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A variety of methodologies to use embedded multicore controllers efficiently has been discussed in the last years. Several assumptions are usually made in the automotive domain, such as static assignment of tasks to the cores. This paper shows an approach for efficient task allocation depending on different system modes. An engine management system (EMS) is used as application example, and the performance improvement compared to static allocation is assessed. The paper is structured as follows: First the control algorithms for the EMS will be classified according to operating modes. The classified algorithms will be allocated to the cores, depending on the operating mode. We identify mode transition points, allowing a reliable switch without neglecting timing requirements. As a next step, it will be shown that a load distribution by mode-dependent task allocation would be better balanced than a static task allocation.

The newly released Distributed System Interface 3 (DSI3) Bus Standard specification defines three modulation levels form which 16 valid symbols are coded. This complex structure is best decoded with symbol pattern recognition. This paper proposes a simplification of the correlation score calculation that sharply reduces the required number of operations. Additionally, the paper describes how the pattern recognition is achieved using correlation scores and a decoding algorithm. The performance of this method is demonstrated by mean of simulations with different load models between the master and the sensors and varying noise injection on the channel. We prove than the pattern recognition can decode symbols without any error for up to 24dBm.

This paper discusses the RDC method utilizing delta-sigma analog-to-digital converter hardware module (DSADC) integrated in the Infineon's microcontroller family. With its higher resolution capability when compared to the regularly used ADC with successive-approximation (SAR), DSADC seems to have more potential. On the other hand, DSADC's inherent properties, such as asynchronous sampling rate and group delay, which when not handled properly, would have negative effects to the rotor positioning system. The solution to overcome those side-effects involves utilization of other internal microcontroller's resources such as timers and capture units, as well as additional software processing run inside CPU. The rotor positioning system is first modeled and simulated in high-level simulation language environment (Matlab and Simulink) in order to predict the transient- and steady state behaviors. The group delay itself is obtained by simulating the model of DSADC module implementation.

The primary function of an airbag control module is to detect crashes, discriminate and predict if a deployment is necessary, then deploy the restraint systems including airbags and where applicable, pretensioners. At General Motors (GM), the internal term for airbag control module is Sensing and Diagnostic Module (SDM). In the 1994 model year, GM introduced its SDM on some of its North American airbag-equipped vehicles. A secondary function of that SDM and all subsequent SDMs is to record crash related data. This data can include data regarding impact severity from internal accelerometers and pre-crash vehicle data from various chassis and powertrain modules. Previous researchers have addressed the accuracy of both the velocity change data, recorded by the SDM, and the pre-crash data, but the assessment of the timing of the pre-crash data has been limited to a single family of modules (Delphi SDM-G).

The paper describes a new strategy for real-time sensor diagnostics that is based on the statistical correlation of various sensor signal pairs. During normal fault-free operation there is a certain correlation between the sensor signals which is lost in the event of a fault. The proposed algorithm quantifies the correlation between sensor signal pairs using real-time scalar metrics based on the Mahalanobis-distance concept. During normal operation all metrics follow a similar pattern, however in the event of a fault; metrics involving the faulty sensor would increase in proportion to the magnitude of the fault. Thus, by monitoring this pattern and using a suitable fault-signature table it is possible to isolate the faulty sensor in real-time. Preliminary simulation results suggest that the strategy can mitigate the false-alarms experienced by most model-based diagnostic algorithms due to an intrinsic ability to distinguish nonlinear vehicle behavior from actual sensor faults.

This paper includes a numerical and experimental study of fluid flow in automotive catalytic converters. The numerical work involves using computational fluid dynamics (CFD) to perform three-dimensional calculations of turbulent flow in an inlet pipe, inlet cone, catalyst substrate (porous medium), outlet cone, and outlet pipe. The experimental work includes using hot-wire anemometry to measure the velocity profile at the outlet of the catalyst substrate, and pressure drop measurements across the system. Very often, the designer may have to resort to offset inlet and outlet cones, or angled inlet pipes due to space limitations. Hence, it is very difficult to achieve a good flow distribution at the inlet cross section of the catalyst substrate. Therefore, it is important to study the effect of the geometry of the catalytic converter on flow uniformity in the substrate.

Computer simulation was used as a complement to crash and injury field data analysis and physical sled and barrier tests to investigate and predict the spinal injuries of a rear impact in an IndyCar. The model was expected to relate the spinal loads to the observed injuries, thereby predicting the probability and location of spinal fractures. The final goal is to help reduce the fracture risk by optimizing the seat and restraint system design and the driver's position using computer modeling and sled testing. MADYMO Full FE Human Body Model (HBM) was selected for use because of its full spinal structural details and its compatibility with the vehicle and restraint system models. However, the IndyCar application imposed unique challenges to the HBM. First, the driver position in a race car is very different from that in a typical passenger car.

Production software validation is critical during software development, allowing potential quality issues that could occur in the field to be minimized. By developing automated and repeatable software test methods, test cases can be created to validate targeted areas of the control software for confirmation of the expected results from software release to release. This is especially important when algorithm/software development timing is aggressive and the management of development activities in a global work environment requires high quality, and timely test results. This paper presents a hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software. The powertrain model used within the HIL consists of an engine model and a detailed automatic transmission dynamics model. The model runs in an OPAL-RT TestDrive based HIL system.

This paper describes a new tool to capture cylinder pressure information, calculate combustion parameters, and implement control algorithms. There are numerous instrumentation and prototyping systems which can provide some or all of this capability. The Cylinder Pressure Development Controller (CPDC) is unique in that it uses advanced high volume automotive grade circuitry, packaging, and software methodologies. This approach provides insight regarding the implementation of cylinder pressure based controls in a production engine management system. A high performance data acquisition system is described along with a data reduction technique to minimize data processing requirements. The CPDC software architecture is discussed along with model-based algorithm development and autocoding. Finally, CPDC calculated combustion parameters are compared with those from a well established combustion analysis system and thermodynamic simulations.

The increasingly stringent requirements in relation to emission reduction and onboard diagnostics are pushing the Chinese automotive industry toward more innovative solutions and a rapid increase in electronic control performance. To manage the system complexity the architecture will require being well structure on hardware and software level. The paper introduces GEMS-K1 (Gasoline Engine Management System - Kit 1). GEMS-K1 is a platform being compliant with Euro IV emission regulation for gasoline engines. The application software is developed using modeling language, the code is automatically generated from the model. The driver software has a well defined structure including microcontroller abstraction layer and ECU abstraction layer. The hardware is following design rules to be robust, 100% testable and easy to manufacture. The electronic components use the latest innovation in terms of architecture and technologies.

An analytical study of spray from an outwardly opening pressure swirl injector has been presented in this paper. A number of model injectors with varying design configurations have been used in this study. The outwardly opening injection process has been modeled using a modified spray breakup model presented in an earlier study. It has been observed that simulation results from the study clearly capture the mechanism by which an outwardly opening conical spray interacts with the downstream flow field. Velocity field near the tip of the injector shows that the conical streams emanating from an outwardly opening injector have the tendency to entrap air into the flow stream which is responsible for finer spray. A deviation from the optimum set of physical parameters showed a high propensity to produce large spray droplets. This study also emphasizes the importance of computational fluid dynamics (CFD) as an engineering tool to understand the complex physical processes.

During the production controller and software development process, one critical step is the controller and software verification. There are various ways to perform this verification. One of the commonly used methods is to utilize an HIL (hardware-in-the-loop) test bench to emulate powertrain hardware for development and validation of powertrain controllers and software. A key piece of an HIL bench is the plant dynamics model used to emulate the external environment of a modern controller, such as engine (ECM), transmission (TCM) or powertrain controller (PCM), so that the algorithms and their software implementation can be exercised to confirm the desired results. This paper presents a 6-speed automatic transmission plant dynamics model development for hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software. The modeling method, model validation, and application in an HIL test environment are described in details.

This paper presents a hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software, with a focus on a closed-loop vehicle drive-train model incorporating a detailed automatic transmission plant dynamics model developed for certain applications. Specifically, this paper presents the closed-loop integration of a 6-speed automatic transmission model developed for our HIL transmission controller and algorithm test bench (Opal-RT TestDrive based). The model validation, integration and its application in an HIL test environment are described in details.

In recent years, gasoline direct injection (GDi) engines have been popular due to their inherent potential for reduction of exhaust emissions and fuel consumption to meet stringent EPA standards. These engines require high-pressure fuel injection in order to improve the atomization process and accelerate mixture preparation. The high-pressure fuel pump is an essential component in the GDi system. Therefore, understanding the flow characteristics of this device and its associated behavior is critical for improving the performance of this category of engines. In this paper, the fluid flow characteristics in a high-pressure single-piston pump for use in GDi engines are analyzed using 1-D LMS Imagine.Lab AMESim system and 3-D Ansys Fluent computational fluid dynamics (CFD) models. The flow rate of the fuel pump under various cam speeds has been examined along with characteristics of the pump's control valve.

This paper describes a correlation study on fuel spray pattern recognition of multi-hole injectors for gasoline direct injection (GDi) engines. Spray pattern is characterized by patternation length, which represents the distance of maximum droplet concentration from the axis of the injector. Five fuel injectors with different numbers and sizes of nozzle holes were considered in this study. Experimental data and CFD modeling results were used separately to develop regression models for spray patternation. These regressions predicted the influence of a number of injector operating and design parameters, including injection system operating pressure, valve lift, injector hole length-to-diameter ratio (L/d) and the orientation of the injector hole. The regression correlations provided a good fit with both experimental and CFD spray simulation results. Thus CFD offers a good complement to experimental validation during development efforts to meet a desired injector spray pattern.

In a CAS system, the distance and relative velocity between front and host vehicles are estimated to calculate time-to-collision (TTC). The distance estimates by different methods will certainly include noise which should be removed to ensure the accuracy of TTC calculations. Kalman filter is a good tool to filter such type of noise. Nevertheless, Kalman filter is a model based filter, which means a correct model is important to get the good filtering results. Usually, a vehicle is either moving with a constant velocity (CV) or constant acceleration (CA) maneuvers. This means the distance data between front and host vehicles can be described by either constant velocity or constant acceleration model. In this paper, first, CV and CA models are used to design two Kalman filters and an interacting multiple model (IMM) is used to dynamically combine the outputs from two filters.

This paper considers the verification of non-standard CAN network topologies of the physical layer at high speed transmission rate (500.0Kbps and 1.0Mbps). These network topologies including single star, multiple stars, and hybrid topologies (multiple stars in combination with linear bus or with ring topology) are simulated by using behavior modeling language (VHDL-AMS) in comparison to measurement. Throughout the verification process, CAN transceiver behavioral model together with other CAN physical layer simulation components have been proved to be very accurate. The modeling of measurement environment of the CAN network is discussed, showing how to get the measurement and simulation results well matched. This demonstrates that the simulation solution is reliable, which is highly desired and very important for the verification requirement in CAN physical layer design.

This paper presents the development of a transmission closed loop pressure control system. The objective of this system is to improve transmission pressure control accuracy by employing closed-loop technology. The control system design includes both feed forward and feedback control. The feed forward control algorithm continuously learns solenoid P-I characteristics. The closed loop feedback control has a conventional PID control with multi-level gain selections for each control channel, as well as different operating points. To further improve the system performance, Robust Optimization is carried out to determine the optimal set of control parameters and controller hardware design factors. The optimized design is verified via an L18 experiment on spin dynamometer. The design is also tested on vehicle.

The verification of an in-vehicle network often requires to look at more than one level of abstraction at a time. At the moment, this is not addressed by existing methods, which are dedicated either to physical or application layer, but not both. This paper fills this gap by introducing a methodology to insert the protocol related software execution as well as the motor behavior into the physical layer mixed-signal (i.e. analog/digital) simulation. Electronics and mechanics are covered by the hardware description language VHDL-AMS, while the software is given in C.

The perceived interior noise has been one of the major driving factors in the design of automotive interior assemblies. Buzz, Squeak and Rattle (BSR) issues are one of the major contributors toward the perceived quality in a vehicle. Traditionally BSR issues have been identified and rectified through extensive hardware testing. In order to reduce the product development cycle and minimize the number of costly hardware builds, however, one must rely on engineering analysis and simulation upfront in the design cycle. In this paper, an analytical and experimental study to identify potential BSR locations in a cockpit assembly is presented. The analytical investigation utilizes a novel and practical methodology, implemented in the software tool Nhance.BSR, for identification and ranking of potential BSR issues. The emphasis here is to evaluate the software for the BSR predictions and the identification of modeling issues, rather than to evaluate the cockpit design itself for BSR issues.