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Deep Learning-based Eco-driving System for Battery Electric Vehicles

Citation

Wu, Guoyuan et al. (2019), Deep Learning-based Eco-driving System for Battery Electric Vehicles, UC Riverside Dash, Dataset, https://doi.org/10.6086/D1FW9G

Abstract

The uninterrupted growth in transportation activities, for both people and goods movement, have been exerting significant pressure on our socio-economics and environment. However, emerging technologies such as connected and automated vehicles (CAVs), transportation electrification, and edge computing have been stimulating more and more dedicated efforts by engineers, researchers and policymakers to tackle the transportation-related problems, including those energy and environment focused. The eco-driving strategies based on CAV technology particularly attract significant interest from all over the world due to its great potential in energy saving as well as tail-pipe emissions reduction. Among all CAV based eco-driving strategies, the Eco-Approach and Departure (EAD) at Signalized Intersections application has shown most significant promise. In this system, an equipped vehicle can take advantage of the signal phase and timing (SPaT) and geometric intersection description (GID) information from the upcoming signalized intersection and calculate the optimal speed to pass on a green light or to decelerate to a stop in the most eco-friendly manner. Speed recommendations may be provided to the driver using a driver-vehicle-interface (DVI) or to the vehicle systems that support automated longitudinal control capabilities.

In this project, the research team conducted a thorough literature review on EAD algorithms, and identified a few major research gaps in the corresponding area, including 1) the balance between system optimality and computational efficiency; 2) designated algorithms for electric vehicles (e.g., consideration of regenerative braking); and 3) taking into account of downstream traffic information (e.g., prediction of preceding vehicle’s state). To address these gaps, the research team proposed a deep-learning based trajectory planning algorithm (DLTPA) for EAD application, which can be considered as an approximation of a global optimal algorithm (called graph-based trajectory planning algorithm or GTPA) that the research team previously developed. The proposed DLTPA has two processes: offline (training) and online (implementation), and is composed of two major modules: 1) solution feasibility checker which identifies if there is a feasible trajectory subject to all the system constraints, e.g., maximum acceleration or deceleration; and 2) a regressor to predict the speed of next time step.

Preliminary simulation study in VISSIM showed that the proposed DLTPA can achieve a great balance of energy savings vs. computational efforts, compared to the baseline scenario where no EAD was implemented and the optimal solution (in terms of energy savings) provided by GTPA.