EL FORT 2 - Electric Fleet Optimization in Real Time (Phase 2)

Important results from the project

The project objectives were achieved as follows: 1. Develop an intelligent stochastic method for predicting energy consumption and route planning for commercial electric vehicles. Maximize the use of battery capacity and range. 2. Develop an intelligent dynamic route planning model that monitors energy use and traffic conditions in real time. Be able to react quickly to unforeseen events and plan additional charging if necessary. 3. Implement and evaluate the methods. Collect data for assessment and fine-tuning of the developed methods.

Expected long term effects

The main results are two papers and the doctoral thesis covering the topics described. The papers cover important gaps in the literature and are therefore expected to have an impact in the area. One of the most important novelties is the use of machine learning for energy prediction and route planning. Some of the results from this project and its predecessor are already included in product development projects. The PhD student completed his studies and will continue to work in the field.

Approach and implementation

Most of the project was executed without problems. However, the industrial PhD student had to be involved in internal activities in the company, which slightly delayed the project. Furthermore, due to the pandemic situation, the student was on short-term lay-off, which further delayed finishing the project. However, despite the delays all results were achieved according to the proposal.