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One of the most widely used computer simulation tools for hybrid electric vehicles (HEVs) is the ADvanced VehIcle SimulatOR (ADVISOR) developed by the National Renewable Energy Laboratory. The capability to quickly perform parametric and sensitivity studies for specific vehicles is a unique and invaluable feature of ADVISOR. However, no simulation tool is complete without being validated against measured vehicle data to insure the reliability of its predictions. This paper details the validation of ADVISOR using data from the Virginia Tech FutureCar Challenge Lumina, a series HEV. The modeling process is discussed in detail for each of the major components of the hybrid system: transmission; electric motor and inverter; auxiliary power unit (fuel and emissions); batteries; and miscellaneous vehicle parameters. The integration of these components into the overall ADVISOR model is also described. The results of the ADVISOR simulations are then explained.

The objective of this work is to provide a relatively simple battery energy storage and loss model that can be used for technology screening and design/sizing studies of hybrid electric vehicle powertrains. The model dynamic input requires only power demand from the battery terminals (either charging or discharging), and outputs internal battery losses, state-of-charge (SOC), and pack temperature. Measured data from a vehicle validates the model, which achieves reasonable accuracy for current levels up to 100 amps for the size battery tested. At higher current levels, the model tends to report a higher current than what is needed to create the same power level shown through the measured data. Therefore, this battery model is suitable for evaluating hybrid vehicle technology and energy use for part load drive cycles.

Fuel cells are being considered for transportation primarily because they have the ability to increase vehicle energy efficiency and significantly reduce or eliminate tailpipe emissions. A proton exchange membrane fuel cell is an electrochemical device for which the operational characteristics depend heavily upon temperature. Thus, it is important to know how the thermal design of the system affects the performance and efficiency of a fuel cell vehicle. More specifically, this work addresses issues of the initial thermal transient known to the automotive community as “cold start” effects for a direct hydrogen fuel cell system. Cold start effects play a significant role in power limitations in a fuel cell vehicle, and may require hybridization (batteries) to supplement available power. The results include a comparison of cold-start and hot-start fuel cell power, efficiency and fuel economy for a hybrid fuel cell vehicle.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is currently developing a control strategy for a parallel plug-in hybrid electric vehicle (PHEV). The hybrid powertrain is being implemented in a 2016 Chevrolet Camaro for the EcoCAR 3 competition. Fuzzy rule sets determine the torque split between the motor and the engine using the accelerator pedal position, vehicle speed and state of charge (SOC) as the input variables. The torque producing components are a 280 kW V8 L83 engine with active fuel management (AFM) and a post-transmission (P3) 100 kW custom motor. The vehicle operates in charge depleting (CD) and charge sustaining (CS) modes. In CD mode, the model drives as an electric vehicle (EV) and depletes the battery pack till a lower state of charge threshold is reached. Then CS operation begins, and driver demand is supplied by the engine operating in V8 or AFM modes with supplemental or loading torque from the P3 motor.

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is currently going through several modeling and testing stages to develop models that represent the P3 PHEV powertrain the team is building as part the EcoCAR 3 competition. The model development process consists of several major steps. First, Model-in-the-Loop (MIL) testing is conducted to validate a conventional vehicle model, down-select a desired powertrain configuration, and generate initial vehicle technical specifications. HEVT is pursuing a performance powertrain that balances high performance with minimal energy consumption. Initial MIL modeling results yield an IVM-60 mph time of 4.9 seconds and an overall UF-weighted 4-cycle energy consumption of 560 Wh/km. MIL modeling provides an initial reference to compare subsequent vehicle modeling. Following the MIL process, Software-in-the-Loop (SIL) is used to develop a vehicle model from the ground-up that facilitates the transition to Hardware-in-the-Loop (HIL) testing.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2008 - 2011 EcoCAR: The NeXt Challenge Advanced Vehicle Technology Competition series organized by Argonne National Laboratory (ANL) and sponsored by General Motors (GM) and the U.S. Department of Energy (DoE). Following GM's vehicle development process, HEVT established goals that meet or exceed the competition requirements for EcoCAR in the design of a plug-in, range-extended hybrid electric vehicle. The challenge involves designing a crossover SUV powertrain to reduce fuel consumption, petroleum energy use and well-to-wheels (WTW) greenhouse gas (GHG) emissions. In order to interface with and control the vehicle, the team added a National Instruments (NI) CompactRIO (cRIO) to act as a hybrid vehicle supervisory controller (HVSC).

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 improve and redesign a stock Vue XE donated by GM. The result of this design process is an Extended-Range Electric Vehicle (E-REV) that uses grid electric energy and E85 fuel for propulsion. The vehicle design is predicted to achieve an SAE J1711 utility factor corrected fuel consumption of 2.9 L(ge)/100 km (82 mpgge) with an estimated all electric range of 69 km (43 miles) [1].

Improving fuel economy and overall vehicle emissions are very important in today's society with strict new regulations throughout the world. To help in the education process for the next generation of design engineers, this paper seeks to define a powertrain model created and developed to help users understand the basics behind hybrid vehicles and the effects of these advanced technologies. One of the main goals of this research is to maintain a simplified approach to model development. The 1 Hz model described within this work aims to allow energy to be simply and understandably traced through a hybrid powertrain. Through the use of a “backwards” energy tracking method, demand for a drive cycle is found, and, after tracing the energy demand through each powertrain component, the resulting fuel to meet vehicle demand and associated powertrain losses is found.

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.