Fuzzy Logic Based Energy Management Research Articles

Fuzzy Logic-Based Energy Management in Sustainable management for Renewable Integration

Fuzzy Logic-Based Energy Management in Sustainable management for Renewable Integration

Fuzzy logic-based energy management in smart grids for renewable integration

This study explores the creation and execution of energy management methods using fuzzy logic in smart grids, with the goal of effectively incorporating renewable energy sources. The research employs empirical data that includes information on renewable energy production, changes in energy use, the current state of battery storage, and control measures taken. The data analysis demonstrates significant variations in renewable energy sources, namely solar energy ranging from 350 kW to 410 kW, wind energy changing from 180 kW to 220 kW, and hydro energy varied from 120 kW to 150 kW. The energy consumption in different sectors exhibits varied patterns. Residential consumption ranges from 250 kW to 275 kW, industrial demand increases from 300 kW to 330 kW, and commercial consumption fluctuates from 200 kW to 225 kW. The battery storage status shows changes, with Battery 1 seeing an increase from 150 kWh to 165 kWh, Battery 2 fluctuating between 180 kWh and 195 kWh, and Battery 3 maintaining a stable range of 200 kWh to 215 kWh. The use of control actions based on fuzzy logic demonstrates flexibility, where Control Action 1 ranges from 0.6 to 0.8, Control Action 2 fluctuates from 0.5 to 0.7, and Control Action 3 varies from 0.6 to 0.9. The study highlights the flexibility and quick response of the energy management system based on fuzzy logic. It can adjust control actions in real-time to accommodate changes in renewable energy generation, consumption patterns, and battery storage. This indicates its potential to optimize energy flow and ensure grid stability in smart grids, facilitating the efficient integration of renewable energy.

Real-Time Fuzzy Logic Based Energy Management System for Microgrid Using Hardware in the Loop

This research presents a hierarchical energy management strategy for isolated microgrids (MG). The strategy’s objectives are achieved through a master-slave topology where local controllers are managed and controlled through a central controller. This can provide many technical advantages, particularly regarding the microgrid’s performance and the supply of energy. The local controller is designed to meet the local objectives of the microgrid, such as stabilization of DC voltage and maximization of sources’ extracted power. The objectives of the central controller are achieved through a centralized approach based on fuzzy logic to preserve battery life and manage the energy balance between generation and consumption. The microgrid’s performances were investigated under a steady-state and faulty regime. A Hardware in the Loop (HIL) test based on the Simulink platform is established by RT-LAB real-time simulator. Results are presented to validate the proposed hierarchical control. The OP1400 test bench, based on the OP4150 digital simulator, is utilized to test and validate the proposed hierarchical control strategy. The results are compared to international standards IEEE 1547 and IEC 61727, which demonstrate excellent consistency.

Fuel Cell Hybrid Locomotive with Modified Fuzzy Logic Based Energy Management System

As one of the most environmentally friendly energy sources today, fuel cells have become the focus of research in countries around the world, especially in the electric transportation field. This paper mainly studies the modeling of fuel cell hybrid locomotives (FCHL) including fuel cells, batteries, motors, and energy management systems. To increase the operating efficiency and improve the performance of FCHL, a modified fuzzy logic-based energy management system (MFL-EMS) is proposed and compared with the traditional power flow energy management system (PF-EMS). Meanwhile, a modified fuel cell hybrid power system model for locomotives is proposed, taking into account the traction motor features that, compared with a simplified controlled source load, can directly reflect the status of the locomotive running speed and the output power of the traction motor load. The proposed system parameters and configurations are determined by combining the characteristics of power and energy density, response characteristics, and charging/discharging characteristics of fuel cells and batteries. The precise simulation results revealed that adopting the proposed MFL-EMS in comparison to the traditional PF-EMS, reduced the hydrogen consumption by 2.943%. Comparing the battery output voltage, it is confirmed that with MFL-EMS it tends to be steeper than the one with PF-EMS, showing the proposed strategy’s robustness. Overall, the obtained results revealed an improved performance in terms of power distribution as well as SOC, which means less hydrogen consumption and therefore a more economical solution.

Design of fuzzy logic based energy management and traffic predictive model for cyber physical systems

Cyber-Physical Systems (CPS) is a multidisciplinary effort that links cyber and physical vectors. Cyber systems may be incorporated into any physical system, such as intelligent transportation systems (ITS), industries, and cities, to improve its intelligence, energy efficiency, and comfort. The design of self-driving cars has a large influence on vehicle travel demand and energy consumption. ITS also heavily relies on the Traffic Flow Prediction (TFP) system. With this rationale, this work focuses on the development of a Fuzzy Logic-based Energy Management and TFP model (FLEM-TFP) for CPS in ITS. The FLEM-TFP approach proposed here involves two primary processes: energy management and TFP. An adaptive neuro-fuzzy inference system (ANFIS) model is also utilized to compute the engine torque required based on various readings. In addition, a sailfish optimizer (SFO)-based fuzzy wavelet neural network (FWNN) approach is used in the ITS to estimate traffic flow. According to the findings of the experiments, the FLEM-TFP approach outperformed previous state-of-the-art procedures.

Fuzzy logic based energy management for grid connected hybrid PV system

Renewable energy resources are at the forefront for addressing the rapidly increasing energy demand. Renewable energy resources possess numerous advantages over conventional energy resources, but the major issue is the intermittency of its supply. This issue is partially addressed by designing a hybrid system with energy sources and battery storage systems, which can also be connected to the grid. In this paper, an optimal energy management system is proposed for a hybrid PV-Battery storage system. Fuzzy logic is used to control the battery storage system and grid-connected inverter, and its associated control is used to control power flow in the grid-tie line. The control strategy is designed, so that power flow from the grid happens only during contingency and normal operation; the battery storage system handles any power fluctuation and ensures optimal utilization of resources. The proposed scheme also ensures that battery capacity requirement is minimal yet sufficient to address all day-to-day variations that the system would typically face.

Energy Management Strategies for Hybrid Power Systems Considering Dynamic Characteristics of Power Sources

Due to the rising concern of environmental problems, fuel cell hybrid power systems have been proposed to be an ideal substitution to diesel engines. However, the hybrid power system has brought many challenges, one of which is the power splitting among different power generation technologies. In this paper, a wavelet transform and fuzzy logic based energy management strategy is proposed for a fuel cell/battery/ultra-capacitor hybrid power cruiser considering their dynamic characteristics. The energy demand of the cruiser is decoupled into different frequencies by wavelet transform and distributed to different energy sources according to their dynamic characteristics. To ensure long-term reliable operation of the hybrid power system, fuzzy logic is proposed to keep the state of charges of the battery and ultra-capacitor within safe range. The proposed energy management strategy is applied to a hybrid power cruiser in MATLAB/Simulink and two cases are studied considering different initial states of charge of the energy storage system. The results show that the proposed energy management strategy enables the fuel cell to supply about 50% of the high frequency power and reduce the peak power supplied by the fuel cell by 80% - 100% when the initial state of charge of the ultra-capacitor is low, and the fuel cell supplies around 400% higher than the low peak power and 20% of the high peak power when the initial state of charge of the battery is low.

Adaptive optimal fuzzy logic based energy management in multi-energy microgrid considering operational uncertainties

The intelligent decision making of multi-energy management in a microgrid is a non-trivial task due to the intermittent and stochastic nature of highly penetrated renewable energy sources and demand. To address such a challenge, the energy management system often adopts the prediction based day-ahead energy scheduling and real-time energy dispatch to optimally coordinate the operation of dispatchable components, e.g., battery-based energy storage and thermal units. This paper presents an adaptive optimal fuzzy logic based energy management solution to develop appropriate day-ahead fuzzy rules for real-time energy dispatch adaptively in the presence of operational uncertainties. The solution determines the optimal fuzzy inference system (e.g., the membership function shape and the inference rules set) based on the predicted information over a certain period through a novel offline meta-heuristic optimization algorithm. The real-time energy dispatch based on the obtained optimal fuzzy logic rules can be further carried out to meet the various operational criteria, e.g., minimal power fluctuation and operational cost. The proposed solution is extensively evaluated through simulation experiments in comparison with two existing approaches: the online rule-based dispatch method and the meta-heuristic optimization-based offline scheduling method. The numerical results demonstrate the superior performance of the proposed energy management solution.

Fuzzy Logic Based Energy Controlling Strategy for Isolated Wind-PV Hybrid System

The paper proposes a fuzzy logic based energy management strategy for isolated hybrid PV-Wind system. This PV-Wind system comprises of 500 W photovoltaic (PV) systems and 600 W permanent magnet based alternator for conversion of wind energy. A step down converter is connected to the system in order to charge the 48V battery electric storage system (BESS). The load of 1 kW system is supplied with the 1.1 kW hybrid systems. The renewable energy sources are intermittent in nature and hence BESS system is used to support the load when there is shortage of energy generated. To optimize the output voltage of the fuzzy logic approach based maximum power point tracking (MPPT) method is employed which is also utilized for the energy management strategy. The proposed topology is modeled and simulated using mathworks tool MATLAB/Simulink and its performance is evaluated.

Advanced Energy Management System For Hybrid Vehicle

The battery and ultra-capacitor hybrid storage system (HESS) is a system which can be adopted in the EV. By utilizing Ultra-capacitors, the batteries can be protected from high peak currents, which can be especially harming to the batteries. Consequently it is conceivable to expand the battery life. Battery and ultra-capacitor are connected through a bidirectional non-isolated multi input converter which has numerous points of interest. Fuzzy logic based energy management strategy is an efficient method to manage energy through HESS and furthermore regulating the SoC of ultra-capacitor while smoothing the battery power profile.

Fuzzy Logic Based Energy Management For Wind Turbine, Photo Voltaic And Diesel Hybrid System

Rapid population growth with high living standards and high electronics use for personal comfort has raised the electricity demand exponentially. To fulfill this elevated demand, conventional energy sources are shifting towards low production cost and long term usable alternative energy sources. Hybrid renewable energy systems (HRES) are becoming popular as stand-alone power systems for providing electricity in remote areas due to advancement in renewable energy technologies and subsequent rise in prices of petroleum products. Wind and solar power are considered feasible replacement to fossil fuels as the prediction of the fuel shortage in the near future, forced all operators involved in energy production to explore this new and clean source of power. Presented paper proposes fuzzy logic based Energy Management System (EMS) for Wind Turbine (WT), Photo Voltaic (PV) and Diesel Generator (DG) hybrid micro-grid configuration. Battery backup system is introduced for worst environmental conditions or high load demands. Dump load along with dump load controller is implemented for over voltage and over speed protection. Fuzzy logic based supervisory control system performs the power flow control between different scenarios such as battery charging, battery backup, dump load activation and DG backup in most intellectual way.

Real time energy management strategy for a fast charging electric urban bus powered by hybrid energy storage system

In this paper, an innovative real time energy management strategy design approach is proposed for a fast charging electric urban bus with hybrid energy storage system composed of conventional batteries and supercapacitors. After modeling, a multi-objective optimization problem taking into account cycle life of the battery, total energy consumption and specific requirement that minimizing the use of battery is formulated. A quantifiable evaluation model is firstly derived to evaluate different kinds of strategies. Then a conventional fuzzy logic control based energy management strategy with features of intelligence and adaptability is proposed, but the simulation result shows that even after long time tuning it can not achieve the desired result with the manual set membership functions. Thereafter, an optimal energy management strategy based on dynamic programming is developed as a benchmark to see the room for improvement. Finally, an innovative model in the loop optimization approach based on genetic algorithm is proposed to optimize the membership functions of the conventional fuzzy logic based energy management strategy. Simulation results demonstrate that the overall performance of optimized fuzzy logic based energy management strategy can be improved significantly and can even approach the optimal results of dynamic programming.

Fuzzy Logic Based Energy Management System for Hybrid Electric Vehicle

Hybrid electric vehicles have gained attention throughout the globe with its advantage of green technology and reduced greenhouse gases emission. Moreover, hybrid vehicles being powered by battery would be the best option of replacing current petrol or gas dependent vehicles. There are drawbacks though; battery has limited lifetime and is very costly. Hence, it is hybridized with other energy storage systems such as supercapacitor. This paper focuses on the energy management system for the energy storage system consisting battery and supercapacitor of a hybrid electric vehicle using fuzzy logic based controller. The energy management system, which manages energy feed between battery and supercapacitor, is then simulated in Matlab/Simulink to verify its reliability and validity of operation.

Experimental performance assessment of an online energy management strategy for varying renewable power production suppression

Lately, interest in renewable sources, especially wind and solar energy, has shown a significant increase in all over the world that mostly depends on climate-threatening conventional fossil fuels. Besides, hybrid use of these power sources with suitable back-up units provides many advantages compared to sole use of these sources. In this regard, a hybrid system consisting of a wind turbine for utilizing the wind energy, photovoltaic panels for solar energy, fuel cell for providing back-up power and a battery unit for storing the possible excess energy production and supplying the transient load is considered in this study. Experimental assessment of this system in different case studies including the real time measured dynamic power demand of an office block is realized. The collaborative actions of the proposed hybrid system with a fuzzy logic based energy management strategy during fluctuations of renewable-based power production are investigated. Thus, results of this study may be valuable for evaluating the feasibility of stand-alone hybrid renewable energy units for future power systems.

Fuel cell and ultra-capacitor hybridization: A prototype test bench based analysis of different energy management strategies for vehicular applications

An energy storage system with sufficient power capacity should be incorporated with fuel cells (FCs) to compensate the slow dynamics of FCs. Ultra-capacitors (UCs) are potential candidates for a solution in this aspect. A test bench of such an FC/UC hybrid configuration that can emulate the dynamics of vehicular systems is presented in this paper. Namely, the test bench performance verification of wavelet transform and fuzzy logic based energy management strategy that was discussed in earlier simulation-based studies of the authors is investigated. Of equal importance is the comparison of the cascade wavelet-fuzzy logic based strategy with the case of using only fuzzy logic in the energy management is presented. Experimental results with small-scale devices, a PEMFC (5 kW, 48 V) manufactured by Plug Power ® Company, and a UC bank composed of 430 F, 16 V and 165 F, 48 V UC modules manufactured by Maxwell Technologies ® Company, illustrate the successful performance analysis of employed energy management schemes during similar motor cycles.

Slide Mode and Fuzzy Logic Based Powertrain Controller for the Energy Management and Battery Lifetime Extension of Series Hybrid Electric Vehicles

A control strategy was developed to improve fuel economy, enhance engine efficiency as well as extend battery cycle life in the series hybrid electric vehicle (SHEV) powertrain. The controller was based on fuzzy logic, fixedboundary-layer sliding mode controllers (FBLSMCs) and an optimized battery charge scenario. The fuzzy logic based energy management controller is developed to determine the engine power based on two inputs, battery state-of-charge (SOC) and vehicle power demand. The goal of the fuzzy logic based controller is to enhance the engine and battery operation efficiency and at the mean time, extend battery life. An appropriate battery charge scenario is designed to remove surge charge current, and avoid persistently-high charge power, which are positive factors to the battery lifetime extension. Besides, two robust FBLSMCs against uncertain disturbances are configured in the powertrain control system, responsible for engine speed control and engine torque control, respectively. Simulation results are obtained for comparison between the proposed and conventional powertrain control schemes. Through these simulations, the effectiveness and superiority of the proposed powertrain control strategy are validated.

A wavelet-fuzzy logic based energy management strategy for a fuel cell/battery/ultra-capacitor hybrid vehicular power system

Abstract Due to increasing concerns on environmental pollution and depleting fossil fuels, fuel cell (FC) vehicle technology has received considerable attention as an alternative to the conventional vehicular systems. However, a FC system combined with an energy storage system (ESS) can display a preferable performance for vehicle propulsion. As the additional ESS can fulfill the transient power demand fluctuations, the fuel cell can be downsized to fit the average power demand without facing peak loads. Besides, braking energy can be recovered by the ESS. This study focuses on a vehicular system powered by a fuel cell and equipped with two secondary energy storage devices: battery and ultra-capacitor (UC). However, an advanced energy management strategy is quite necessary to split the power demand of a vehicle in a suitable way for the on-board power sources in order to maximize the performance while promoting the fuel economy and endurance of hybrid system components. In this study, a wavelet and fuzzy logic based energy management strategy is proposed for the developed hybrid vehicular system. Wavelet transform has great capability for analyzing signals consisting of instantaneous changes like a hybrid electric vehicle (HEV) power demand. Besides, fuzzy logic has a quite suitable structure for the control of hybrid systems. The mathematical and electrical models of the hybrid vehicular system are developed in detail and simulated using MATLAB®, Simulink® and SimPowerSystems® environments.