Hybrid Electric Power System Research Articles

Correction: Tierielnyk, S.; Lukovtsev, V. Emergency Prevention Control as a Means of Power Quality Improvement in a Shipboard Hybrid Electric Power System. Energies 2024, 17, 398

There was an error in the original publication [...]

Parasitism-predation optimization algorithm-based energy management system for hybrid electric power system

Efficient management of hybrid energy sources, including fuel cells, batteries, and supercapacitors, is crucial for optimizing fuel consumption during emergency aircraft landings. Researchers are increasingly recognizing metaheuristic optimization algorithms for their effective convergence properties. This paper introduces a Parasitism-Predation Optimization Algorithm (PPOA)-based Energy Management System (EMS) hybridized with an Equivalent Consumption Minimization Strategy (ECMS) and an External Energy Maximization Strategy (EEMS) to reduce fuel consumption in Hybrid Energy Sources (HES). The proposed PPOA-EMS is developed using MATLAB/SIMULINK and validated on the Opal-RT 5700 real-time Hardware-in-the-Loop (HIL) simulator. The PPOA-ECMS configuration achieved fuel consumption of 17.5 g in simulations and 17.44 g in real-time simulation, with an efficiency of 88.61%. Similarly, the PPOA-EEMS configuration further reduces the fuel consumption to 15.2 g in simulations and 15.36 g in real-time simulations, with an efficiency of 93.17%. This work highlights the PPOA-EMS optimization in hydrogen fuel consumption when compared to conventional and metaheuristic algorithms, including State Machine Control (SMC), External Energy Maximization Strategy (EEMS), Equivalent Consumption Minimization Strategy (ECMS), Classical PI Controller (CPI), Gray Wolf Algorithm (GWO), Mine Blast Algorithm (MBA), and Slaps Swarm Algorithm (SSA). Finally, the findings demonstrate the effectiveness of PPOA-EMS in enhancing fuel efficiency and ensuring robust energy management.

Heuristic power management of hybrid source electric vehicle based on PV-battery-PEMFC

Renewable energy sources are now being focused on for usage in electric vehicles. Although majority of electric vehicles run solely on batteries, they have eliminated local emissions but not pollution. To tackle this issue, this research looks into the performance and power management aspects of an electric vehicle's hybrid power system, with a particular emphasis on the interactions between fuel cells, batteries, and photovoltaic (PV) cells. Stable operation is ensured via a bi-directional buck-boost converter, which maintains the voltage from all sources around reference 430 V. Initially, the fuel cell supplies electricity under low light conditions. The PV system and battery take over when the irradiance surpasses 100 W/m2, with the battery first draining and then charging in tandem with the increase in irradiance. 1750 RPM is the constant motor speed that is reached after making the required torque adjustments. A model full electric vehicle with multiple sources has been proposed with an efficient power management algorithm. When the battery is in stationary mode and the state of charge (SOC) is less than 100%, extra solar power is used to charge the battery. The model also takes into consideration using the car for entertainment or gadget charging when the ignition is off. The biogas output can be regulated using an inferential control technique using artificial neural network (ANN) based on the fuel demand or consumption. The system also includes anaerobic digestion of organic waste to provide hydrogen for the fuel cell sustainably from biogas. Power, current, SOC profiles, motor performance graphs, and other validations show that the extensive simulations exhibit efficient power management and a strong model for hybrid electric car power systems. The model has been simulated in the MATLAB Simulink environment and has been tested under realistic conditions.

Energy management strategy for electro-hydraulic hybrid electric vehicles considering optimal mode switching: A soft actor-critic approach trained on a multi-modal driving cycle

Hybrid electric vehicles (HEVs) feature multiple working modes. Thoughtful selection of these modes can optimally balance driving performance, power demands, and energy consumption, thereby enhancing the overall efficiency of the vehicle. This paper presents a soft actor-critic (SAC) approach trained on a multi-modal driving cycle (MDC) for selecting operational modes of electro-hydraulic hybrid electric vehicle (EHHEV). Firstly, characteristic parameters are extracted and clustered for five typical driving cycles through principal component analysis and K-means clustering, creating a multi-modal driving cycle. Secondly, based on the operational characteristics of EHHEV, state variables, action variables, reward functions, learning rates, and other parameters are set for the SAC algorithm, and the EMS framework is built based on the electro-hydraulic hybrid electric power system. Subsequently, the SAC algorithm is trained using the MDC to construct the SAC-MDC EMS. Results demonstrate that compared to EV, RB EMS, and SAC EMS, IREC achieves maximum improvements of 22.38 %, 5.55 % and 0.80 %, respectively. The dynamic performance and the motor load optimization capability are also enhanced. To further validate the practicality and reliability of the SAC-MDC EMS, this paper validates it using actual driving data, revealing that it still exhibits outstanding performance.

An improved energy management strategy for hybrid electric powered aircraft based on deep reinforcement learning

Electric airplanes have attracted a great deal of attentions because of their strategic commercial values and low emissions. One of the essentials for efficient control and management of hybrid electric propulsion aircraft is a well-designed energy management strategy (EMS). A new EMS approach based on deep learning is introduced in this study to realize thrust distribution and power distribution, consequently lowering aircraft fuel consumption. A model of hybrid electric propulsion power system including turbine engines and lithium-ion battery pack is constructed to act as an application environment. An intelligent energy management strategy based on deep reinforcement learning (DRL) is then proposed in this paper. Two DRL agents based on twin delayed deep deterministic policy gradient (TD3) are combined in the hierarchical framework. The multi-agents are trained to handle the energy management problem in this paper. In a finite period of time, the training process is accomplished. After training, the simulation result of the test shows that this DRL-based EMS reduces the equivalent fuel consumption by 2.2% compared with that using the existing method, and preserves 99.9% of the battery state of health (SOH). The battery pack's state of charge (SOC) regresses precisely to the reference value at the end. Particularly, the proposed EMS is able to work properly for different flight profiles and various initial SOCs. The results of this paper provide preliminary supports for the feasibility and superiority of deep reinforcement learning applied to the energy management strategy of series hybrid electric aircraft.

Micro-electromechanical systems sensor characterization for acoustic prognostic health management

Prognostic Health Management (PHM) is the process used to optimize machinery use by detecting faults and predicting failures and a machine’s remaining useful life. PHM is especially important to electro-mechanical systems in order to maximize a system’s availability and effective operation. The use of Micro-Electromechanical Systems (MEMS) accelerometers and microphones for PHM analysis is beneficial because it enables non-destructive acoustic testing with reduced cost, reduced size, and more efficient vehicle integration with other electronics than current methods. This research quantitatively studies a variety of MEMS sensors to understand how they measure acoustic data in a lab setting and on a truck with a diesel engine compared to piezoelectric accelerometers and condenser microphones. Diesel engines are common in many vehicles and hybrid-electric power systems, so the results will be applicable to a variety of situations. By applying signal processing techniques and PHM condition indicators to the data, standardized metrics can be developed to determine the best sensor for different PHM analyses.

Preliminary design of a retrofitted ultralight aircraft with a hybrid electric fuel cell power system

Emission-free aerial propulsion can be achieved with a proton-exchange membrane fuel cell (PEM-FC). In the present investigation, this potential is addressed by designing a hybrid electric power system with fuel cells for an ultralight aerial vehicle to be retrofitted from a conventional fossil-fuelled piston engine configuration. The proposed power system includes a fuel cell, a lithium battery, and a compressed hydrogen vessel. A procedure is proposed to find the size of these components that minimizes the total mass and satisfies the target of a size below 200L and uses performance data of commercially available components. A comparison of different energy management approaches, with and without on-board charge of the battery, is performed. The results underline that the optimal solution is to select the size of the fuel cell to meet the cruise electric request and point out that the maximum discharge current of the battery must be regarded as a key issue in sizing this component, because of the very high take-off power.

PRINCIPLES OF FORMING THE OPTIMAL STRUCTURE OF THE ELECTRIC POWER SYSTEM WITH THE INTEGRATED USE OF DISTRIBUTED GENERATION SOURCES

Abstract. The article presents the results of developing the optimal configuration of a hybrid electric power system of distributed generation, taking into account the energy potential of alternative energy in Ukraine. The issues of further development of distributed energy based on the introduction of hybrid systems with renewable sources of distributed energy generation are considered. The potential of alternative energy technologies in Ukraine is analyzed. A comparison of the efficiency of implementation of different types of alternative energy sources by the method of hierarchy analysis is carried out in order to identify their level of prospects for use in complex systems of distributed generation. The general optimal structure of the electric power system of distributed generation is developed. A method for optimizing the composition of integrated distributed generation power systems with renewable energy sources based on expert analysis of energy efficiency and the potential for the development of modern alternative energy technologies in Ukraine is proposed. The application of the performed research will increase the reliability of distributed generation systems and their energy efficiency in the hybrid use of renewable energy sources, provided that the system control is fully automated and its structure is optimised.

Emergency Prevention Control as a Means of Power Quality Improvement in a Shipboard Hybrid Electric Power System

The problems associated with the greenhouse effect have increased the desire to limit carbon dioxide (CO2) emissions into the atmosphere, including emissions produced by shipboard electrical power systems. This has led to a further search for solutions in this area, such as shipboard hybrid electric power systems (SHEPSs). These systems do not yet have a wide application compared with traditional shipboard electrical power systems for several reasons, including the lack of studies establishing the fault tolerance of such systems. Therefore, in this work, problems related to generated power quality deterioration under large disturbances are studied. To achieve the fault-free operation of SHEPS, an emergency prevention control (EPC) system based on controlled parameter forecasting, along with a system structure and operation algorithm, is developed. The goals of improving electrical power quality are achieved by increasing the control efficiency of the power system’s generating sources. To validate the feasibility of the proposed control system, a computer simulation was carried out after developing a mathematical model of the SHEPS under study. The results of the study show that the use of the proposed EPC system will improve power quality when the controlled parameters are within acceptable limits. At the same time, further research is needed, as the problem of false control action as a consequence of EPC system hardware or software faults remains unstudied.

PLANNING STUDY OF HYBRID POWER PLANT SOLAR PV-DIESEL GENERATOR ON KODINGARE ISLAND, SINJAI REGENCY

Kodingare Island is located in Pulau Sembilan District, Sinjai Regency, one of nine islands. Currently, most people still rely on conventional energy from diesel power plants. The reason is that this island does not yet receive an electricity supply from the electricity grid due to the geographical limitations of the archipelago. It is known that the most potential renewable energy source on Kodingare Island is solar energy, with a potential for solar radiation reaching 5.86 kWh/m2/day. This research aims to analyze an innovation that combines PV and solar, where PV acts as the main electricity generator, while solar functions as a backup and additional energy source. The method used in this research uses simulation methods, layout modeling, and financial analysis using HOMER Pro simulation software to determine the potential and performance of hybrid power plants and SketchUp Pro software to produce three-dimensional layouts and economic and feasibility values obtained through financial analysis. Technical aspects include producing an electrical energy system of 37,029 Wh/year, consisting of PV of 32,981 Wh/year and solar of 4,048 Wh/year with energy consumption of 33,850 Wh/year. The required fuel consumption is 2,086 L/year, with excess electricity of 931 kWh/year and renewable energy penetration of 89.1%. From an economic perspective, planning this hybrid power system requires an investment of 258.290.000 IDR, O&M costs of 19.350.600 IDR, and the cost of energy value of 1,352/kWh IDR. In contrast, from the feasibility aspect of planning a hybrid electric power system, it is said to be feasible because it produces a Net Present Value of 9,870,151 IDR, is more significant than zero, the Profitability Index is 1.03 greater than one, the Internal Rate of Return is 8.90% greater than the credit interest rate of 8.43% and the Payback Period required for return of capital is nine years nine months.

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell–CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal MG operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.

Coyote Optimization Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Power Systems

This research proposes an improved energy management strategy (EMS) for a fuel cell hybrid power system for an electric aircraft based on a recently developed coyote optimization algorithm (COA). The suggested hybrid system consists of fuel cells and an energy storage system (ESS) to supply the required load in stable conditions. The distribution and performance of the hybrid electrical power system are determined by various energy sources. Consequently, having the best energy management system is essential for completing this work. The suggested EMS’s main objectives are to reduce hydrogen energy utilization and increase power source longevity. The proposed coyote optimization algorithm with external energy maximization strategy (COA-EEMS) and coyote optimization algorithm with equivalent consumption minimisation strategy (COA-ECMS) are tested with the help of the Opal-RT 5700 real-time HIL simulator and MATLAB/Simulink. The proposed algorithms confirm their robustness and higher efficiency by minimizing hydrogen fuel consumption compared to existing algorithms. The merits of the proposed algorithms are presented in detailed and compared with existing algorithms.

A deep reinforcement learning approach to energy management control with connected information for hybrid electric vehicles

Considering the importance of the energy management strategy for hybrid electric vehicles, this paper is aiming at addressing the energy optimization control issue using reinforcement learning algorithms. Firstly, this paper establishes a hybrid electric vehicle power system model. Secondly, a hierarchical energy optimization control architecture based on networked information is designed, and a traffic signal timing model is used for vehicle target speed range planning in the upper system. More specifically, the optimal vehicle speed is optimized by a model predictive control algorithm. Thirdly, a mathematical model of vehicle speed variation in connected and unconnected states is established to analyze the effect of vehicle speed planning on fuel economy. Finally, three learning-based energy optimization control strategies, namely Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) algorithms, are designed under the hierarchical energy optimization control architecture. It is shown that the Q-learning algorithm is able to optimize energy control; however, the agent will meet the ”dimension disaster” once it faces a high-dimensional state space issue. Then, a DQN control strategy is introduced to address the problem. Due to the limitation of the discrete output of DQN, the DDPG algorithm is put forward to achieve continuous action control. In the simulation, the superiority of the DDPG algorithm over Q-learning and DQN algorithms in hybrid electric vehicles is illustrated in terms of its robustness and faster convergence for better energy management purposes.

Design and Reliability Analysis of a Series/Parallel Hybrid System with a Rotary Engine for Safer Ultralight Aviation

The conventional powertrain for ultralight aviation consists of a fixed pitch propeller connected to an internal combustion engine (ICE). Since ICEs have a limited thermal efficiency (<40%), new and more efficient powerplant configurations have recently been proposed in the scientific literature by adopting hybrid electric solutions. Hybridization has the additional benefit of increased safety thanks to redundancy. This is a very important issue in ultralight aviation, where a high percentage of accidents are caused by engine failure. In a previous investigation, the authors proposed the design of a series/parallel hybrid electric power system to increase safety and optimize fuel economy by controlling the engine working points during flight. A new powertrain, derived from an automotive Honda i-MMD system, is analyzed in this study and a reliability analysis is performed to underline the improved safety obtained with the proposed system.

THE DEVELOPMENT STRATEGY OF UKRAINE'S ENERGY SYSTEM AND THE PLACE OF SMALL MODULAR REACTORS IN IT

The paper describes the characteristics of small modular reactors (SMR), their differences from existing nuclear power plants and the features of their use in modern hybrid electric power systems in combination with renewable energy sources and Nuclear Renewable Hybrid Energy System (NRHES) electricity storage, their advantages and problems of use in the future power system of Ukraine. Prospects for cooperation in this area with companies from the United States of America were considered. Small modular SMR reactors are suitable for electrical systems of various capacities, their modular system allows flexibility and speed of construction, reduces capital investment needs and eases financing requirements. The smaller size and variety of reactors can also mean that they can be built in locations not traditionally suitable for large nuclear power plants and, importantly, near energy-intensive industries or remote communities, i.e. as elements of distributed generation. They can ensure a reliable supply of not only electricity, but also heat. SMRs can also be deployed at decommissioned coal-fired power plants. Taking advantage of existing infrastructure, including switchgear and coal-fired turbines, could reduce SMR construction costs and avoid the need to add new transmission lines from these facilities. Problems with the implementation of such networks are revealed. Attention was drawn to the increased complexity of the system in connection with the use of various sources of generation and processes of distribution and consumption of electricity. The main technical characteristics, features of the reactor design, the station building, safety systems and control systems are given. The study concludes that SMRs have significant advantages for use in modern networks, due to their modular design and modular deployment, to meet a variety of power switching and scaling requirements.

Fault Alarms and Power Performance in Hybrid Electric Vehicles Based on Hydraulic Technology

In order to improve the fault alarm effect on the power performance of hydraulic hybrid electric vehicles (HEV), this paper proposes a fault alarm method for hybrid electric vehicle power performance based on hydraulic technology, builds a hybrid electric vehicle power system model, uses hydraulic technology to extract the characteristic signals of key components, uses support vector mechanisms to build a hybrid electric vehicle classifier, and obtains the fault alarm results for dynamic performance based on hydraulic technology. The results show that the proposed method can improve real-time diagnosis and alarm for engine faults in HEV, and the fault can be diagnosed after 5 s of injection, thus ensuring the dynamic stability of HEV.

Tuning Ba/Sr ratios of BaxSr1-xTiO3 of polymer nanocomposites toward significant enhancement of energy storage performance by optimized phase composition

The rapid development of portable electronic equipment, hybrid electric vehicles and pulse power systems have put forward higher requirements for new dielectric materials, making flexible polymer dielectrics attractive. Barium strontium titanate nanoparticles with different compositions were successfully prepared by using oxalate. A series of PVDF/BaxSr1-xTiO3 nanoparticles nanocomposites were prepared using the semi-crystalline ferroelectric polymer polyvinylidene fluoride PVDF as the polymer matrix. The microstructure and electrical properties of the nanocomposites were characterized to explore the effects of the composition and intrinsic properties of the filling phase on the nanocomposites. Among all the groups of nanocomposites, the 1 vol% PVDF/Ba0.6Sr0.4TiO3@DA NPs has the highest discharge energy density at the breakdown field strength, which is 7.8 J/cm3. The excellent discharge energy efficiency of 1 vol% PVDF/Ba0.6Sr0.4TiO3@DA NPs is maintained at 65 % at 300 kV/mm. This paper can provide experimental guidance and theoretical basis for the development of high energy storage density dielectric materials and their application in dielectric energy storage capacitors.

Feed-Forward Neural Network for health monitoring of a parallel hybrid electric power system

Hybrid engines are becoming more and more widespread. Electric energy instead is a valid help to reduce the environmental impact. In hybrid engines, the number of components is higher and this results in a decrease in reliability. With Engine Health Monitoring (EHM) we mean the set of techniques used to monitor the health status of a system based on the values assumed by some related parameters. Artificial Intelligence (AI) methods are widely used nowadays in this discipline. In this paper, an EHM approach was developed to monitor the health status of some components constituting an hybrid turboshaft. The dynamic model of the hybrid electric power system is described in an accompanying paper. Feed-Forward Neural Network (FFNN) is used as AI tool to built the just cited system. The engine modelled with Simulink, was used to perform a series of steady-state simulations implementing a degradation condition in some selected components. The degradation condition was simulated by changing the value of the Performance Parameters (PPs) related to each of the selected components. The results of the simulation were used to obtain a dataset useful to train the FFNN to predict the values of the same PPs in a degraded case.

Reinforcement learning based power management integrating economic rotational speed of turboshaft engine and safety constraints of battery for hybrid electric power system

Hybrid electric power system (HEPS) with turboshaft engine is a promising solution for the land and air vehicle, and the power management strategy (PMS) is the key to obtaining better performance of HEPS. In this paper, a reinforcement learning (RL)-based PMS integrating economic rotational speed (ERS) of turboshaft engine and safety constraints-based variable action space (SC-VAS) approach is proposed. First, an efficient algorithm based on the turbine performance map for calculating ERS is proposed, with low complexity which is 5.5% of the conventional algorithm. Second, based on the ERS feature, the SC-VAS approach is presented to further optimize the action space to prevent the discharging/charging power and state of charge of the battery from violating the safety constraints. Comparison results show that with no violation of constraints of battery, the convergence speed of RL agent incorporating the SC-VAS approach increases by 5 times, and the size of the optimized Q table decreases to 21.9% of that of the basic Q table. The proposed PMS with the ERS feature and SC-VAS approach can bring a 4.29% reduction in the fuel consumption under an air-land driving condition. Moreover, the results of the hardware-in-the-loop experiment demonstrate the real-time performance of the proposed strategy.

The Evaluation of an Electric Hybrid Power System for the High Endurance Drone

This research shows the test performance of a 6 kW-scale hybrid electric power system for the high endurance drone. The power system is composed of a two-stroke reciprocal engine, starter-generator and battery, and they are integrated as one power unit. The engine is designed to provide the house for holding the starter-generator at the end of a crankshaft in turn the engine and starter-generator can maintain the same speed during the operational period. In this way, the generated power is readily controlled by just manipulating an engine throttle movement. Moreover, the starter-generator can initiate an engine operation with an aid of battery power until the combustion process becomes stabilized. In consequence, integration mechanism between an engine and generator is simplified, which results in weight reduction achieved. The duty of back-up battery is to provide a starting power to generator via a system controller in addition to covering momentarily power shortage. Therefore, the electric power system is vindicated to provide 6 kW power through a ground test.