EL FORT 2 - Electric Fleet Optimization in Real Time (Phase 2)
|Coordinator||Volvo Technology AB - Electromobility|
|Funding from Vinnova||SEK 1 900 000|
|Project duration||March 2018 - December 2020|
Purpose and goal
The project objective is to develop a new intelligent energy consumption prediction and route planning methodology to real-time optimize the energy use of electric vehicles in cities. The project will integrate machine learning methods to improve the precision of energy estimation and being able to dynamically re-plan the routes when needed.
Expected results and effects
The main expected results are: 1) An intelligent stochastic method for energy consumption estimation and route planning for electric commercial vehicles, in order to maximize the use of the battery capacity and driving range. 2) An intelligent dynamic routing model that follows up battery state of charge and traffic conditions in real-time to support the driver and reduce range anxiety, planning for additional charging whenever necessary. 3) Implementation and evaluation of the methods. Implement initial demonstrators and document the results.
Planned approach and implementation
An industrial PhD student will perform the research tasks, collaborating with Chalmers and other Volvo researchers. The first year will focus on developing the machine learning techniques (result 1 above) and incorporating that into the routing algorithms. The second year will focus on the real-time aspects of routing (result 2 above). Throughout the project the methods will be implemented, evaluated and fine-tuned. The results will be documented in 2 journal articles and a PhD dissertation.