Eco-Drive Experiment on Rolling Terrain for Fuel Consumption Optimization

EXECUTIVE SUMMARY

Eco-drive is one of the many research topics that address the issue of increasing vehicle fuel efficiency and improving the sustainability of the entire transportation system. Connected and automated vehicle (CAV) data are now being used to allow vehicles to cooperate better with current and future environments, including traffic conditions, signal timing, and terrain information. This study proposes an eco-drive algorithm for vehicle fuel consumption optimization on rolling terrains, which frequently cause additional fuel waste because of inefficient transformation between kinetic and potential energy. The proposed algorithm uses the Relaxed Pontryagin’s Minimum Principle (RPMP); it is computationally efficient and applicable in real time. While similar algorithms have proven effective in simulation with many assumptions, it is necessary to test these algorithms in the field to better understand the algorithm’s performance and thus enable optimal vehicle control in support of eco-driving. Therefore, this study further tested and verified the newly developed algorithms on an innovative CAV platform and quantified the fuel saving benefits of eco-drive. The proposed eco-drive system is compared against conventional constant-speed cruise control on a total of seven road segments over 47 miles. Experimental data show that more than 20 percent of fuel consumption can be avoided on certain terrains. Detailed analysis through linear models also reveals the main geometrical contributors to the eco-drive fuel savings. This conclusion can enable a rough estimate of fuel saving potential on given roadways and help State departments of transportation to identify roadways where eco-drive could be beneficial. The algorithm and the experiment can also support original equipment manufacturers in developing and marketing this technology to reduce fuel consumption and emissions in the future. Further research is still needed to study the impact of this univariate finding on following traffic, including automated and nonautomated vehicles.