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Porous wall permeability is one of the most critical factors for the estimation of backpressure, a key performance indicator in automotive particulate filters. Current experimental and analytical filter models could be calibrated to predict the permeability of a specific filter. However, they fail to provide a reliable estimation for the dependence of the permeability on key parameters such as wall porosity and pore size. This study presents a novel methodology for experimentally determining the permeability of filter walls. The results from four substrates with different porosities and pore sizes are compared with several popular permeability estimation methods (experimental and analytical), and their validity for this application is assessed. It is shown that none of the assessed methods predict all permeability trends for all substrates, for cold or hot flow, indicating that other wall properties besides porosity and pore size are important.

Dynamic modelling of the contact between the tires of automobiles and the road surface is crucial for accurate and effective vehicle dynamic simulation and the development of various driving controllers. Furthermore, an accurate prediction of the rolling resistance is needed for powertrain controllers and controllers designed to reduce fuel consumption and engine emissions. Existing models of tires include physics-based analytical models, finite element based models, black box models, and data driven empirical models. The main issue with these approaches is that none of these models offer the balance between accuracy of simulation and computational cost that is required for the model-based development cycle. To address this issue, we present a volumetric approach to model the forces/moments between the tire and the road for vehicle dynamic simulations.

Accurately predicting the future trajectories of surrounding traffic agents is important for ensuring the safety of autonomous vehicles. To address the scenario of frequent interactions among traffic agents in the highway merging area, this paper proposes a trajectory prediction method based on interactive graph attention mechanism. Our approach integrates an interactive graph model to capture the complex interactions among traffic agents as well as the interactions between these agents and the contextual map of the highway merging area. By leveraging this interactive graph model, we establish an agent-agent interactive graph and an agent-map interactive graph. Moreover, we employ Graph Attention Network (GAT) to extract spatial interactions among trajectories, enhancing our predictions. To capture temporal dependencies within trajectories, we employ a Transformer-based multi-head self-attention mechanism.

This paper proposes a vehicle performance/safety method using combined active steering and differential braking to achieve yaw stability and rollover avoidance. The advantages and disadvantages of active steering and differential braking control methods are identified under a variety of input signals, such as J-turn, sinusoidal, and fishhook inputs by using the implemented linear 4 DOF model. Also, the nonlinear model of the vehicle is evaluated and verified through individual and integrated controller. Each controller gives the correction steering angle and correction moment to the simplified steering and braking actuators. The integrated active steering and differential braking control are shown to be most efficient in achieving yaw stability and rollover avoidance, while active steering and differential braking control has been shown to improve the vehicle performance and safety only in yaw stability and rollover avoidance, respectively.

The increasing severity in emission standards around the world has been accompanied by the development of more active, durable catalysts. With a view to investigating the effects of high thermal aging on the catalyst performance and structure, the relationships of washcoat composition, washcoat structure, and PGM location with respect to catalyst activity were clarified using a model gas test, as well as physical and chemical characterization methods. The influence of newly developed washcoat components and PGM location on catalyst performance are also demonstrated by engine bench tests. The results obtained in this study indicate the newly developed Pt/Rh catalyst techologies are appropriate for future applications in which the catalyst will be exposed to extremely high temperature and flowrates.

The performance, life cycle cost, and safety of electric and hybrid electric vehicles (EVs and HEVs) depend strongly on their energy storage system. Advanced batteries such as lithium-ion (Li-ion) polymer batteries are quite viable options for storing energy in EVs and HEVs. In addition, thermal management is essential for achieving the desired performance and life cycle from a particular battery. Therefore, to design a thermal management system, a designer must study the thermal characteristics of batteries. The thermal characteristics that are needed include the surface temperature distribution, heat flux, and the heat generation from batteries under various charge/discharge profiles. Therefore, in the first part of the research, surface temperature distribution from a lithium-ion pouch cell (20Ah capacity) is studied under different discharge rates of 1C, 2C, 3C, and 4C.

In electrified vehicle applications, the heat generated of lithium-ion (Li-ion) cells may significantly affect the vehicle range and state of health (SOH) of the pack. Therefore, a major design task is creation of a battery thermal management system with suitable control and cooling strategies. To this end, the thermal behavior of Li-ion cells at various temperatures and operating conditions should be quantified. In this paper, two different commercial pouch cells for plug-in hybrid electric vehicles (PHEVs) are studied through comprehensive thermal performance tests. This study employs a fractional factorial design of experiments to reduce the number of tests required to characterize the behavior of fresh cells while minimizing the effects of ageing. At each test point, the effects of ambient temperature and charge/discharge rate on several types of cell efficiencies and surface heat generation are evaluated.

New advanced Pd only, Pd:Rh and Pt:Pd:Rh catalysts are compared with a current platinum rhodium catalyst after poisoning and thermal ageing. The results indicate that at equivalent precious metal cost (at 1994 prices) the advanced palladium based catalysts achieve significantly improved performance compared with current Pt, Rh and Pd technology. The new Pd:Rh formulation is recommended for close coupled locations and the Pt:Pd:Rh formulation recommended for underfloor locations where residual fuel lead may be present. The formation of H2S is shown to be low with the palladium based catalysts. Finally, it is shown that the new catalysts with balanced oxidation and reduction capability perform better in multi-brick systems than addition of a highly loaded palladium only front brick.

Safety assurance is a central concern for the development and societal acceptance of automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on Machine Learning (ML). Despite the known challenges with the safety assurance of ML-based components, proposals have recently emerged for unit-level safety cases addressing these components. Unfortunately, AD safety cases express safety requirements at the system level and these efforts are missing the critical linking argument needed to integrate safety requirements at the system level with component performance requirements at the unit level. In this paper, we propose the Integration Safety Case for Perception (ISCaP), a generic template for such a linking safety argument specifically tailored for perception components. The template takes a deductive and formal approach to define strong traceability between levels.

Traditional electrode materials for lithium-ion storage cells are typically crystalline layered structures such as metal oxides, and graphitic carbons. These materials power billions of portable electronic devices in today's society. However, large-scale, high-capacity storage devices capable of powering hybrid electric vehicles (HEV″s) or their plug-in versions (PHEV's) have much more demanding requirements with respect to safety, cost, and the power they must deliver. Recently, nanostructured solid state materials, which are comprised of two more compositional or structural phases, have been found to show exciting possibilities to meet these criteria.

With the advent of stricter vehicle emission standards, the improvement of three way catalyst performance and durability remains a pressing issue. A critical consideration in catalyst design is the potential for variations in fuel sulfur levels to have a significant impact on the ability to reach TLEV, LEV, and ULEV emission levels. As a result, a better understanding of the role of PGM composition in the interplay between thermal durability and sulfur tolerance is required. Three way catalysts representative of standard Pd-only, Pd/Rh and Pt/Rh formulations were studied over a variety of aging and evaluation conditions. The parameters investigated included aging temperature, air fuel ratio and sulfur level. Evaluations were performed on a 1994 TLEV vehicle using different sulfur level fuels. The effect of PGM loading was also included within the study.

The performance characteristics of a commercial lean-NOX trap catalyst were evaluated between 200 and 500°C, using H2, CO, and a mixture of both H2 and CO as reductants before and after different high-temperature aging steps, from 600 to 750°C. Tests included NOX reduction efficiency during cycling, NOX storage capacity (NSC), oxygen storage capacity (OSC), and water-gas-shift (WGS) and NO oxidation reaction extents. The WGS reaction extent at 200 and 300°C was negatively affected by thermal degradation, but at 400 and 500°C no significant change was observed. Changes in the extent of NO oxidation did not show a consistent trend as a function of thermal degradation. The total NSC was tested at 200, 350 and 500°C. Little change was observed at 500°C with thermal degradation but a steady decrease was observed at 350°C as the thermal degradation temperature was increased.

In a collaborative programme, the effects of gasoline sulphur content on regulated emissions from three-way catalyst equipped vehicles have been studied. The programme evaluated the sulphur tolerance of three different catalyst formulations on the same range of vehicles. The catalyst chemistries were chosen to be representative of typical current formulations in different markets, as follows: 1. Platinum/Rhodium (Pt/Rh) 2. Platinum/Rhodium/Nickel (Pt/Rh/Ni) 3. Palladium/Rhodium (Pd/Rh) Each vehicle/catalyst combination was tested with fuels containing sulphur at nominal levels of 50, 250 and 450 ppm weight. All fuels were produced using the low sulphur fuel as a base and doping to 250 and 450 ppm S with a mixture of nine sulphur compounds, typical of those actually occurring in European gasolines. The results show clear differences between the magnitudes of the sulphur effect with different catalyst formulations.

A cold worked and induction hardened SAE1045 steel component exhibited excessive distortion after cold working and straightening, as well as cracking during straightening after induction hardening. Since the problems occurred only in certain heats of electric furnace (EF) steel, in which nitrogen content can vary widely and in some cases be quite high, and never occurred for basic oxygen furnace (BOF) steel for which nitrogen contents are uniformly low it was suspected that the source of the problem was low temperature nitrogen strain aging in heats of EF steel with a high nitrogen content. The measured distortion and mechanical properties at various stages in the fabrication process showed that while nitrogen content had no significant effect on the hot rolled steel the component distortion and strength after cold working and after induction hardening increased with increasing nitrogen content.

Progress made in reducing engine-out particulate emissions has prompted a revival in the design of flow-through oxidation catalysts for diesel engine applications. Effort in this area has focused primarily in the area of SOF control for the further reduction of particulate emissions. The work reported here covers some of the catalyst design parameters important for SOF and gas phase pollutant control. This is illustrated with both laboratory reactor and engine evaluation data for several formulary and operating parameters. Platinum-based catalysts are shown to be generally the most active, but they require treatments or additives to reduce the inherently high activity of platinum for the oxidation of SO2 present in the exhaust. The effect of additives and their loading on the oxidation activity of Pt/alumina for HC, CO, SOF and SO2 oxidation is discussed in detail and additives are identified which reduce SO2 oxidation with minimal effect on HC, CO or SOF oxidation activity.

Driving Automation Systems (DAS) are subject to complex road environments and vehicle behaviors and increasingly rely on sophisticated sensors and Artificial Intelligence (AI). These properties give rise to unique safety faults stemming from specification insufficiencies and technological performance limitations, where sensors and AI introduce errors that vary in magnitude and temporal patterns, posing potential safety risks. The Safety of the Intended Functionality (SOTIF) standard emerges as a promising framework for addressing these concerns, focusing on scenario-based analysis to identify hazardous behaviors and their causes. Although the current standard provides a basic cause-and-effect model and high-level process guidance, it lacks concepts required to identify and evaluate hazardous errors, especially within the context of AI. This paper introduces two key contributions to bridge this gap.

This paper presents a road classifier based on the system response with consideration of the tire enveloping. The aim is to provide an easily applicable yet accurate road classification approach for automotive engineers. For this purpose, tire enveloping effect is firstly modeled based on the flexible roller contact (FRC) theory, then transfer functions between road input and commonly used suspension responses i.e. the sprung mass acceleration, unsprung mass acceleration, and rattle space, are calculated for a quarter vehicle model. The influence of parameter variations, vehicle velocity, and measurement noise on transfer functions are comprehensively analyzed to derive the most suitable system response thereafter. In addition, this paper proposes a vehicle speed correction mechanism to further improve the classification accuracy under complex driving conditions.

In a consortium of European industrial partners and research institutes, a combination of industrial development and scientific research was organised. The objective was to improve the catalytic NOx conversion for lean burn cars and heavy-duty trucks, taking into account boundary conditions for the fuel consumption. The project lasted for three years. During this period parallel research was conducted in research areas ranging from basic research based on a theoretical approach to full scale emission system development. NOx storage catalysts became a central part of the project. Catalysts were evaluated with respect to resistance towards sulphur poisoning. It was concluded that very low sulphur fuel is a necessity for efficient use of NOx trap technology. Additionally, attempts were made to develop methods for reactivating poisoned catalysts. Methods for short distance mixing were developed for the addition of reducing agent.

The present paper offers a status report on round-robin tests conducted with the participation of ten laboratories, with drawbead simulation (DBS) as the test method. The results showed that, in most laboratories, the coefficient of friction (COF) derived from the test is repeatable within an acceptable range of ±0.01. Repeatability between laboratories was less satisfactory. Five laboratories reported results within the desirable band, while some laboratories found consistently higher values. In one instance this could be traced to incomplete transfer of clamp forces to the load cell, in other instances inaccurate test geometry is suspected. Therefore, numerical values of COF from different laboratories are not necessarily comparable. Irrespective of these inter-laboratory variations, the relative ranking of lubricants was not affected, and data generated within one laboratory can be used for relative evaluations and for a resolution of production problems.

This paper seeks to identify the refrigeration load of a hybrid electric truck in order to find the demand power required by the energy management system. To meet this objective, in addition to the power consumption of the refrigerator, the vehicle mass needs to be estimated. The Recursive Least Squares (RLS) method with forgetting factors is applied for this estimation. As an example of the application of this parameter identification, the estimated parameters are fed to the energy control strategy of a parallel hybrid truck. The control system calculates the demand power at each instant based on estimated parameters. Then, it decides how much power should be provided by available energy sources to minimize the total energy consumption. The simulation results show that the parameter identification can estimate the vehicle mass and refrigeration load very well which is led to have fairly accurate power demand prediction.