Title: Modelling and implementation of an energy management simulator based on agents using optimised fuzzy rules: application to an electric vehicle
Authors: Rachid El Amrani; Sanaa Faquir; Ali Yahyaouy; Hamid Tairi
Addresses: LIIAN Laboratory, Faculty of Sciences Dhar Mehraz, Sidi Mohamed Ben Abdellah University, P.O. Box 1796, Fes-Atlas 30003, Fez, Morocco ' Faculty of Sciences of the Engineer, Private University of Fez, Ain Chkef Road, 30000, Fez, Morocco ' Department of Computer Sciences, Faculty of Sciences Dhar Mehraz, Sidi Mohamed Ben Abdellah University, 30003, Fez, Morocco ' Department of Computer Sciences, Faculty of Sciences Dhar Mehraz, Sidi Mohamed Ben Abdellah University, 30003, Fez, Morocco
Abstract: This paper presents an intelligent algorithm based on multi agent systems to manage the energy in a hybrid electrical vehicle using a model of lithium metal polymer (LMP) battery and a model of an electrical double layer capacitor (EDLC). The algorithm uses fuzzy rules optimised by a genetic algorithm to control the flow of energy inside the system. The LMP battery is linked to a boost converter to insure the autonomy of the electrical vehicle, while the EDLC is linked to a back boost converter that provides the highly demanded energy in a short time and guarantees the temporarily energy storage when the vehicle is braking (no energy is demanded). The hybrid electrical vehicle is simulated in different driving cycles to analyse the behaviour of the LMP battery and the EDLC. Results showed that the used hybrid strategy was able to ensure the autonomy of the vehicle in terms of energy since it has performed a minimum energy cost and a maximum profit in autonomy, which means a longer life of the hybrid electric source.
Keywords: hybrid vehicle; battery; capacitor; modelling; genetic optimisation; fuzzy control; multi-agent system; MAS; energy management.
International Journal of Innovative Computing and Applications, 2018 Vol.9 No.4, pp.203 - 215
Received: 05 Jun 2017
Accepted: 08 Mar 2018
Published online: 22 Oct 2018 *