Connected and automated vehicles (CAVs) have shown increased development in recent years. Currently, they’re mostly used for safety and driver convenience, but they present new opportunities to improve the energy efficiency of vehicles.
All drivers operate with an information gap: a level of uncertainty that negatively affects the energy efficiency of their car. Safe driving demands that drivers leave appropriate space between vehicles and cautiously approach intersections, because they can never completely anticipate the intentions of nearby vehicles and traffic conditions. Closing this information gap can enable vehicles to operate in more energy efficient ways.
Southwest Research Institute is leading an effort to demonstrate a 20 percent energy consumption reduction in a plug-in hybrid electric vehicle (PHEV) by leveraging improvements from engine control products and connected vehicle technologies. While cooperative behavior among vehicles has been the subject of much research, the full potential of improved powertrain control on the resultant composite energy efficiency of individual vehicles has not yet been fully explored.
ARPA-E NEXTCAR CAV Research
SwRI’s efforts are a part of the Next-Generation Energy Technologies for Connected and Automated on-Road Vehicles (NEXTCAR) program funded by the Advanced Research Project Agency – Energy (ARPA-E). As part of this three-year project that draws on automotive software and electronics as well as engine control products, SwRI is collaborating with University of Michigan, Ann Arbor and Toyota Motor North America.
The SwRI team is augmenting a vehicle with a Dedicated Short-Range Communication (DSRC) radio to enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity and interfacing with the production Dynamic Radar Cruise Control (DRCC) feature of the vehicle. NEXTCAR employs four primary mechanisms to achieve a target of 20 percent reduction of energy consumption:
- ECO-Routing: This block utilizes route and traffic info to calculate the route that consumes the least amount of energy.
- ECO-Approach and Departure at Signalized Intersections (V2I): This block optimizes vehicle speed to stop in an energy efficient manner.
- Velocity Smoothing: This block utilizes connected information from neighboring vehicles (V2V) to minimize accelerations.
- Power-split Optimization: Optimizes battery and engine operation to meet the user power demand.
Real-World Traffic Simulator
SwRI researchers have also created a traffic simulator that produces real-world traffic patterns and provides repeatable driving scenarios to test new control algorithms. The traffic simulator also helps evaluate robustness of algorithms by artificially injecting disturbances in a controlled fashion.
The simulator is based on real data from traffic sensors in Fort Worth, Texas. A custom wrapper written on top of commercial, off-the-shelf traffic flow simulation software enables basic safety messages from neighboring vehicles as well as signal phasing and timing information from traffic lights. The traffic simulator in conjunction with a chassis or hub dynamometer provides a unique platform for CAV testing.