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Technical Paper

The Importance of Maximizing Grid Electricity Usage in the Component Selection and Design of a Midsize PHEV

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.
Technical Paper

ESS Design Process Overview and Key Outcomes of Year Two of EcoCAR 2: Plugging in to the Future

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.
Technical Paper

Improving Fuel Economy of Thermostatic Control for a Series Plugin-Hybrid Electric Vehicle Using Driver Prediction

This study investigates using driver prediction to anticipate energy usage over a 160-meter look-ahead distance for a series, plug-in, hybrid-electric vehicle to improve conventional thermostatic powertrain control. Driver prediction algorithms utilize a hidden Markov model to predict route and a regression tree to predict speed over the route. Anticipated energy consumption is calculated by integrating force vectors over the look-ahead distance using the predicted incline slope and vehicle speed. Thermostatic powertrain control is improved by supplementing energy produced by the series generator with regenerative braking during events where anticipated energy consumption is negative, typically associated with declines or decelerations.