Search Results

The front camera module is a fundamental component of a modern vehicle’s active safety architecture. The module supports many active safety features. Perception of the road environment, requests for driver notification or alert, and requests for vehicle actuation are among the camera software’s key functions. This paper presents a novel method of testing these functions virtually. First, the front camera module software is compiled and packaged in a Docker container capable of running on a standard Linux computer as a software in the loop (SiL). This container is then integrated with the active safety simulation tool that represents the vehicle plant model and allows modeling of test scenarios. Then the following simulation components form a closed loop: First, the active safety simulation tool generates a video data stream (VDS). Using an internet protocol, the tool sends the VDS to the camera SiL and other vehicle channels.

This paper presents recent advances in automotive microprocessor, operating system, and supporting software technology that supports regulatory and/or functional safety graphics within vehicle cockpit displays. These graphics include “virtual switches” that replace physical switches in the vehicle, as well as “virtual indicators” that replace physical indicator lights. We discuss the functional safety design process and impacts to software and hardware architecture as well as the software design methods to implement End-To-End [E2E] network protection between different ECUs and software processes. We also describe hardware monitoring requirements within the display panel, backlighting, and touch screen and examine an example system design to illustrate the concepts.

Disc brakes operate with very close proximity of the brake pads and the brake rotor, with as little as a tenth of a millimeter of movement of the pads required to bring them into full contact with the rotor to generate braking torque. It is usual for a disc brake to operate with some amount of residual drag in the fully released state, signifying constant contact between the pads and the rotor. With this contact, every miniscule movement of the rotor pushes against the brake pads and changes the forces between them. Sustained loads on the brake corner, and maneuvers such as cornering, can both produce rotor movement relative to the caliper, which can push it steadily against one or both of the brake pads. This can greatly increase the residual force in the caliper, and increase drag. This dependence of drag behavior on the movement of the brake rotor creates some vehicle-dependent behavior.

The establishment of highly-diluted combustion strategies is one of the major challenges that the next generation of sustainable internal combustion engines must face. The desirable use of high EGR rates and of lean mixtures clashes with the tolerable combustion stability. To this aim, the development of numerical models able to reproduce the degree of combustion variability is crucial to allow the virtual exploration and optimization of a wide number of innovative combustion strategies. In this study ignition experiments using a conventional coil system are carried out in a closed volume combustion vessel with side-oriented flow generated by a speed-controlled fan. Acquisitions for four combinations of premixed propane/air mixture quality (Φ=0.9,1.2), dilution rate (20%-30%) and lateral flow velocity (1-5 m/s) are used to assess the modelling capabilities of a newly developed spark-ignition model for large-eddy simulation (GLIM, GruMo-UniMORE LES Ignition Model).

In the context of vehicle electrification, improving vehicle aerodynamics is not only critical for efficiency and range, but also for driving experience. In order to balance the necessary trade-offs between drag and downforce without significant impact on the vehicle styling, we see an increasing amount of active aerodynamic solutions on high-end passenger vehicles. Active rear spoilers are one of the most common active aerodynamic features. They deploy at high vehicle speed when additional downforce is required [1, 2]. For a vehicle with an active rear spoiler, the aerodynamic performance is typically predicted through simulations or physical testing at different static spoiler positions. These positions range from fully stowed to fully deployed. However, this approach does not provide any information regarding the transient effects during the deployment of the rear spoiler, which can be critical to understanding key performance aspects of the system.

Turbocharger housings in internal combustion engines are subjected to severe mechanical and thermal cyclic loads throughout their life-time or during engine testing. The combination of thermal transients and mechanical load cycling results in a complex evolution of damage, leading to thermo-mechanical fatigue (TMF) of the material. For the computational TMF life assessment of high temperature components, the DTMF model can provide reliable TMF life predictions. The model is based on a short fatigue crack growth law and uses local finite-element (FE) results to predict the number of cycles to failure for a technical crack. In engine applications, it is nowadays often acceptable to have short cracks as long as they do not propagate and cause loss of function of the component. Thus, it is necessary to predict not only potential crack locations and the corresponding number of cycles for a technical crack, but also to determine subsequent crack growth or even a possible crack arrest.

The present paper describes the results of a cooperative research project between GM Powertrain Europe and Istituto Motori - CNR aimed at studying the capability of GM Combustion Closed-Loop Control (CLCC) in enabling seamless operation with high biodiesel blending levels in a modern diesel engine for passenger car applications. As a matter of fact, fuelling modern electronically-controlled diesel engines with high blends of biodiesel leads to a performance reduction of about 12-15% at rated power and up to 30% in the low-end torque, while increasing significantly the engine-out NOx emissions. These effects are both due to the interaction of the biodiesel properties with the control logic of the electronic control unit, which is calibrated for diesel operation. However, as the authors previously demonstrated, if engine calibration is re-tuned for biodiesel fuelling, the above mentioned drawbacks can be compensated and the biodiesel environmental inner qualities can be fully deployed.

The automotive industry is continuously striving to reduce vehicle mass by reducing the mass of components including wheel bearings. A typical wheel bearing assembly is mostly steel, including both the wheel and knuckle mounting flanges. Mass optimization of the wheel hub has traditionally been accomplished by reducing the cross-sectional thickness of these components. Recently bearing suppliers have also investigated the use of alternative materials. While bearing component performance is verified through analysis and testing by the supplier, additional effects from system integration and performance over time also need to be comprehended. In a recent new vehicle architecture, the wheel bearing hub flange was reduced to optimize it for low mass. In addition, holes were added for further mass reduction. The design met all the supplier and OEM component level specifications.

In traditional automotive electronic design, software update has been a component oriented, manual process rather than a systematic designed in capability suitable for automation. In recent days as software content in vehicles grow, the need to update software in vehicles more frequently is becoming a necessity. Moreover, additional attributes for software updates, for example timely delivery of security related update for vehicles, desire to add features using software update, control cost of software updates, etc., requires a system engineered design rather than a component oriented approach. As the automobile domain utilizes various means of mobility (Combustion Engine, Hybrid, Battery, etc.) and various functional domains (Infotainment, Safety, Mobility, Telematics, ADAS (Advance Driving Assist service), Autonomous, etc.), to control the overall cost of future software update for such a diverse environment, it is beneficial to introduce automation in the software update process.

An investigation of high speed direct injection (DI) compression ignition (CI) engine combustion fueled with gasoline (termed GDICI for Gasoline Direct-Injection Compression Ignition) in the low temperature combustion (LTC) regime is presented. As an aid to plan engine experiments at full load (16 bar IMEP, 2500 rev/min), exploration of operating conditions was first performed numerically employing a multi-dimensional CFD code, KIVA-ERC-Chemkin, that features improved sub-models and the Chemkin library. The oxidation chemistry of the fuel was calculated using a reduced mechanism for primary reference fuel combustion. Operation ranges of a light-duty diesel engine operating with GDICI combustion with constraints of combustion efficiency, noise level (pressure rise rate) and emissions were identified as functions of injection timings, exhaust gas recirculation rate and the fuel split ratio of double-pulse injections.

Third generation advanced high strength steels (AHSS) that rely on the transformation of austenite to martensite have gained growing interest for implementation into vehicle architectures. Previous studies have identified a dependency of the rate of austenite decomposition on the amount of strain and the associated strain path imposed on the sheet. The rate and amount of austenite transformation can impact the work hardening behavior and tensile properties. However, a deeper understanding of the impact on toughness, and thus crash performance, is not fully developed. In this study, the strain path and strain amounts were systematically controlled to understand the associated correlation to impact toughness in the end application condition (strained and baked). Impact toughness was evaluated using an instrumented Charpy machine with a single sheet v-notch sample configuration.

Steel crankshafts are subjected to an induction heat treatment process for improving the operational life. Metallurgical phase transformations during the heat treatment process have direct influence on the hardness and residual stress. To predict the structural performance of a crankshaft using Computer Aided Engineering (CAE) early in the design phase, it is very important to simulate the complete induction heat treatment process. The objective of this study is to establish the overall analysis procedure, starting from capturing the eddy current generation in the crank shaft due to rotating inductor coils to the prediction of resultant hardness and the induced residual stress. In the proposed methodology, a sequentially coupled electromagnetic and thermal model is developed to capture the resultant temperature distribution due to the rotation of the inductor coil.

This work reports a CFD study on a 2-stroke (2-S) opposed piston high speed direct injection (HSDI) Diesel engine. The engine main features (bore, stroke, port timings, et cetera) are defined in a previous stage of the project, while the current analysis is focused on the assembly made up of scavenge ports, manifold and cylinder. The first step of the study consists in the construction of a parametric mesh on a simplified geometry. Two geometric parameters and three different operating conditions are considered. A CFD-3D simulation by using a customized version of the KIVA-4 code is performed on a set of 243 different cases, sweeping all the most interesting combinations of geometric parameters and operating conditions. The post-processing of this huge amount of data allow us to define the most effective geometric configuration, named baseline.

Energy management in electric vehicles plays a significant role in both reducing energy consumption and limiting the rate of battery capacity degradation. It is especially important for systems with multiple energy storage units where optimally arbitrating power demand among the energy storage units is challenging. While many optimal control methods exist for designing a good energy management system, in this work a Reinforcement-Learning (RL) methodology is explored to design an energy management system for an electric vehicle with a Hybrid Energy Storage System (HESS) that included a battery and a supercapacitor. The energy management system is designed to optimally divide the traction power request among a battery and a super-capacitor in real-time; while trying to minimize the overall energy consumption and battery degradation.

Transmission Control Module (TCM) bracket is mounted on the vehicle chassis and is subjected to the random load excitation due to the uneven surface of the road. Assembly of the TCM bracket on the vehicle chassis induces some constant stress on it due to bolt preload, which acts as a mean stress along with the varying random loads. It is important for a design engineer and CAE analyst to understand the effect of all sources of loads on vehicle mount brackets while designing them. The objective of this study is to consider the effect of mean stress in the random vibration fatigue assessment of TCM bracket. The random vibration fatigue analyses are performed for all the three directions without and with consideration of mean loads and results are compared to show the significance of mean stresses in random vibration fatigue life.

The purge pump is used to pull evaporative gases from canister and send to engine for combustion in Turbocharged engines. The purge pump with impeller at one end and electric motor at the other end is supported by the ball bearing assembly. A bearing kinematic model to predict forcing function due to defect in ball bearing arrangement, coupled with bearing dynamic model of rotor because of rotating component, is proposed in this paper to get accumulated effect on transmitted force to the purge pump housing. Rotor dynamic of purge pump rotor components only produces certain order forcing responses which can be simulated into the multibody software environment, knowing the ball bearing geometry parameters hence providing stiffness parameter for rotor system.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

The objective of this research is a detailed investigation of particulate sizing and number count from a spark-ignited, direct-injection (SIDI) engine at different operating conditions. The engine is a 549 [cc] single-cylinder, four-valve engine with a flat-top piston, fueled by Tier II EEE. A baseline engine operating condition, with a low number of particulates, was established and repeatability at this condition was ascertained. This baseline condition is specified as 2000 rpm, 320 kPa IMEP, 280 [°bTDC] end of injection (EOI), and 25 [°bTDC] ignition timing. The particle size distributions were recorded for particle sizes between 7 and 289 [nm]. The baseline particle size distribution was relatively flat, around 1E6 [dN/dlogDp], for particle diameters between 7 and 100 [nm], before dropping off to decreasing numbers at larger diameters. Distributions resulting from a matrix of different engine conditions were recorded.

A model-based torque control strategy which is simple and easily adaptable to various types of engines is developed in this paper. A torque model is derived from constant-volume combustion model, and applications of the model to engine torque control problem are also discussed. As examples, the torque model is calibrated with experimental data collected from two different engines, and simulation and experimental results from the torque control strategy are presented as well.

Multi-core systems are promising a cost-effective solution for (1) advanced vehicle features requiring dramatically more software and hence an order of magnitude more processing power, (2) redundancy and mixed-IP, mixed-ASIL isolation required for ISO 26262 functional safety, and (3) integration of previously separate ECUs and evolving embedded software business models requiring separation of different software parts. In this context, designing, optimizing and verifying the mapping and scheduling of software functions onto multiple processing cores becomes key. This paper describes several multi-core task design and scheduling design options, including function-to-task mapping, task-to-core allocation (both static and dynamic), and associated scheduling policies such as rate-monotonic, criticality-aware priority assignment, period transformation, hierarchical partition scheduling, and dynamic global scheduling.