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The reliable prediction of pollutant emissions generated by IC engine powertrains during the WLTP driving cycle is a key aspect to test and optimize different configurations, in order to respect the stringent emission limits. This work describes the application of an integrated modeling tool in a co-simulation environment, coupling a 1D fluid dynamic code for engine simulation with a specific numerical code for aftertreatment modelling by means of a robust numerical approach, to achieve a complete methodology for detailed simulations of driving cycles. The main goal is to allow an accurate 1D simulation of the unsteady flows along the intake and exhaust systems and to apply advanced thermodynamic combustion models for the calculation of cylinder-out emissions.

The human-robot interaction (HRI) is involved in a lift assistant system of manufacturing assembly line. The admittance model is applied to control the end effector motion by sensing intention from force of applied by a human operator. The variable admittance including virtual damping and virtual mass can improve the performance of the systems. But the tuning process of variable admittance is un-convenient and challenging part during the real test for designers, while the offline simulation is lack of learning process and interaction with human operator. In this paper, the Iterative learning algorithm is proposed to emulate the human learning process and facilitate the variable admittance control design. The relationship between manipulate force and object moving speed is demonstrated from simulation data. The effectiveness of the approach is verified by comparing the simulation results between two admittance control strategies.

This study evaluated the interactions between sheet steel, lubricant and measurement system under typical sheet forming conditions using a fixed draw bead simulator (DBS). Deep drawing quality mild steel substrates with bare (CR), electrogalvanized (EG) and hot dip galvanized (HDG) coatings were tested using a fixed DBS. Various lubricant conditions were targeted to evaluate the coefficient of friction (COF) of the substrate and lubricant combinations, with only rust preventative mill oil (dry-0 g/m2 and 1 g/m2), only forming pre-lube (dry-0 g/m2, 1 g/m2, and >6 g/m2), and a combination of two, where mixed lubrication cases, with incremental amounts of a pre-lube applied (0.5, 1.0, 1.5 and 2.0 g/m2) over an existing base of 1 g/m2 mill oil, were analyzed. The results showed some similarities as well as distinctive differences in the friction behavior between the bare material and the coatings.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

This report describes a new driving simulator capability at General Motors (GM) Research and Development's (R&D) Vehicle Development Research (VDR) Laboratory and its application in an iterative HMI development process. The paper also provides an overview of three recent simulator usability tests supporting HMI development.

ELEVATE (European Low Emission V4 Automotive Two-stroke Engine) was a research project part funded by the European Commission to design and develop a compact and efficient gasoline two-stroke automotive engine. Five partners were involved in the project, IFP (Institut Français Du Pétrole) who were the project leaders, Lotus, Opcon (Autorotor and SEM), Politecnico di Milano and Queen's University Belfast. The general project targets were to achieve Euro 3 emissions compliance without DeNOx catalisation, and a power output of 120 kW at 5000 rev/min with maximum torque of 250 Nm at 2000 rev/min. Specific targets were a 15% reduction in fuel consumption compared to its four-stroke counterpart and a size and weight advantage over the four-stroke diesel with significant reduction in particulate and NOx emissions. This paper describes the design philosophy of the engine as well as the application of the various partner technologies used.

Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.

In today’s race for improved fuel economy and lower emissions from gasoline engines, precise metering of delivered fuel is essential. Gasoline Direct Injection fuel systems provide the means for improved combustion efficiency through mixture preparation and better atomization. These improvements can be achieved from both increasing fuel pressure and using multiple injection events, which significantly reduce the required energizing time per injection, and in a number of cases, force the injector to operate at less than full stroke. When the injector operates in this condition, the influence of variation in injector dynamics account for a large percentage of the delivered fuel and require compensation to ensure accurate fuel delivery. Injector dynamics such as opening delay and closing time are influenced by operating conditions such as fuel pressure, energizing time, and temperature.

Fabric materials have diverse applications in the automotive industry which include upholstery, carpeting, safety devices, and interior trim components. The textile industry has invested substantial effort toward development of standard testing techniques for characterizing mechanical properties of different fabric types (e.g. woven and knitted). However, there are presently no standards for determination of Young's modulus, Poisson's ratio and tensile stress-strain properties required for the detailed modeling of fabric materials in vehicle structural simulations. This paper presents results from uniaxial tensile tests of different automotive seat cover fabric materials. Digital image correlation, a full field optical method for measuring surface deformation, was used to determine tensile properties in both the warp/wale and the weft/course directions. The fabrics were tested with and without the foam backing.

Although advanced driver assistance systems (ADAS) have been widely introduced in automotive industry to enhance driving safety and comfort, and to reduce drivers’ driving burden, they do not in general reflect different drivers’ driving styles or customized with individual personalities. This can be important to comfort and enjoyable driving experience, and to improved market acceptance. However, it is challenging to understand and further identify drivers’ driving styles due to large number and great variations of driving population. Previous research has mainly adopted physical approaches in modeling drivers’ driving behavior, which however are often very much limited, if not impossible, in capturing human drivers’ driving characteristics. This paper proposes a reinforcement learning based approach, in which the driving styles are formulated through drivers’ learning processes from interaction with surrounding environment.

Since steering wheel torque feedback is one of the crucial factors for drivers to gain road feel and ensure driving safety, it is especially important to simulate the steering torque feedback for a driving simulator. At present, steering wheel feedback torque is mainly simulated by an electric motor with gear transmission. The torque response is typically slow, which can result in drivers’ discomfort and poor driving maneuverability. This paper presents a novel torque feedback device with magnetorheological (MR) fluid and coil spring. A phase separation control method is also proposed to control its feedback torque, including spring and damping torques respectively. The spring torque is generated by coil spring, the angle of coil spring can be adjusted by controlling a brushless DC motor. The damping torque is generated by MR fluid, the damping coefficient of MR fluid can be adjusted by controlling the current of excitation coil.

Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.

Zinc-based electrogalvanized (EG) and hot-dip galvanized (HDGI) coatings have been widely used in automotive body-in-white components for corrosion protection. The formability of zinc coated sheet steels depends on the properties of the sheet and the interactions at the interface between the sheet and the tooling. The frictional behavior of zinc coated sheet steels is influenced by the interfacial conditions present during the forming operation. Friction behavior has also been found to deviate from test method to test method. In this study, various lubrication conditions were applied to both bending under tension (BUT) test and a draw bead simulator (DBS) test for friction evaluations. Two different zinc coated steels; electrogalvanized (EG) and hot-dip galvanized (HDGI) were included in the study. In addition to the coated steels, a non-coated cold roll steel was also included for comparison purpose.

Due to growing interest in automated driving, the need for better understanding of human driving behavior in uncertain environment, such as driving behavior at un-signalized crossroad and roundabout, has further increased. Driving behavior at roundabout is greatly influenced by different dynamic factors such as speed, distance and circulating flow of the potentially conflicting vehicles, and drivers should choose whether to leave or wait at the upcoming exit according to these factors. In this paper, the influential dynamic factors and driving behavior characteristics at the roundabout is analyzed in detail, random forest method is then deployed to predict the driving behavior. For training the driving behavior model, four typical roundabout layouts were created under a real-time driving simulator with PanoSim-RT and dSPACE. Traffic participants with different motion style were also set in the simulation platform to mimic real driving conditions.

With the recent advances in rapid modeling and rapid prototyping, accurate simulation models for tires are very desirable. Selection of a tire slip model depends on the required frequency range and nonlinearity associated with the dynamics of the vehicle. This paper presents a brief overview of three major slip concepts including “Stationary slip”, “Physical transient slip”, and “Pragmatic transient slip”; tire models use these slip concepts to incorporate tire slip behavior. The review illustrates that there can be no single accurate slip model which could be ideally used for all modes of vehicle dynamics simulations. For this study, a rigid ring based semi-analytical tire model for intermediate frequency (up to 100 Hz) is used.

For virtual simulation of the vehicle attributes such as handling, durability, and ride, an accurate representation of pneumatic tire behavior is very crucial. With the advancement in autonomous vehicles as well as the development of Driver Assisted Systems (DAS), the need for an Intelligent Tire Model is even more on the increase. Integrating sensors into the inner liner of a tire has proved to be the most promising way in extracting the real-time tire patch-road interface data which serves as a crucial zone in developing control algorithms for an automobile. The model under development in Kettering University (KU-iTire), can predict the subsequent braking-traction requirement to avoid slip condition at the interface by implementing new algorithms to process the acceleration signals perceived from an accelerometer installed in the inner liner on the tire.

Connected and automated vehicle (CAV) technologies promise a substantial decrease in traffic accidents and traffic jams, and bring new opportunities for improving vehicle’s fuel economy. However, testing autonomous vehicles in a real world traffic environment is costly, and covering all corner cases is nearly impossible. Furthermore, it is very challenging to create a controlled real traffic environment that vehicle tests can be conducted repeatedly and compared fairly. With the capability of allowing testing more scenarios than those that would be possible with real world testing, simulations are deemed safer, more efficient, and more cost-effective. In this work, a full-scale simulation platform was developed to simulate the infrastructure, traffic, vehicle, powertrain, and their interactions. It is used as an effective tool to facilitate control algorithm development for improving CAV’s fuel economy in real world driving scenarios.

The engine operating point (EOP), which is determined by the engine speed and torque, is an important part of a vehicle's powertrain performance and it impacts FC, available propulsion power, and emissions. Predicting instantaneous EOP in real-time subject to dynamic driver behaviour and environmental conditions is a challenging problem, and in existing literature, engine performance is predicted based on internal powertrain parameters. However, a driver cannot directly influence these internal parameters in real-time and can only accommodate changes in driving behaviour and cabin temperature. It would be beneficial to develop a direct relationship between the vehicle-level parameters that a driver could influence in real-time, and the instantaneous EOP. Such a relationship can be exploited to dynamically optimize engine performance.

Automotive manufacturers relentlessly explore engine technology combinations to achieve reduced fuel consumption under continued regulatory, societal and economic pressures. For example, technologies enabling advanced combustion modes, increased expansion to effective compression ratio and reduced parasitics continue to be developed and integrated within conventional and hybrid propulsion strategies across the industry. A high-efficiency gasoline engine capable for use in conventional or hybrid electric vehicle platforms is highly desirable. This paper is the second of two papers describing the multi-cylinder integration of a technology package combining lean-stratified combustion with Miller cycle for downsized boosted applications. The first paper describes the design, analysis and single-cylinder testing conducted to down-select the combustion system deployed to the multi-cylinder engine.

The rising environmental awareness has led to a growing interest in electric and lightweight vehicles. Four-wheeled Ultra-Efficient Lightweight Vehicles (UELVs) have the potential to improve the quality of urban life, reduce environmental impact and make efficient use of land. However, the safety of these vehicles in terms of dynamic behaviour needs to be better understood. This paper aims to provide a quantitative assessment of the handling behaviour of UELVs. An analytical single-track model and a numerical simulation by VI-CarRealTime are analysed to evaluate the dynamic performance of a UELV compared to a city car. This analysis shows that the lightweight vehicle has a higher readiness (i.e. lower reaction time to yaw rate) for step steering and lower steering effort (i.e. higher steady-state value). Experimental analysis through real-time driving sessions on the Dynamic Driving Simulator assesses vehicle responses and subjective perception for different manoeuvres.