Hybrid Energy Management System Research Articles

A backpropagation neural network-based hybrid energy recognition and management system

For several decades, small electronic devices like wireless sensor network nodes (WSNs) tend to be powered by ambient energy, and the multi-input energy platform attracts much attention because sensors are usually used in complicated surroundings. However, for multi-input energy platform energy management is complex and the demand of the consumers is stochastic. To solve the problems, this paper presents a backpropagation neural network (BPNN) based hybrid energy recognition and management System (ERMS). The design applies artificial intelligence algorithms to energy forecasting recognition. And it achieves energy-matching management according to recognition results. Besides, we implemented the energy recognition algorithm on an application specific integrated circuit (ASIC) innovatively, which is manufactured in a standard 180 nm CMOS technology. The energy recognition chip area is 1.45mm × 1.45 mm. The experimental data present that the system can identify different types of input energy and control the energy flows automatically. The current consumption of the ASIC is 65μA at 1 MHz and the recognition accuracy can reach 98 %. Moreover, the hybrid energy recognition and management system platform worked effectively. The measurement results show that the power conversion efficiency of the system to photovoltaic energy input is 85 %. Furthermore, when the input is piezoelectric energy, the power management system output power can achieve 7.4 mW.

Research and optimization of energy management system for photovoltaic vehicles

To address the drawbacks of low energy utilization and high cost in traditional photovoltaic (PV) vehicle energy management systems, a hybrid energy management system for PV vehicles is proposed, which can automatically manage energy under complex conditions. An improved PV model and the solar irradiation S – temperature T transfer function are used to construct Simulink simulation models. The simulation compares the charging effects of a PV cell and a charging station, as well as the performance of four energy management schemes. Finally, validation experiments are conducted for the improved PV model and the PV charging model. The simulation results show that: (1) The charging speed of the PV cell can reach 3.37 times that of the charging station, highlighting the practicality of PV power supply research. (2) In complex environments, Energy Management Scheme 2 has a more effective energy management capability compared to other schemes. The experimental results show that: (1) Compared to the original PV model, the improved PV model provides simulated data that are closer to the experimental data of the PV cell. (2) The accuracy of simulating the charging voltage and current in the PV charging model has significantly improved, reaching over 94.2% and 89.5%, respectively.

A deep reinforced markov action learning based hybridized energy management strategy for electric vehicle application

Due to the severe lack of fossil fuels and existing environmental pollution, developing Electric Vehicles (EV) powered by clean, new energy is essential to finding a way to address this problem. The hybrid EV powered by fuel cell is the most practical and suitable option in present days. Several researchers have concluded that a key factor in determining an EV's performance is the hybrid energy management system's output performance. Moreover, proper energy management and performance optimization are the most essential solutions to ensure the normal operation of the hybridized system. In the existing studies, several energy management methodologies are developed using a hybridized energy sources, yet it facing some challenges in terms of high cost, increased system designing complexity, stability issues, and unreliable power supply. Therefore, the proposed work intends to implement a novel Energy Management System (EMS) for EVs with the use of advanced learning algorithms. In the proposed topology, the combination of Proton Exchange Membrane Fuel Cell (PEMFC), battery, and Super-Capacitor (SC) have been used as the major energy sources. Then, a high gain DC-DC converter is utilized to boost the regulate output voltage with reduced stress and loss factors. For improving the performance of converter, a recently developed Gorilla Troop Optimization Algorithm (GTOA) is implemented, which provides the best solution to choose the controlling parameters for generating the switching pulses. Moreover, the Deep Reinforced Markov Action Learning (DRLA) is employed to assure an efficient energy management in EV systems. For validation, the simulation and comparative analyses are carried out in this study with the use of numerous parameters. Index termsElectric Vehicle (EV), Proton Exchange Membrane Fuel Cell (PEMFC), Battery, Super-Capacitor (SC), Renewable Energy Sources (RES), High Gain DC-DC Converter, Gorilla Troop Optimization Algorithm (GTOA), and Deep Reinforced Markov Action Learning (DRLA).

Lagrange Multiplier-Based Optimization for Hybrid Energy Management System with Renewable Energy Sources and Electric Vehicles

The issues of energy scarcity and environmental harm have become major priorities for both business and human progress. Hence, it is important and useful to focus on renewable energy research and efficient utilization of distributed energy sources (DERs). A microgrid (MG) is a self-managed system that encompasses these energy resources as well as interconnected consumers. It has the flexibility to function in both isolated and grid-connected configurations. This study aims to design an effective method of power management for a MG in the two operating modes. The proposed optimization model seeks to strike a balance between energy usage, protecting the life of batteries, and maximizing economic benefits for users in the MG, with consideration of the real-time electricity price and constraints of the power grid. Furthermore, in order to accurately account for the dynamic nature of not only the stationary battery banks used as the energy storage systems (ESS) but also the built-in batteries of electric vehicles (EVs), the model is presented as a multi-objective, multiparametric and constrained problem. The solution is proposed to be found using the Lagrange multiplier theory, which helps to achieve good performance with less computational burden. Lastly, simulation results from both the isolated and grid-connected modes also demonstrate the effectiveness of the designed method.

Optimization of hybrid energy management system based on high-energy solid-state lithium batteries and reversible fuel cells

Integrating a high power source, like a super capacitor (SCAP), and a lithium-ion battery (LIB) for electric vehicle (EV) applications yields achievement improvements, including maximum reliability, long lifetime (LT), small size, and competitive pricing for the overall source. A hybrid energy storage system (ESS) controlled by an intelligent energy management strategy (EMS) may be substantially included in multi-source EV design and development. Therefore, this paper proposes a hybrid chimp optimization algorithm (ChOA) and Levy walk technique to create an optimum EMS. The proposed technique reduces battery power (BP) stress and increases the LT, which is accomplished by using a hybrid ChOA-Levy walk (ChOA-LW) optimization algorithm with a rule-based approach in accordance with understanding the performance of LIB and SCAP. In order to optimize the rule-based EMS's control settings, the latter strategy is suggested. The control approach can be implemented online once the offline optimization procedure is finished. The presented technique is evaluated via simulation and on an experimental platform by means of a power emulator testbed of a LIB/SCAP hybrid ESS. In terms of BP stress and LT, the findings are compared with a conventional rule-based approach and a mono-source containing a regular high-power LIB. Results obtained demonstrate the effectiveness of the suggested technique, which enables the requested performance to be satisfied with better energy utilization. The assessment results also show notable LT improvements for the LIB, an improvement of up to 19% over the mono-source in reference to a conventional single cell LIB.

Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System

Growing Electric vehicle (EV) ownership leads to an increase in charging stations, which raises load demand and causes grid outages during peak hours. Microgrids can significantly resolve these issues in the electrical distribution system by implementing an effective energy management approach. The suggested hybrid optimization approach aims to provide constant power regardless of the generation discrepancy and should prevent the early deterioration of the storage devices. This study suggests using a dynamic control system based on the Fuzzy-Sparrow Search Algorithm (SSA) to provide a reliable power balance for microgrid (MG) operation. The proposed DC microgrid integrating renewable energy sources (RES) and battery storage system (BSS) as sources are designed and evaluated, and the findings are further validated using MATLAB Simulink simulation. In comparing the hybrid SSA strategy with the most widely used Particle Swarm Optimization (PSO)-based power management, it was observed that the hybrid SSA approach was superior in terms of convergence speed and stability. The effectiveness of the given energy management system is evaluated using two distinct modes, the variation of solar irradiation and the variation of battery state of charge, ensuring the microgrid’s cost-effective operation. The enhanced response characteristics indicate that the Fuzzy-SSA can optimise power management of the DC microgrid, making better use of energy resources. These results show the relevance of algorithm configuration for cost-effective power management in DC microgrids, as it saves approximately 7.776% in electricity expenses over a year compared to PSO.

Decentralised Hybridised Energy Management Systems (DHEMS) in power grids

The integration of electric batteries along the power supply chain is crucial for the transformation of the energy sector towards a new flexible grid that allows the penetration of renewable power generation while ensuring stability and supply security. Batteries penetration in the grid can be boosted through an efficient management of heterogeneous generation sources, controllable loads and batteries, according to different criteria of stability, efficiency, cost, maintenance and power flow requirements. The Distributed Hybrid Energy Management System (DHEMS) is a management software tool able to solve an optimization problem maximizing renewable energy sources exploitation. The DHEMS has been designed with two control layers. First, the Cloud DHEMS layer accepts external setpoints (from a VPP, DSO or TSO) and dispatchs the total active and reactive power to be exchanged with the grid by a set of distributed plants. Second, the Local DHEMSs are in charge of distributing received set points and commands among the local sets that form each power plant. Different real control and communication tests have been done, in La Plana facility (owned by Siemens Gamesa Renewable Energy).

Hybrid Energy Management System Consisting of Battery and Supercapacitor for Electric Vehicle

This paper is mainly focused on Hybrid Energy Management System (HEMS) consisting ofBattery (BT) and Super capacitor (SC). Two energy sources connected in with same DC link in parallel manner with the help of Bidirectional DC-DC converter, which is used to separate control of power flow of each source. Here Permanent magnet dc motor (PMDC) motor used as a load and speed control of PMDC motor can be done by PWM method for this purpose chopper circuit is used. Input of chopper circuit is DC link and output of the chopper is given to PMDC motor. This method of energy management gives power splitting between two sources based on State of Charge (SOC) of each individual source during different state of vehicle such as acceleration, constant running and deceleration. Improved filter-based power splitting techniques is implemented. Three acceleration reference points were taken for power splinting at different SOC levels of both energy sources. Objective of this proposed method is best use of both the sources i.e. battery and supercapacitor and maximum use of supercapacitor energy at the time of transient conditions. Battery supply energy during normal running condition or very less load condition. Hence during transient condition SC directly react with system and gives peak power requirement, so stress on battery is reduces hence lifetime of battery is increase, also power available during braking is store in SC and battery, so independence of Electric Vehicle (EV) is increases. Because of less peak power requirement, batteries with less peak output power is used so it is reduced size and cost of batteries. Matlab-Simulink software is used for simulation and also small scale hardware is also implemented of proposed method.

Engine RPM and Battery SOC Activation Optimization in Hybrid Vehicle Energy Management System Utilizing BPNN - Genetic Algorithm and BPNN – Particle Swarm Optimization

The energy used in the hybrid vehicle needs to be regulated to gain further mileage and lower fuel consumption. It is achieved by selecting the correct levels of hybrid energy management system (EMS) parameters (i.e., vehicle speed, engine RPM, and activation State of Charge (SOC) of battery). This study focused on the modeling and optimization of Sepuluh Nopember Institute of Technology (ITS)’s series plug-in hybrid electric vehicle (PHEV) car mileage and fuel consumption by comparing the backpropagation neural network (BPNN) method – genetic algorithm (GA) and BPNN – particle swarm optimization (PSO). The BPNN was used to model the character of ITS’s series PHEV EMS and predict mileage and fuel consumption. The BPNN’s model obtained the best EMS parameters, most extended mileage, and minimum fuel consumption. The result of the validation experiment showed that both the integration of BPNN - GA and BPNN - PSO were able to predict and optimize the multi-objective characteristic with the same results.

Multi-objective optimization of hybrid energy management system for expressway chargers

Fuel combustion is considered a vast energy source for transportation, resulting in the emission of pollutants into the environment. In contrast, electric vehicles (EVs) do not emit direct emissions and protect the environment from hazardous emissions. The rapid and enormous expansion of electric vehicles in China made electric chargers a crucial component of the transportation management system. The present study suggests a hybrid energy management approach to boost the amount of renewable energy going into the electric charger. Wind turbines and solar power generating modules generate energy for electric charging. Excessive energy is stored in energy storage modules and sent to power grid modules, making reverse supply more flexible. Three optimization algorithms, including PSO, GA, and BBO, were employed to find the highest energy cost and power grid dependence with different combinations. The system is considered on-grid and can purchase or sell electricity to maintain energy balance for electric charging devices. The optimization results show that the highest energy cost in a reasonable scheme can reach 0.034546 $/kWh with a share of maximum renewable energy of 96.50 %. Finally, the balance between energy cost and dependency on the power grid can be reached based on the present analysis findings, as the energy cost of the final equilibrium scheme is 0.057475 $/kWh, with a share of 90.82 % in renewable energy. The proposed study can effectively balance energy supply and demand and further support the global agenda of UN-2030 for sustainable development, especially SDG-7.

Hybrid Energy Storage to Control and Optimize Electric Propulsion Systems

Today, ship development has concentrated on electrifying ships in commercial and military applications to improve efficiency, support high-power missile systems and reduce emissions. However, the electric propulsion of the shipboard system experiences torque fluctuation, thrust, and power due to the rotation of the propeller shaft and the motion of waves. In order to meet these challenges, a new solution is needed. This paper explores hybrid energy management systems using the battery and ultracapacitor to control and optimize the electric propulsion system. The battery type and ultracapacitor are ZEBRA and MAXWELL, respectively. The 3-, 4-and 5-blade propellers are considered to produce power and move rapidly. The loss factor has been reduced, and the sea states have been found through the Elephant Herding Optimization algorithm. The efficiency of the proposed system is greatly enhanced through torque, thrust and power. The model predictive controller control strategy is activated to reduce load torque and drive system Root Average Square (RMS) error. The implementations are conducted under the MATLAB platform. The values for torque, current, power, and error are measured and plotted. Finally, the performance of the proposed methodology is compared with other available algorithms such as BAT and Dragonfly (DF). The simulation results show that the results of the proposed method are superior to those of various techniques and algorithms such as BAT and Dragonfly.

Optimization of Hybrid Energy Management System Based on High-Energy Solid-State Lithium Batteries and Reversible Fuel Cells

Optimization of Hybrid Energy Management System Based on High-Energy Solid-State Lithium Batteries and Reversible Fuel Cells

Fuzzy Controller based Hybrid Energy Management System for Electric Vehicle

Fuzzy Controller based Hybrid Energy Management System for Electric Vehicle

A Novel Hybrid Home Energy Management System Considering Electricity Cost and Greenhouse Gas Emissions Minimization

This article proposes a multiobjective hybrid energy management system that minimizes both the electricity expenses and the household greenhouse gas emissions released due to consumption, considering the entire life cycle of the generation assets used to provide energy. The global warming potential indicator is used to decide if it is more sustainable to purchase electricity from the grid or use the household's flexible generation sources like photovoltaic panels and the energy storage system. Results prove that it is possible to reduce greenhouse gas emissions without incurring expensive electricity bill costs thanks to the hybrid-based home energy management system approach. This method gives the end-users a more influential role in the climate change solution, allowing them to give more or less importance to the economic or environmental component, according to their preferences.

High temperature superconducting material based energy storage for solar-wind hybrid generating systems for fluctuating power management

The green energy infrastructure plays an important part in existing world of power scarcity. Owing to their dependence on environmental factors, renewable energy technologies continue to fluctuate. Using the energy storage device will solve the problem. The technical and economical requirements of the energy storage process can be met with the use of hybrid combinations. This paper proposes a hybrid energy management system based on the Super conductive magnetic material and the lead acid battery to provide power stabilization in the grid-connected unstable microgrid. Here, second-generation High Temperature Superconducting (HTS) material is used as Super Conducting Magnet Energy Storage (HTSMES) which exhibits a high irreversibility field and critical current density within an active magnetic field. The proposed system may be a good solution to minimize the impact of the Point of Common Coupling (PCC) power variability during fault condition. To improve the efficiency and flexibility of the proposed system, the two hybrid power sources are connected via dual input single output zeta converter. The HTSMES is used to minimize the difference in active power during fault and to give the reactive current to handle the fault. Grid connected microgrid with HTSMES-battery was simulated using the MATLAB Simulink platform and tested using an energy management algorithm with and without the presence of fault. The system proposed is demonstrated with detailed simulation results.

Nonlinear controllers for fuel cell, photovoltaic cell and battery based hybrid energy management system

Abstract Due to limitations of conventional energy sources like petrol, diesel, hydro, nuclear etc., hybridization of green energy sources like photovoltaic cell, wind, fuel cell etc. has become a challenging task. In this research photovoltaic (PV) and fuel cells (FC) have been considered as primary sources of energy while battery as a storage unit for a Hybrid Energy Storage System (HESS). Extraction of maximum power from PV panel and output voltage regulation is another challenge due to its nonlinear characteristics. In this paper, single stage multi input multi output (MIMO) buck converter has been considered and four controllers: Fuzzy Logic Based (FLB) robust, nonlinear Backstepping (BS), Integral Backstepping (IBS) and Synergetic controllers have been proposed to track the output voltage, to control the battery charging and to extract maximum power from the PV cell. Asymptotic stability of the proposed controllers has been proved using Lyapunov theory. The performance of nonlinear controllers has been checked under input power and load variations on the converter having only one inductor and two input sources. They have been compared with each other and conventional PID controller under different operating conditions by simulations in MATLAB/Simulink. The results have also been validated by an experimental setup on a lab prototype.

Design of a hybrid energy management system using designed rule‐based control strategy and genetic algorithm for the series‐parallel plug‐in hybrid electric vehicle

Design of a hybrid energy management system using designed <scp>rule‐based</scp> control strategy and genetic algorithm for the series‐parallel plug‐in hybrid electric vehicle

Enhancing the operation of fuel cell-photovoltaic-battery-supercapacitor renewable system through a hybrid energy management strategy

It is necessary to have an energy management system based on one or more control strategies to sense, monitor, and control the behavior of the hybrid energy sources. In renewable hybrid power systems containing fuel cells and batteries, the hydrogen consumption reduction and battery state of charge (SOC) utilizing are the main objectives. These parameters are essential to get the maximum befits of cost reduction as well as battery and hydrogen storage lifetime increasing. In this paper, a novel hybrid energy management system (HEMS) was designed to achieve these objectives. A renewable hybrid power system combines: PV, PEMFC, SC, and Battery was designed to supply a predetermined load with its needed power. This (REHPS) depends on the PV power as a master source during the daylight. It uses the FC to support as a secondary source in the night or shading time. The battery is helping the FC when the load power is high. The supercapacitor (SC) is working at the load transient or load fast change. The proposed energy management system uses fuzzy logic and frequency decoupling and state machine control strategies working together as a hybrid strategy where the switching over between both strategies done automatically based on predetermined values to obtain the minimum value of hydrogen consumption and the maximum value of SOC at the same time. The proposed HEMS achieves 19.6% Hydrogen consumption saving and 5.4% increase in SOC value compared to the results of the same two strategies when working as a stand-alone. The load is designed to show a surplus power when the PV power is at its maximum value. This surplus power is used to charge the battery. To validate the system, the results were compared with the results of each strategy if working separately. The comparison confirms the achievement of the hybrid energy management system goal.

Control and energy management of hybrid system based on a fuel cell/photovoltaic/super capacitor using an AC-link inverter connected to the grid

This article deals with the power flow control in a hybrid power system, which is composed of a fuel cell, photovoltaic system and a super capacitor. The super capacitor has been employed with an aim to ameliorate the performance of the hybrid power system. In addition, it takes into account the energy fluctuations of photovoltaic energy sources and the slow dynamic of the fuel cell. Each source is connected to a DC/DC converter and their outputs are connected to a common DC-link and they supply a DC load. Furthermore, an energy management algorithm has been carried out in order to commonly share the power between the sources and the load. The proposed energy management method shows its flexibility, and an efficient energy conditioning between different sources is guaranteed in case of multi-source system. The main originality of this work lies in the use of AC-link partial resonant DC–AC converter topology to interface hybrid system and to inject the excess of energy to the grid. In this article, the system description, the control and the modeling of DC/DC converters and the AC-link converter are provided. Finally, simulations under Matlab/Simulink are shown in order to validate the effectiveness of the proposed control strategy and the energy management algorithm. An implementation on real-time using dSpace 1104 is presented to illustrate the feasibility of the proposed control strategy.

Cooperative energy management system for networked microgrids

Abstract This paper proposes a hybrid Energy Management Systems (EMS) architecture based on canonical coalition games for cooperative power exchange management of networked microgrids, interconnected with the main grid through a macro station. A central EMS and local EMSs perform three main processes: scheduling process, monitoring and rescheduling process, and benefits distribution process. A value function verifying the superadditivity property, which guarantees the grand coalition is the best coalition structure, and a computationally efficient non-linear scheduling model, which allows obtaining the power exchange strategy that minimizes transmission and transformation power losses, are defined. The scheduling process uses the scheduling model to generate a cooperative schedule for a rolling horizon. In order to enhance the performance of networked microgrids, the monitoring process uses efficiency thresholds for monitoring the schedule execution to identify schedule disruptions. For preserving the private information of microgrids, each local EMS only informs the central EMS of their state profiles, which specifies its power surplus or deficit for each time period of the scheduling horizon. The benefit-distribution process uses a Core-based model for distributing saved power loss obtained as a result of the power exchanged during the schedule execution. By comparing the proposed approach with a coalition formation game-based algorithm recently reported in the literature, it is concluded that the problem of power exchange management of networked microgrids should be modeled as a canonical coalition game and not as a coalition formation game. Results obtained from a prototype testing of the proposed EMS are presented.