Eco-Driver Assistance System for Electric Vehicles

Electric vehicles are expected to become one of the key elements of future sustainable transportation systems. First electric cars are already commercially available but still suffer from problems and constraints that have to be solved before a mass market might be created. Key aspects that will play an important role in modern electric vehicles are range extension, energy efficiency, safety, comfort as well as communication. An overall solution approach to integrate all these aspects is the development of advanced driver assistance systems to make electric vehicles more “intelligent”.

Driver assistance systems are based on the integration of suitable sensors and actuators as well as electronic devices and software enabled control functionality to automatically support the human driver.

Driver assistance for electric vehicles will in some points differ from the systems already used in fuel-powered cars. A stronger focus must be put on assistance systems that increase the vehicle efficiency to extend the cruising range and the energy consumption of the driver assistance systems as well.

In this work, an eco-driving assistance system as the first step towards those new driver assistance systems for electric vehicles will be investigated. Using information about the internal state of the car as well as information about the environment coming from a navigation system and sensor systems, an algorithm will be developed that will adapt the speed of the vehicle automatically to minimize the energy consumption.

From an algorithmic point of view, a Model Predictive Control (MPC) approach will be applied and adapted to the special constraints of the problem. MPC utilizes a model of system in order to predict its upcoming behavior, and then computes an optimal input trajectory based on the predictions. Due to model uncertainties or external disturbances; however, perfect model may not be acquired in practical applications. Stochastic MPC (SMPC) is offered as an alternative to deal with model uncertainties or external disturbances. In SMPC, the constraints are construed probabilistically, making it possible to be considered in the optimization problem.

Finally, the MPC and SMPC solutions will be tested in simulations as well as in first experiments with a commercial electric vehicle in the SnT automation Lab.

Dynamometer test bench at Delphi, Bascharage

The Smart E-Cars of SnT


Experimental evaluation at the CFC test track, Colmar-Berg, Luxembourg.


Researchers: Tim SchwickartAmin Sajadi, Prof. Dr. Holger VoosProf. Dr. Jean-Régis Hadji-MinaglouProf. Dr. Mohamed Darouach.