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Hybrid vehicle technology is beginning to make a significant mark in the automotive industry, most notably by the Toyota Prius THS-II and its one-mode technology, but also by two-mode architectures recently introduced. GM-Allison, Renault, and the Timken Company have attempted to capitalize on the advantages over simpler series and parallel architectures that the series-parallel configuration confers on the Prius while also improving the design by allowing the powertrain configuration to physically shift and operate in two different modes depending on the driving load. This work provides an overview of the state-of-the-art in two-mode hybrid vehicle architectures, and demonstrates the performance of this technology in comparison to the market-leading Toyota Prius one-mode hybrid vehicle technology and conventional ICE technology. Simulations in the NREL ADVISOR® software compare the performances of the one- and two-mode architectures against a parallel-full design and the ICE baseline for four different drive cycles and a vehicle with varying weight that simulates a commercial vehicle application. A configuration that is a variation of those designed by GM-Allison was chosen as the representative of the two-mode architectures. The performance metric was fuel economy. The fuel economy was measured over the course of the drive cycles: (1) Urban Dynamometer Driving Schedule for Heavy Duty Vehicles (UDDSHDV); (2) New York City Truck (NYCT); (3) City-Suburban Heavy Vehicle Route (CSHVR); and (4) Highway Fuel Economy Test (HWFET). The vehicle model uses a module developed in-house for a Kenworth T400 truck with a payload that varies from empty to completely full. The results demonstrate that the two-mode architecture provides significantly improved performance to that of the conventional non-hybrid design and comparable performance to that of the parallel-full hybrid design. Furthermore, the one-mode design is shown to be sub-optimal for this vehicle type. Development and optimization of the control strategy, which is the direction of the current research, should allow for additional improvement in fuel economy; optimization of vehicular components could result in improvements in acceleration ability, gradeability, and top speed performance, which lags behind the performance capabilities of the conventional powertrain vehicle in these metrics. The study confirms that two-mode architecture presents unique advantages for constantly changing driving cycles and vehicle payloads and represents the future of hybrid vehicle technology.Copyright © 2007 by ASME

Design optimization is performed by presenting a systematic method to obtain the optimal operating conditions of a Proton Exchange Membrane (PEM) fuel cell system targeted towards a vehicular application. The fuel cell stack model is a modified version of the semi-empirical model introduced by researchers at the Royal Military College of Canada and one that is widely used by industry. Empirical data obtained from tests of PEM fuel cell stacks are used to determine the empirical parameters of the fuel cell performance model. Based on this stack model, a fuel cell system model is built in MATLAB. Included in the system model are heat transfer and gas flow considerations and the associated Balance of Plant (BOP) components. The modified ADVISOR vehicle simulation tool is used to integrate the New York City Cycle (NYCC) drive cycle and vehicle model to determine the power requirements and hence the load cycle of the fuel cell system for a low-speed fuel cell hybrid electric vehicle (LSFCHEV). The optimization of the powerplant of this vehicle type is unique. The vehicle model has been developed in the work to describe the characteristics and performance of an electric scooter, a simple low-speed vehicle (LSV). The net output power and system exergetic efficiency of the system are maximized for various system operating conditions using the weighted objective function based on the load cycle requirement. The method is based on the coupling of the fuel cell system model with three optimization algorithms (a) sequential quadratic programming (SQP); (b) simulated annealing (SA); and (c) genetic algorithm (GA). The results of the optimization provide useful information that will be used in future study on control algorithms for LSFCHEVs. This study facilitates research on more complex fuel cell system modeling and optimization, and provides a basis for experimentation to verify the fuel cell system model.Copyright © 2006 by ASME