SmartDrive: traction energy optimization and applications in rail systems
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
This paper presents the development of SmartDrive package to achieve the application of energy-efficient driving strategy. The results are from collaboration between Ricardo Rail and the Birmingham Centre for Railway Research and Education (BCRRE). Advanced tram and train trajectory optimization techniques developed by BCRRE as part of the UKTRAM More Energy Efficiency Tram project have been now incorporated in Ricardo's SmartDrive product offering. The train trajectory optimization method, associated driver training and awareness package (SmartDrive) has been developed for use on tram, metro, and some heavy rail systems. A simulator was designed that can simulate the movement of railway vehicles and calculate the detailed power system energy consumption with different train trajectories when implemented on a typical AC or DC powered route. The energy evaluation results from the simulator will provide several potential energy-saving solutions for the existing route. An enhanced Brute Force algorithm was developed to achieve the optimization quickly and efficiently. Analysis of the results showed that by implementing an optimal speed trajectory, the energy usage in the network can be significantly reduced. A driver practical training system and the optimized lineside driving control signage, based on the optimized trajectory were developed for testing. This system instructed drivers to maximize coasting in segregated sections of the network and to match optimal speed limits in busier street sections. The field trials and real daily operations in the Edinburgh Tram Line, U.K., have shown that energy savings of 10%-20% are achievable.
|Number of pages||10|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Publication status||Published - 1 Jul 2019|
- Optimization, Rails, Rail transportation, Vehicles, Energy consumption, Trajectory, Genetic algorithms