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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 engine operating point (EOP), which is determined by the engine speed and torque, is an important part of a vehicle's powertrain performance and it impacts FC, available propulsion power, and emissions. Predicting instantaneous EOP in real-time subject to dynamic driver behaviour and environmental conditions is a challenging problem, and in existing literature, engine performance is predicted based on internal powertrain parameters. However, a driver cannot directly influence these internal parameters in real-time and can only accommodate changes in driving behaviour and cabin temperature. It would be beneficial to develop a direct relationship between the vehicle-level parameters that a driver could influence in real-time, and the instantaneous EOP. Such a relationship can be exploited to dynamically optimize engine performance.

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.

Currently, Model Based Development (MBD) is the de-facto methodology in automotive industry. This has led to conversions of legacy code to Simulink models. Our previous work was related to implementing the C2M tool to automatically convert legacy code to Simulink models. While the tool has been implemented and deployed on few OEM pilot code-sets there were several improvement areas identified w.r.t. the generated models. One of the improvement areas identified was that the generated model used atomic blocks instead of abstracted blocks available in Simulink. E.g. the generated model used an ADD block and feedback loop to represent an integration operation instead of using an integrator block directly. This reduced the readability of the model even though the functionality was correct. Thus, as a user of the model, an engineer would like to see abstract blocks rather than atomic blocks.

Electric motor whine is a major NVH source for electric vehicles. Traditional mitigation methods focus on e-motor hardware optimization, which requires long development cycles and may not be easily modified when the hardware is built. This paper presents a control- and software-based strategy to reduce the most dominant motor order of an IPM motor for General Motors’ Ultium electric propulsion system, using the patented active Torque Ripple Cancellation (TRC) technology with harmonic current injection. TRC improves motor NVH directly at the source level by targeting the torque ripple excitations, which are caused by the electromagnetic harmonic forces due to current ripples. Such field forces are actively compensated by superposition of a phase-shifted force of the same spatial order by using of appropriate current.

This paper describes the design of a supervisory multivariable constrained Model Predictive Control (MPC) system for driver requested axle torque tracking with real-time fuel economy optimization that is scheduled for production by General Motors starting in 2018. The control system has been conceived and co-developed by General Motors and ODYS. The control approach consists of a set of linear MPC controllers scheduled in real-time based on powertrain operating conditions. For each MPC controller, a linear model is obtained by system identification with vehicle and dynamometer data. The supervisory MPC coordinates in real time desired Continuously Variable Transmission (CVT) ratio and desired engine torque to satisfy the system requirements, based on estimates of axle torque and engine fuel rate, by solving a constrained optimization problem at each sampling step. Each linear MPC controller is equipped with a Kalman filter to reconstruct the system state from available measurements.

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.

Prior to making rotational vibration measurements with a laser vibrometer, it is good practice to establish that the instrument is operating properly. This can be accomplished by comparative measurement of a rotational vibration source with known amplitude and frequency. This paper describes the design and development of a rotational vibration apparatus with known amplitude and frequency to be used as a reference for comparison to concurrent and co-located measurements made by a rotational laser vibrometer (RLV). The comparative measurements acquired with the apparatus are helpful to verify proper laser vibrometer operation in between regular calibration intervals, and/or whenever the functionality of the vibrometer is suspect. In the subject apparatus, a Cardan shaft with variable input speed and angle is used to provide output torsional vibration with variable frequency and amplitude.

The REAL NOx regulation requires tracking and reporting of NOx emissions starting in 2022MY for both medium-duty and heavy-duty diesel vehicles with potential to be considered during the next light-duty rulemaking. The regulation includes minimum NOx mass measurement accuracy requirements of either +/−20 percent or +/− 0.1 g/bhp-hr. Existing NOx sensor technology may not be able to meet the regulated accuracy requirements especially when exposed to other sources of variation within the emissions control system. This paper provides an assessment of real-world NOx sensor accuracy and the impact of other sources of variation and noise factors on NOx measurement accuracy. Noise factors investigated include NOx sensor tolerance, exhaust flow rate estimation, NOx sensor ammonia (NH3) cross sensitivity, mass air flow (MAF) sensor accuracy, NOx sensor placement, and laboratory emissions measurement capability.

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.

More than four decades ago, the concept of zero defects was coined by Phillip Crosby. It was only a vision at the time, but the introduction of Artificial Intelligence (AI) in manufacturing has since enabled it to become attainable. Since most mature manufacturing organizations have merged traditional quality philosophies and techniques, their processes generate only a few Defects Per Million of Opportunities (DPMO). Detecting these rare quality events is one of the modern intellectual challenges posed to this industry. Process Monitoring for Quality (PMQ) is an AI and big data-driven quality philosophy aimed at defect detection and empirical knowledge discovery. Detection is formulated as a binary classification problem, where the right Machine Learning (ML), optimization, and statistics techniques are applied to develop an effective predictive system.

Given continued industry focus on reducing parasitic losses, the ability to accurately measure the magnitude of losses on all driveline components is required. A standardized test procedure enables manufacturers and suppliers to measure component losses consistently, in addition to offering a reliable process to assess enablers for efficiency improvements. This paper reviews the development of SAE draft standard J3218, which is a comprehensive test procedure to break-in and characterize the efficiency of beam axles. Focus areas of the study included ensuring the axle’s efficiency does not change as it is being characterized, building a detailed map of efficiency at a wide range of operating points, and minimizing test time. The resulting break-in procedure uses an asymptotic regression approach to predict fully broken in efficiency of the axle and determine how much the efficiency of the axle changes during the characterization phase.

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.

Range anxiety in current battery electric vehicles is a challenging problem, especially for commercial vehicles with heavy payloads. Therefore, the development of electrified propulsion systems with multiple power sources, such as fuel cells, is an active area of research. Optimal speed planning and energy management, referred to as eco-driving, can substantially reduce the energy consumption of commercial vehicles, regardless of the powertrain architecture. Eco-driving controllers can leverage look-ahead route information such as road grade, speed limits, and signalized intersections to perform velocity profile smoothing, resulting in reduced energy consumption. This study presents a comprehensive analysis of the performance of an eco-driving controller for fuel cell electric trucks in a real-world scenario, considering a route from a distribution center to the associated supermarket.

Electric motor whine is one of the main noise sources of hybrid and electric vehicles. Compared with permanent magnetic motors, characterization and prediction of traction induction motor is particularly challenging due to high computational costs to calculate the electro-magnetic (EM) forces as noise source, as well as motor slip and harmonic orders change at different torque/speed operating conditions. Historically, induction motor NVH is designed qualitatively by optimizing motor topology including rotor bar, pole number and slot counts etc. A new integrated electromagnetic and NVH analysis method is developed and successfully validated at all dominant motor orders for an automotive traction motor, which enables quantitative prediction of induction motor N&V performance in early design stage: First, a new equivalent rotor current method is used that significantly reduces the computational time required to calculate the EM force over transient responses.

A multi-stage system level method is used to design, optimize and enhance electric motor NVH performance of General Motors’ Chevrolet Bolt battery electric vehicle (BEV). First, the rotor EM (electromagnetic) design optimizes magnet placement between adjacent poles asymmetrically, along with a pair of small slots stamped near the rotor outer surface to lower torque ripple and radial force. The size and placement of stator slot openings under each pole are optimized to lower torque ripple and radial force. Next, motor stator level FE (Finite Element) analysis and modal test correlation are performed to benchmark the orthotropic stator material properties and accurately predict modal results within 7% error below 2 kHz. Furthermore, tangential and radial EM forces are applied on motor-in-fixture subsystem FE model, which predicts surface vibration and pseudo sound power on the motor housing.

Exterior component integration presents competing performance challenges for balanced exterior styling, safety, ‘structural feel’ [1] and durability. Industry standard practices utilize noise and vibration mode maps and source-path-receiver [2] considerations for component mode frequency placement. This modal frequency placement has an influence on ‘structural feel’ and durability performance. Challenges have increased with additional styling content, geometric overhang from attachment points, component size and mass, and sensor modules. Base excitation at component attachment interfaces are increase due to relative positioning of the suspension and propulsion vehicle source inputs. These components might include headlamps, side mirrors, end gates, bumpers and fascia assemblies. Here, we establish basic expectations for the behavior of these systems, and ultimately consolidate existing rationales that are applied to these systems.

With the emphasis of electrification in automotive industry, tremendous efforts are made to develop electric motors with high efficiency and power density, and reduce noise, vibration and harshness (NVH). A multiphysics simulation workflow is used to predict the eccentricity-induced noise for GM’s Bolt EV motor. Both static and dynamic eccentricities are investigated along with axial tilt. Analysis results show that these eccentricities play a critical role in the NVH behavior of the motor assembly. Transient electromagnetic (EM) analysis is performed first by extruding 2D stator and rotor sections to form 3D EM models. Sector model is duplicated to form full 360-degree model. Stator is split into three rotated sections to characterize stator skew, and the skew between two sections of rotor and magnets are also modelled. Sinusoidal current is applied and lumped-sum forces on each stator tooth are computed.

A computational study based on unsteady Reynolds-Averaged-Navier-Stokes that resolves the gas-liquid interface was performed to examine the unsteady multiphase flow in a 4 cylinder Inline (i-4) engine. In this study, the rotating motion of the crankshaft and reciprocating motion of the pistons were accounted for to accurately predict the oil distribution in various parts of the engine. Three rotational speeds of the crankshaft have been examined: 1000, 2800, and 4000 rpm. Of particular interest is to examine the mechanisms governing the process of oil drawdown from the engine head into the case. The oil distributions in other parts of the engine have also been investigated to understand the overall crankcase breathing process. Results obtained show the drawdown of oil from the head into the case to be strongly dependent on the venting strategy for the foul air going out of the engine through the PCV system.

Torque ripple from electric motor can excite a system resonance perceived as vibration at the vehicle seat track. The CAE simulation procedure was applied to analyze the seat track acceleration excited by electric motor torque ripple. In this study, the transfer function between the electric motor torque and vehicle level seat track acceleration was developed, and it incorporates the control capability and vehicle sensitivity subfunctions. The motor level torque ripple requirement was developed, which can support motor design in early vehicle development stage based on vehicle level criteria. The analysis results obtained for motor level torque ripple requirement shows good agreement with the experimental validation using vehicle test data. The variation study on control capability and vehicle sensitivity was investigated, and the results can help to identify the solution to improve vehicle torque ripple response.