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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.

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 gaining interest, and the use of green H2 as fuel for ICE is considered a high potential solution with fast and easy adoption. NOx emission is still a problem for H2 ICE and can be managed operating the engine with lean air fuel ratio all over the engine map. This combustion strategy will challenge the boosting system as lean H2 combustion will require quite higher air flow compared to diesel for the same power density in steady state. Similar problem will show up in transient response particularly when acceleration starts from low load and the exhaust gases enthalpy is very poor and insufficient to spin the turbine. The analysis presented in this paper will show and quantify the positive impact that a supercharger has on both the above mentions problems.

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.

For the purpose of achieving carbon-neutrality in the mobility sector by 2050, hydrogen can play a crucial role as an alternative energy carrier, not only for direct usage in fuel cell-powered vehicles, but also for fueling internal combustion engines. This paper focuses on the numerical investigation of high-pressure hydrogen injection and the mixture formation inside a high-tumble engine 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 engines. An alternative and more pragmatic lagrangian 3D-CFD approach offers opportunities for a significant reduction in computational effort without sacrificing reliability.

Heavy duty truck engines are quite difficult to electrify, due to the large amount of energy required on-board, in order to achieve a range comparable to that of diesels. This paper considers a commercial 6-cylinder engine with a displacement of 12.8 L, developed in two different versions. As a standard diesel, the engine is able to deliver more than 420 kW at 1800 rpm, whereas in the CNG configuration the maximum power output is 330 kW at 1800 rpm. Maintaining the same combustion chamber design of the last version, a theoretical study is carried out in order to run the engine on Hydrogen, compressed at 700 bar. The study is based on GT-Power simulations, adopting a predictive combustion model, calibrated with experimental results. The study shows that the implementation of a combustion system running on lean mixtures of Hydrogen, permits to cancel the emissions of CO2, while maintaining the same power output of the CNG engine.

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.

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.

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.

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.

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.

After the COVID-19 pandemic, leisure activities and cultures have undergone significant transformations. Particularly, there has been an increased demand for outdoor camping. Consequently, the need for capabilities that allow vehicles to navigate not only paved roads but also unpaved and rugged terrains has arisen. In this study, we aim to address this demand by utilizing AI to introduce a 'Stuck Probability Estimation Algorithm' for vehicles on off-road. To estimate the 'Stuck Probability' of a vehicle, a mathematical model representing vehicle behavior is essential. The behavior of off-road driving vehicles can be characterized in two main aspects: firstly, the harshness of the terrain (how uneven and rugged it is), and secondly, the extent of wheel slip affecting the vehicle's traction.

Heavy Vehicle Event Data Recorders (HVEDRs) have the ability to capture important data surrounding an event such as a crash or near crash. Efforts by many researchers to analyze the capabilities and performance of these complex systems can be problematic, in part, due to the challenges of obtaining a heavy truck, the necessary space to safely test systems, the inherent unpredictability in testing, and the costs associated with this research. In this paper, a method for simulating vehicle speed sensor (VSS) inputs to HVEDRs to trigger events is introduced and validated. Full-scale instrumented testing is conducted to capture raw VSS signals during steady state and braking conditions. The recorded steady state VSS signals are injected into the HVEDR along with synthesized signals to evaluate the response of the HVEDR. Brake testing VSS signals are similarly captured and injected into the HVEDR to trigger an event record.

The absence of combustion information continues to be one of the key obstacles to the intelligent development of engines. Currently, the cost of integrating cylinder pressure sensors remains too high, prompting attention to methods for extracting combustion information from existing sensing data. Mean-value combustion models for engines are unable to capture changes of combustion parameters. Furthermore, the methods of reconstructing combustion information using sensor signals mainly depend on the working state of the sensors, and the reliability of reconstructed values is directly influenced by sensor malfunctions. Due to the concentration of operating conditions of hybrid vehicles, the reliability of priori calibration map has increased. Therefore, a combustion information reconstruction method based on priori calibration information and the fused feature deviations of existing sensing signals is proposed and named the "Deviation-based Centroid Displacement Method" (DCDM).

The current focus in the ongoing development of autonomous driving systems (ADS) for heavy duty vehicles is that of vehicle operational safety. To this end, developers and researchers alike are working towards a complete understanding of the operating environments and conditions that autonomous vehicles are subject to during their mission. This understanding is critical to the testing and validation phases of the development of autonomous vehicles and allows for the identification of both the nominal and edge case scenarios encountered by these systems. Previous work by the authors saw the development of a comprehensive scenario generation framework to identify an operating domain specification (ODS), or external and internal conditions an autonomous driving system can expect to encounter on its mission to form critical scenario groups for autonomous vehicle testing and validating using statistical patterns, clustering, and correlation.

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.

Lane changing is an essential action in commercial vehicles to prevent collisions. However, steering system malfunctions significantly escalate the risk of head-on collisions. With the advancement of intelligent chassis control technologies, some autonomous commercial vehicles are now equipped with a four-wheel independent braking system. This article develops a lane-changing control strategy during steering failures using torque vectoring through brake allocation. The boundaries of lane-changing capabilities under different speeds via brake allocation are also investigated, offering valuable insights for driving safety during emergency evasions when the steering system fails. Firstly, a dual-track vehicle dynamics model is established, considering the non-linearity of the tires. A quintic polynomial approach is employed for lane-changing trajectory planning. Secondly, a hierarchical controller is designed.

Brake judder affects vehicle safety and comfort, making it a key area of research in brake NVH. Transfer path analysis is effective for analyzing and reducing brake judder. However, current studies mainly focus on passenger cars, with limited investigation into commercial vehicles. The complex chassis structures of commercial vehicles involve multiple transfer paths, resulting in extensive data and testing challenges. This hinders the analysis and suppression of brake judder using transfer path analysis. In this study, we propose a simulation-based method to investigate brake judder transfer paths in commercial vehicles. Firstly, road tests were conducted to investigate the brake judder of commercial vehicles. Time-domain analysis, order characteristics analysis, and transfer function analysis between components were performed.

An experiment is carried out to measure creep groan of a drum brake located in a trailer axle of a truck. The noise nearby the drum brake and accelerations on brake shoes, axle and trailer frame are collected to analyze the occurring conditions and characteristics of the creep groan. A multi-body dynamics model with 1/4 trailer chassis structures is established for analyzing brake component vibrations that generates the creep groan. In the model, the contact force between brake cam and brake shoes, the contact friction characteristics between brake linings and inner circular surface of brake drum, and the properties of chassis structure are included. Dynamic responses of brake shoes, axle and trailer frame during the braking process are estimated using the established model and the responses are compared with the measured results, which validate the model.

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.