Modelling and implementation of an energy management simulator based on agents using optimised fuzzy rules: application to an electric vehicle. (2018)

• Record Type: Journal Article
• Authors: Amrani, Rachid El; Faquir, Sanaa; Yahyaouy, Ali; Tairi, Hamid
• Is Part Of: International journal of innovative computing and applications. Volume 9:Number 4(2018)
• Page Start: 203
• Page End: 215
• Publication Date: 2018
• Subjects: hybrid vehicle -- battery -- capacitor -- modelling -- genetic optimisation -- fuzzy control -- multi-agent system -- MAS -- energy management; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

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.