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In this paper a yaw stability control algorithm along with an emergency roll control strategy have been developed. The yaw stability controller and emergency roll controller were both developed using linear two degree-of-freedom vehicle models. The yaw stability controller is based on Lyapunov stability criteria and uses vehicle lateral acceleration and yaw rate measurements to calculate the corrective yaw moment required to stabilize the vehicle yaw motion. The corrective yaw moment is then applied by means of a differential braking strategy in which one wheel is selected to be braked with appropriate brake torque applied. The emergency roll control strategy is based on a rollover coefficient related to vehicle static stability factor. The emergency roll control strategy utilizes vehicle lateral acceleration measurements to calculate the roll coefficient. If the roll coefficient exceeds some predetermined threshold value the emergency roll control strategy will deploy.

A new and unique electric vehicle powertrain model based on bidirectional power flow for propel and regenerative brake power capture is developed and applied to production battery electric vehicles. The model is based on a Willans line model to relate power input from the battery and power output to tractive effort, with one set of parameters (marginal efficiency and an offset loss) for the bidirectional power flow through the powertrain. An electric accessory load is included for the propel, brake and idle phases of vehicle operation. In addition, regenerative brake energy capture is limited with a regen fraction (where the balance goes to friction braking), a power limit, and a low-speed cutoff limit. The purpose of the model is to predict energy consumption and range using only tractive effort based on EPA published road load and test mass (test car list data) and vehicle powertrain parameters derived from EPA reported unadjusted UDDS and HWFET energy consumption.

A graphical user interface (GUI) toolbox called Vehicle System Simulator (VSS) is developed in MATLAB to ease the use of this vehicle model and hopefully encourage its widespread application in the future. This toolbox uses the inherent MATLAB discrete-time solvers and is mainly based on Level-2 s-function design. This paper describes its built-in vehicle dynamics model based on multibody dynamics approach and nonlinear tire models, and traction/braking control systems including Cruise Control and Differential Braking systems. The built-in dynamics model is validated against CarSim 8 and the simulation results prove its accuracy. This paper illustrates the application of this new MATLAB toolbox called Vehicle System Simulator and discusses its development process, limitations, and future improvements.

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is ready to compete in the Year 3 Final Competition for EcoCAR 2: Plugging into the Future. The team is confident in the reliability of their vehicle, and expects to finish among the top schools at Final Competition. During Year 3, the team refined the vehicle while following the EcoCAR 2 Vehicle Development Process (VDP). Many refinements came about in Year 3 such as the implementation of a new rear subframe, the safety analysis of the high voltage (HV) bus, and the integration of Charge Sustaining (CS) control code. HEVT's vehicle architecture is an E85 Series-Parallel Plug-In Hybrid Electric Vehicle (PHEV), which has many strengths and weaknesses. The primary strength is the pure EV mode and Series mode, which extend the range of the vehicle and reduce Petroleum Energy Usage (PEU) and Greenhouse Gas (GHG) emissions.

In the past few years, the price of petroleum based fuels, especially vehicle fuels such as gasoline and diesel, have been increasing at a significant rate. Consequently, there is much more consumer interest related to reducing fuel consumption of conventional and hybrid electric vehicles (HEVs). The “pulse and glide” (PnG) driving strategy is first applied to a conventional vehicle to quantify the fuel consumption benefits when compared to steady state speed (cruising) conditions over the same time and distance. Then an HEV is modeled and tested to investigate if a hybrid system can further reduce fuel consumption with the proposed strategy. Note that the HEV used in this study has the advantage that the engine can be automatically shut off below a certain speed (∼40 mph, 64 kph) at low loads, however a driver must shut off the engine manually in a conventional vehicle to apply this driving strategy.

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech has achieved the Year 2 goal of producing a 65% functional mule vehicle suitable for testing and refinement, while maintaining the series-parallel plug-in hybrid architecture developed during Year 1. Even so, further design and expert consultations necessitated an extensive redesign of the rear powertrain and front auxiliary systems packaging. The revised rear powertrain consists of the planned Rear Traction Motor (RTM), coupled to a single-speed transmission. New information, such as the dimensions of the high voltage (HV) air conditioning compressor and the P2 motor inverter, required the repackaging of the hybrid components in the engine bay. The P2 motor/generator was incorporated into the vehicle after spreading the engine and transmission to allow for the required space.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2005 - 2007 Challenge X advanced technology vehicle competition series, sponsored by General Motors Corporation, the U.S. Department of Energy, and Argonne National Lab. This report documents the Equinox REVLSE (Renewable Energy Vehicle, the Larsen Special Edition) design and specifies how it meets the Vehicle Technical Specifications (VTS) set by Challenge X and HEVT through simulation and test results. The report also documents the vehicle control development process, specifies the control code generation, demonstrates an analysis of hybrid powertrain losses, and presents the REVLSE vehicle balance in its intended market.

This study evaluated the accuracy of 75 Event Data Recorders (EDRs) extracted from model year 2010-2012 Chrysler, Ford, General Motors, Honda, Mazda, and Toyota vehicles subjected to side-impact moving deformable barrier crash tests. The test report and vehicle-mounted accelerometers provided reference values to assess the EDR reported change in lateral velocity (delta-v), seatbelt buckle status, and airbag deployment status. Our results show that EDRs underreported the reference lateral delta-v in the vast majority of cases, mimicking the errors and conclusions found in some longitudinal EDR accuracy studies. For maximum lateral delta-v, the average arithmetic error was −3.59 kph (−13.8%) and the average absolute error was 4.05 kph (15.9%). All EDR reports that recorded a seatbelt buckle status data element correctly recorded the buckle status at both the driver and right front passenger locations.

This study evaluates the accuracy of 41 Event Data Recorders (EDR) extracted from model year 2012 General Motors, Chrysler, Ford, Honda, Mazda, Toyota, and Volvo vehicles subjected to New Car Assessment Program 56 kph full-frontal barrier crash tests. The approach was to evaluate (1) the vehicle longitudinal change in velocity or delta-V (ΔV) as measured by EDRs in comparison with the high-precision accelerometers mounted onboard test vehicles and (2) the accuracy of pre-crash speed, seatbelt buckle status, and frontal airbag deployment status. On average the absolute error for pre-crash speed between the EDR and reference instrumentation was only 0.58 kph, or 1.0% of the nominal impact speed. In all cases in which the EDRs recorded the seatbelt buckle status of the driver or right front passenger, the modules correctly reported that the occupants were buckled. EDRs reported airbag deployment correctly in all of the tests.

A crucial step to designing and building more efficient vehicles is modeling powertrain energy consumption. While accurate modeling is indeed key to effective and efficient design, a fundamental understanding of the powertrain and auxiliary systems that contribute to the energy consumption of a vehicle is equally as important. This paper presents a methodology that has been packaged into a tool, called VTool (short for Vehicle Tool), which can be used to estimate the energy consumption of a vehicle powertrain. The method is intrinsically designed to foster understanding of the vehicle powertrain as it relates to energy consumption and losses while still providing reasonably accurate results. This paper briefly explains the methodology of VTool and demonstrates the capability of VTool as a design tool by presenting 4 example exercises.

This paper outlines the development of a simulation-based process for assessing the handling performance of a given set of tires on a specific vehicle. Based on force and moment data, a Pacejka tire model was developed for each of the five sets of tires used in this study. To begin with, simple handling metrics including under-steer gradient were calculated using cornering stiffness derived from the Pacejka model. This Pacejka tire model was subsequently combined with a 3DOF non-linear vehicle model to create a simulation model in MATLAB/Simulink®. Other handling metrics were calculated based on simulation results to step and sinusoidal (General Motors Company) steering inputs. Calculated performance metrics include yaw velocity overshoot, yaw velocity response time, lateral acceleration response time and steering sensitivity. In addition to this, the phase lag in lateral acceleration and yaw rate of the vehicle to a sinusoidal steering input were also calculated.

This paper describes a three part analysis to characterize the interaction between the female upper extremity and a helicopter cockpit side airbag system and to develop dynamic hyperextension injury criteria for the female elbow joint. Part I involved a series of 10 experiments with an original Army Black Hawk helicopter side airbag. A 5th percentile female Hybrid III instrumented upper extremity was used to demonstrate side airbag upper extremity loading. Two out of the 10 tests resulted in high elbow bending moments of 128 Nm and 144 Nm. Part II included dynamic hyperextension tests on 24 female cadaver elbow joints. The energy source was a drop tower utilizing a three-point bending configuration to apply elbow bending moments matching the previously conducted side airbag tests. Post-test necropsy showed that 16 of the 24 elbow joint tests resulted in injuries.

Due to increased urgency regarding environmental concerns within the transportation industry, sustainable solutions for combating climate change are in high demand. One solution is a widespread transition from internal combustion engine vehicles (ICEVs) to battery electric vehicles (BEVs). To facilitate this transition, reliable energy consumption modeling is desired for providing quick, high-level estimations for a BEV without requiring extensive vehicle and computational resources. Therefore, the goal of this paper is to create a simple, yet reliable vehicle model, that can estimate the energy consumption of most electric vehicles on the market by using parameter normalization techniques. These vehicle parameters include the vehicle test weight and performance to obtain a unified net Willans line to describe the input/output power using a linear relationship.

Safety and minimal transit time are vital during transportation of essential commodities and passengers, especially in winter conditions. Icy roads are the worst driving conditions with the least available friction, leaving valuable cargo and precious human lives at stake. The study investigates the available friction at the tire-ice interface due to changes in key operational parameters. Experimental analysis of tractive performance of tires on ice was carried out indoor, using the terramechanics rig located at the Advanced Vehicle Dynamics Laboratory (AVDL) at Virginia Tech. The friction-slip ratio curves obtained from indoor testing were inputted into TruckSIM, defining tire behavior for various ice scenarios and then simulating performance of trucks on ice. The shortcomings of simulations in considering the effects of all the operational parameters result in differences between findings of indoor testing and truck performance simulations.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is currently modeling and bench testing powertrain components for a parallel plug-in hybrid electric vehicle (PHEV). The custom powertrain is being implemented in a 2016 Chevrolet Camaro for the EcoCAR 3 competition. The engine, a General Motors (GM) L83 5.3L V8 with Active Fuel Management (AFM) from a 2014 Silverado, is of particular importance for vehicle integration and functionality. The engine is one of two torque producing components in the powertrain. AFM allows the engine to deactivate four of the eight cylinders which is essential to meet competition goals to reduce petroleum energy use and greenhouse gas emissions. In-vehicle testing is performed with a 2014 Silverado on a closed course to understand the criteria to activate AFM. Parameters required for AFM activation are monitored by recording vehicle CAN bus traffic.

Ride control of military vehicles is challenging due to varied terrain and mission requirements such as operating weight. Achieving top speeds on rough terrain is typically considered a key performance parameter, which is always constrained by ride discomfort. Many military vehicles using passive suspensions suffer with compromised performance due to single tuning solution. To further stretch the performance domain to achieving higher speeds on rough roads, semi-active suspensions may offer a wide range of damping possibilities under varying conditions. In this paper, various semi-active control strategies are examined, and improvements have been made, particularly, to the acceleration-driven damper (ADD) strategy to make the approach more robust for varying operating conditions. A seven degrees of freedom ride model and a quarter-car model were developed that were excited by a random road process input modeled using an auto-regressive time series model.

Dynamic “Game Theory” brings together different features that are keys to many situations in control design: optimization behavior, the presence of multiple agents/players, enduring consequences of decisions and robustness with respect to variability in the environment, etc. In previous studies, it was shown that vehicle stability can be represented by a cooperative dynamic/difference game such that its two agents (players), namely, the driver and the vehicle stability controller (VSC), are working together to provide more stability to the vehicle system. While the driver provides the steering wheel control, the VSC command is obtained by the Nash game theory to ensure optimal performance as well as robustness to disturbances. The common two-degree of freedom (DOF) vehicle handling performance model is put into discrete form to develop the game equations of motion. This study focus on the uncertainty in the inputs, and more specifically, the driver's steering input.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2009 - 2011 EcoCAR: The NeXt Challenge Advanced Vehicle Technology Competition series organized by Argonne National Lab (ANL), and sponsored by General Motors Corporation (GM), and the U.S. Department of Energy (DOE). Following GM's Vehicle Development Process (VDP), HEVT established team goals that meet or exceed the competition requirements for EcoCAR in the design of a plug-in extended range hybrid electric vehicle. The competition requires participating teams to re-engineer a stock crossover utility vehicle donated by GM. The result of this design process is an Extended Range Electric Vehicle (EREV) that uses grid electric energy and E85 fuel for propulsion. The vehicle design has achieved an SAE J1711 utility factor corrected fuel consumption of 2.9 L(ge)/100 km (82 mpgge) with an all-electric range of 87 km (54 miles) [1].

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is excited about the opportunity to apply for participation in the next Advanced Vehicle Technology Competition. EcoCAR 3 is a new four year competition sponsored by the Department of Energy and General Motors with the intention of promoting sustainable energy in the automotive sector. The goal of the competition is to guide students from universities in North America to create new and innovative technologies to reduce the environmental impact of modern day transportation. EcoCAR 3, like its predecessors, will give students hands-on experience in designing and implementing advanced technologies in a setting similar to that of current production vehicles.

In modern aircraft power systems, active power converters are promising replacements for transformer rectifier units concerning efficiency and weight. To assess the benefits of active power converters, converter design and optimization should be carefully done under the operation requirements of aircraft applications: electromagnetic interference (EMI) standards, power quality standards, etc. Moreover, certain applications may have strict limits on other converter specifications: weight, size, converter loss, etc. This paper presents the methodology for performance optimization of different active power converters (active front-ends, isolated DC/DC converters and three-phase isolated converters) for aircraft applications. Key methods for power converter component (e.g. inductors, semiconductor devices, etc.) performance optimization and loss calculation are introduced along with the converter optimization procedure.