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Vehicle manufacturers are suffering from increasing expenses for fixing software issues. This fact is mainly driving their desire to use mobile communication channels for doing Software Updates Over The Air (SOTA). Software updates today are typically done at vehicle service stations by connecting the vehicles’ electronic network via the On Board Diagnostic (OBD) interface to a service computer. These operations are done under the control of trained technicians. SOTA means that the update process must get handled by the driver. Two critical aspects need to get considered when doing SOTA at Electronic Brake Control (EBC) systems. Both will determine the acceptance of SOTA by legal authorities and by the passengers: The safety and security of the vehicle The availability of the vehicle for the passengers The security aspect includes the necessity to protect the vehicle and the manufacturers IP from unwanted attacks.

Remote diagnostic systems support diagnostic communication by having the capability of sending diagnostic request services to a vehicle and receiving diagnostic response services from a vehicle. These diagnostic services are specified in diagnostic protocols, such as SAE J1979, SAE J1939 or ISO 14229 (UDS). For the purpose of diagnostic communication, the tester needs access to the electronic control units as communication partners. Physically, the diagnostic tester gets access to the entire vehicle´s E/E system, which consists of connectors, wiring, the in-vehicle network (e.g. CAN), the electronic control units, sensors, and actuators. Any connection of external test equipment and the E/E system of a vehicle poses a security vulnerability. The combination can be used for malicious intrusion and manipulation.

In order to detect degradation of engine oil lubricant, bench testing along with a number of diesel-powered Ford trucks were instruments and tested. The purpose of the bench testing was primarily to determine performance aspects such as repeatability, hysteresis effects and so on. Vehicle testing was conducted by designing and installing a separate oil reservoir along with a circulation system which was mounted in the vicinity of the oil pan. An innovative oil sensor was directly installed on the reservoir which can measure five (5) independent oil parameters (viscosity, density, permittivity, conductance, temperature). In addition, the concept is capable of detecting the oil level continuously during normal engine operation. The sensing system consists of an ultrasonic transducer for the oil level detection as well as a Tuning Fork mechanical resonator for the oil condition measurement.

Empirical models for engine-out oxides of Nitrogen (NOx) and smoke emissions have been developed for the purpose of minimizing transient emissions while maintaining transient response. Three major issues have been addressed: data acquisition, data processing and modeling method. Real and virtual transient parameters have been identified for acquisition. Accounting for the phase shift between transient engine events and transient emission measurements has been shown to be very important to the quality of model predictions. Several methods have been employed to account for the transient transport delays and sensor lags which constitute the phase shift. Finally several different empirical modeling methods have been used to determine the most suitable modeling method for transient emissions. These modeling methods include several kinds of neural networks, global regression and localized regression.

Predominant brake noise evaluation and rating was developed many years ago and no longer fulfills the need of modern development work. An extended description of a noisy brake event (European expert group guideline EKB 3006) and a standardized test data exchange format, allowing the comparison of different source test results (EKB 3008) are presented. Today's noise rating systems are described and compared by selected examples. The paper proposes an open 4 level noise rating system (EKB 3007). It starts with simple occurrence statistics, noise rating based on sound levels, situational noise rating including duration and finally based on the human perception, described by psychoacoustics.

Inferential sensing, as it relates to the equipment operator, can be viewed as human intuition [1]. The person operating the equipment can sense there is something wrong while their intuition tells them when and what needs troubleshooting and repair. Attempts have been made to implement this human intuition model to monitor a vehicle operation and detect abnormalities. In many approaches traditional sensors are added to the vehicle which increases cost, complexity, and another failure point. After years of developments and techniques, there are few highly reliable on-board systems that can duplicate the human intuition model since the specific failure cannot be directly measured but must be inferred from a variety of symptoms. This paper describes an engineering approach using Physics of Failure (PoF) for specific subsystems, developing the applicable fatigue models, and then collecting, monitoring, and manipulating the real-time on-vehicle data to complement the “operator intuition”.

FlexRay is a time triggered automotive communication protocol that connects ECUs (Electronic Control Units) on which distributed automotive applications are executed. If exact agreement (e.g. on physical values measured by redundant sensors on different ECUs) must be reached in the presence of asymmetric communication faults, a byzantine agreement protocol like Signed Messages (SM) can be utilized. This paper gives examples of how byzantine faults can emerge in a FlexRay-based system and proposes optimizations for a FlexRay-specific implementation of the SM protocol. The protocol modifications allow for a reduction in the number of protocol messages under a slightly relaxed fault model, as well as for a reduction in the number of messages to be temporarily stored by the ECUs.

The design of a safe transportation system requires numerous design decisions that should be based on data acquired by rigorous scientific method. Naturalistic data collection and analysis methods are a relatively new addition to the engineer's toolbox. The naturalistic method is based on unobtrusively monitoring driver and vehicle performance under normal, everyday, driving conditions; generally for extended collection periods. The method generates a wealth of data that is particularly well-suited for identifying the underlying causes of safety deficiencies. Furthermore, the method also provides robust data for the design and evaluation of safety enhancement systems through field studies. Recently the instrumentation required to do this type of study has become much more cost effective allowing larger numbers of vehicles to be instrumented at a fraction of the cost. This paper will first provide an overview of the naturalistic method including comparisons to other available methods.

MIL-STD-704 defines power quality in terms of transient, steady-state, and frequency-domain metrics that are applicable throughout a military aircraft electric power system. Maintaining power quality in more electric aircraft power systems has become more challenging in recent years due to the increase in load dynamics and power levels in addition to stricter requirements of power system characteristics during a variety of operating conditions. Further, power quality is often difficult to assess directly during experiments and aircraft operation or during data post-processing for the integrated electric power system (including sources, distribution, and loads). While MIL-STD-704 provides guidelines for compliance testing of electric load equipment, it does not provide any instruction on how to assess the power quality of power sources or the integrated power system itself, except the fact that power quality must be satisfied throughout all considered operating conditions.

This study outlines an approach for speeding up the simulation of the dynamic response of vehicle models that include hysteretic nonlinear tire components. The method proposed replaces the hysteretic nonlinear tire model with a surrogate model that emulates the dynamic response of the actual tire. The approach is demonstrated via a dynamic simulation of a quarter vehicle model. In the proposed methodology, training information generated with a reduced number of harmonic excitations is used to construct the tire hysteretic force emulator using a Neural Network (NN) element. The proposed approach has two stages: a learning stage, followed by an embedding of the learned model into the quarter car model. The learning related main challenge stems from the attempt to capture with the NN element the behavior of a hysteretic element whose response depends on its loading history.

Field reprogramming of electronic control units (ECU) via the serial communication bus for feature upgrades, software fixes is an area which has potential cost impacts to the OEMs. Due to increasing software complexity, feature content, up-integration considerations for ECUs in next generation vehicle platforms the expectation is for flash memory requirements to increase significantly in the future. A reduced reprogramming cycle that builds on top of the existing system framework would be of interest from a cost and timing aspects. Additionally deployment of the next generation of telematics based remote programming techniques would also benefit from shorter reprogramming time in ECUs. An approach to address field reprogramming time would be to migrate to high baud rate communication protocols like FlexRay or Ethernet from the traditional CAN based systems currently in use.

Two-poster rig plays a very important role in accelerated durability evaluation in a motorcycle industry, similar to what a four-poster rig does in a car industry. The rig simulates the exact road conditions in the vertical direction through tire coupling by applying feedback control on displacement. On account of its ability to simulate to the exact customer usage conditions, it reproduces the failures realistically as it happens on the field. However, as complete vehicle is required for testing on the rig, the testing happens mostly in the advanced stages of product development. Any failures beyond the concept stage have a huge impact on the development time and cost and the same should be avoided. Therefore, in this paper, a virtual testing methodology is proposed, based on which potential failures on the vehicles can be captured at the concept design stage itself. An ADAMS model of a motorcycle was created.

The recent development of diesel engine fuel injection systems has been dominated by how to manage the degrees of freedom that common rail multi-pulse systems now offer. A number of production engines already use four injection events while in research, work based on up to eight injection events has been reported. It is the degrees of freedom that lead to a novel experimental requirements. There is a potentially complex experimental program needed to simply understand how injection parameters influence the combustion process in steady state. Combustion behavior is not a continuum and as both injection and EGR rates are adjusted, distinct combustion modes emerge. Conventional calibration processes are severely challenged in the face of large number of degrees of freedom and as a consequence new development approaches are needed.

Hat sections made of steel are frequently encountered in automotive body structural components such as front rails. These components can absorb significant amount of impact energy during collisions thereby protecting occupants of vehicles from severe injury. In the initial phase of vehicle design, it will be prudent to incorporate the sectional details of such a component based on an engineering target such as peak load, mean load, energy absorption, or total crush, or a combination of these parameters. Such a goal can be accomplished if efficient and reliable data-based models are available for predicting the performance of a section of given geometry as alternatives to time-consuming and detailed engineering analysis typically based on the explicit finite element method.

The topic of timing has already been recognized as a major challenge when designing safety-critical automotive architectures. Consequently the availability of appropriate performance and timing analysis methods is key to building reliable automotive electric and electronics (E/E) and software architectures. Due to the potential performance increase, power reduction and cost-efficiency multicore solutions for automotive real-time environments receive growing attention. But the prediction of the timing behavior for multicore electronic control unit (ECU) systems becomes more complicated. Even in setups with static task-to-processor mapping, the execution of the tasks is usually not independent. The use of the same physical hardware, such as memories, coprocessors, or network components, makes inter-core interference unavoidable and may introduce hard-to-find timing problems including missed deadlines that can finally make the entire system fail.

The debate surrounding roof deformation and occupant injury potential has existed in the automotive community for over 30 years. In analysis of real-world rollovers, assessment of roof deformation and occupant compartment space starts with the post-accident roof position. Dynamic movement of the roof structure during a rollover sequence is generally acknowledged but quantification of the dynamic roof displacement has been limited. Previous assessment of dynamic roof deformation has been generally limited to review of the video footage from staged rollover events. Rollover testing for the evaluation of injury potential has typically been studied utilizing instrumented test dummies, on-board and off-board cameras, and measurements of residual crush. This study introduces an analysis of previously undocumented real-time data to be considered in the evaluation of the roof structure's dynamic behavior during a rollover event.

In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.

This research was to model a 6×4 tractor-trailer rig using TruckSim and simulate severe braking maneuvers with hardware in the loop and software in the loop simulations. For the hardware in the loop simulation (HIL), the tractor model was integrated with a 4s4m anti-lock braking system (ABS) and straight line braking tests were conducted. In developing the model, over 100 vehicle parameters were acquired from a real production tractor and entered into TruckSim. For the HIL simulation, the hardware consisted of a 4s4m ABS braking system with six brake chambers, four modulators, a treadle and an electronic control unit (ECU). A dSPACE simulator was used as the “interface” between the TruckSim computer model and the hardware.

Collision avoidance systems for rear-end collisions have been researched and developed. It is necessary to activate collision warnings and automatic braking systems with appropriate timing determined by a monitoring system of a driver's braking action. Although there are various systems to monitor driving behavior, this study aims to create a monitoring system using a driver model. This study was intended to construct a model of a driver's braking action with the Time Delay Neural Network (TDNN). An experimental scenario focuses on rear-end collisions on a highway, such as the driver of a host vehicle controlling the brake to avoid a collision into a leading vehicle in a stationary condition caused by a traffic jam. In order to examine the accuracy of the TDNN model, this study used four parameters: the number of learning, the number of neurons in the hidden layer, the sampling time with 0.01 second as a minimum value, and the number of the delay time.

The presentation describes a technical solution for 100 Mbit/s Ethernet Data transmission cabling. This solution considers the specific requirements of automotive wiring harness and manufacturing. It bases on standard automotive connectors and headers. Currently the development of automotive electronic architecture considers central ECU or data backbone structure for the upcoming EE architecture (e. g. single ECU for network; SEN). For these structures solid and cost effective data backbone solutions are essential. Ethernet, a wide distributed and well-known bus system for office and industry data distribution provide a wide range of software tools and many physical layer solutions. Several cabling systems are available. Based on this we propose a solution for automotive application.