Hybrid Electric System Research Articles

Empirical Evaluation of the Replacement of Conventionally Powered Vehicles with Hybrid and Electric Vehicles on the Example of the Poznań Agglomeration

From the collected data of the Central Register of Vehicles, an average of 15,000 vehicles were registered per month in the Greater Poland region in 2020–2022. It should be borne in mind that most of them were conventionally powered cars-spark-ignition or compression-ignition engines, the daily operation of which negatively affects the environment. Institutions responsible for regulating the homologation of passenger vehicles are introducing increasingly stringent emission standards to reduce the harmful effects of vehicles on the environment. In addition, more and more public campaigns are being conducted on the use of cars equipped with alternative propulsion systems, e.g., electric, full-hybrid or increasingly popular hydrogen-powered vehicles. The publication presents forecasts for the replacement of vehicles equipped with spark-ignition and compression-ignition engines with cars equipped with hybrid propulsion systems or electric systems. Tests of vehicles in real operating conditions were carried out in the Poznań agglomeration in an urban area, where the number of vehicles per area is higher than in the case of a nonurban zone. The research objects were both cars equipped with a conventional drivetrain and a hybrid system. The research made it possible to draw up a hybridization index, which was then applied to the number of vehicles registered over the past few years.

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.

Research on Aircraft Engines and Advanced Power in Land, Sea and Air

This paper provides a comprehensive overview of advanced propulsion technologies in military applications across land, sea, and air domains. It examines the theoretical foundations of propulsion systems, tracing the evolution of aircraft engines from early piston designs to modern jet propulsion. The study explores current state-of-the-art technologies, including advanced turbofan engines, hybrid electric systems, and nuclear propulsion for naval vessels. Special attention is given to emerging fields such as unmanned aerial vehicle propulsion, hypersonic technologies, and stealth engine designs. The research also delves into futuristic concepts like magnetohydrodynamic propulsion and pulse detonation engines. Throughout the analysis, common themes of improved efficiency, enhanced power-to-weight ratios, and increased operational capabilities are highlighted. This paper aims to provide a holistic view of the current landscape and future trajectory of military propulsion technologies, underlining their critical role in shaping modern warfare capabilities.

Configuration design methodof hybrid electric agricultural machinery system based on layered two-dimensional matrix

The working environment and objects of agricultural machinery are different from those of automobiles, andagricultural machinery is greatly affected by the working environment and working conditions, and the power output systemismore complex. Agricultural machinery not only has drive output, but also PTO output and hydraulic output, whichtogetherconstitute the output system of agricultural machinery. Agricultural machinery conditions can be dividedintoroadtransportation working conditions and field operation working conditions. The working conditions of agricultural machinerycan be divided into different load conditions according to the different traction tools and whether the hydraulic andPTOwork,such as ploughing, rotary tillage, fertilization, and transportation. Therefore, developing hybrid electric agricultural machinerysystems that are suitable for various complex working conditions holds great theoretical significance and practical value. Giventhe complex working conditions of agricultural machinery systems in agricultural work and the intricate challengesindesigning hybrid agricultural machinery systems. In this paper, the two-dimensional matrix is used to represent thephysicalstructure and dynamics of the multi-channel power output agricultural mechanism. A hierarchical two-dimensional matrixmethod for the generation and screening of hybrid electric agricultural machinery systems with multi-power output powerisproposed. The components of agricultural machinery are divided into an input layer and an output layer, and these componentsare coded and defined, and then transformed into a matrix. The hierarchical two-dimensional matrix method is usedtogenerateand screen the hybrid electric agricultural mechanism type. Through the stratification of the matrix, the complexityoftheconfiguration generation is reduced, and the constraints are applied to the basic screening of the generated configurations. Therationality of the configurations obtained after generation and screening is verified by Simulink simulation. The resultsshowthat the configuration screened by this method can meet the performance requirements of agricultural machinery.

The Challenges of Naval Electromobility in Amazônia

Introduction: This study aims to assess the feasibility of replacing a diesel-powered tourist boat with a Solar Electric Hybrid Catamaran with a capacity for fifty people to promote sustainable tourism in the Amazon, specifically in the Belém-Pará region. The initiative seeks to take advantage of the opportunity presented by COP 2030 to innovate in the waterway transportation matrix through renewable energy sources. The research is based on sustainability concepts, the environmental impacts of using fossil fuels and the advantages of using solar energy for waterway transportation. The methodology includes a comparative analysis of diesel-powered vessels' operational and environmental costs and a Hybrid Electric system based on data compiled from a master's thesis. The expected results include identifying significant benefits in terms of reduced carbon emissions. The discussion will address the challenges faced in implementation, such as the initial investment costs, the maintenance of the new technologies, and the adaptation of operators and tourists to the latest transportation conditions. The implications of this research are vast; it can serve as an incentive for energy transition in the river transportation sector. Furthermore, this research pioneers the proposal of replacing diesel-powered tourist boats with solar electric hybrid models in the Amazon region. The study adds value by presenting a practical and sustainable solution to the environmental challenges the tourism sector faces, in line with the global sustainability goals set by COP 2030. Objective: This research outlines the importance and potential of naval electric mobility in the Eastern Amazon region, highlighting its relevance within COP 2030 and the SDGs for promoting ecotourism for the Amazon region. Theoretical Framework: The research is based on sustainability concepts, the environmental impacts of using fossil fuels, and the advantages of using solar energy for water transportation. In addition, previous experiences of implementing similar technologies in other regions and the potential for replicability in the Amazon are considered, focusing on environmental conservation and economic development. Method: This research is a compilation of the master's thesis entitled "Waterway electric propulsion technologies: conceptual design of a hybrid vessel with a focus on meeting environmental, social and Governance (ESG) practices," where the study was carried out in the municipality of Belém, Pará in the year 2023. Results and Discussion: The expected results include identifying significant benefits in terms of reduced carbon emissions, lower noise pollution and improved air quality. The discussion will address the challenges faced in implementation, such as initial investment costs, maintaining the new technologies and adapting operators and tourists to the new transportation conditions. Social acceptance and the potential for a positive impact on local tourism will also be assessed. Research Implications: This research has vast implications, ranging from contributing to climate change mitigation to promoting more sustainable tourism practices in the Amazon. Adopting the Solar Electric Hybrid Catamaran can serve as a model for other areas with similar geographical characteristics, encouraging public policies aimed at using renewable energies in the transportation sector. Originality/Value: This research pioneers the proposal of the replacement of diesel-powered tourist boats with a solar electric hybrid model in the Amazon region. The study adds value by presenting a practical and sustainable solution to the environmental challenges faced by the tourism sector, which is in line with the global sustainability goals set by COP 2030.

Expansion planning of hybrid electrical and thermal systems using reconfiguration and adaptive bat algorithm

_ This study introduces a comprehensive model for the concurrent expansion planning of various energy systems and their associated equipment. The need to reduce network costs, emissions, losses, and feeder loading, as well as to enhance network reliability and voltage profile, mandates the utilization of proper multi-objective planning models that respect all network constraints. The introduced framework includes units for generating both electrical and thermal energies. The model leverages conventional expansion alternatives such as the installation of new lines, network reconfiguration, rewiring, and the addition of new thermal and electrical generating units to the network. Expansion planning involves determining the optimal time, location, and type of new installations to meet future energy demands while minimizing costs and emissions. Reconfiguration refers to altering the network topology to improve reliability and reduce losses. The proposed expansion planning is formulated as a discrete, nonlinear, and non-convex optimization problem, which is solved using the Self Adaptive Learning Bat Algorithm (SALBA). This algorithm improves convergence speed and increases the diversity of the search population, enhancing the likelihood of finding the global optimum. Numerical simulations of the proposed methodology on two modified standard IEEE test systems corroborate the efficacy and feasibility of the suggested approach. Key innovations include the comprehensive modeling for concurrent expansion planning, the use of an advanced optimization algorithm, and a focus on reducing costs, emissions, losses, and feeder loading while enhancing network reliability and voltage profile.

Challenges in the Guidance, Navigation and Control of Autonomous and Transport Vehicles

In recent years, autonomous and transportation vehicles have been evolving towards more electric or hybrid electric systems, with new challenges arising in their guidance, navigation and control (GNC) systems [...]

A Hybrid RBFNN-SPOA Technique for Multi-Source EV Power System with Single-Switch DC-DC Converter

With a single-switched DC-DC converter, a hybrid approach is proposed for a fast-charging (FC) dc to dc converter with the controller for an electric vehicle charging station (EVCS) that combines solar photovoltaic (PV), fuel cells (FC), and a battery energy storage system (BESS). The proposed hybrid technique integrates a radial-basis-function-neural-network (RBFNN) and a student-psychology-optimization-algorithm (SPOA); commonly known as the RBFNN-SPOA technique. The PV-FC hybrid electric vehicle systems, like controllers, electric vehicle, and the vehicle’s power source, are displayed. The proposed converter architectures have less voltage waveform changes, a low voltage stress on semiconductor power sources, and a high-voltage ratio of conversion. The proposed method is to use FC successfully at battery charge states and varied radiation levels. The FC in the controller of SPOA may change the inputs and outputs depending on the battery state of charge (SoC) and PV irradiation. Similarly, the needed input-torque for the motor is forecasted by the recalling recurrent neural network controller using several manually produced torque signals. The performance of the proposed technique is evaluated in MATLAB and is compared to ant colony optimization (ACO), crow optimization algorithm (COA), and fuzzy logic controller (FLC) approaches. According to the results, the efficiency of the proposed technique is 99.17%.

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.

A centralized fleet management system for electrified transportation

Abstract Hybrid Electric Vehicles are a promising alternative to Conventional and Electric Vehicles, as they offer better fuel economy, lower emissions, and long drive range without requiring extensive charging infrastructure. Limited driving range and battery degradation in electric vehicles are the main challenges in electrified fleets working in large metropolitan. For these challenges, the hybrid electric vehicle fleet management system is an effective solution to optimize power consumption and mitigate electric vehicle and conventional vehicle challenges. This paper proposes a hybrid electric vehicle fleet management system for public transportation, to analyze and investigate the performance and energy consumption rate of conventional, electric, and hybrid electric vehicles on different routes using representative driving cycles. The hybrid electric vehicle fleet management system enhances operational efficiency for the fleet management system and reduces power consumption in transportation. The proposed work comprises a hybrid electric vehicle model that is based on object-oriented programming. The proposed work offers the ability to add the input of the initial state of charge, power split ratio, and route data for each bus, and the hybrid electric vehicle model computes the state of charge and fuel consumption rate along the road. Finally, The proposed work shows how a hybrid electric vehicle fleet management system has a significant potential to be a solution for public transportation in Egyptian urban areas at this time compared with conventional vehicle and electric vehicle fleet management systems.

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.

A hybrid electric power supply system for a livestock farm using battery electric transport.

A hybrid electric power supply system for a livestock farm using battery electric transport.

Reinforcement learning-based energy management for hybrid electric vehicle enhanced by velocity prediction

Abstract This paper proposes a novel approach for optimal energy management of engine-battery hybrid electric systems. It addresses two limitations in existing research, namely inaccurate speed prediction and low control accuracy. The proposed method integrates Markov theory and clustering theory to achieve high-precision prediction of future velocity under diverse driving styles. Furthermore, a learning-based energy management strategy is developed using a deep deterministic strategy gradient algorithm. This strategy considers the predicted velocity and aims to minimize engine fuel consumption, battery state fluctuations, and engine start-stop occurrences. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance under specific operating conditions.

Bidirectional DC - DC Converter Topology for Electric Vehicles Using Fuzzy Logic Controller

This project organizes an application of hybrid electric vehicle systems operated with novel designed bidirectional dc-dc converter (BDC) which interfaces a main energy storage (ES1), an auxiliary energy storage (ES2) and dc bus of different voltage levels. Proposed BDC converter can operate both step up and step-down mode. In which step up mode represents low voltage dual source -powering mode and step-down mode represents high voltage dc link energy –regenerating mode, both the modes are operated under the control of bidirectional power flow. This model can independently control power flow between low voltage dual source buck/boost modes. Here in, the circuit configuration, operation, steady-state analysis, and closed-loop control of the proposed BDC are discussed according to its three modes of power transfer. In this project fuzzy logic controller is used and also system results are validating through MATLAB/SIMULINK software. Index Terms—Bidirectional dc/dc converter (BDC), dual battery storage, hybrid electric vehicle, Fuzzy logic controller.

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.

Bi-LSTM predictive control-based efficient energy management system for a fuel cell hybrid electric vehicle

Energy management systems (EMS) in hybrid electric vehicles play a vital role in achieving optimal energy utilization, improved fuel efficiency, lower emissions, and improved vehicle performance. Thus, this paper proposes a Bidirectional Long-Term Memory (Bi-LSTM) model based on an efficient EMS for a hybrid electric vehicle that manages the powertrain elements of the IC engine, electric motor(s), fuel cell, and energy storage system. The proposed EMS also continuously monitors various parameters, including vehicle speed, driver inputs, battery state of charge, engine load, and environmental conditions, to make real-time decisions on power distribution and operation modes. Further, the proposed system leverages the capabilities of the Bi-LSTM model to capture intricate temporal dependencies and bidirectional context in driving patterns using the real-time dataset. Empirical studies are conducted to substantiate the efficacy of the proposed energy management strategy vis-à-vis diverse controllers, encompassing Model Predictive Control and Sliding Mode Control. Real-world driving data is employed as the basis for these investigations, aiming to provide a robust assessment of the proposed energy management approach in practical scenarios. Results demonstrate the efficacy of the suggested methodology compared to traditional EMS approaches, showcasing improvements in energy utilization and reduced environmental impact. Finally, this research emphasizes a comparative analysis of the proposed topology with the existing approaches in a real-world fuel cell hybrid electric vehicle system. The findings provide insights into the feasibility of deploying intelligent EMS strategies for improved sustainability and efficiency in a modern energy-efficient environment.

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.

Design and operational strategy optimization of a hybrid electric heating system with phase change materials for energy storage in nearly zero energy buildings

Nearly zero energy buildings (nZEBs) and the associated research on heating energy systems are gaining increasing attention. To enhance PV self-consumption capacity in nZEBs, a hybrid electric heating system with phase change materials (PCM) for energy storage using photovoltaic (PV) and grid power was developed. To study the system's performance, an experimental bench was set up, and mathematical models for energy efficiency and operational strategy were developed. Furthermore, a research building was chosen as the scenario to investigate the operational performance of the system. The analysis focused on assessing the impact of varying PV and battery capacity, specifically concentrating on load flexibility transfer capability and energy flow within the system. The results indicated that when implementing the hybrid electric heating system with varying PV and battery capacities, significant transfers of both heat and electricity loads could be achieved, leading to peak price electricity usage remaining below 10 %. Regardless of the manner in which PV is utilized, the system facilitates the transfer of both heat and electricity loads, resulting in peak price electricity usage remaining below 5 %. Through an analysis involving the optimization of system capacity and operational strategies during a simulated one-week period in the heating season, the results revealed that the utilization of a battery with a rated capacity as low as 3.69 kWh led to an increase of 10.93 kWh in PV self-consumption, resulting in a noteworthy cost reduction of 15.6 %.

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.