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This paper explains why software for efficient model-based development is needed to improve the efficiency of automakers and suppliers when implementing solutions with next generation automotive embedded systems. The resulting synergies are an important contribution for the automotive industry to develop safer, smarter, and more eco-friendly cars. To achieve this, it requires implementations of algorithms for machine learning, deep learning and model predictive control within embedded environments. The algorithms’ performance requirements often exceed the capabilities of traditional embedded systems with a homogeneous multicore architecture and, therefore, additional computing resources are introduced. The resulting embedded systems with heterogeneous computing architectures enable a next level of safe and secure real-time performance for innovative use cases in automotive applications such as domain controllers, e-mobility, and advanced driver assistance systems (ADAS).

In this paper a method for the identification of most significant vehicle parameters influencing the behavior of a lateral control system of autonomous car is presented. Requirements for the design stage of the controller need to consider many uncertainties in the plant. While most vehicle properties can be compensated by an appropriate tuning of the control parameters, other vehicle properties can change significantly during usage. The control system is evaluated based on performance measures. Analyzed parameters comprise functional tire characteristics, mass of the vehicle and position of its center of gravity. Since the parameters are correlated, but Sobol’ sensitivity analysis assumes decorrelated inputs, random variation yields no reasonable results. Furthermore, the variation of each parameter or set of parameters is not applicable since the numbers of required simulations is increased significantly according to input dimension.

The current automotive electronic and electrical (EE) architecture has reached a scalability limit and in order to adapt to the new and upcoming requirements, novel automotive EE architectures are currently being investigated to support: a) an Ethernet backbone, b) consolidation of hardware capabilities leading to a centralized architecture from an existing distributed architecture, c) optimization of wiring to reduce cost, and d) adaptation of service-oriented software architectures. These requirements lead to the development of Zonal EE architectures as a possible solution that require appropriate adaptation of used security mechanisms and the corresponding utilized hardware trust anchors. 1 The current architecture approaches (ECU internal and in-vehicle networking) are being pushed to their limits, simultaneously, the current embedded security solutions also seem to reveal their limitations due to an increase in connectivity.

The specification of automotive ventilation / defrosting systems has often utilized “trial-and-error” and “prior experience” techniques. But design development and production efficiency has generated a strong interest in using more sophisticated design tools such as computational fluid dynamics. For this purpose a joint experimental and numerical study was undertaken. This comprehensive investigation was divided into two parts. First, the three dimensional defroster flow field was measured using LDA in an actual automobile. Second, LDA and infrared thermography was used to map the flow and temperature fields for a two dimensional jet impinging upon a slanted plate -- a simplified representation of a car defroster geometry.

The new BMW Aerodynamisches Versuchszentrum (AVZ) wind tunnel center includes a full-scale wind tunnel, "The BMW Windkanal" and an aerodynamic laboratory "The BMW AEROLAB." The AVZ facility incorporates numerous new technology features that provide design engineers with new tools for aerodynamic optimization of vehicles. The AVZ features a single-belt rolling road in the AEROLAB and a five-belt rolling road in the Windkanal for underbody aerodynamic simulation. Each of these rolling road types has distinct advantages, and BMW will leverage the advantages of each system. The AEROLAB features two overhead traverses that can be configured to study vehicle drafting, and both static and dynamic passing maneuvers. To accurately simulate "on-road" aerodynamic forces, a novel collector/flow stabilizer was developed that produces a very flat axial static pressure distribution. The flat static pressure distribution represents a significant improvement relative to other open jet wind tunnels.

Recent regulations on greenhouse gas (GHG) emission standards for heavy duty vehicles have prompted government agencies to standardize procedures to assess aerodynamic performance of Class 8 tractor-trailers. The coastdown test procedure is the primary reference method to assess vehicle drag and other valid alternatives include wind tunnel testing and computational fluid dynamics (CFD) simulations. While there have been many published studies comparing results between simulations and wind tunnel testing, it is less well understood how to compare results with coastdown testing. Both the wind tunnel and simulation directly measure aerodynamic drag forces in controlled conditions, while coastdown testing is conducted in an open road environment, aerodynamic forces are calculated from a road load equation, and variable wind and vehicle speed introduce additional complexity.

The implementation of an advanced process for the aerodynamic development of cab-over type heavy trucks at China FAW Group Corporation (FAW) requires a rigorous validation of the tools employed in this process. The final objective of the aerodynamic optimization of a heavy truck is the reduction of the fuel consumption. The aerodynamic drag of a heavy truck contributes up to 50% of the overall resistance and thus fuel consumption. An accurate prediction of the aerodynamic drag under real world driving conditions is therefore very important. Tools used for the aerodynamic development of heavy trucks include Computational Fluid Dynamics (CFD), wind tunnels and track and road testing methods. CFD and wind tunnels are of particular importance in the early phase development.

The technology in the automotive industry is evolving rapidly in recent times. Thus, with the development of new technologies, the challenges are also ever-increasing from an Electromagnetic Interference and Susceptibility (EMI/EMC) perspective. A lot of the latest technologies in Adaptive Driver Assistance Systems (ADAS), which include Rear Drive Assist, Blind Spot Detection (BSD), Lane Change Assist (LCA) to name a few, and other features like Anti-Braking System (ABS), Emergency Brake Assist (EBD) etc. rely heavily on different types of sensors and their detection circuitry. In addition, a lot of other internal functions in the Engine Control Unit (ECU) also depend on such sensors’ functionalities. Thus, it becomes imperative to study the potential impact of higher field emissions on the immunity behaviour of the sensors.

The validation of vehicle aerodynamic simulation results to wind tunnel test results and simulation accuracy improvement attract considerable attention of many automotive manufacturers. In order to improve the simulation accuracy, a simulation model of the ground effects simulation system of the aerodynamic wind tunnel of the Shanghai Automotive Wind Tunnel Center was built. The model includes the scoop, the distributed suction, the tangential blowing, the moving belt and the wheel belts. The simulated boundary layer profile and the pressure distribution agree well with test results. The baseline model and multiple design changes of the new Buick Excelle GT are simulated. The simulation results agree very well with test results.

As part of the United States Department of Energy's SuperTruck program, Volvo Trucks and its partners were tasked with demonstrating 50% improvement in overall freight efficiency for a tractor-trailer, relative to a best in class 2009 model year truck. This necessitated that significant gains be made in reducing aerodynamic drag of the tractor-trailer system, so trailer side-skirts and a trailer boat-tail were employed. A Lattice-Boltzmann based simulation method was used in conjunction with a Kriging Response Surface optimization process in order to efficiently describe a design space of seven independent parameters relating to boat-tail and side-skirt dimensions, and to find an optimal configuration. Part 1 concerns a fully-skirted tractor-trailer system, and consists of an initial phase of optimization, followed by a mid-project re-evaluation of constraints, and an additional period of optimization.

Aerodynamic efficiency plays an increasingly important role in the automotive industry, as the push for increased fuel economy becomes a larger factor in the engineering and design process. Longitudinal drag is used as the primary measure of aerodynamic performance, usually cited as the coefficient of drag (CD). This drag is created mostly by the body shape of the vehicle, but the wheel and tire system also contributes a significant portion. In addition to the longitudinal drag created by the body and wheels, rotational drag can add an appreciable amount of aerodynamic resistance to the vehicle as well. Reducing power consumption is an especially vital aspect in electric vehicle (EV) design. As the world's first luxury electric sedan, the Tesla Model S combines a premium driving experience with an electric drivetrain package that allows for unique solutions to many vehicle subsystems.

For the past approximately 20 years, the Serial Peripheral Interface (SPI) has been the established standard for serial communication between a host or central microprocessor and peripheral devices. This standard has been used extensively in control modules covering the entire spectrum of automotive applications, as well as non-automotive applications. As the complexity of engine control modules grows, with the number of vehicle actuators being controlled and monitored increasing, the number of loads the central microprocessor has to manage is growing accordingly. These loads are typically controlled using discrete and pulse-width modulated (PWM) outputs from the microcontroller when real-time operation is essential or via SPI when real-time response is not critical. The increase of already high pin-count on microcontrollers, the associated routing effort and demand for connected power stages is a concern of cost and reliability for future ECU designs.