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Simulink is a very successful and popular method for modelling and auto-coding embedded automotive features, functions and algorithms. Due to its history of success, university feeder programs, and large third party tool support, it has, in some cases, been applied to areas of the software system where other methods, principles and strategies may provide better options for the software and systems engineers and architects. This paper provides approaches to determine when best to apply UML and when best to apply Simulink to a typical automotive feature. Object oriented software design patterns as well as general guidelines are provided to help in this effort. This paper's intent is not to suggest a replacement for Simulink but to provide the software architects and designers additional options when decomposing high level requirements into reusable software components.

Modern vehicles become increasingly software intensive. Software development therefore is critical to the success of the manufacturer to develop state of the art technology. Standards like ISO 26262 recommend requirement-based verification and test cases that are derived from requirements analysis. Agile development uses continuous integration tests which rely on test automation and evaluation. All these drove the development of a new model-based software verification environment. Various aspects had to be taken into account: the test case specification needs to be easily comprehensible and flexible in order to allow testing of different functional variants. The test environment should support different use cases like open-loop or closed-loop testing and has to provide corresponding evaluation methods for continuously changing as well as for discrete signals.

The proposed technique is a tailored deep neural network (DNN) training approach which uses an iterative process to support the learning of DNNs by targeting their specific misclassification and missed detections. The process begins with a DNN that is trained on freely available annotated image data, which we will refer to as the Base model, where a subset of the categories for the classifier are related to the automotive theater. A small set of video capture files taken from drives with test vehicles are selected, (based on the diversity of scenes, frequency of vehicles, incidental lighting, etc.), and the Base model is used to detect/classify images within the video files. A software application developed specifically for this work then allows for the capture of frames from the video set where the DNN has made misclassifications. The corresponding annotation files for these images are subsequently corrected to eliminate mislabels.

Engine air induction systems hydrocarbon trap (HC trap) designs to limit evaporative fuel emissions, have evolved over time. This paper discusses a range of HC traps that have evolved in engine air induction systems. (AIS) The early zeolite flow through HC trap utilized an exhaust catalyst technology internal stainless steel furnace brazed substrate coated with zeolite media. This HC trap was installed in the AIS clean air tube. This design was heavy, complicated, and expensive but met the urgency of the implementation of the new evaporative emissions regulation. The latest Ford Motor Company HC trap is a simple plastic tray containing activated carbon with breathable non-woven polyester cover. This design has been made common across multiple vehicle lines with planned production annual volume in the millions. The cost of the latest HC trap bypass design is approximately 5% of the original stainless steel zeolite flow through HC trap.

Thermal modeling of liquid-cooled vehicle traction battery assemblies using Computational Fluid Dynamics (CFD) usually involves large models to accurately resolve small cooling channel details, and intensive computation to simulate drive-cycle transient solutions. This paper proposes a segregated method to divide the system into three parts: the cells, the cold plate and the interface between them. Each of the three parts can be separated and thermally characterized and then combined to predict the overall system thermal behavior for both steady-state and transient operating conditions. The method largely simplifies battery thermal analysis to overcome the limitations of using large 3D CFD models especially for pack level dynamic drive cycle simulations.

The die wear up to 80,800 hits on a prog-die setup for bare DP1180 steel was investigated in real production condition. In total, 31 die inserts with the combination of 11 die materials and 9 coatings were evaluated. The analytical results of die service life for each insert were provided by examining the evolution of surface wear on inserts and formed parts. The moments of appearance of die defects, propagation of die defects, and catastrophic failure were determined. Moreover, the surface roughness of the formed parts for each die insert was characterized using Wyko NT110 machine. The objectives of the current study are to evaluate the die durability of various tooling materials and coatings for flange operations on bare DP 1180 steel and update OEM tooling standards based on the experimental results. The current study provides the guidance for the die material and coating selections in large volume production for next generation AHSSs.

In recent years, implementation of dual phase (DP) Advanced High Strength Steels (AHSS) and Ultra High Strength Steels (UHSS) is increasing in automotive components due to their superior structural performance and vehicle weight reduction capabilities. However, these materials are often sensitive to trimmed edge cracking if stretching along sheared edge occurs in such processes as stretch flanging. Tool wear is another major issue in the trimming of UHSS because of higher contact pressures at the interface between cutting tools and sheet metal blank caused by UHSS’s higher flow stresses and the presence of a hard martensitic in the microstructure. The objective of the present paper is to discuss the methodology of analyzing die wear for trimming operations of UHSS components and illustrate it with some examples of tool wear analysis for trimming 1.5mm thick DP980 steel.

Mechanical shock tests for lithium metal and lithium-ion batteries often require that each cell or battery pack be subjected to multiple shocks in the positive and negative directions, of three mutually perpendicular orientations. This paper focuses on the no-disassembly requirement of those testing conditions and on the CAE methodology specifically developed to perform this assessment. Ford Motor Company developed a CAE analysis method to simulate this type of test and assess the possibility of cell dislodging. This CAE method helps identify and diagnose potential failure modes, thus guiding the Design Team in developing a strategy to meet the required performance under shock test loads. The final CAE-driven design focuses on the structural requirement and optimization, and leads to cost savings without compromising cell or pack mechanical performance.

This paper proposes an approach that uses the road preview data to optimize a shift schedule for a vehicle equipped with an automatic transmission. The road preview is inferred here from the so-called electronic horizon of a digital map that includes road attributes such as road grade, curvature, segment speed limit, functional class, etc. The optimized shift schedule selects the gear ratio whose optimization is conducted through applying a hybrid model predictive control method to the powertrain system, which is modelled as the multiple plants associated with multiple gears together with engine models. The goal of this optimization of shift schedule includes improving real world fuel economy and drivability. The real-world fuel economy gains using the proposed approach are achieved through optimizing gear ratio w.r.t. the road grade variations of the road ahead.

Rivets, especially self-piercing rivets (SPR), are a primary joining technology used in aluminum bodied vehicles. SPR are mechanical joining elements used to connect sheets to create a body in white (BiW) structure. To ensure the structural performance of a vehicle in crash load cases it is necessary to describe physical occurring failure modes under overloading conditions in simulations. One failure mode which needs to be predicted precisely by a crash simulation is joint separation. Within crash simulations a detailed analysis of a SPR joint would require a very high computational effort. The conflict between a detailed SPR joint and a macroscopic vehicle model needs to be solved by developing an approach that can handle an accurate macroscopic prediction of SPR behavior with a defined strength level with less computational effort. One approach is using a cohesive material model for a SPR connection. The paper describes cohesive element characteristics and calibration effort.

An intervention of vehicle stability control systems is more likely on slippery surfaces, e.g. when the road is covered with snow or ice. Contrary to testing on dry asphalt, testing on such surfaces is restricted by weather and proving grounds. Another drawback in testing is the reproducibility of measurements, since the surface condition changes during the tests, and the vehicle reaction is more sensitive on slippery surface. For that, simulation enables a good pre-assessment of the control systems independent from testing conditions. Essential for this is a good knowledge about the contact between vehicle and road, meaning a good tyre model and a reasonable set of tyre model parameters. However, the low friction surface has a high variation in the friction coefficient. For instance, the available lateral acceleration on scraped ice could vary between 0.2 and 0.4 g within a day. These facts lead to the idea of using generic tyre parameters that vary in a certain range.

Subjective steering feel tuning and objective verification tests are conducted on vehicle prototypes that are a subset of the total number of buildable combinations of body style, drivetrain and tires. Limited development time, high prototype vehicle cost, and hence limited number of available prototypes are factors that affect the ability to tune and verify all the possible configurations. A new model-based process and a toolset have been developed to enhance the existing steering development process such that steering tuning efficiency and performance robustness can be improved. The innovative method utilizes the existing vehicle dynamics simulation and/or physical test data in conjunction with steering system control models, and provides users with simple interfaces which can be used by either CAE or development engineers to perform virtual tuning of the vehicle steering feel to meet performance targets.

With the continuing challenges of future fuel economy targets carbon fiber composite materials are one facet of a lightweighting strategy to enable reduced fuel consumption. In general, use of lightweight materials such as carbon fiber composites in vehicle design generates vehicle NVH performance degradation. To address this potential issue at the design phase, there is a need to develop correlated CAE models for carbon fiber vehicle parts to evaluate the NVH impact of carbon fiber composite material use in vehicle design. To develop correlated CAE models for lightweight vehicle design with the use of carbon fiber composite vehicle body parts, an experimental study was conducted to determine the material and NVH characteristics of the carbon fiber composite materials. In this paper, the damping properties and NVH modal analysis results for structural carbon fiber thermoset composite panels and body parts (B-pillar upper insert and B-pillar lower insert) is presented.

Engine downsizing, boosting, direct injection and variable valve actuation, have become industry standards for reducing CO2 emissions in current production vehicles. Because of the increasing complexity of the engine air path system and the high number of degrees of freedom for engine charge management, the design of air path control algorithms has become a difficult and time consuming process. One possibility to reduce the control development time is offered by Software-in-the-Loop (SIL) or Hardware-in-the-Loop (HIL) simulation methods. However, it is significantly challenging to identify engine air path system simulation models that offer the right balance between fidelity, mathematical complexity and computational burden for SIL or HIL implementation.

The paper deals with the design of shift scheduling maps based on dynamic programing (DP) optimization algorithm. The recorded data related to a delivery vehicle fleet are used, along with a model of delivery truck equipped with a 12-gear automated manual transmission, for an analysis and reconstruction of the truck-implemented shift scheduling patterns. The same map reconstruction procedure has been applied to a set of DP optimization-based operating points. The cost function of DP optimization is extended by realistic clutch energy losses dissipated during shift transients, in order to implicitly introduce hysteresis in the shift scheduling maps for improved drivability. The different reconstructed shift scheduling maps are incorporated within the truck model and validated by computer simulations for different driving cycles.

Per California Air Resources Board (CARB) regulations, On-board diagnostic (OBD) of vehicle powertrain systems are required to continuously monitor key powertrain components, such as the circuit discontinuity of actuators, various circuit faults of sensors, and out-of-range faults of sensors. The maturing and clearing of these continuous monitoring faults are critical to simplification of algorithm design, save of engineering cost (i.e., calibration), and reduction of warranty issues. Due to the nature of sensors (to sense different physical quantities) and actuators (to output energy in desired ways), most of OEM and supplies tend to choose different fault maturing and clearing strategy for sensors and actuators with different physics nature, such as timer-based, counter-based, and other physical-quantity-based strategies.

Age and experience influence driver ability to cope with transitions between automated and manual driving, especially when drivers are engaged in media use. This study evaluated three age cohorts (young/new drivers, adults, and seniors) on their performance in transitions from automated driving to manual vehicle control in a laboratory driving simulator. Drivers were given three tasks to perform during the automated driving segments: to watch a movie on a tablet, to read a story on a tablet, or to supervise the car's driving. We did not find significant differences in people's driving performance following the different tasks. We also did not find significant differences in driving performance between the people in each age group who successfully completed the study; however, the rejection rate of the senior age group was over 30% because many of the people in this age group had difficulty hearing instructions, understanding tasks, or remembering what to do.

Evaluation of electric steering (EPAS) system performance using vehicle specific load conditions is important for steering system design validation and vehicle steering performance tuning. Using real-time vehicle dynamics mathematical models is one approach for generating steering loads in steering hardware-in-the-loop (HIL) testing. However achieving a good correlation of simplified mathematical models with real vehicle dynamics is a challenge. Using rack force models from measured steering tie rod forces or from simulations using a high-fidelity vehicle dynamics model is an effective data-driven modelling method for testing EPAS systems under vehicle specific load conditions. Rack force models are identified from physical measurements or validated vehicle simulations of selected steering test maneuvers. The rack force models have been applied in steering system performance evaluation, benchmarking, and steering model validation.

Joining technology is a key factor to utilize dissimilar materials in vehicle structures. Adaptable insert weld (AIW) technology is developed to join sheet steel (HSLA350) to cast magnesium alloy (AM60) and is constructed by combining riveting technology and electrical resistance spot welding technology. In this project, the AIW joint technology is applied to construct front shock tower structures composed with HSLA350, AM60, and Al6082 and a method is developed to predict the fatigue life of the AIW joints. Lap-shear and cross-tension specimens were constructed and tested to develop the fatigue parameters (load-life curves) of AIW joint. Two FEA modeling techniques for AIW joints were used to model the specimen geometry. These modeling approaches are area contact method (ACM) and TIE contact method.

This work reports on measurement and analysis of the galvanic interaction between steel self-piercing rivets (SPRs) having several different surface conditions and magnesium alloy substrates under consideration for use in automotive structural assemblies. Rivet surface conditions included uncoated steel, conventional Zn-Sn barrel plating and variations of commercial aluminizing processes, including supplemental layers and sealants. Coating characteristics were assessed using open circuit potential (OCP) measurement, potentiodynamic polarization scanning (PDS), and electrochemical impedance spectroscopy (EIS). The degree of galvanic coupling was determined using zero-resistance ammeter (ZRA) and the scanning vibrating electrode technique (SVET), which also permitted characterization of galvanic current flows in situ.