Given a spatial road network, an origin, a destination, and trajectory data of vehicles on the network, the Energy-efficient Path Selection (EPS) problem aims to find the most energy-efficient path (i.e., with least energy consumption) between the origin and the destination. With world energy consumption growing rapidly, estimating and reducing the energy consumption of road transportation is becoming critical. The main challenge of this problem is to adopt energy consumption as the cost metric of paths, which is neglected by the related work in shortest path selection problem whose typical metrics are distance and time. Additionally, negative energy consumption caused by the use of regenerative braking on electrified vehicles prevents classical algorithms like Dijkstra’s algorithm from functioning correctly. We introduce a Physics-guided Energy Consumption (PEC) model based on a low-order physics model, which estimates energy consumption as a function of the vehicle parameters (e.g., mass and powertrain system efficiency) and use the estimation in the proposed adaptive dynamic programming algorithm for path selection. Our PEC model treats energy consumption as a unique metric that is determined not only by the path and vehicle’s motion along the path, but also on properties of the vehicle itself. Experiments show that the PEC model estimates are more similar to real trajectory data than the estimates represented by the mean or histogram of historical data. Also, the path found by the proposed method is more energy-efficient than both the currently used path and the fastest path found by a commercial routing package. As far as we know, this is the first paper to use a physics-guided method to estimate the vehicle energy consumption and perform path selection.
|Original language||English (US)|
|Title of host publication||26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018|
|Editors||Li Xiong, Roberto Tamassia, Kashani Farnoush Banaei, Ralf Hartmut Guting, Erik Hoel|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|State||Published - Nov 6 2018|
|Event||26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 - Seattle, United States|
Duration: Nov 6 2018 → Nov 9 2018
|Name||GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems|
|Other||26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018|
|Period||11/6/18 → 11/9/18|
Bibliographical notePublisher Copyright:
© 2018 Association for Computing Machinery.
Copyright 2019 Elsevier B.V., All rights reserved.
- Energy efficiency
- Shortest path