Neuro-Fuzzy Paradigms for Intelligent Energy Management

2004 ◽  
pp. 285-314 ◽  
Author(s):  
Ajith Abraham ◽  
Muhammad Riaz Khan
Keyword(s):  
Energy Management ◽  
Neuro Fuzzy

scholarly journals Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1745 ◽  
Author(s):  
Duong Phan ◽  
Alireza Bab-Hadiashar ◽  
Reza Hoseinnezhad ◽  
Reza N. Jazar ◽  
Abhijit Date ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Autonomous Vehicles ◽  
Pid Controller ◽  
Autonomous Vehicle ◽  
Subtractive Clustering ◽  
Energy Management System ◽  
Power Demand ◽  
Neuro Fuzzy ◽  
Simulation Results

This paper investigates the energy management system (EMS) of a conventional autonomous vehicle, with a view to enhance its powertrain efficiency. The designed EMS includes two neuro-fuzzy (NF) systems to produce the optimal torque of the engine. This control system uses the dynamic road power demand of the autonomous vehicle as an input, and a PID controller to regulate the air mass flow rate into the cylinder by changing the throttle angle. Two NF systems were trained by the Grid Partition (GP) and the Subtractive Clustering (SC) methods. The simulation results show that the proposed EMS can reduce the fuel consumption of the vehicle by 6.69 and 6.35 l/100 km using the SC and the GP, respectively. In addition, the EMS based on NF trained by GP and NF trained by SC can reduce the fuel consumption of the vehicle by 11.8% and 7.08% compared with the case without the controller, respectively.

scholarly journals An Optimal Home Energy Management Paradigm With an Adaptive Neuro-Fuzzy Regulation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 19614-19628 ◽  
Author(s):  
Vahid Hosseinnezhad ◽  
Miadreza Shafie-Khah ◽  
Pierluigi Siano ◽  
Joao P. S. Catalao
Keyword(s):  
Energy Management ◽  
Neuro Fuzzy ◽  
Home Energy Management

scholarly journals Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic

2021 ◽  
Vol 3 (3) ◽  
pp. 149-162
Author(s):  
G Ranganathan ◽  
Jennifer S Raj
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Control Strategies ◽  
Electrical Energy ◽  
Energy Management Strategy ◽  
Total Energy Consumption ◽  
Supervisory Controller ◽  
Neuro Fuzzy

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.

scholarly journals Neuro-Fuzzy-Based Model Predictive Energy Management for Grid Connected Microgrids

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 900 ◽  
Author(s):  
Ahsen Ulutas ◽  
Ismail Hakki Altas ◽  
Ahmet Onen ◽  
Taha Selim Ustun
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Technology Use ◽  
Energy Use ◽  
Renewable Energy Sources ◽  
Solar Irradiation ◽  
Pv System ◽  
Inference System ◽  
Management Algorithm ◽  
Neuro Fuzzy

With constant population growth and the rise in technology use, the demand for electrical energy has increased significantly. Increasing fossil-fuel-based electricity generation has serious impacts on environment. As a result, interest in renewable resources has risen, as they are environmentally friendly and may prove to be economical in the long run. However, the intermittent character of renewable energy sources is a major disadvantage. It is important to integrate them with the rest of the grid so that their benefits can be reaped while their negative impacts can be mitigated. In this article, an energy management algorithm is recommended for a grid-connected microgrid consisting of loads, a photovoltaic (PV) system and a battery for efficient use of energy. A model predictive control-inspired approach for energy management is developed using the PV power and consumption estimation obtained from daylight solar irradiation and temperature estimation of the same area. An energy management algorithm, which is based on a neuro-fuzzy inference system, is designed by determining the possible operating states of the system. The proposed system is compared with a rule-based control strategy. Results show that the developed control algorithm ensures that microgrid is supplied with reliable energy while the renewable energy use is maximized.

Design of Neuro-Fuzzy based Intelligent Inference Algorithm for Energy Management System with Legacy Device

2015 ◽  
Vol 64 (5) ◽  
pp. 779-785 ◽  
Author(s):  
In-Hwan Choi ◽  
Sung-Hyun Yoo ◽  
Jun-Ho Jung ◽  
Myo-Taeg Lim ◽  
Jung-Jun Oh ◽  
...  
Keyword(s):  
Energy Management ◽  
Management System ◽  
Energy Management System ◽  
Inference Algorithm ◽  
Neuro Fuzzy
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