Hybrid Energy Management System Research Articles

Optimal power management of a stand-alone hybrid energy management system: Hydro-photovoltaic-fuel cell

The field of electrical engineering with renewable energy systems has witnessed a remarkable improvement due to an increase in the world's growing population as available resources become limited. This study proposes an optimal and efficient hybrid energy management system that combines photovoltaic, hydro, and fuel cell renewable energy resources that can solve the drawbacks of current energy setups and offer a dependable power source for stand-alone purposes. This novel AC-linked hybrid energy system features a comprehensive power management strategy with actual weather data and a realistic load demand profile. The HOMER Pro technique is employed for calculating the emission of toxic gases, system economic benefits, sensitivity analysis and cost minimization of the system while providing sustainability. The numerical outcomes show optimal results as compared to the existing systems in the literature. The system consists of 55 kW of PV modules, a 30 kW FC generator, and 40 kW of hydropower, suggesting optimal and excellent economic and environmental sustainability. This system appeared to be an especially cost-effective solution, with a net present cost (NPC) of $248,773 and a cost of energy (COE) of $0.0546/kWh. The results show that the system can meet energy needs while utilising the available resources efficiently in different weather conditions. The proposed system has the potential to increase the sustainability and dependability of power generation in a variety of applications and aid in the design and implementation of alternative energy systems, particularly in off-grid or remote areas. The proposed system can be extended to other such locations that will provide a scalable and flexible solution for electricity generation, making it suitable for a variety of applications, from small rural power plants to large industrial power supplies. Overall, the proposed study provides an important resource for development in hybrid energy system design and operation, with far-reaching implications for the transition to a sustainable and low-carbon future.

High gain super lift Luo converter for hybrid energy management systems

Recently, there has been a resurgence of interest in the development of alternative energy supplies due to rising fossil fuel prices and issues about the effects of greenhouse gas emissions on the environment. Today's demand is the design of a hybrid electric power generation system that uses solar and wind energy for remote places. This paper presents the design of an optimized hybrid renewable energy system consisting of a photo voltaic wind turbine (savinous type) with a battery and a super lift LUO converter. A solar PV module with an intelligent inverter facilitates the tracking of the maximum power point and the implementation of a battery charge controller for greater battery lifespan as well as reliable performance. Higher power output and clean energy are available from the hybrid power generation system, which combines innovative techniques with solar and wind power.

Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation

Considering the rapidly evolving microgrid technology and the increasing complexity associated with integrating renewable energy sources, innovative approaches to energy management are crucial for ensuring sustainability and efficiency. This paper presents a novel Fuzzy Logic-Based Particle Swarm Optimization (FLB-PSO) technique to enhance the performance of hybrid energy management systems. The proposed FLB-PSO algorithm effectively addresses the challenge of balancing exploration and exploitation in optimization problems, thereby enhancing convergence speed and solution accuracy with robustness across diverse and complex scenarios. By leveraging the adaptability of fuzzy logic to adjust PSO parameters dynamically, the method optimizes the allocation and utilization of diverse energy resources within a grid-connected microgrid. Under fixed grid tariffs, the investigation demonstrates that FLB-PSO achieves grid power purchase and battery degradation costs of $1935.07 and $49.93, respectively, compared to $2159.67 and $61.43 for the traditional PSO. This results in an optimal cost of $1985.00 for FLB-PSO, leading to a cost saving of $236.09 compared to the $2221.10 of PSO. Furthermore, under dynamic grid tariffs, FLB-PSO incurs grid power purchase and battery degradation costs of $2359.20 and $64.66, respectively, in contrast to $2606.47 and $54.61 for PSO. The optimal cost for FLB-PSO is $2423.86, representing a cost reduction of $237.23 compared to the $2661.08 of PSO. The FLB-PSO algorithm proficiently manages energy sources while addressing complexities associated with battery storage degradation. Overall, the FLB-PSO algorithm outperforms traditional PSO in terms of robustness to system dynamics, convergence rate, operational cost reduction, and improved energy efficiency.

Optimal power-split of hybrid energy storage system using Pontryagin’s minimum principle and deep reinforcement learning approach for electric vehicle application

The battery supercapacitor hybrid energy storage system (HESS) based electric vehicles (EVs) require an efficient online energy management system (EMS) to enhance the battery life. The indirect optimal control method, like Pontryagin’s minimum principle (PMP), is gaining attention for its inherent instantaneous optimization and computational simplicity. However, determining the costate, non-casual variable of PMP for a given driving scenario is challenging and significantly affects the optimal solution of minimizing the battery degradation. In this study, we propose an online hybrid EMS by combining PMP and deep reinforcement learning (RL) to precisely predict the optimal costate and simultaneously minimize the battery degradation in a battery supercapacitor HESS assisted EV. Also, we propose an analytical approach to estimate the initial costate to create the costate action space of RL, which could also be used to estimate the initial costate guess for similar applications. We validate the performance of the proposed EMS under driving cycle uncertainties and untrained driving cycles via simulation. The results demonstrate the effectiveness of deep RL for optimal costate estimation to satisfy the charge sustainability of the supercapacitor. The predicted costate maintains the supercapacitor state-of-charge in the desired range while minimizing battery current for uncertain driving profiles. Further, we have compared the battery energy depletion in the proposed EMS and an offline optimal EMS to study the feasibility of the proposed intelligent EMS. The proposed EMS outperforms the offline EMS, achieving a significant improvement of 900 charge–discharge cycles.

Optimization of Endurance Performance for Quadrotor Unmanned Aerial Vehicles Driven by a Hybrid System of Solar Photovoltaic Cells and Energy Storage Batteries

The potential applications of unmanned aerial vehicles (UAVs) are vast and exciting, yet their scope could be significantly expanded if their flight endurance could be extended. The primary objective of this study is to design a hybrid power system combining solar energy and lithium batteries to enhance the endurance and energy management efficiency of unmanned aerial vehicles (UAVs). Through comparative analysis of series, parallel, and hybrid power systems, a parallel hybrid power system was chosen as the power solution for UAVs. In this paper, we detail the selection process for solar panels and lithium batteries and elucidate the design of a solar endurance enhancement system, including the application of anti-backflow and voltage stabilization modules. Additionally, we propose an energy management strategy based on finite state machines, which ensures efficient and stable operation of the system through real-time monitoring and dynamic adjustment. The methodology of this study provides an innovative and practical framework for the design and implementation of solar and energy storage battery hybrid energy management systems for UAVs.

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.