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EcoCAR 3 is a university based competition with the goal of hybridizing a 2016 Chevrolet Camaro to increase fuel economy, decrease environmental impact, and maintain user acceptability. To achieve this goal, university teams across North America must design, test, and implement automotive systems. The Colorado State University (CSU) team has designed a parallel pretransmission plug in hybrid electric design. This design will add torque from the engine and motor onto a single shaft to drive the vehicle. Since both the torque generating devices are pre-transmission the torque will be multiplied by both the transmission and final drive. To handle the large amount of torque generated by the entire powertrain system the vehicle's rear half-shafts require a more robust design. Taking advantage of this, the CSU team has decided to pursue the use of composites to increase the shaft's robustness while decreasing component weight.

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.

The environmental implications of converting vehicles powered by Internal Combustion Engines (ICE) to battery powered and hybrid battery/ICE powered are evaluated for the case of Chile, one of the worldwide leaders in the production of lithium (Li) required for manufacturing of Li-ion batteries. The economic and environmental metrics were evaluated by techno-economic analysis (TEA) and Life Cycle Assessment (LCA) tools - SuperPro Designer and Gabi®/GREET® models. The system boundary includes both the renewable and nonrenewable energy sources available in Chile and well-to-pump energy consumptions and GHG emissions due to Li mining and Li-ion battery manufacturing. All the major input data required for TEA and LCA were generated using Autonomie vehicle modeling software. This study compares economic and environmental indicators of three vehicle models for the case of Chile including compact, mid-size, and a light duty truck.

Maintaining and diagnosing vehicle systems often involves a technician connecting a service computer to the vehicle diagnostic port through a vehicle diagnostics adapter (VDA). This creates a connection from the service software to the vehicle network through a protocol adapter. Often, the protocols for the personal computer (PC) hosted diagnostic programs use USB, and the diagnostic port provides access to the controller area network (CAN). However, the PC can also communicate to the VDA via WiFi or Bluetooth. There may be scenarios where these wireless interfaces are not appropriate, such as maintaining military vehicles. As such, a method to defeature the wireless capabilities of a typical vehicle diagnostic adapter is demonstrated without access to the source code or modifying the hardware. The process of understanding the vehicle diagnostic adapter system, its hardware components, the firmware for the main processor and subsystems, and the update mechanism is explored.

Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into whether near-term technologies can be utilized to improve FE and the impact of real-world prediction error on potential FE improvements. In this study, a speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication with real-world driving data and a drive cycle database was developed to understand if incorporating near-term technologies could be utilized in a predictive energy management strategy to improve vehicle FE. This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a validated high-fidelity fuel economy model of a Toyota Prius.

Autonomous truck and trailer configurations face challenges when operating in reverse due to the lack of sensing on the trailer. It is anticipated that sensor packages will be installed on existing trailers to extend autonomous operations while operating in reverse in uncontrolled environments, like a customer's loading dock. Power Line Communication (PLC) between the trailer and the tractor cannot support high bandwidth and low latency communication. This paper explores the impact of using Ethernet or a wireless medium for commercial trailer-tractor communication on the lifecycle and operation of trailer electronic control units (ECUs) from a Systems Engineering perspective to address system requirements, integration, and security. Additionally, content-based and host-based networking approaches for in-vehicle communication, such as Named Data Networking (NDN) and IP-based networking are compared.

Cyber assurance of heavy trucks is a major concern with new designs as well as with supporting legacy systems. Many cyber security experts and analysts are used to working with traditional information technology (IT) networks and are familiar with a set of technologies that may not be directly useful in the commercial vehicle sector. To help connect security researchers to heavy trucks, a remotely accessible testbed has been prototyped for experimentation with security methodologies and techniques to evaluate and improve on existing technologies, as well as developing domain-specific technologies. The testbed relies on embedded Linux-based node controllers that can simulate the sensor inputs to various heavy vehicle electronic control units (ECUs). The node controller also monitors and affects the flow of network information between the ECUs and the vehicle communications backbone.

Modern vehicles have incorporated numerous safety-focused Advanced Driver Assistance Systems (ADAS) in the last decade including smart cruise control and object avoidance. In this paper, we aim to go beyond using ADAS for safety and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy. We combine ADAS sensor data with a previously developed prediction model, dynamic programming optimal energy management control, and a validated model of a 2010 Toyota Prius to explore fuel economy. First, a unique ADAS detection scope is defined based on optimal vehicle control prediction aspects demonstrated to be relevant from the literature. Next, during real-world city and highway drive cycles in Denver, Colorado, a camera is used to record video footage of the vehicle environment and define ADAS detection ground truth. Then, various ADAS algorithms are combined, modified, and compared to the ground truth results.

Optimal energy management of hybrid electric vehicles has previously been shown to increase fuel economy (FE) by approximately 20% thus reducing dependence on foreign oil, reducing greenhouse gas (GHG) emissions, and reducing Carbon Monoxide (CO) and Mono Nitrogen Oxide (NOx) emissions. This demonstrated FE increase is a critical technology to be implemented in the real world as Hybrid Electric Vehicles (HEVs) rise in production and consumer popularity. This review identifies two research gaps preventing optimal energy management of hybrid electric vehicles from being implemented in the real world: sensor and signal technology and prediction scope and error impacts. Sensor and signal technology is required for the vehicle to understand and respond to its environment; information such as chosen route, speed limit, stop light locations, traffic, and weather needs to be communicated to the vehicle.

Research on alternative fuels has made significant progress as demands for cleaner and more efficient engine operation intensifies. Liquefied petroleum gas (LPG) can offer a potential alternative fuel route in the Diesel fuel dominated heavy-duty transportation sector due to its low cost, high anti-knock limit relative to gasoline, and reduced emission levels. In this work, experimental investigations are performed to study the effects of LPG compositions on performance, emissions, and combustion behavior of a spark-ignited (SI) cooperative fuel research (CFR) engine under stoichiometric conditions. Four LPG blends (chemically pure propane, a representative US blend, HD-5, and a representative European blend) representing the present LPG market are chosen. The impact of fuel composition is studied under different compression ratios (CR), ranging from 7:1 to 10:1 with one-unit increments, and at constant engine speed, intake manifold air pressure (IMAP) and 50% burn crank angle (CA50).

A technology path has been identified for development of a high efficiency, durable, gasoline engine, targeted at achieving performance and emissions levels necessary to meet heavy-duty, on-road standards of the foreseeable future. Initial experimental and numerical results for the proposed technology concept are presented. This work summarizes internal research efforts conducted at Southwest Research Institute. An alternative combustion system has been numerically and experimentally examined. The engine utilizes gasoline as the fuel, with a combination of enabling technologies to provide high efficiency operation at ultra-low emissions levels. The concept is based upon very highly-dilute combustion of gasoline at high compression ratio and boost levels. Results from the experimental program have demonstrated engine-out NOx emissions of 0.06 g/hp/hr, at single-cylinder brake thermal efficiencies (BTE) above thirty-four percent.

Achieving maximum downforce during cornering is critical in the aerodynamic design of a race car. During cornering, all race cars will be at some angle of yaw relative to the vehicle velocity vector, and in certain forms of racing the yaw angle can be large. For this reason it is important to take into consideration the effect of yaw on the aerodynamic characteristics of the vehicle. Most aerodynamic elements on vehicles have been examined in some detail in straight ahead motion. However, an element such as a wing/end plate combination optimized for straight forward motion may not perform well under yaw conditions. This effect may substantially diminish the aerodynamic advantage for race cars in high yaw, such as those raced at the Pike's Peak Hill Climb. As a part of a comprehensive research program relating to optimized aerodynamics at high yaw angles, the effect of end plate design on lift and drag of a rear wing in free stream is being considered.

Recent developments in materials science related to nano-technology have led to the creation and investigation of a new group of potential coolants known as nanofluids. These suspensions have been shown to have improved heat transfer capabilities over fluids without nanoparticles. The application of such fluids in motor racing shows potential for improvements in engine thermal management and aerodynamics. This study investigates the effect of additions of nanoparticles on the heat capacity of water using Differential Scanning Calorimetry (DSC). Fluids incorporating a variety of nanoparticles, of varying size and volume fraction, are investigated. It is shown that the volumetric heat capacity of water is only slightly affected by the addition of nanoparticles. The similarity in volumetric heat capacities, coupled with proven increased thermal conductivity of nanofluids, yields fluids with high thermal diffusivities that respond more quickly to changes in thermal environment.

Previous research has demonstrated an increase in Fuel Economy (FE) using an optimal controller based on limited foreknowledge using methods such as Engine Equivalent Minimization Strategy (ECMS) and Stochastic Dynamic Programming (SDP) with stochastic error in the prediction signal considerations. This study seeks to quantify the sensitivity of prediction-derived vehicle FE improvements to prediction signal quality assuming optimal control. In this research, a hill pattern and route type identification scenario control subjected to varying prediction signal quality is selected for in depth study. This paper describes the development of a baseline Toyota Prius Hybrid Vehicle (HV) simulation models, real world drive cycles and real-world disturbances, and an optimal controller incorporating a prediction of vehicle power requirements.

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.

It is not uncommon in motorsport for a team to chase a chassis setup throughout a race weekend, changing many different suspension settings, yet not getting consistent response from the chassis. In at least some of these cases it has been later determined that these inconsistencies stemmed from either chassis damage or fastener loosening, leading to a decrease in chassis stiffness. The current research investigates a method for quickly and accurately measuring the torsional stiffness, or static compliance, of a racecar chassis and suspension at various stations along the length, which can be utilized in the paddock area. When compared to baseline measurements of a newly assembled racecar, the post-race static compliance of the vehicle can be used to reveal the length-wise region of new damage or softening of components.

In racing, understanding how the setup and setup changes affect the racecar's ability to produce optimum longitudinal and lateral acceleration is vital to producing a winning team. To better understand and characterize how setup and setup changes affect the racecar, the use of state transition diagrams to model the vehicle's transient roll and pitch while negotiating the track is being investigated. State transition diagrams are made up of statistically significant patterns or events, and show the interconnection or transition from one state to another.1 The basic application of a state transition diagram to the phenomena of a racecar's roll and pitch is to identify the locations on the race course of the major events that make up the vehicle braking into, maneuvering through, and accelerating out of a corner. Major events that are examined include the maximum roll and pitch displacements, velocities, and accelerations.

A current general need within Six Sigma methodologies is to utilize statistical methods including experimental design in the confirmation of new processes and their parameters. This is typically done in the improve step of the DMAIC process. This need is even more evident in microjoining (small scale resistance welding) due to the number and complexity of the process variables. This paper outlines the improve step of a Six Sigma project in which statistical methods are applied to a microjoining process. These statistical methods include linear experimental design, regression analysis with linear transformation and mathematical modeling. The paper documents the methodology used to establish process parameters in microjoining of an electrical lead frame design.

Fiber reinforced, shape memory, polymer matrix, composites have recently been demonstrated in a variety of applications. Once cured, these composites, based on thermoset shape memory resins, have the ability to be semi-permanently deformed from the cured shape at elevated temperatures and then subsequently returned to the original shape. However, the vast majority of the applications demonstrated have made use of very thin composite laminates. The current research considers composite sandwich panel structures formed from shape memory composite facesheets and a rigid foam core created from shape memory resin. The goal is to investigate the potential deformability in these much more rigid geometries to assess the potential for use in conformable, structural applications.

Practical applications of recently developed sensor fusion algorithms perform poorly in the real world due to a lack of proper evaluation during development. Existing evaluation metrics do not properly address a wide variety of testing scenarios. This issue can be addressed using proactive performance measurements such as the tools of resilience engineering theory rather than reactive performance measurements such as root mean square error. Resilience engineering is an established discipline for evaluating proactive performance on complex socio-technical systems which has been underutilized for automated vehicle development and evaluation. In this study, we use resilience engineering metrics to assess the performance of a sensor fusion algorithm for vehicle localization. A Kalman Filter is used to fuse GPS, IMU and LiDAR data for vehicle localization in the CARLA simulator.