Intelligent energy management in a photovoltaic installation using neuro-fuzzy technique

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.

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Neuro-Fuzzy Energy Management System in Elevator Drive Applications for Maximum Braking Energy Regenerative Capability

7th IET International Conference on Power Electronics, Machines and Drives (PEMD 2014) ◽

10.1049/cp.2014.0494 ◽

2014 ◽

Cited By ~ 3

Author(s):

A. Mesemanolis ◽

C. Mademlis ◽

I. Kioskeridis

Keyword(s):

Energy Management ◽

Management System ◽

Energy Management System ◽

Neuro Fuzzy ◽

Fuzzy Energy

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

IEEE Access ◽

10.1109/access.2020.2968038 ◽

2020 ◽

Vol 8 ◽

pp. 19614-19628 ◽

Cited By ~ 4

Author(s):

Vahid Hosseinnezhad ◽

Miadreza Shafie-Khah ◽

Pierluigi Siano ◽

Joao P. S. Catalao

Keyword(s):

Energy Management ◽

Neuro Fuzzy ◽

Home Energy Management

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Towards Buildings Energy Management: Using Seasonal Schedules Under Time of Use Pricing Tariff via Deep Neuro-Fuzzy Optimizer

2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) ◽

10.1109/iwcmc.2019.8766673 ◽

2019 ◽

Author(s):

Sakeena Javaid ◽

Muhammad Abdullah ◽

Nadeem Javaid ◽

Tanzeela Sultana ◽

Jawad Ahmed ◽

...

Keyword(s):

Energy Management ◽

Neuro Fuzzy ◽

Time Of Use Pricing

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

Journal of Soft Computing Paradigm - September 2019 ◽

10.36548/jscp.2021.3.002 ◽

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.

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

Electronics ◽

10.3390/electronics9060900 ◽

2020 ◽

Vol 9 (6) ◽

pp. 900 ◽

Cited By ~ 1

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.

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