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While technologies such as adaptive cruise control and platooning can increase energy efficiency of freight trucks on highways, to date less attention has been given to technologies that can improve energy efficient of these trucks during their first-mile/last-mile operations. In this paper, we present a Connected Vehicle-based truck eco-driving system, called Eco-Drive, which uses Signal Phase and Timing (SPaT) information from the upcoming traffic signal along with the information about the equipped vehicle and preceding traffic to determine the most energy-efficient speed trajectory to pass through the intersection. The system is pre-calibrated using real-world acceleration and deceleration profile data to create a graph-based vehicle trajectory planning algorithm. A machine learning algorithm is then developed and trained to enable a fast and accurate determination of optimal vehicle trajectory. During the operation, the system uses real-time information about the host vehicle, SPaT, and preceding traffic to optimize vehicle trajectory for energy while ensuring a safe passage through the intersection. The system evaluation in traffic microsimulation environment shows that the proposed system provides statistically significant energy savings for the host vehicle, while maintaining similar travel time, as a result of reduced number of stops and milder acceleration/deceleration. The results presented are specific to the simulation settings used in this paper. More research is needed to better understand the levels of energy savings that the proposed system could provide under a wide variety of settings.

Hybrid electric and electric trucks are potential technology solutions for reducing emissions at ports. However, developing an advanced, low-emission technology driveline entails thoroughly understanding typical truck operations in the real-world environment. This paper presents the work performed to develop a novel, more representative drayage duty cycle that characterizes drayage truck operations in the ports of San Pedro Bay in California. Unlike a conventional vehicle, an optimized hybrid driveline requires detailed understanding not only of torque requirements and vehicle speeds but also of the potential recovery of dynamic brake energy, charging opportunities, stopping and idling times, and many other operational requirements. Keeping this in mind, the duty cycle presented in this paper incorporated real-world, near-dock activities of Class 8 drayage trucks such as daily hours of operation, mileage, altitude profiles of routes, and idling and key-off patterns. The empirical duty cycle model was subsequently integrated with a complete vehicle simulation to explore the best solutions to minimize energy consumption for drayage applications in and around the ports. The analysis presented indicates that trucks spent most of the generated power in overcoming aerodynamic drag and rolling resistance of tires for a complete drayage shift and that electrical auxiliary loads dominated for near-dock operations because of idling and low-speed profiles. Therefore, achieving zero-emission near-dock operations entails focusing on auxiliary loads and rolling resistance. By using simulations, it was estimated that a hybrid truck with electrical power limited to about 100 kW could deliver a greenhouse gas emission reduction of about 30%.