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To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.

Almost every automotive noise, vibration, and harshness (NVH) engineer who has ever looked at a fast Fourier transform knows the difference between instruments with 90 dB and instruments with 120 dB of dynamic range. NVH engineers understand instrumentation specs such as 24-bit resolution analog-to-digital converters and alias-free signal bandwidth. However, with modern noise and vibration instrumentation systems now being almost completely built on the PC, these specs neglect the most important X factor: the PC itself. No other aspect can affect the performance of an instrumentation system for a sensor array more than the components of the PC. Fortunately, a variety of off-the-shelf PC technologies built on industry standards are available to make it easier and less expensive than ever before to instrument and manage data from large systems. But an NVH engineer must wade through a sea of options to choose the right PC technologies for desired instrumentation system performance.

Modular instrumentation is being widely used in noise and vibration measurement systems that demand higher channel counts and the wider dynamic range that 24-bit delta-sigma ADCs make available at lower costs. This is an overview how flexible modular instrumentation employing the latest software technology can be used in making high precision noise and vibration measurements where higher sampling rates, higher channel counts, increased dynamic range, and distributed architectures were needed in smaller packages. An example where this is being used is in acoustic beam forming in aircraft pass by noise tests to measure and distinguish engine and airframe noise sources.

LIN is a low-cost, low-speed vehicle communication sub-bus becoming increasingly pervasive in automotive subsystems. It is a simple, UART-based master-slave protocol designed as a low-speed supplement to a CAN or FlexRay bus. Its primary application is cabin comfort and human interface hardware such as dashboard controls, power seat harnesses, and power door/window systems. As automotive network designers attempt to reduce wiring complexity and lower system cost, modular, inexpensive sub-buses like LIN become an attractive option. This paper presents an overview of the LIN standard and its applications, and then proposes an architecture for rapid development of LIN networks via hardware simulations of LIN nodes. Using inexpensive, off-the-shelf hardware, LIN sensor and actuator applications can be tested in-place without microcode development, speeding overall network development time.

Companies typically invest significant time and money in choosing the proper test equipment for new automotive test systems. Yet, the architecture for proper data storage and management of the mounds of data these systems produce is often times an afterthought. Although data management may not appear as an obstacle during initial design, as the system expands, changes, and interfaces with other systems, the ability to easily access and exchange technical data becomes a critical challenge to overcome. This paper will introduce new technologies giving engineers the power to search and mine data sets to find key information and trends in the data for to rapidly turn the raw data into results.

In many measurement applications, there is a need to correlate data acquired from different systems or synchronize systems together with precise timing. Signal Based and Time Based are the two basic methods of synchronizing instrumentation. In Signal Based synchronization, clocks and triggers are physically connected between systems. Typically this provides the highest precision synchronization. In many NVH applications size and distance constrains physically connecting the systems needed for making measurements though the inter-channel phase information of simultaneously sampled signals is crucial. In Time Based synchronization, system components have a common reference of what time it is. Events, triggers and clocks can be generated based on this time.

Several advanced signal processing algorithms beyond the FFT such as time-frequency analysis, quefrency, cestrum, wavelet analysis, and AR modeling uses are outlined. These advanced algorithms can solve some sound and vibration challenges that FFT-based algorithms cannot solve. Looking at signal characteristics of a unit under test in the time-frequency plane, it is possible to get a better understanding of signal characteristics. This is an overview of these algorithms and some application examples, such as speaker testing, bearing fault detection, dashboard motor testing, and engine knock detection where they can be applied to NVH applications.

Military jet aircraft expose both ground maintenance personnel and the community to high levels of noise. The U.S. Department of Defense is funding research to develop advanced modeling tools for noise reduction techniques and community noise exposure. A large-scale microphone array for portable near-field acoustic holography (NAH) and data acquisition system was created for this purpose. The system was designed for measuring high-amplitude jet noise from current and next-generation military aircraft to provide model refinement and benchmarking, evaluate performance of noise control devices, and predict ground maintenance personnel and community noise exposure. The acoustical instrumentation system was designed to be easy to use with scalable data processing as the primary focus. The data acquisition system allowed up to 152 channels simultaneously sampled at a rate of 96 kHz.

48V mild hybrid engine is one of major eco-friendly technology for global CO2 reduction policy. The 48V mild hybrid engine enables to operate torque boost, recuperation and ISG status by MHSG(Mild Hybrid Starter and Generator). The FEAD(Front End Auxiliary Drive) system is a very important role to transfer MHSG power to crankshaft at the mild hybrid engine. The conventional FEAD configuration is relatively simple because it transfers power from crankshaft to auxiliary drive components in one direction. But the FEAD configuration of 48V mild hybrid engine is not simple due to bidirectional power transmission between crankshaft and MHSG. For instance, in case of torque boost mode, the tight side of auxiliary belt is entry span of MHSG. On the contrary, the tight side of auxiliary belt is exit span of MHSG at recuperation mode.

Automotive HW/SW architectures are becoming increasingly complex to support the deployment of new safety, comfort, and energy-efficiency features. Such architectures include several software tasks (100+), messages (1000+), computational and communication resources (70+ CPUs, 10+ buses), and (smart) sensors and actuators (20+). To cope with the increasing system complexity at lowest development and product costs, highest safety, and fastest time to market, model-based rapid-prototyping development processes are essential. The processes, coupled with optimization steps aimed at reducing the number of software and hardware resources while satisfying the safety requirements, enable reduction of the system complexity and ease downstream testing/validation efforts. This paper describes a novel model-based design exploration and optimization process for the deployment of a set of software tasks on a dual-processor control module implementing a fail-safe strategy.

Vehicle communication networks are challenged by increasing demands for bandwidth, safety, and security. New data is coming into the vehicle from personal devices (e.g. mobile phones), infotainment systems, camera-based driver assistance, and wireless communication with other vehicles and infrastructure. Ethernet (IEEE 802.3) provides high levels of bandwidth and security, making it a potential solution to the challenges of vehicle communication networks. However, in order to be used in control applications, Ethernet must provide known timing performance (e.g. bounded latency and jitter), and in some cases redundancy. This paper explores use of Ethernet for in-vehicle control applications.

This is an overview of the development of a portable, real-time acoustic beamformer based on FPGA (Field Programmable Gate Arrays) and digital microphones for noise source identification. Microphone arrays can be a useful tool in identifying noise sources and give designers an image of noise distribution. The beamforming algorithm is a classic and efficient algorithm for signal processing of microphone arrays and is the core of many microphone array systems. High-speed real-time beamforming has not been implemented much in a portable instrument because it requires large computational resources. Utilizing a beamforming algorithm running on a Field Programmable Gate Array (FPGA), this camera is able to detect and locate both stationary and moving noise sources. A high-resolution optical camera located in the middle of the device records images at a rate of 25 frames per second. The use of the FPGA technology and digital microphones provides increased performance, reduced cost and weight.

Test and validation of control systems for hybrid vehicle power trains provide a unique set of challenges. Not only does the electronic control unit (ECU), or pair of ECUs, need to smoothly coordinate power flow between two or more power plants, but it also must handle the power electronics' high-speed dynamics due to PWM signals frequently in the 10-20 kHz range. The trend in testing all-electric and hybrid-electric ECUs has moved toward using field-programmable gate arrays (FPGAs) as the processing node for simulating inverter and electric motor dynamics in real time. Acting as a purpose-built processor colocated with analog and digital input and output, the FPGA makes it possible for real-time simulation loop rates on the order of one microsecond.