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

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Listed:
  • Wu, Guoyuan
  • Ye, Fei
  • Hao, Peng
  • Esaid, Danial
  • Boriboonsomsin, Kanok
  • Barth, Matthew J.

Abstract

Eco-driving strategies based on connected and automated vehicles (CAV) technology, such as Eco-Approach and Departure (EAD), have attracted significant worldwide interest due to their potential to save energy and reduce tail-pipe emissions. In this project, the research team developed and tested a deep learning–based trajectory-planning algorithm (DLTPA) for EAD. The DLTPA has two processes: offline (training) and online (implementation), and it is composed of two major modules: 1) a solution feasibility checker that identifies whether 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 the next time-step. Preliminary simulation with microscopic traffic modeling software PTV VISSIM showed that the proposed DLTPA can achieve the optimal solution in terms of energy savings and a greater balance of energy savings vs. computational efforts when compared to the baseline scenarios where no EAD is implemented and the optimal solution (in terms of energy savings) is provided by a graph-based trajectory planning algorithm. View the NCST Project Webpage

Suggested Citation

  • Wu, Guoyuan & Ye, Fei & Hao, Peng & Esaid, Danial & Boriboonsomsin, Kanok & Barth, Matthew J., 2019. "Deep Learning–based Eco-driving System for Battery Electric Vehicles," Institute of Transportation Studies, Working Paper Series qt9fz140zt, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt9fz140zt
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    Keywords

    Engineering; Eco-driving; deep-learning; energy and emissions; VISSIM; Algorithms; Automatic speed control; Ecodriving; Electric vehicles; Energy conservation; Energy consumption; Machine learning; Simulation; Traffic speed;
    All these keywords.

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