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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.

Autonomous Driving is being utilized in various settings, including indoor areas such as industrial halls. Additionally, LIDAR sensors are currently popular due to their superior spatial resolution and accuracy compared to RADAR, as well as their robustness to varying lighting conditions compared to cameras. They enable precise and real-time perception of the surrounding environment. Several datasets for on-road scenarios such as KITTI or Waymo are publicly available. However, there is a notable lack of open-source datasets specifically designed for industrial hall scenarios, particularly for 3D LIDAR data. Furthermore, for industrial areas where vehicle platforms with omnidirectional drive are often used, 360° FOV LIDAR sensors are necessary to monitor all critical objects. Although high-resolution sensors would be optimal, mechanical LIDAR sensors with 360° FOV exhibit a significant price increase with increasing resolution.

The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering.

Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving style based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel.

Increasingly stringent regulations relating to the emissions of passenger cars and commercial vehicles demand alternative powertrain technologies in order to effectively achieve the climate targets. Hydrogen can play a crucial role as alternative energy carrier regarding the EU targets for CO2-neutral mobility towards 2050. Therefore, it represents a reasonable choice not only for fuel cell powered vehicles, but also for fueling internal combustion engines (ICE). This paper focuses on the numerical investigation of high-pressure hydrogen injection and the mixture formation inside a high-tumble ICE with a conventional liquid fuel injector for passenger cars. Since the traditional 3D-CFD approach of simulating the inner flow of an injector requires a very high spatial and temporal resolution, the enormous computational effort, especially for full engine simulations, is a big challenge for an effective virtual development of modern ICEs.

Commercial vehicle powertrain is called to respect a challenging roadmap for CO2 emissions reduction, quite complex to achieve just improving technologies currently on the market. In this perspective alternative solutions are taking interest, and the use of green H2 as fuel for ICE is considered a high potential solution with fast and easy adoption. To assure the required low engine out NOx emission to fulfill future legislations the engine should be operated with lean air fuel rations all over the engine map. A challenge following this strategy is to supply sufficient boost pressure for sufficient air mass flow rate to target same power output as the diesel engine. At the same time the transient response improvement is the key to keep NOx emission low also during transient engine operation. The analysis presented in this paper will show and quantify the positive impact that a supercharger has on both the above mentions problems.

Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based operational strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based Control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by validated Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.

As part of the safety validation of advanced driver assistance systems (ADAS) and automated driving (AD) functions, it is necessary to demonstrate that the frequency at which the system exhibits hazardous behavior (HB) in the field is below an acceptable threshold. This is typically tested by observation of the system behavior in a field operational test (FOT). For situations in which the system under test (SUT) actively intervenes in the dynamic driving behavior of the vehicle, it is assessed whether the SUT exhibits HB. Since the accepted threshold values are generally small, the amount of data required for this strategy is usually very large. This publication proposes an approach to reduce the amount of data required for the evaluation of emergency intervention systems with a state machine based intervention logic by including the time periods between intervention events in the validation process.

The pace of innovations in battery development is revolutionizing the landscape and opportunities for energy storage applications leading to a stronger market segmentation enabling a better suitability to fulfill specific application requirements. For automotive applications, several approaches to increase energy densities, to improve fast charging performance, and to reduce cost on a pack level are considered. Among them, a promising example is the direct integration of battery cells into the battery pack (Cell-to-pack; CTP) or vehicle (Cell-to-chassis, CTC) to increase energy densities and to reduce costs, as already commercialized by Tesla, CATL and others. In the pack development, especially Asian players are one of the frontrunners, where e.g., hybrid cell battery systems with a mixture of cells with different cathode chemistries as introduced by NIO, are experiencing a high interest of the market.

Today’s engines used in Agriculture, Mining and Construction are designed for robustness and cost. Here, the Diesel powertrain is the established mainstream solution, offering long operation times without refueling at any desired power rating. In view of the steps towards Carbon Neutrality by 2050 this segment of the Transportation Sector needs to reduce its CO2 emissions. Currently, the EU and US emissions legislations (EU Stage V / EPA Tier4) do not include a CO2 reduction scheme but is expected to change with the next update towards EU Stage VI / EPA Tier5 coming into effect 2030 and after. Larger power and operation range still require the use of renewable, liquid fuels or hydrogen. The cost-up of such fuels could be counterbalanced by more efficient engines in combination with a hybridized powertrain.

Authors : Thomas ANTOINE, Christophe THEVENARD, Pierrick BOTTA, Jerome DESTREE, Alain Le Quenven Future noise emission limits for passenger car are going to lower levels by 2024 (Third phase of R51-03, with a limit of 68dBA for the pass by noise) –Social cost of noise for France in 2021, shows clearly that the dominant source of noise pollution is indeed road traffic (81 Bn€ for a total of 146 Bn€) This R51 regulation is meant to lower the noise pollution from road traffic, however when looking closer to the sound source and their contributions, in particular the tire/road noise interaction, the environmental efficiency of this regulation is questionable. Indeed: Tire/Road interaction involves tires characteristics, that are constrained by an array of specification for energy efficiency, safety (wet grip, braking, etc…) and it has been proven that there is a physical limit to what could be expected from the tire as far as tire/road interaction noise is concerned.

For battery-electric vehicles (BEVs), the climate control and the driving range are crucial criteria in the ongoing electrification of automobiles in Europe towards the targeted carbon neutrality of the automotive industry. The thermal management system makes an important contribution to the energy efficiency and the cabin comfort of the vehicle. In addition to the system architecture, the refrigerant is crucial to achieve high cooling and heating performance while maintaining high efficiency and thus low energy consumption. Due to the high efficiency requirements for the vehicle, future system architectures will largely be heat pump systems. The alternative refrigerant R-474A based on the molecule R-1132(E) achieved top performance for both parameters in various system and vehicle tests.

Composite materials, pioneered by aerospace engineering due to their lightweight, strength, and durability properties, are increasingly adopted in the high-performance automotive sector. Besides the acknowledged composite components’ performance, enabled lightweighting is becoming even more crucial for energy efficiency, and therefore emissions along vehicle use phase from a decarbonization perspective. However, their use entails energy-intensive and polluting processes involved in raw material production, in manufacturing processes, and, in particular, in end-of-life disposal. Carbon footprint is the established indicator to assess the environmental impact of climate-changing factors on products or services. Research on different carbon footprint sources reduction is increasing, and even the European Composites Industry Association is demanding the development of specific Design for Sustainability approaches.

The societies around the world remain far from meeting the agreed primary goal outlined under the 2015 Paris Agreement on climate change: reducing greenhouse gas (GHG) emissions to keep global average temperature rise to well below 20°C by 2100 and making every effort to stay underneath of a 1.5°C elevation. Current emissions are rebounding from a brief decline during the economic downturn related to the Covid-19 pandemic. To get back on track to support the realization of the goal of the Paris Agreement, research suggests that GHG emissions should be roughly halved by 2030 on a trajectory to reach net zero by around mid-century.2 Although these are averaged global targets, every sector and country or market can and must contribute, especially higher-income and more developed countries bear the greater capacity to act. In 2020 direct tailpipe emissions from transport represented around 8 GtC02e, or nearly 15% of total emissions.

In contrast to refrigeration circuits in internal combustion engine vehicles (ICEVs) mainly used for cabin cooling, in electric vehicles (EVs) additional functions need to be taken into consideration, e.g., cabin heating, which in ICEVs is realized by the combustion engine’s waste heat, conditioning of the electric battery and drive train components. Additionally, each of these functions demands a different temperature level. Therefore, requirements towards the thermal management in EVs are more challenging. In modern EVs most of these functions are realized by direct refrigerant circuits, which are optimal in terms of efficiency and response time, however, result in greater complexity and different architectures for almost every vehicle model. In addition, the vast majority of EVs worldwide use chemical refrigerants that contain PFAS, e.g. R1234yf, which are known to be persistent and harmful for human health and environment.

The transition from ICE to electric power trains in new vehicles along with the application of advanced active and passive noise reduction solutions has intensified the perception of noise sources not directly linked to the propulsion system. This includes road noise as amplified by the tire cavity resonance. This resonance mainly depends on tire geometry, air temperature inside the tire and vehicle speed and is increasingly audible for larger wheels and heavier vehicles, as they are typical for current electrical SUV designs. Active technologies can be applied to significantly reduce narrow band tire cavity noise with low costs and minimal weight increase. Like ANC systems for ICE powertrains, they make use of the audio system in the vehicle. In this paper, a novel low-cost system for road induced tire cavity noise control (RTNC) is presented that reduces the tire cavity resonance noise inside a car cabin.

Electric aircraft have emerged as a promising solution for sustainable aviation, aiming to reduce greenhouse gas emissions and noise pollution. Efficiently estimating and optimizing energy consumption in these aircraft is crucial for enhancing their design, operation, and overall performance. This paper presents a novel framework for analyzing and modeling energy consumption patterns in lightweight electric aircraft. A mathematical model is developed, encompassing key factors such as aircraft weight, velocity, wing area, air density, coefficient of drag, and battery efficiency. This model estimates the total energy consumption during steady-level flight, considering the power requirements for propulsion, electrical systems, and auxiliary loads. The model serves as the foundation for analyzing energy consumption patterns and optimizing the performance of lightweight electric aircraft.

With the modernization of agriculture, the application of unmanned agricultural special vehicles is becoming increasingly widespread, which helps to improve agricultural production efficiency and reduce labor. Vehicle path-tracking control is an important link in achieving intelligent driving of vehicles. This paper designs a controller that combines path tracking with vehicle lateral stability for four-wheel steer/drive agricultural special electric vehicles. First, based on a simplified three-degrees-of-freedom vehicle dynamics model, a model predictive control (MPC) controller is used to calculate the front and rear axle angles. Then, according to the Ackermann steering principle, the four-wheel independent angles are calculated using the front and rear axle angles to achieve tracking of the target trajectory.

It is an important factor in electric vehicles to show customers how much they can drive with the energy of the remaining battery. If the remaining mileage is not accurate, electric vehicle drivers will have no choice but have to feel anxious about the mileage. Additionally, the potential customers have range anxiety when they consider Electric Vehicles. If the remaining mileage to drive is wrong, drivers may not be able to get to the charging station and may not be able to drive because the battery runs out. It is important to show the remaining available driving range exactly for drivers. The previous study proposed an advanced model by predicting the remaining mileage based on actual driving data and based on reflecting the pattern of customers who drive regularly. The Bayesian linear regression model was right model in previous study.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.