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In applications ranging from design of customized vehicle interiors to virtual testing of biomedical devices, the processes of modeling, design and analysis involve the simultaneous treatment of artifacts (i.e., parts designed by humans) and anatomical structures. An inherent conflict arises because the geometric descriptions are completely different. Artifact descriptions are typically the output of computer-aided design (CAD) software and consist of a collection of parametric patches that comprise the boundary of the artifact. In stark contrast, the native description of an anatomical structure typically consists of an image stack obtained using a volumetric scanning technology such as computed tomography (CT) or magnetic resonance imaging (MRI). Current practice for simultaneously dealing with both categories of entities involves working primarily in the world of CAD.

This paper details the control system development process for the University of Washington (UW) EcoCAR 2 team over the three years of the competition. Particular emphasis is placed upon the control system development and validation process executed during Year 3 of the competition in an effort to meet Vehicle Technical Specifications (VTS) established and refined by the team. The EcoCAR 2 competition challenges 15 universities across North America to reduce the environmental impact of a 2013 Chevrolet Malibu without compromising consumer acceptability. The project takes place over a three year design cycle, where teams select a hybrid architecture and fuel choice before defining a set of VTS goals for the vehicle. These VTS are selected based on the desired static and dynamic performance targets to balance fuel consumption and emissions with consumer acceptability requirements.

The University of Washington Advanced Vehicle Works team is currently in the process of designing Plug-in Hybrid Electric Vehicle (PHEV) for the EcoCAR2 Challenge. This competition challenges 15 universities across North America to reduce the environmental impact of a 2013 Chevrolet Malibu without compromising consumer acceptability. The architecture chosen by the team to address these goals is a Parallel Through The Road (PTTR) PHEV which provides all electric operation to displace petroleum usage, four wheel drive mode to improve utility performance for consumers, and effective charge-sustaining operation. The PTTR architecture is the lowest cost architecture to provide all of these benefits, and it does so without compromising safety performance of the platform.

EcoCAR 2: Plugging in to the Future (EcoCAR) is North America's premier collegiate automotive engineering competition, challenging students with systems-level advanced powertrain design and integration. The three-year Advanced Vehicle Technology Competition (AVTC) series is organized by Argonne National Laboratory, headline sponsored by the U. S. Department of Energy (DOE) and General Motors (GM), and sponsored by more than 30 industry and government leaders. Fifteen university teams from across North America are challenged to reduce the environmental impact of a 2013 Chevrolet Malibu by redesigning the vehicle powertrain without compromising performance, safety, or consumer acceptability. During the three-year program, EcoCAR teams follow a real-world Vehicle Development Process (VDP) modeled after GM's own VDP. The EcoCAR 2 VDP serves as a roadmap for the engineering process of designing, building and refining advanced technology vehicles.

In this paper, we demonstrate a system for synthesizing high-resolution, realistic 3D human body shapes according to user-specified anthropometric parameters. We begin with a corpus of whole-body 3D laser range scans of 250 different people. For each scan, we warp a common template mesh to fit each scanned shape, thereby creating a one-to-one vertex correspondence between each of the example body shapes. Once we have a common surface representation for each example, we then use principal component analysis to reduce the data storage requirements. The final step is to relate the variation of body shape with concrete parameters, such as body circumferences, point-to-point measurements, etc. These parameters can then be used as “sliders” to synthesize new individuals with the required attributes, or to edit the attributes of scanned individuals.

Map matching determines which road a vehicle is on based on inaccurate measured locations, such as GPS points. Simple algorithms, such as nearest road matching, fail often. We introduce a new algorithm that finds a sequence of road segments which simultaneously match the measured locations and which are traversable in the time intervals associated with the measurements. The time constraint, implemented with a hidden Markov model, greatly reduces the errors made by nearest road matching. We trained and tested the new algorithm on data taken from a large pool of real drivers.

In order to reduce painful and injurious shear stresses for lower limb amputees, prosthetic ankle joints need to provide torsional control in the transverse plane. This paper attempts to characterize biological ankle function in the transverse plane with simple mechanical elements to assist in the design of a biomimetic prosthetic ankle joint. Motion capture data was collected from ten subjects walking in a straight trajectory to model four states of stance phase. Passive elements were chosen to model the ankle in each state. The ankle was observed to act as a quadratic torsional spring in State 1 and as linear torsional springs in States 2, 3 and 4. The results of this study should assist with the mechanical design and control of a biomimetic torsional prosthesis by suggesting a finite state control system and by providing the stiffness coefficients to be controlled for straight walking.

This paper develops and tests algorithms for predicting the end-to-end route of a vehicle based on GPS observations of the vehicle's past trips. We show that a large portion a typical driver's trips are repeated. Our algorithms exploit this fact for prediction by matching the first part of a driver's current trip with one of the set of previously observed trips. Rather than predicting upcoming road segments, our focus is on making long term predictions of the route. We evaluate our algorithms using a large corpus of real world GPS driving data acquired from observing over 250 drivers for an average of 15.1 days per subject. Our results show how often and how accurately we can predict a driver's route as a function of the distance already driven.

Regenerative braking is an important factor in improving hybrid electric vehicle efficiency. This paper proposes a new regenerative braking strategy that activates preemptively during a distracted driving scenario, before service brakes are utilized. The strategy uses onboard advanced driver assistance systems, such as forward facing radar, to detect when an object is approaching fast enough to enable regenerative braking in response. The proposed strategy is simulated on a full-vehicle model of a series plug-in hybrid electric vehicle. A driver model is developed to mimic the behavior of a distracted driver through delayed response time to the changing speed of a lead vehicle. Multiple trials are simulated using different combinations of existing regenerative braking strategies along with the proposed strategy. Results show that a preventative regenerative braking control strategy can recuperate significant amounts of energy while also improving vehicle safety.

This paper details the development process and model architecture used in the University of Washington's EcoCAR 2 hybrid supervisory controller. The EcoCAR 2 project challenges 15 universities across North America to create a hybrid vehicle that most effectively minimizes emissions and fuel consumption while still maintaining consumer acceptability. The supervisory controller for the University of Washington was designed to distribute torque to the various electric and combustion drive systems on a parallel though the road plug-in hybrid electric vehicle using Simulink and Stateflow. The graphical interface of Simulink offers some distinct advantages over text-based programming languages. However, there are also significant challenges posed by the software, particularly when several controls engineers are working in parallel on a large model with some type of version control.

The University of Washington EcoCAR2 team (UWEC2) is currently in the process of building a Plug-in Hybrid Electric Vehicle (PHEV) for the EcoCAR2 Challenge. This competition challenges 15 universities across North America to reduce the environmental impact of a 2013 Chevrolet Malibu without compromising consumer acceptability. In order to be competitive in EcoCAR2, grid electricity is relied on heavily and the use of the Utility Factor method presented in SAE J2841 - Utility Factor Definitions must be used to compare emissions and consumption results with traditional vehicle results. Powertrain simulation in Autonomie was performed to explore many different hybrid architectures. The simulation results were normalized using the Utility Factor method to reach final architecture and component decisions.