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The present production General Motors 2-Mode Hybrid system for full-size SUVs and pickup trucks integrates truck utility functions with a full hybrid system. The 2-mode hybrid system incorporates two electro-mechanical power-split operating modes with four fixed-gear ratios. The combination provides fuel savings from electric assist, regenerative braking and low-speed electric vehicle operation. The combination of two power-split modes reduces the amount of mechanical power that is converted to electric power for continuously variable transmission operation, meeting the utility required for SUVs and trucks. This paper describes how fuel economy functionality was blended with full-size truck utility functions. Truck functions described include: Manual Range Select, Cruise Control, 4WD-Low and continuous high load operation.

As the automotive industry prepares to roll out an unprecedented range of fully electric propulsion vehicle models over the next few years - it really brings to a head for folks responsible for brakes what used to be the subject of hypothetical musings and are now pivotal questions for system design. How do we really go about designing brakes for electric vehicles, in particular, for the well-known limit condition of descending a steep grade? What is really an “optimal’ design for brakes considering the imperatives for the entire vehicle? What are the real “limit conditions” for usage that drive the fundamental design? Are there really electric charging stations planned for or even already existing in high elevations that can affect regenerative brake capacity on the way down? What should be communicated to drivers (if anything) about driving habits for electric vehicles in routes with significant elevation change?

The Lockheed Martin Low-Speed Wind Tunnel (LSWT) is a closed-return wind tunnel with two solid-wall test sections. This facility originally entered into service in 1967 for aerodynamic research of aircraft in low-speed and vertical/short take-off and landing (V/STOL) flight. Since this time, the client base has evolved to include a significant level of automotive aerodynamic testing, and the needs of the automotive clientele have progressed to include acoustic testing capability. The LSWT was therefore acoustically upgraded in 2016 to reduce background noise levels and to minimize acoustic reflections within the low-speed test section (LSTS). The acoustic upgrade involved detailed analysis, design, specification, and installation of acoustically treated wall surfaces and turning vanes in the circuit as well as low self-noise acoustic wall and ceiling treatment in the solid-wall LSTS.

EMC Component Validation Responsibilities encompass many realms. One of these realms is the effect of magnetic fields on silicon-based devices. This article describes a method for exposing these devices to magnetic fields with waveforms other than the traditional sinusoidal excitation. The method commonly used to explore the sensitivity of active silicon devices is exposure of the device to a representative sinusoidal field and observation of its reaction or lack thereof. The challenge is to characterize the representative field and be able to verify its effectiveness. Recent vehicle level testing of new designs has brought our attention to time-varying or transient magnetic field shapes that create deviations not previously detected with Military Standard 461 (MIL-STD-461) type sinusoidal magnetic field exposure.

The purchase of a new automobile is unquestionably a significant investment for most customers, and with this recognition, comes a correspondingly significant expectation for quality and reliability. Amongst automotive systems -when it comes to considerations of reliability - the brakes (perhaps along with the tires) occupy a rarified position of being located in a harsh environment, subjected to continuous wear throughout their use, and are critical to the safe performance of the vehicle. Maintenance of the brake system is therefore a fact of life for most drivers - something that almost everyone must do, yet given the potentially considerable expense, it is something that of great benefit to minimize.

Environmental perception is considered an essential module for autonomous driving and Advanced Driver Assistance System (ADAS). Recently, deep Convolutional Neural Networks (CNNs) have become the State-of-the-Art with many different architectures in various object detection problems. However, performances of existing CNNs have been dropping when detecting small objects at a large distance. To deploy any environmental perception system in real world applications, it is important that the system achieves high accuracy regardless of the size of the object, distance, and weather conditions. In this paper, a robust sensor fused object detection system is proposed by utilizing the advantages of both vision and automotive radar sensors. The proposed system consists of three major components: 1) the Coordinate Conversion module, 2) Multi level-Sensor Fusion Detection (MSFD) system, and 3) Temporal Correlation filtering module.

The adoption of simulation is critical to reducing development time and enhancing system robustness for Advanced Driver Assistance Systems (ADAS). Automotive companies typically have an abundance of real data recorded from a vehicle which is suitable for open-loop simulations. However, recorded data is often not suitable to test closed-loop control systems since the recorded data cannot react to changes in vehicle movement. This paper introduces a methodology to create virtual driving scenarios from recorded vehicle data to enable closed-loop simulation. This methodology is applied to test a lane centering application. A lane centering application helps a driver control steering to stay in the current lane and control acceleration and braking to maintain a set speed or to follow a preceding vehicle. The driver’s vehicle is referred to as the ego vehicle. Other vehicles on the road are referred to as target vehicles.

Modern hybrid technologies, especially mild and micro-hybrids with auto start/stop feature, demand a starter with higher power, better performance and longer life than conventional brush-type starters. In this paper, a new starter design using a brushless motor is proposed. This improves the engine crank performance during autostarts due to lower inertia, higher torque and wider power band capability of the brushless motor, especially at higher speeds. The overall integrated system includes the motor, inverter and controller all packaged in the same form factor of the original starter housing as a “drop-in replacement”. The prototype starter motor is designed to operate at 48V with a peak power of 4kW but can be designed to operate at the standard 12V. This paper will describe in detail the functionalities of the overall system and the simulation and experimental results of the prototype that was tested on a 4-cylinder engine in a production crossover vehicle.

Forward collision warning is one of the most challenging concerns in the safety of autonomous vehicles. A cooperation between many sensors such as LIDAR, Radar and camera helps to enhance the safety. Apart from the importance of having a reliable object detector, the safety system should have requisite capabilities to make reasonable decisions in the moment. In this work, we concentrate on detecting front vehicles of autonomous cars using a monocular camera, beyond only a detection method. In fact, we devise a solution based on a cooperation between a deep object detector and a reinforcement learning method to provide forward collision warning signals. The proposed method models the relation between acceleration, distance and collision point using the area of the bounding box related to the front vehicle. An agent of learning automata as a reinforcement learning method interacts with the environment to learn how to behave in eclectic hazardous situations.

As advanced vehicle controls and autonomy become mainstream in the automotive industry, the need to employ traditional mathematical models and control strategies arises for the purpose of simulating autonomous vehicle handling maneuvers. This study focuses on lane change maneuvers for autonomous vehicles driving at low speeds. The lane change methodology uses PID (Proportional-Integral-Derivative) controller to command the steering wheel angle, based on the yaw motion and lateral displacement of the vehicle. The controller was developed and tested on a bicycle model of an electric vehicle (a Chevrolet Bolt 2017), with the implementation done in MATLAB/Simulink. This simple mathematical model was chosen in order to limit computational demands, while still being capable of simulating a smooth lane change maneuver under the direction of the car’s mission planning module at modest levels of lateral acceleration.

In a hydraulic system, parameter variation, contamination, and/or operating conditions can lead to instabilities in the pressure response. The resultant erratic pressure profile reduces performance and can lead to hardware damage. Specifically, in a transmission control system, the inability to track pressure commands can result in clutch or variator slip which can cause driveline disturbance and/or hardware damage. A variator is highly sensitive to slip and therefore, it is advantageous to identify such pressure events quickly and take remedial actions. The challenge is to detect the condition in the least amount of time while minimizing false alarms. A Neyman-Pearson and an energy detector (based on auto-correlation) are evaluated for the detection of pressure disturbances. The performance of the detectors is measured in terms of speed of detection and robustness to measurement noise.

The fatal automobile accidents can be attributed to fatigued and distracted driving by drivers. Driver Monitoring Systems alert the distracted drivers by raising alarms. Most of the image based driver drowsiness detection systems face the challenge of failure proof performance in real time applications. Failure in face detection and other important part (eyes, nose and mouth) detections in real time cause the system to skip detections of blinking and yawning in few frames. In this paper, a real time robust and failure proof driver drowsiness detection system is proposed. The proposed system deploys a set of detection systems to detect face, blinking and yawning sequentially. A robust Multi-Task Convolutional Neural Network (MTCNN) with the capability of face alignment is used for face detection. This system attained 97% recall in the real time driving dataset collected. The detected face is passed on to ensemble of regression trees to detect the 68 facial landmarks.

Reliable driveaway from frozen condition is one of the challenging design and control problem for fuel cell applications. Different approaches for warmup from frozen conditions have been developed by OEMs, e.g. low voltage inefficient operation, or use of coolant heaters. However, most methods result in water generation which risk icing and blocking the valves and rendering them nonfunctional till they thaw. One such valve is the anode drain valve which is needed to remove water that crosses over across the membrane to anode side. This work discusses characterization of dynamics of water crossover to anode balance of plant via step response experiments on full scale systems, and development of an online estimator to detect onset of anode water crossover via this online observer. In addition, detection via voltage dip-based feedback is also presented.

The diesel particulate filters (DPF) are considered the most robust technologies for particle emission reduction both in terms of mass and number. On the other hand, the increase of the backpressure in the exhaust system due to the accumulation of the particles in the filter walls leads to an increase of the engine fuel consumption and engine power reduction. To limit the filter loading, and the backpressure, a periodical regeneration is needed. Because of the growing interest about particle emission both in terms of mass, number and size, it appears important to monitor the evolution of the particle mass and number concentrations and size distribution during the regeneration of the DPFs. For this matter, in the presented work the regeneration of a catalyzed filter was fully analyzed. Particular attention was dedicated to the dynamic evolution both of the thermodynamic parameters and particle emissions.

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.

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.

Lean applications in product development usually start with manufacturing due to the relative experience of measuring improvements and identifying wastes in physical settings. The full potential of lean implementation in any product development, however, can only be realized when applied throughout the process, starting with early process. Considering that the first and most essential principle in lean implementation is the characterization of value from the customer's perspective, it is imperative that the proper definition of value is realized at the beginning of the process. In addition, streaming and flowing of this customer's specified value should be realized throughout the process from start to finish. This paper discusses the application of lean principles to integrated design and manufacturing phases of the Product Development Process.

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.

Typically, when someone needs to perform occasional towing tasks, such as towing a boat on a trailer, they have two choices. They can either purchase a larger, more powerful vehicle than they require for their regular usage, or they can rent a larger vehicle when they need to tow something. In this project, we propose a third alternative: a trailer with an on-board power supply, which can be towed by a small vehicle. This system requires a means of sensing how much power the trailer's power supply should provide, and an appropriate control system to provide this power. In this project, we design and model the trailer, a standard small car, and the control system, and evaluate the concept's feasibility. We have selected a suitable power source for the trailer, a DC motor, coupled directly to the trailer's single drive wheel, which allow us to dispense with the need for a differential.

The General Motors Reduced Scale Wind Tunnel Facility, which came into operation in the fall of 2015, is a new state-of-the-art scale model aerodynamic test facility that expands GM’s test capabilities. The new facility also increases GM’s aerodynamic testing through-put and provides the resources needed to achieve the growing demand for higher fuel economy requirements for next generation of vehicles. The wind tunnel was designed for a nominal model scale of 40%. The nozzle and test section were sized to keep wind tunnel interference effects to a minimum. Flow quality and other wind tunnel performance parameters are on par with or better than the latest industry standards. A 5-belt system with a long center belt and boundary layer suction and blowing system are used to model underbody flow conditions. An overhead probe traverse system is installed in the test section along with a model positioning robot used to move the model in an out of the test section.