Eco-Predictive Control of Electric Vehicles.

Conference · Tue Jan 14 04:00:00 EST 2020

OSTI ID:1593328

Ahn, Kyoungho ^[1]; Rakha, Hesham; Park, Sangjun

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1. Virginia Polytechnic Institute and State University

Roadway grade is one of the major variables that affects a vehicle’s energy consumption rate. This study demonstrates the potential benefits of an eco-predictive control for battery electric vehicles (BEVs). The eco-predictive control was developed for the lead vehicle control of electric vehicle platooning. The eco-predictive control predicts optimum speed and acceleration levels within a preset speed window to minimize energy consumption considering instantaneous electricity used and regenerative power. The developed eco-predictive control system for a BEV integrates a BEV energy consumption model and a powertrain model to save fuel while maintaining vehicle speed within a user-specified window. In addition, the study demonstrated the impacts of roadway grade on energy consumption for BEVs. The results show that BEVs can significantly improve energy consumption on downhill roads compared to internal combustion engine vehicles due to regenerative braking. Specifically, testing showed that BEVs could even produce extra energy on downhill roads. The study also tested the eco-predictive control on an 18-km section of I-81 and found that the system saved 6.12% and 11.16% of energy on the uphill road and downhill road sections, respectively. This study demonstrated that regenerative energy in BEVs is a critical factor in energy efficiency and the developed eco-predictive control significantly improved the energy efficiency for BEVs using a given road topography in a predictive manner.

• Research Organization: Virginia Polytechnic Institute and State University, Blacksburg, VA.
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
• DOE Contract Number: EE0008209
• OSTI ID: 1593328
• Report Number(s): DOE-VT-0008209-C06
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
54 ENVIRONMENTAL SCIENCES
97 MATHEMATICS AND COMPUTING
Energy impacts
electric vehicle
BEV
eco-driving
regenerative energy.