are Hydro-fluorocarbon (HFC), which are potent green house gases. With the rising popularity of electric vehicles, there has been an increased demand for effective and efficient cooling system, not only for cabin cooling for bus but for battery pack Thermal Management Systems also. This has led to an increase in the refrigerant amount and it is even more in case of commercial electric vehicle. Alternate refrigerant with low global warming potential like R1234yf (GWP = 4), Co2 (GWP = 1), R- 152a (GWP = 124) emerged to cater this challenge; But due to their high cost, risk regarding flammability and relative performance, some researchers found secondary loop cooling system as the next possible solution.

Enhancing truck-sensor modularity Kodiak Robotics' fifth-generation sensor stack and new SensorPods boost sensor and GPU performance and improve power efficiency. Bosch high on hydrogen The supplier is committed to all facets of the H2 economy as volume production of its power module kicks off for Nikola's Class 8 fuel-cell truck. Constructing bus structures with stainless steel Outokumpu and collaborators show a possible weight reduction of up to 35% by using high-strength stainless steel in place of carbon steel. Volta Zero is U.S. bound The startup plans to apply lessons learned in Europe to the U.S. market, bringing a "small fleet" of electric trucks for potential customers by the end of the year.

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.

The influence of engine cooling fan on the working state of engine cooling system under different driving forms and control strategy is studied, and a simulation model of engine thermal management system of a commercial vehicle is established. The model takes into account the measured performance parameters of the cooling system components, the gear shift logic of the transmission, the effect of vehicle speed on the airflow rate of the radiator, and proposes a modeling method for different cooling fan driving forms. The performance parameters such as engine outlet coolant temperature and corresponding cooling fan speed under different vehicle speeds and engine loads are calculated and analyzed by using the established model. The road measurement test of the engine thermal management system under the same working condition was carried out to read the relevant data from the engine ECU and confirm the reliability of the data.

In the future, conventional powertrains will increasingly be supplied by sustainable energy sources. Long-haul freight transport requires efficient energy storage and the ability to refuel quickly. For this reason, hydrogen-powered PEM fuel cells are being discussed as a future energy source for long-distance vehicles. However, there are numerous challenges in packaging, system cooling and service life. Above all, the dissipation of the fuel cell’s heat losses places high demands on the design of the cooling system due to the relatively low operating temperature. In the presented study, a complete generic drive train of a long-distance commercial vehicle was set up within a suitable simulation environment to investigate the required sizes of the fuel cell stack, the HV battery, the hydrogen tanks, and the cooling circuit.

Heating, ventilation and air conditioning systems play a crucial role in our day-to-day activities. With rise in global warming, leading to climate change, HVAC unit is the need of the hour. With average temperatures on the rise, it is quite imperative that the unit provides better thermal comfort to the passengers. Off-road vehicles like tractor, is also no exclusion. Tractor drivers have to experience adverse weather conditions out in the open field. Thus it is quite fundamental that sufficient airflow reaches every point inside the driver cabin, ensuring proper cool-down. To ensure proper distribution of airflow inside the cabin, optimization of HVAC unit needs to be properly carried out. The present study shows how an HVAC of an off-road vehicle is properly optimized with the help of Computational Fluid Dynamics. STAR-CCM+ v2021.2.1 is used as solver for the simulation.