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Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams. Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way. In this paper we will demonstrate a data-driven approach for validation testing.

Beside the automotive industry, where 2-cylinder inline engines are catching attention again, twin-cylinder configurations are quite usual in the small engine world. From stationary engines and range-extender use to small motorcycles up to big cruisers and K-Cars this engine architecture is used in many types of applications. Because of very good overall packaging, performance characteristics and not least the possibility of parts-commonality with 4-cylinder engines nearly every motorcycle manufacturer provides an inline twin in its model range. Especially for motorcycle applications where generally the engine is a rigid member of the frame and vibrations can be transferred directly to the rider an appropriate balancing system is required.

Car manufacturers put large efforts into reducing wind noise to improve the comfort level of their cars. Each component of the vehicle is designed to meet its individual noise target to ensure the wind noise passenger comfort level inside the vehicle is met. Sunroof designs are tested to meet low-frequency buffeting (also known as boom) targets and broadband noise targets for the fully open sunroof with deflector and for the sunroof in vent position. Experimentally testing designs and making changes to meet these design targets typically involves high cost prototypes, expensive wind tunnel sessions, and potentially late design changes. To reduce the associated costs as well as development times, there is strong motivation for the use of a reliable numerical prediction capability early in the vehicle design process.

This paper describes the Lean Boost System, a gasoline engine concept for improved fuel economy. The system combines direct injection, lean operation and pressure charging, and allows significant reduction in swept volume, or ‘downsizing’. Engine tests have been undertaken which demonstrate the validity of the combustion concept. The strategy a typical manufacturer might adopt in order to meet future European requirements for CO2 emissions is proposed. Vehicle simulation results for typical North American and European vehicles are presented. Using the exhaust gas emission levels from engine tests and drive cycle simulation, aftertreatment requirements and configurations are considered.

Vehicle efficiency and range, along with the DC charging speed, are deemed as the most important criteria for an electric vehicle currently. The electric vehicle energy consumption is impacted by the change in temperature along with the driving style and average speed of a customer, all other factors being constant. Hence understanding the patterns and impact of different aspects of an EV range & charging speed is crucial in delivering an electric vehicle with robust efficiency across all weather conditions. In this paper we have analysed vehicle parameters of global Jaguar I-PACE customer data. We present and analyse the collated big data of around 50,000+ unique vehicles with a data aggregate of well over 482 million km. In moderate ambient conditions the analysis indicated a good correlation with 50th to 75th percentile drivers’ energy consumption to the EPA label figure.

Complexity of electronics and embedded software systems in automobiles has been increasing over the years. This necessitates the need for an effective and exhaustive development and validation process in order to deliver fault free vehicles at reduced time to market. Model-based Product Engineering (MBPE) is a new process for development and validation of embedded control software. The process is generic and defines the engineering activities to plan and assess the progress and quality of the software developed for automotive applications. The MBPE process is comprised of six levels (one design level and five verification and validation levels) ranging from the vehicle requirements phase to the start of production. The process describes the work products to be delivered during the course of product development and also aligns the delivery plan to overall vehicle development milestones.

In order to meet current and future emission and CO2 targets, an efficient vehicle thermal management system is one of the key factors in conventional as well as in electrified powertrains. Global vehicle simulation is already a well-established tool to support the vehicle development process. In contrast to conventional vehicles, electrified powertrains offer an additional challenge to the thermal conditioning: the durability of E-components is not only influenced by temperature peaks but also by the duration and amplitude of temperature swings as well as temperature gradients within the components during their lifetime. Keeping all components always at the preferred lowest temperature level to avoid ageing under any conditions (driving, parking, etc.) will result in very high energy consumption which is in contradiction to the efficiency targets.

Reducing emissions and the carbon footprint of our society have become imperatives requiring the automotive industry to adapt and develop technologies to strive for a cleaner sustainable transport system and for sustainable economic prosperity. Electrified hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV) and range extender powertrains provide potential solutions for reducing emissions, but they present challenges in terms of thermal management. A key requirement for meeting these challenges is accurately to predict the thermal loading and temperatures of an internal combustion engine (ICE) quickly under multiple full-load and part-load conditions. Computational Fluid Dynamics (CFD) and thermal survey database methods are used to derive thermal loading of the engine structure and are well understood but typically only used at full-load conditions.

The high-frequency whining noise produced by motors in modern electric vehicles causes a significant issue, leading to annoyance among passengers. This noise becomes even more noticeable due to the quiet nature of electric vehicles, which lack other noises to mask the high-frequency whining noise. To improve the noise caused by motors, it is essential to optimize various motor design parameters. However, this task requires expert knowledge and a considerable time investment. In this study, we explored the application of artificial intelligence to optimize the NVH performance of motors during the design phase. Firstly, we selected and modeled three benchmark motor types using Motor-CAD. Machine learning models were trained using Design of Experiment methods to simulate batch runs of Motor-CAD inputs and outputs.

The advent of 3D displays offers Human-Machine Interface (HMI) designers and engineers new opportunities to shape the user's experience of information within the vehicle. However, the application of 3D displays to the in-vehicle environment introduces a number of new parameters that must be carefully considered in order to optimise the user experience. In addition, there is potential for 3D displays to increase driver inattention, either through diverting the driver's attention away from the road or by increasing the time taken to assimilate information. Manufacturers must therefore take great care in establishing the ‘do’s and ‘don’t's of 3D interface design for the automotive context, providing a sound basis upon which HMI designers can innovate. This paper describes the approach and findings of a three-part investigation into the use of 3D displays in the instrument cluster of a road car, the overall aim of which was to define the boundaries of the 3D HMI design space.

The field of vision of the driver during wet road conditions is essential for safety at all times. Additionally, the safe use of the increasing number of sensors integrated in modern cars for autonomous driving and intelligent driver assistant systems has to be ensured even under challenging weather conditions. To fulfil these requirements during the development process of new cars, experimental and numerical investigations of vehicle soiling are performed. This paper presents the surface contamination of self- and foreign-soiling tested in the wind tunnel. For these type of tests, the fluorescence method is state-of-the-art and widely used for visualizing critical areas. In the last years, the importance of parameters like the contact angle have been identified when designing the experimental setup. In addition, new visualization techniques have been introduced.

Many electric (EV) and hybrid-electric (HEV) vehicles are designed to operate using only electric propulsion at low road speeds. This has resulted in significantly reduced vehicle noise levels in urban situations. Although this may be viewed by many as a benefit, a risk to safety exists for those who rely on the engine noise to help detect the presence, location and behaviour of a vehicle in their vicinity. In recognition of this, legislation is being introduced globally which will require automotive manufacturers to implement external warning sound systems. A key challenge for premium vehicle manufacturers is the development of a suitable warning sound signature which also conveys the appropriate brand aspirations for the product. A further major difficulty exists when trying to robustly evaluate potential exterior sounds by running large-scale trials in the real world.

The automotive domain has seen safety engineering at the forefront of the industry’s priorities for the last decade. Therefore, additional safety engineering efforts, design approaches, and well-established safety processes have been stipulated. Today many connected and automated vehicles are available and connectivity features and information sharing are increasingly used. This increases the attractiveness of an attack on vehicles and thus introduces new risks for vehicle cybersecurity. Thus, just as safety became a critical part of the development in the late 20th century, the automotive domain must now consider cybersecurity as an integral part of the development of modern vehicles. Aware of this fact, the automotive industry has, therefore, recently taken multiple efforts in designing and producing safe and secure connected and automated vehicles.

High-speed planar laser Mie scattering and Laser Induced Fluorescence (PLIF) were employed for the determination of Sauter Mean Diameter (SMD) distribution in non-evaporating diesel sprays. The effect of rail pressure, distillation profile, and consequent fuel viscosity on the drop size distribution developing during primary and secondary atomization was investigated. Samples of conventional crude-oil derived middle-distillate diesel and light distillate kerosene were delivered into an optically accessible mini-sac injector, using a customized high-pressure common rail diesel fuel injection system. Two optical channels were employed to capture images of elastic Mie and inelastic LIF scattering simultaneously on a high-speed video camera at 10 kHz. Results are presented for sprays obtained at maximum needle lift during the injection. These reveal that the emergent sprays exhibit axial asymmetry and vorticity.

For electric vehicles (EVs), driving range is one of the major concerns for wider customer acceptance and the cabin climate system represents the most significant auxiliary load for battery consumption. Unlike internally combustion engine (ICE) vehicles, EVs cannot utilize the waste heat from an engine to heat the cabin through the heating, ventilation and air conditioning (HVAC) system. Instead, EVs use battery energy for cabin heating, this reduces the driving range. To mitigate this situation, one of the most promising solutions is to optimize the recirculation of cabin air, to minimize the energy consumed by heating the cold ambient air through the HVAC system, whilst maintaining the same level of cabin comfort. However, the development of this controller is challenging, due to the coupled, nonlinear and multi-input multi-output nature of the HVAC and thermal systems.

If fuel cell vehicles are to compete in the same marketplace as conventional vehicles, then they must provide the consumer with the same, or improved, levels of convenience, comfort, refinement and performance at the same, or lower, price. In 2003/4, several vehicle manufacturers are planning to launch their first commercial fuel cell vehicles onto the market. In this remarkably short timeframe, many systems must be integrated into a vehicle including the fuel cell system plus thermal and water management, cabin heating, ventilation and air-conditioning, control and on-board diagnostics, power electronics, electric motor and gearbox, suspension, steering, braking, refinement and crash protection. This paper presents a range of modelling techniques which allow the user to design and develop key systems, including the power management system, compressed air supply, thermal management and control algorithms.

For the automotive industry, the quality and level of the wind noise contribution has a growing importance and therefore should be addressed as early as possible in the development process. Each component of the vehicle is designed to meet its individual noise target to ensure the wind noise passenger comfort level inside the vehicle is met. Sunroof broadband noise is generated by the turbulent flow developed over the roof opening. A strong shear layer and vortices impacting on the trailing edge of the sunroof are typical mechanisms related to the noise production. Sunroof designs are tested to meet broadband noise targets. Experimentally testing designs and making changes to meet these design targets typically involves high cost prototypes, expensive wind tunnel sessions and potentially late design changes.

Validation of highly automated or autonomous vehicles is nowadays still a major challenge for the automotive industry. Furthermore, the homologation of ADAS/AD vehicles according to global regulations is getting more essential for their safe development and deployment around the world. In order to assure that the autonomous driving function is able to cope with the huge number of possible situations during operation, comprehensive testing of the functions is required. However, conventional testing approaches such as driving distance-based validation approach in the real world, can be time- and cost-consuming. Therefore, a scenario-based virtual validation and testing method is considered to be a proper solution. In this paper, we propose a virtual assessment framework using a fully automated test case generation method. This framework is embedded into the continuous development and validation process.

This paper presents a methodology for simulating Fluid Structure Interaction (FSI) for a typical vehicle bonnet (hood) under a range of onset flow conditions. The hood was chosen for this study, as it is one of the panels most prone to vibration; particularly given the trend to make vehicle panels lighter. Among the worst-case scenarios for inducing vibration is a panel being subjected to turbulent flow from vehicle wakes, and the sudden peak loads caused by emerging from a vehicle wake. This last case is typical of a passing manoeuvre, with the vehicle suddenly transitioning from being immersed in the wake of the leading vehicle, to being fully exposed to the free-stream flow. The transient flowfield was simulated for a range of onset flow conditions that could potentially be experienced on the open road, which may cause substantial vibration of susceptible vehicle panels.

Calibrating a vehicle’s powertrain for dynamic operation needs to focus on efforts to mitigate the risks of thermal overload which may arise in the stator or rotor components of an e-motor. Risks also may arise for expected NVH or durability targets, with torque and torque “oscillations” acting as primary sources for the vehicles’ NVH behavior. Both topics, temperature measurement of stator and rotor as well as dynamic torque measurements of the powertrain’s drive shaft are addressed with examples demonstrating the sensors applications in normal test bed and vehicle configurations.