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Automotive engines are regularly utilized in the material handling market where LPG is often the primary fuel used. When compared to gasoline, the use of gaseous fuels (LPG and CNG) as well as alcohol based fuels, often result in significant increases in valve seat insert (VSI) and valve face wear. This phenomenon is widely recognized and the engine manufacturer is tasked to identify and incorporate appropriate valvetrain material and design features that can meet the ever increasing life expectations of the end-user. Alternate materials are often developed based on laboratory testing – testing that may not represent real world usage. The ultimate goal of the product engineer is to utilize accelerated lab test procedures that can be correlated to field life and field failure mechanisms, and then select appropriate materials/design features that meet the targeted life requirements.

Although hybrid variants of Automated Mechanical Transmissions such as the Dual Clutch Transmissions are less affected by driveline torque interrupts, actuator dynamics is very critical in the speed of gear pre-selection and during multiple gear shifts. To avoid torque interrupts, such systems require precise gearshift duration hence the actuators are expected to have fast, stable and predictable responses. However, actuator dynamics and controls remain barriers to attaining the full benefits of such complex systems, demanding precise timing and coordination of the gearshifts alongside the clutches engagement and disengagement. To overcome such challenges, a dynamic model of an electro-hydraulic gearshift actuator, the synchronizer and the shift fork has been developed. The model predicts the gearshift actuator dynamics for a given set of input parameters, which can be correlated against experimental data.

The human thermal comfort, which has been a subject of extensive research, is a principal objective of the automotive climate control system. Applying the results of research studies to the practical problems require quantitative information of the thermal environment in the passenger compartment of a vehicle. The exposure to solar radiation is known to alter the thermal environment in the passenger compartment. A photovoltaic-cell based sensor is commonly used in the automotive climate control system to measure the solar radiation exposure of the passenger compartment of a vehicle. The erroneous information from a sensor however can cause thermal discomfort to the occupants. The erroneous measurement can be due to physical or environmental parameters. Shading of a solar sensor due to the opaque vehicle body elements is one such environmental parameter that is known to give incorrect measurement.

This paper describes the work performed in predicting and measuring the contribution of oil churning to the no-load losses of a commercial truck axle at typical running speeds. A computational fluid dynamics (CFD) analysis of the churning losses was conducted. The CFD model accounts for design geometry, operating speed, temperature, and lubricant properties. The model calculates the oil volume fraction and the torque loss caused by oil churning due to the viscous and inertia effects of the fluid. CFD predictions of power losses were then compared with no-load measurements made on a specially developed, dynamometer-driven test stand. The same axle used in the CFD model was tested in three different configurations: with axle shafts, with axle shafts removed, and with ring gear and carrier removed. This approach to testing was followed to determine the contribution of each source of loss (bearings, seals, and churning) to the total loss.

The Chevrolet Volt is an electric vehicle with extended-range that is capable of operation on battery power alone, and on engine power after depletion of the battery charge. First generation Chevrolet Volts were driven over half a billion miles in North America from October 2013 through September 2014, 74% of which were all-electric [1, 12]. For 2016, GM has developed the second-generation of the Volt vehicle and “Voltec” propulsion system. By significantly re-engineering the traction power inverter module (TPIM) for the second-generation Chevrolet Volt extended-range electric vehicle (EREV), we were able to meet all performance targets while maintaining extremely high reliability and environmental robustness. The power switch was re-designed to achieve efficiency targets and meet thermal challenges. A novel cooling approach enables high power density while maintaining a very high overall conversion efficiency.

Prediction based optimal energy management systems are a topic of high interest in the automotive industry as an effective, low-cost option for improving vehicle fuel efficiency. With the continuing development of connected and autonomous vehicle (CAV) technology there are many data streams which may be leveraged by transportation stakeholders. The Suite of CAVs-derived data streams includes advanced driver-assistance (ADAS) derived information about surrounding vehicles, vehicle-to-vehicle (V2V) communications for real time and historical data, and vehicle-to-infrastructure (V2I) communications. The suite of CAVs-derived data streams have been demonstrated to enable improvements in system-level safety, emissions and fuel economy.

Recently, a new technology, termed 2-way SCR/DPF by the authors, has been developed by several catalyst suppliers for diesel exhaust emission control. Unlike a conventional emission control system consisting of an SCR catalyst followed by a catalyzed DPF, a wall-flow filter is coated with SCR catalysts for controlling both NOx and PM emissions in a single catalytic converter, thus reducing the overall system volume and cost. In this work, the potential and limitations of the Cu/Zeolite-based SCR/DPF technology for meeting future emission standards were evaluated on a pick-up truck equipped with a prototype light-duty diesel engine.

Sequel is the third vehicle in GM's Reinvention of the Automobile and is the first zero emissions passenger vehicle to drive more than 300 miles on public roads without refueling or recharging. It is purpose-built around the hydrogen storage and fuel cell systems and uses the skateboard principle introduced in the Autonomy vision concept and the Hy-wire proof-of-concept vehicles. Sequel's aluminum structure, Flexray controlled chassis-by-wire systems and AWD system comprising a single front electric motor and two rear wheel motors make it, perhaps, the most technically advanced automobile ever built. The paper describes the vehicle's design and performance characteristics.

A fully flexible valve actuation (FFVA) system was developed for a single cylinder research engine to investigate high efficiency clean combustion (HECC) in a diesel engine. The main objectives of the study were to examine the emissions, performance, and combustion characteristics of the engine using late intake valve closing (LIVC) to determine the benefits and limitations of this strategy to meet Tier 2 Bin 5 NOx requirements without after-treatment. The most significant benefit of LIVC is a reduction in particulates due to the longer ignition delay time and a subsequent reduction in local fuel rich combustion zones. More than a 95% reduction in particulates was observed at some operating conditions. Combustion noise was also reduced at low and medium loads due to slower heat release. Although it is difficult to assess the fuel economy benefits of LIVC using a single cylinder engine, LIVC shows the potential to improve the fuel economy through several approaches.

This paper suggests a scientific approach to designing conservative tests based on computer simulation of the influence of the sources of variations. The idea is to design the conservative test so that, even in the presence of variation, there is a high probability that a random test will have a better result than the conservative test. Therefore, if the conservative test meets the requirement, one has a scientific reason to believe that any random test would have a high probability of meeting it. This new approach is illustrated for FMVSS301 80 kph 70% rear offset deformable barrier impact.

Over the past decade, the automotive industry has seen a rapid decrease in product development cycle time and an ever increasing need by original equipment manufacturers and their suppliers to differentiate themselves in the marketplace. This differentiation is increasingly accomplished by introducing new technology while continually improving the performance of existing automotive systems. In the area of automotive brake system design, and, in particular, the brake apply subsystem, an increased focus has been placed on the development of electrohydraulic apply systems and brake-by-wire systems to replace traditional pneumatic and hydraulic systems. Nevertheless, the traditional brake apply systems, especially vacuum-based or pneumatic systems, will continue to represent the majority of brake apply system production volume into the foreseeable future, which underscores the need to improve the performance and application of these traditional systems in passenger cars and light-trucks.

With increasingly stringent emissions regulations and concurrent requirements for enhanced engine thermal efficiency, a comprehensive characterization of the automotive gasoline fuel spray has become essential. The acquisition of accurate and repeatable spray data is even more critical when a combustion strategy such as gasoline direct injection is to be utilized. Without industry-wide standardization of testing procedures, large variablilities have been experienced in attempts to verify the claimed spray performance values for the Sauter mean diameter, Dv90, tip penetration and cone angle of many types of fuel sprays. A new SAE Recommended Practice document, J2715, has been developed by the SAE Gasoline Fuel Injection Standards Committee (GFISC) and is now available for the measurement and characterization of the fuel sprays from both gasoline direct injection and port fuel injection injectors.

A key element of General Motors' Advanced Propulsion Technology Strategy is the electrification of the automobile. The objectives of this strategy are reduced fuel consumption, reduced emissions and increased energy security/diversification. The introduction of hybrid vehicles was one of the first steps as a result of this strategy. To determine future opportunities and direction, an extensive study was completed to better understand the ability of Plug-in Hybrid Electric Vehicles (PHEV) and Extended-Range Electric Vehicles (E-REV) to address societal challenges. The study evaluated real world representative driving datasets to understand actual vehicle usage. Vehicle simulations were conducted to evaluate the merits of PHEV and E-REV configurations. As derivatives of conventional full hybrids, PHEVs have the potential to deliver a significant reduction in petroleum usage.

The auto industry has had decades of experience with designing safe vehicles. The introduction of highly integrated features brings new challenges that require innovative adaptations of existing safety methodologies and perhaps even some completely new concepts. In this paper, we describe some of the new challenges that will be faced by all OEMs and suppliers. We also describe a set of generic top-level potential hazards that can be used as a starting point for the Preliminary Hazard Analysis (PHA) of a vehicle software-intensive motion control system. Based on our experience with the safety analysis of a system of this kind, we describe some general categories of hazard causes that are considered for software-intensive systems and can be used systematically in developing the PHA.

While machine learning in autonomous vehicles development has increased significantly in the past few years, the use of reinforcement learning (RL) methods has only recently been applied. Convolutional Neural Networks (CNNs) became common for their powerful object detection and identification and even provided end-to-end control of an autonomous vehicle. However, one of the requirements of a CNN is a large amount of labeled data to inform and train the neural network. While data is becoming more accessible, these networks are still sensitive to the format and collection environment which makes the use of others’ data more difficult. In contrast, RL develops solutions in a simulation environment through trial and error without labeled data. Our research expands upon previous research in RL and Proximal Policy Optimization (PPO) and the application of these algorithms to 1/18th scale cars by expanding the application of this control strategy to a full-sized passenger vehicle.

Operation of an autonomous vehicle (AV) carries risk if it acts on inaccurate information about itself or the environment. The perception system is responsible for interpreting the world and providing the results to the path planning and other decision systems. The perception system performance is a result of the operating state of the sensors, e.g. is a sensor in fault or being adversely affected by the weather or environmental conditions, and approach to sensor measurement interpretation. We propose a trailing horizon switched system observer that minimizes the difference between reference tracking values developed from sensor fusion performed at an upper level and the values from a potentially faulty sensor based upon the convex combination of different sensor observation model outputs; the sensor observations models are associated with different sensor operating errors.

The GREEN-MAC-LCCP© [Global Refrigerants Energy & Environmental - Mobile Air Condition - Life Cycle Climate Performance] model described here is an evolution of a previous GM model that assesses the lifecycle energy and GHG emissions associated with the production, use and disposal of alternative refrigerants and MAC components. This new model reduces the complexity of inputs and provides a consistent output analysis. This model includes Microsoft Excel Visual Basic© code to automatically make the calculations once inputs are complete.

The purpose of this paper is to present the results of hot surface ignition (HSI) testing and American Society for Testing and Materials (ASTM) auto-ignition testing (AIT) performed on gasoline fuel mixtures containing varying levels of ethanol. With the increased consumer interest in ethanol-based fuels as a measure of reducing the United States dependence on foreign oil, the use of E85 and other ethanol/petroleum fuel blends is on the increase. While some autoignition data for summer and winter blends of gasoline on hot surfaces exist beyond the standard ASTM E659-78 test procedure [1], there is little data on ethanol-based fuels and their HSI characteristics.

Global environmental and economic concerns regarding increasing fuel consumption and greenhouse gas emission are driving changes to legislative regulations and consumer demand. As regulations become more stringent, advanced engine technologies must be developed and implemented to realize desired benefits. Discrete variable valve lift technology is a targeted means to achieve improved fuel economy in gasoline engines. By limiting intake air flow with an engine valve, as opposed to standard throttling, road-load pumping losses are reduced resulting in improved fuel economy. This paper focuses on the design and development of a switching roller finger follower system which enables two mode discrete variable valve lift on end pivot roller finger follower valvetrains. The system configuration presented includes a four-cylinder passenger car engine with an electro-hydraulic oil control valve, dual feed hydraulic lash adjuster, and switching roller finger follower.

Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide low error velocity prediction. We developed an LSTM deep neural network that uses different groups of datasets collected in Fort Collins, Colorado.