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The design of vehicles, particularly hybrid and other advanced technology vehicles, is typically complex and benefits from systems engineering processes. Vehicle modeling and simulation have become increasingly important system design tools to improve the accuracy, repeatability, and flexibility of the design process. In developing vehicle computational models and simulation, there is an inevitable compromise between the level of detail and the development/computational cost. The tradeoff is specific to the requirements of each vehicle design effort. The assumptions and detail limitations used for vehicle simulations lead to a varying degree of result uncertainty for each design effort. This paper provides a literature review to investigate the state of the art vehicle simulation methods, and quantifies the uncertainty associated with components that are commonly allocated uncertainty.

There are many design parameters in designing multi-element racecar wings even after the airfoil to be used has been determined. To choose the best parameter values for the wings of a Formula SAE car, Computational Fluid Dynamics combined with highly fractional factorial design of experiments was used. The CFD results were analyzed for the effectiveness of each parameter in increasing the down force, and effective parameters were used for the next CFD analyses with a fractional factorial design for choosing the best parameter values. The designed wings satisfied the target performance criteria.

When a two-element airfoil for the wing of a racecar has to be inside a rectangle space dictated by regulations or dictated by the available space, the ratio of the flap chord length to the main element chord length, the overlap and gap sizes between the main element and the flap are design parameters, besides the element shapes. To find the configuration for the high downforce-to-drag ratio, CFD simulations were performed in 2D using the FX63-137 airfoil for both the main element and the flap. Some important findings are that the flap chord length should be 50 to 70% of the main element chord length to achieve the high lift-to-drag ratio. This finding will help design better two-element wings.

Motorsport Engineering is developing a foothold, around the World, as a field of academic preparation at the post-graduate level. To gain the appropriate practical skills to augment classroom education, and thus, for the graduates to successfully compete for employment in the Motorsport Industry, it is critical that the degree program has a strong experiential component. This paper describes the need to take an engineering approach to motorsport education by combining a discovery-based education with the traditional lecture format to realize synergistic results. The idea is that to effectively “engineer” the graduate, the student must have a strong skill set or a strong grasp of the fundamentals. The growth of the current educational program at Colorado State University and the effectiveness of merging the “inside-out” process, typical of the research mission, with the instructional practices of the University and with the needs of the Motorsport Industry are discussed.

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.

The EcoCAR3 competition challenges student teams to redesign a 2016 Chevrolet Camaro to reduce environmental impacts and increase energy efficiency while maintaining performance and safety that consumers expect from a Camaro. Energy management of the new hybrid powertrain is an integral component of the overall efficiency of the car and is a prime focus of Colorado State University’s (CSU) Vehicle Innovation Team. Previous research has shown that error-less predictions about future driving characteristics can be used to more efficiently manage hybrid powertrains. In this study, a novel, real-world implementable energy management strategy is investigated for use in the EcoCAR3 Hybrid Camaro. This strategy uses a Nonlinear Autoregressive Artificial Neural Network with Exogenous inputs (NARX Artificial Neural Network) trained with real-world driving data from a selected drive cycle to predict future vehicle speeds along that drive cycle.

From November 2010 until May of 2011, NASA's Icing Remote Sensing System was positioned at Platteville, Colorado between the National Science Foundation's S-Pol radar and Colorado State University's CHILL radar (collectively known as FRONT, or ‘Front Range Observational Network Testbed’). This location was also underneath the flight-path of aircraft arriving and departing from Denver's International Airport, which allowed for comparison to pilot reports of in-flight icing. This work outlines how the NASA Icing Remote Sensing System's derived liquid water content and in-flight icing hazard profiles can be used to provide in-flight icing verification and validation during icing and non-icing scenarios with the purpose of comparing these times to profiles of polarized moment data from the two nearby research radars.

Standard operation of autonomous vehicles on public roads results in significant exposure to high levels of risk. There is a significant need to develop metrics that evaluate safety of an automated system without reliance on the rate of vehicle accidents and fatalities compared to the number of miles driven; a proactive rather than a reactive metric is needed. Resilience engineering is a new paradigm for safety management that focuses on evaluating complex systems and their interaction with the environment. This paper presents the overall methodology of resilience engineering and the resilience assessment grid (RAG) as an evaluation tool to measure autonomous systems' resilience. This assessment tool was used to evaluate the ability to respond to the system. A Pure Pursuit controller was developed and utilized as the path tracking control algorithm, and the Carla simulator was used to implement the algorithm and develop the testing environment for this methodology.

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.

High accuracy hybrid vehicle fuel consumption (FC) and emissions models used in practice today are the product of years of research, are physics based, and bear a large computational cost. However, it may be possible to replace these models with a non-physics based, higher accuracy, and computationally efficient versions. In this research, an alternative method is developed by training and testing a time series artificial neural network (ANN) using real world, on-road data for a hydraulic hybrid truck to predict instantaneous FC and emissions. Parameters affecting model fidelity were investigated including the number of neurons in the hidden layer, specific training inputs, dataset length, and hybrid system status. The results show that the ANN model was computationally faster and predicted FC within a mean absolute error of 0-0.1%. For emissions prediction the ANN model had a mean absolute error of 0-3% across CO2, CO, and NOx aggregate predicted concentrations.