Open AccessEnergy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Author(s) -
Gayathri Nattar Ranganathan,
Jennifer S. Raj
Publication year - 2021
Publication title -
journal of soft computing paradigm
Language(s) - English
Resource type - Journals
ISSN - 2582-2640
DOI - 10.36548/jscp.2021.3.002
Subject(s) - energy management , energy consumption , fuzzy logic , computer science , controller (irrigation) , automotive engineering , energy (signal processing) , supervisory control , fuel efficiency , matlab , work (physics) , efficient energy use , electric vehicle , control engineering , intelligent control , energy management system , electric energy consumption , control (management) , engineering , artificial intelligence , electric energy , electrical engineering , power (physics) , mathematics , mechanical engineering , agronomy , statistics , physics , quantum mechanics , biology , operating system
This paper has proposed a hybrid electric vehicle that uses intelligent energy management strategy to decrease the energy consumption of the vehicle. Here, the total energy consumption of the vehicle is initially modelled and further investigated to reduce the amount of energy used to be identified as a sum of electrical energy provided by consumed fuels and on-board batteries. In particular, an intelligent controller is proposed in this work to execute its ability to decrease the total amount of energy consumed and improve the energy efficiency of the vehicle. A fuzzy system is utilized in an account supervisory controller to decide the appropriate mode of operation for the system. The part of the proposed work involves development of optimal control strategies by using neuro-fuzzy logic. In order to obtain optimal performance, the controllers are used to regulate vehicle subsystems and set points. The biggest advantage of this work is the reduction in energy consumption and their ability to execute the operation online. Simulink/MATLAB is used to simulate and validate the performance of the proposed work under various conditions and under several dataset values.