Search

Fuel cell hybrid electric vehicles (FCHEVs) are gaining recognition as a pivotal innovation in the quest for sustainable transportation, seamlessly blending the advantages of fuel cells with electric vehicle technology. As global initiatives to curtail greenhouse gas emissions and mitigate climate change gain momentum, FCHEVs offer a promising alternative to conventional internal combustion engine vehicles and battery electric vehicles (BEVs). This review aims to examine recent technological progress in enhancing FCHEV fuel efficiency and performance, focusing particularly on cost reduction, long-distance operational capabilities, and the incorporation of intelligent autonomous systems. A key innovation highlighted in this study is the exploration of cost-effective hydrogen storage techniques, including metal hydrides and chemical storage solutions, which have demonstrated potential to significantly increase storage capacity and reduce refueling times. Furthermore, advancements in energy management, particularly the integration of ultracapacitors and high-capacity batteries, offer new avenues for extending vehicle range and boosting overall efficiency. The emergence of autonomous FCHEVs represents another major leap forward, setting the stage for more efficient, self-driving vehicles in the near future. However, persistent challenges remain, including reducing hydrogen production costs, improving fuel cell durability, and developing a widespread hydrogen infrastructure. Addressing these issues will require ongoing innovation in fuel cell materials, hydrogen storage systems, and energy management strategies. This review underscores the transformative role FCHEVs can play in reducing emissions and enhancing energy efficiency in the transport sector. Collaboration between industry, researchers, and policymakers will be vital to unlocking the full potential of FCHEVs in creating a more sustainable future. • This review work explores the diverse advancements aimed at enhancing the fuel economy and performance of FCHEVs. • A primary focus of recent developments is the reduction of FCHEV costs. • Merging the benefits of hydrogen fuel cells with advanced electric vehicle technology. • The work also looks Fuel Cell Hybrid Electric Vehicles (FCHEVs), offering a promising path achieving sustainable transportation. [ABSTRACT FROM AUTHOR]

Lithium-based battery systems (LBS) are used in various applications, from the smallest electronic devices to power generation plants. LBS energy storage technology, which can offer high power and high energy density simultaneously, can respond to continuous energy needs and meet sudden power demands. The lifetime of LBSs, which are seen as a high-cost storage technology, depends on many parameters such as usage habits, temperature and charge rate. Since LBSs store energy electrochemically, they are seriously affected by temperature. High-temperature environments increase the thermal stress exerted on LBS and cause its chemical structure to deteriorate much faster. In addition, the fast charging feature of LBSs, which is generally presented as an advantage, increases the internal temperature of the cell and negatively affects the battery life. The proposed energy management approach ensures that the ambient temperature affects the charging speed of the battery and that the charging speed is adaptively updated continuously. So, the two parameters that harm battery health absorb each other, and the battery has a longer life. A new differential approach has been created for the proposed energy management system. The total amount of energy that can be withdrawn from LBS is increased by 14.18% as compared to the LBS controlled with the standard energy management system using the genetic algorithm optimized parameters. Thus the LBS replacement period is extended, providing both cost benefits and environmentally friendly management by LBSs turning into chemical waste distinctly later. [ABSTRACT FROM AUTHOR]

The transformation of power system networks is slowly taking shape, the advent of interruptive technological platforms dealing with digitalization and real‐time trading of power has gained attention based on incorporation of more renewables into the grid. The stochastic nature of renewables pauses security of supply challenges and other related stability concerns, and for this reason efficient methods are investigated in this review to build an understanding of microgrid energy management system (MG‐EMS) and distribution‐based energy management strategies aimed at transforming the conventional grid network into smart grid network. In essence, propagating a technological shift to microgrids which have proven to be ideal distribution networks for residential and commercial loads, have become indispensable in handling distributed energy resources (DER), such as solar, PV, wind, battery energy storage systems (BESS) and small‐scale microgrids, for example in case of excess supply, energy storage system (ESS) has been formulated as a solution to curb excess supply and can offer ancillary services to the grid network. Within the perspective of electricity generation and distribution, microgrid control methodologies, distribution network (DN) management approaches and incumbent optimization strategies used to coordinate and manage grid‐level uncertainties are investigated. In addition, this study proposes distributionally robust optimization (DRO), to manage and mitigate risks associated to shortage or oversupply of power from RESs. [ABSTRACT FROM AUTHOR]

This research addresses the challenge of accurate load forecasting in cluster microgrids, where distributed energy systems interlink to operate seamlessly. As renewable energy sources become more widespread, ensuring a consistent and reliable power supply in the face of variable weather conditions is a significant challenge for power providers. The variability in energy consumption patterns, influenced by human behavior and environmental conditions, further complicates load prediction. The inherent instability of solar and wind energies adds complexity to forecasting load demand accurately. This paper suggests a solution in addressing some challenges by proposing a Modified Temporal Convolutional Feed Forward Network (MTCFN) for load forecasting in cluster microgrids. The Fire Hawk Optimization algorithm is employed to determine optimal configurations, addressing the intricacies of this complex optimization problem. Data collected from the Microgrid Market Share and Forecast 2024–2032 report, the efficiency of the proposed approach is evaluated through metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and R-squared. The RMSE, MSE, MAE, MAPE, and R-squared values of the MTCFN are 0.4%, 1.5%, 0.6%, 6.8%, and 0.8%, respectively. The optimization algorithm's effectiveness is cross-validated through rigorous testing, training, and validation processes, revealing that the FFNN model based on the Fire Hawk Optimization algorithm yields superior load forecasting results. This research contributes to the advancement of signal, image, and video processing in the context of resilient and accurate energy management in cluster microgrids. [ABSTRACT FROM AUTHOR]

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system. [ABSTRACT FROM AUTHOR]

Green hydrogen is a potential contributor to a more sustainable transportation system. In this paper, we present our experiences and insights gained from an industrial-scale green hydrogen refilling station, marked by the innovative hybrid Edge–Cloud electrolyser system (EMS) employed in this context. Hybrid EMS can improve system performance, adapt to dynamic conditions, and continuously improve efficiency, even under unpredictable load and low storage situations. It also allows the centralized monitoring of future hydrogen production sites to facilitate the operation and maintenance and, in the long-term vision, to enable the joint coordination of the plants flexibilities in the service market as a virtual plant, thus increasing the cost efficiency. Through a real-time digital-model based assessment of the EMS, this study offers valuable lessons learned and findings, contributing to a deeper understanding of EMS in green hydrogen infrastructure. • Description of a commercial Energy Management System (EMS) developed by the company ABB. • Demonstration of the benefits of the hybrid edge–cloud EMS architecture. • Application of an EMS to a hydrogen fueling station with on-site electrolysis. • Real-time digital-model assessment, offering valuable lessons learned contributing to a deeper understanding of EMS for H2. [ABSTRACT FROM AUTHOR]

This study addresses the inherent challenges associated with the limited flexibility of power systems, specifically emphasizing uncertainties in solar power due to dynamic regional and seasonal fluctuations in photovoltaic (PV) potential. The research introduces a novel supervised machine learning model that focuses on regression methods specifically tailored for advanced microgrid energy management within a 100% PV microgrid, i.e. a microgrid system that is powered entirely by solar energy, with no reliance on other energy sources such as fossil fuels or grid electricity. In this context, "PV" specifically denotes photovoltaic solar panels that convert sunlight into electricity. A distinctive feature of the model is its exclusive reliance on current solar radiation as an input parameter to minimize prediction errors, justified by the unique advantages of supervised learning. The performance of four well-established supervised machine learning models—Neural Networks (NN), Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Linear Regression (LR)—known for effectively addressing short-term uncertainty in solar radiation, is thoroughly evaluated. Results underscore the superiority of the NN approach in accurately predicting solar irradiance across diverse geographical sites, including Cairo, Egypt; Riyadh, Saudi Arabia; Yuseong-gu, Daejeon, South Korea; and Berlin, Germany. The comprehensive analysis covers both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), demonstrating the model's efficacy in various solar environments. Additionally, the study emphasizes the practical implementation of the model within an Energy Management System (EMS) using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, showcasing high accuracy in microgrid energy management. This validation attests to the economic efficiency and reliability of the proposed model. The calculated range of error, as the median error for cost analysis, varies from 2 to 6%, affirming the high accuracy of the proposed model. Highlights: Introduction of a novel approach: This study presents a pioneering methodology focusing exclusively on supervised machine learning techniques for short-term uncertainty management within 100% PV microgrids, aimed at optimizing energy management efficiency. Comprehensive comparative analysis: Through meticulous comparative investigation, various regression techniques for predicting solar radiation within PV microgrids are scrutinized, providing valuable insights into their efficacy across diverse environmental conditions. Validation across diverse locations: The validation of findings across four distinct geographical locations, encompassing both PV and concentrated PV (CPV) systems, substantially enhances the generalizability of the results, advancing the understanding of solar radiation prediction dynamics for renewable energy integration strategies. [ABSTRACT FROM AUTHOR]

Energy management in enterprises is an important issue in the context of improving energy efficiency, energy use, and energy consumption. This is consistent with the Sustainable Development Goals. The purpose of this study was to evaluate the energy management system of water and wastewater utility in the context of sustainable development based on the opinions of managers and employees. The results indicate the involvement of the surveyed enterprise in energy management system development activity. This demonstrates the orientation of the surveyed enterprise to support activities to improve energy performance in line with the implementation of sustainable development. The added value is that the developed research tool can be used in studies of other enterprises to assess the level of energy management. [ABSTRACT FROM AUTHOR]

This research presents a solution for improving energy efficiency in convenience stores by implementing a building energy management system (BEMS) that uses new logic control in air conditioning and refrigeration systems. These systems currently consume the most energy in convenience stores. Implementing this system not only reduces the energy consumption of the compressors in both systems but also minimizes energy loss due to low desired temperatures in the sale area while maintaining the cabinet temperature at the same level. An experiment was conducted at a 314-square-meter convenience store that was open from 6:00 a.m. to 11:00 p.m., and we demonstrated a 4.4-year payback period by controlling AC units close to the desired sale-area temperature of 25 degrees Celsius and increasing the suction pressure at a medium-temperature CDU by 0.3 bar or 31 kPa. This resulted in energy savings of 7.1 kilowatt-hours per day, or 2591.5 kilowatt-hours per year, for the air conditioning system and 2.8 kilowatt-hours per day, or 1022.0 kilowatt-hours per year, for the refrigeration system, resulting in a total energy savings of 9.9 kilowatt-hours per day, or 3613.5 kilowatt-hours per year. The convenience store can use the results of this research to improve the energy efficiency of its cooling system, which includes air conditioning and refrigeration systems, thereby promoting sustainable energy conservation. [ABSTRACT FROM AUTHOR]

Advancements in the reduction of carbon dioxide emissions from ships are driving the development of more efficient onboard power systems. The proposed non-equivalent parallel running operation system is explored in this study, which improves the efficiency of the main power generation source compared with traditional equal load-sharing methods used in power management systems. However, the asymmetric method reduces the efficiency of the auxiliary power sources. To address this issue, we propose a control method that integrates a battery system with an efficiency-based algorithm to optimize the overall system performance. The proposed approach involves establishing operation command values based on the characteristics of the power generation source and adjusting these commands according to the battery's state of charge (SOC). MATLAB/Simulink simulations confirmed the effectiveness of this method across various operating modes and revealed no operational issues. When applied to a ship's operating profile over 222 h, the method reduced fuel consumption by approximately 2.98 tons (5.57%) compared with conventional systems. Over 38 annual voyages, this reduction equates to savings of 115.96 tons of fuel or approximately 96.47 million Korean won. This study demonstrates that integrating an optimal efficiency algorithm into the energy management system significantly enhances both the propulsion and overall energy efficiency of ships. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles. [ABSTRACT FROM AUTHOR]

With the rising need to transition from fossil fuel consumption to renewables, the transportation industry is foreseeing large-scale adoption of electric vehicles (EVs). According to various studies, the petroleum resources will last until or around 2052. Utilizing the US Federal Highway Administration (FHWA) published data on the number of registered vehicles by each state, the profile of vehicles by 2052 was forecasted using a time series. The vehicle profile comprised vehicles by each type classified as automobiles, buses, trucks, and motorcycles. This future profile became the basis of the quantification of total EVs by the end of 2052. Based on the available fuel economy data published by the US Department of Energy, the average energy consumption per mile (kWh/mile) of each vehicle type was factored into the energy demand calculation for 100% electrification by 2052. A roof-top solar photovoltaic system is easy to install on unoccupied roof space for US house owners. Obtaining the capacity of such a roof-top solar PV system acts as a good decision-making criterion for both house owners and developers. However, for city-dwellers living in multi-family residential buildings, the potential of utilization of roof-top solar PV becomes a subject of future work when effective sharing of resources for local power generation by renewables and development of community-shared EV charging infrastructure is concerned. How well EVs performed against internal combustion engine (ICE) vehicles in terms of fuel efficiency and reduced carbon emissions is a driving factor for this transition. For energy demand calculations, a base model was framed, and results were obtained by utilizing mathematical and statistical tools. After accounting for the effects of improved fuel efficiency or reduced energy consumption by EVs over time, the model was modified to obtain revised energy demand and, thus, effective energy generation required. Accordingly, the capacity of EV charging infrastructure required by each state determined the need for preparation by utility companies and local jurisdictions. With limitations on battery size and thus increased frequency for drivers to return for charging, additional fast chargers (level 3) at existing gasoline stations become an option that requires further assessment. [ABSTRACT FROM AUTHOR]

In light of global warming, hotels account for one of the highest energy demands within the building sector, offering great decarbonization potential. As electrification increases, so does the demand for electric vehicles (EVs) charging stations at hotels and the proportion of Vehicle-to-Building-capable EVs. Therefore, the study explores the potential of guest-centric energy management. To accomplish this, we develop an optimization model for an energy management system that focuses on either cost-efficiency or carbon dioxide equivalents (CO2)-efficiency, grounded in a real-world case study. Through scenario analyses considering seasons as well as different guest mobility behaviors, this study discusses the expenses associated with CO2 savings using digital solutions. It emphasizes the currently perceived conflict between cost reduction and decarbonization goals to achieve a sustainable design of information systems. Thereby, this study highlights the critical importance of individual mobility behavior in enabling sustainable energy management for hotels. [ABSTRACT FROM AUTHOR]

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. • Hybrid Power System is formed using Fuel cell, battery, and supercapacitor. • This study presents PPOA algorithm to optimize fuel consumption of a fuel cell. • PPOA is hybridized with ECMS and EEMS energy management techniques. • PPOA is tested using MATLAB/Simulink and compered with OPAL-RT Real time simulator. • PPOA-ECMS attained least H 2 consumption (15.36 gm) compared to Existing algorithms. [ABSTRACT FROM AUTHOR]

Reliable energy sources are crucial for both economic growth and quality of life. In developing countries, where expensive fuels are often the primary energy source, governments are exploring innovative solutions like small‐scale, IoT‐based projects to achieve energy independence in buildings. This research investigates the integration of renewable energy technologies, statistical modeling, cloud computing, and IoT to develop a self‐managing energy system for buildings. The system prioritizes renewable sources, specifically monocrystalline solar cells with 20% efficiency for photovoltaic (PV) energy and flat plate collectors with 90% efficiency and minimal energy loss for thermal energy. Thermal energy is stored in paraffin wax, chosen for its high storage efficiency and thermal properties. The system also utilizes an absorption chiller with a high coefficient of performance (COP) to provide cooling using solar thermal energy. The building's energy loads are categorized as A, B, C, and D, each utilizing both PV and thermal energy. A SCADA system oversees the operation, monitoring the on–off status of these loads. The system is designed for continuous operation, with simulations conducted using Anaconda Jupyter Notebook and Python. This model aims to offer a sustainable and efficient energy solution for buildings, meeting energy demands while optimizing energy use. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the potential implementation of the ISO 50001 Energy Management System (EnMS) at PT XYZ to achieve the decarbonization target. The focus is on strategic and technical planning, involving the analysis of internal and external issues to develop energy policies as well as the formation of energy teams. Technical planning includes energy performance reviews, energy consumption analysis, Significant Energy Users (SEUs), Energy Performance Indicators (EnPI), and operation control plans. The results of the 2022 review show that energy consumption is 947.8 thousand GJ with the main SEU in the Hauling and Mining systems, as well as Hauler and Excavator tools that consume more than 80% of Biosolar B30. EnPI level 1 shows an average energy intensity of 0.08 GJ per WMT, while EnPI level 2 results in a regression of Y=2.113X + 264924 with a determination of 0.892, making 2022 a baseline. The implementation of ISO 50001 has the potential to improve energy efficiency and reduce GHG emissions by up to 30% by 2030, with a target of saving 9.7 million liters of Biodiesel B30. The PDCA approach helps companies achieve targets by focusing on SEU systems and tools. [ABSTRACT FROM AUTHOR]

The use of DC microgrids (DC-µGs) offers a variety of environmental benefits; albeit, a successful implementation depends on the implementation of an Energy Management System (EMS). An EMS is broadly implemented with a hierarchical and centralized structure, where the communications layer presents as a key element of the system to achieve a successful operation. Additionally, the relatively low cost of wireless communication technologies and the advantages offered by remote monitoring have promoted the inclusion of the Internet of Things (IoT) and Wireless Sensor and Actuator Network (WSAN) technologies in the energy sector. In this article is presented the development of an IoT EMS based on a WSAN (IoT-EMS-WSAN) for the management of a DC-µG. The proposed EMS is composed of a WiFi-based WSAN that is interconnected to a DC-µG, a cloud server, and a User Web App. The proposed system was compared to a conventional EMS with a high latency wired communication layer. In comparison to the conventional EMS, the IoT-EMS-WSAN increased the updating time from 100 ms to 1200 ms; also, the bus of the DC-µG maintained its stability even though its variations increased; finally, the DC bus responded to an energy-outage scenario with a recovery time of 1 s instead of 150 ms, as seen with the conventional EMS. Despite the reduced latency, the developed IoT-EMS-WSAN was demonstrated to be a reliable tool for the management, monitoring, and remote controlling of a DC-µG. [ABSTRACT FROM AUTHOR]

This review paper critically examines the role of demand response (DR) in energy management, considering the increasing integration of renewable energy sources (RESs) and the rise in electric vehicle (EV) adoption. As the energy landscape shifts toward sustainability, recognizing the synergies and challenges offered by RESs and EVs becomes critical. The study begins by explaining the notion of demand response, emphasizing its importance in optimizing energy usage and grid stability. It then investigates the specific characteristics and possible benefits of incorporating RESs and EVs into DR schemes. This assessment evaluates the effectiveness of DR techniques in leveraging the variability of renewable energy generation and managing the charging patterns of electric vehicles. Furthermore, it outlines important technological, regulatory, and behavioral impediments to DR's mainstream adoption alongside RESs and EVs. By synthesizing current research findings, this paper provides insights into opportunities for enhancing energy efficiency, lowering greenhouse gas emissions, and advancing sustainable energy systems through the coordinated implementation of demand response, renewable energy sources, and electric vehicles. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract With the rising need to transition from fossil fuel consumption to renewables, the transportation industry is foreseeing large-scale adoption of electric vehicles (EVs). According to various studies, the petroleum resources will last until or around 2052. Utilizing the US Federal Highway Administration (FHWA) published data on the number of registered vehicles by each state, the profile of vehicles by 2052 was forecasted using a time series. The vehicle profile comprised vehicles by each type classified as automobiles, buses, trucks, and motorcycles. This future profile became the basis of the quantification of total EVs by the end of 2052. Based on the available fuel economy data published by the US Department of Energy, the average energy consumption per mile (kWh/mile) of each vehicle type was factored into the energy demand calculation for 100% electrification by 2052. A roof-top solar photovoltaic system is easy to install on unoccupied roof space for US house owners. Obtaining the capacity of such a roof-top solar PV system acts as a good decision-making criterion for both house owners and developers. However, for city-dwellers living in multi-family residential buildings, the potential of utilization of roof-top solar PV becomes a subject of future work when effective sharing of resources for local power generation by renewables and development of community-shared EV charging infrastructure is concerned. How well EVs performed against internal combustion engine (ICE) vehicles in terms of fuel efficiency and reduced carbon emissions is a driving factor for this transition. For energy demand calculations, a base model was framed, and results were obtained by utilizing mathematical and statistical tools. After accounting for the effects of improved fuel efficiency or reduced energy consumption by EVs over time, the model was modified to obtain revised energy demand and, thus, effective energy generation required. Accordingly, the capacity of EV charging infrastructure required by each state determined the need for preparation by utility companies and local jurisdictions. With limitations on battery size and thus increased frequency for drivers to return for charging, additional fast chargers (level 3) at existing gasoline stations become an option that requires further assessment.

Abstract This study addresses the inherent challenges associated with the limited flexibility of power systems, specifically emphasizing uncertainties in solar power due to dynamic regional and seasonal fluctuations in photovoltaic (PV) potential. The research introduces a novel supervised machine learning model that focuses on regression methods specifically tailored for advanced microgrid energy management within a 100% PV microgrid, i.e. a microgrid system that is powered entirely by solar energy, with no reliance on other energy sources such as fossil fuels or grid electricity. In this context, “PV” specifically denotes photovoltaic solar panels that convert sunlight into electricity. A distinctive feature of the model is its exclusive reliance on current solar radiation as an input parameter to minimize prediction errors, justified by the unique advantages of supervised learning. The performance of four well-established supervised machine learning models—Neural Networks (NN), Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Linear Regression (LR)—known for effectively addressing short-term uncertainty in solar radiation, is thoroughly evaluated. Results underscore the superiority of the NN approach in accurately predicting solar irradiance across diverse geographical sites, including Cairo, Egypt; Riyadh, Saudi Arabia; Yuseong-gu, Daejeon, South Korea; and Berlin, Germany. The comprehensive analysis covers both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), demonstrating the model’s efficacy in various solar environments. Additionally, the study emphasizes the practical implementation of the model within an Energy Management System (EMS) using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, showcasing high accuracy in microgrid energy management. This validation attests to the economic efficiency and reliability of the proposed model. The calculated range of error, as the median error for cost analysis, varies from 2 to 6%, affirming the high accuracy of the proposed model.

Abstract In light of global warming, hotels account for one of the highest energy demands within the building sector, offering great decarbonization potential. As electrification increases, so does the demand for electric vehicles (EVs) charging stations at hotels and the proportion of Vehicle-to-Building-capable EVs. Therefore, the study explores the potential of guest-centric energy management. To accomplish this, we develop an optimization model for an energy management system that focuses on either cost-efficiency or carbon dioxide equivalents (CO2)-efficiency, grounded in a real-world case study. Through scenario analyses considering seasons as well as different guest mobility behaviors, this study discusses the expenses associated with CO2 savings using digital solutions. It emphasizes the currently perceived conflict between cost reduction and decarbonization goals to achieve a sustainable design of information systems. Thereby, this study highlights the critical importance of individual mobility behavior in enabling sustainable energy management for hotels.

Objective The constructor of Qingdao Metro Line 13 has a higher requirement for energy saving during operation. In the process of construction, in order to implement the concept of energy saving, environmental protection, and intelligence in rail transit and meet the actual demand, it is necessary to seek entry points for energy saving through the control system in metro electromechanical system. Method Ventilation and air conditioning, as the key energy consumers of metro, are the focus of the present energy saving. Therefore, in consideration of their characteristics, CCCC Second Highway Consultants Co. Ltd. creatively sets up an energy-saving energy management system and successfully launches it for online operation. The system for the first time opens up the information transmission channel between energy management and air-water linkage energy-saving control of environmental control system, coordinating the two independent systems. With ana-lysis, regulation and management of energy consumption, the linkage control of air and water systems within the environmental control system is realized by using the energy-saving energy management system model and control strategy, technically achieving the goal of energy saving. Result & Conclusion When the energy-saving mode and the non-energy-saving mode run alternately for the same period, the energy consumption of the air conditioning water and air systems at each testing station is tested, recorded and compared, and the energy-saving rate of the above management system is calculated. Based on the results, it is concluded that the energy-saving effect can fully meet the minimum energy-saving requirements during system design. By comparing the changes in the station internal environment under the two operating modes during the set time period, it is determined that the operation of the energy-saving energy management system can ensure stable and comfortable internal environment of the station under the premise of energy saving.

In this paper, we introduce a strategic model for users and a manager in a community, considering investments in photovoltaic (PV) panels and a shared battery. We also analyse how their strategic behaviour influences the overall social welfare within the community. We first formulate the investment optimization problems for the users and the manager explicitly considering electricity exchanges, where users invest in PV panels and the manager invests in a battery. Considering interactions of information and electricity, the strategic model, which combines the investment optimization problems mentioned above, can be formulated as a game. Then, we point out that the formulated game does not always guarantee the existence of a generalized Nash equilibrium (GNE). To overcome the problem, we present new utility functions that ensure the existence of an equilibrium through potential game theory. Finally, we conduct a case study with actual electricity data to numerically analyse the impact of the strategic behaviour on social welfare as well as the advantages of battery sharing in the presence of such strategic behaviour.

In this article, we develop novel mathematical models to optimize utilization of community energy storage (CES) by clustering prosumers and consumers into energy sharing communities/microgrids in the context of a smart city. Three different microgrid configurations are modeled using a unifying mixed‐integer linear programming formulation. These configurations represent three different business models, namely: the island model, the interconnected model, and the Energy Service Companies model. The proposed mathematical formulations determine the optimal households' aggregation as well as the location and sizing of CES. To overcome the computational challenges of treating operational decisions within a multi‐period decision making framework, we also propose a decomposition approach to accelerate the computational time needed to solve larger instances. We conduct a case study based on real power consumption, power generation, and location network data from Cambridge, MA. Our mathematical models and the underlying algorithmic framework can be used in operational and strategic planning studies on smart grids to incentivize the communitarian distributed renewable energy generation and to improve the self‐consumption and self‐sufficiency of the energy sharing community. The models are also targeted to policymakers of smart cities, utility companies, and Energy Service Companies as the proposed models support decision making on renewable energy related projects investments. [ABSTRACT FROM AUTHOR]

The demand for energy on a global scale is continuously rising due to the expansion of energy infrastructure and the increasing number of new appliances. To address this growing need, an efficient energy management system (EMS) has become indispensable. By implementing EMS, both residential and commercial buildings can significantly improve their energy efficiency and consumption. One crucial aspect of enabling EMS to operate efficiently is load forecasting. The accuracy of load forecasting depends on numerous factors. A reliable load forecast model should consider the region's weather forecast, as it plays a crucial role in developing an accurate prediction. This study is about the medium-term load forecasting (MTLF) for the Province of Taroudant, Morocco, using historical monthly load and weather data for five years (2018 to 2022). To forecast consumed energy three methods are used namely artificial neural network (ANN), fuzzy logic (FL) and adaptive neuro-fuzzy inference system (ANFIS). This paper selects absolute percentage error (APE), mean absolute percentage error (MAPE), correlation coefficient (R) and root mean square error (RMSE) to compare and evaluate the prediction accuracy of models. It has been observed through results analysis that the ANFIS model produces very accurate forecasting prediction with MAPE of 4.75% while ANN and FL models give respectively MAPE of 7.36% and 8.42%. [ABSTRACT FROM AUTHOR]

Supply-side energy management (SSEM) aims to improve efficiency in operations and strategic planning. Both the cost of generating electricity and the amount of emissions from that generation are minimized in SSEM. It is required to formulate an optimization problem with these two competing goals in order to come up with a compromise. Resolving problems with network reliability caused by peak demand on the electricity system is another goal of SSEM. The ultimate goal of this study is to reduce energy use during peak hours while also cutting down on power losses, generation costs, and pollution from power plants. In this paper all goals of the smart grid system are satisfied and addressed optimally through the use of optimal generator scheduling and an improved demand response technique. To formulate this problem standard IEEE 30-bus system is considered as test boat. The suggested system employs the Cuckoo search method and its most recent variant, adaptive Cuckoo search, to solve a stochastic non-linear optimization problem. The adaptive Cuckoo search approach, when combined with the proposed demand side management strategy, reduces fuel costs by 7.84%, emission dispatch by 16.35%, power losses by 10.31%, and peak hour demand by 15.6%. [ABSTRACT FROM AUTHOR]

The electric energy demand has been increasing, following digitalization and development of urbanization, which has led to functional enhancement of home energy management system (HEMS) and its subsystems. A great amount of the produced electricity is used for household loads, whereas self-sufficient smart homes can supply all or a large portion of their electricity consumption by using renewable energy resources. In this study, an MILP model is formulated for energy scheduling on a 24-hour time horizon, to achieve the optimal performance of each home appliance for minimizing the smart home energy bill. The studied smart home can exchange electrical energy with the upstream network. A sensitivity analysis has been performed to show the impact of the changes in scheduling and energy prices on the electricity energy bill. The impact of the presence of renewable resources and electrical storage is studied on the electricity energy bill and the electrical energy sales profit of the house in different scenarios. Numerical results show that using the proposed model in the self-sufficient smart home reduces the amount of power purchased from the grid by 45%, transfers energy to the grid at some hours, and the energy bill is reduced by 65%.

The article discusses the possibility of calculating the expected energy demand based on big data and machine learning for the energy technological processes in oil refineries. In order to obtain predictive data, linear regression, machine learning, and neural networks are proposed to be used to build a mathematical model. The advantages and disadvantages of these methods are discussed, and the accuracy of the models is compared with the possibility of interpreting them. Thanks to the use of advanced statistical methods, the variability of energy consumption can be interpreted through factor analysis. Through pilot tests, the practical significance of these proposed methods for their practical use in an energy management system is demonstrated, as well as the transition to statistical control of the process.

The rapid expansion of energy storage integration has not provided sufficient time to strengthen and expand the transmission and distribution network. This issue can lead to PCC congestion in green multi-microgrid (MMG) systems. In these systems, microgrids operate independently and connect to the grid at a point of common coupling (PCC) without sharing operational data with neighboring microgrids. To address this issue, this paper proposes a bi-level optimization model designed to reschedule hydrogen storage systems. The first level allows each microgrid to optimize its energy transactions with the grid and communicates any unbalanced energy to the second level, where a hydrogen management system (HMS) is introduced. The HMS optimizes virtual hydrogen prices to address the PCC congestion and maximize the MMG's profit. These virtual prices are then sent to the first level, allowing the microgrids to reschedule the hydrogen storage systems based on these virtual prices. Finally, the MMG's profit is fairly allocated among the microgrids using the Shapley value method. The proposed method's effectiveness is demonstrated using simulations, which show a six percent increase in MMG profit compared to scenarios that only share PCC capacity while maintaining the data privacy of all the involved microgrids. [ABSTRACT FROM AUTHOR]

This paper presents an easy-to-implement model to predict the voltage in a class of Li-ion batteries characterized by non-flat, gradually decreasing voltage versus capacity. The main application is for the accurate estimation of the battery state of the charge, as in the energy management systems of battery packs used in stationary and mobility applications. The model includes a limited number of parameters and is based on a simple equivalent circuit representation where an open circuit voltage source is connected in series with an equivalent resistance. The non-linear open circuit voltage is described using a Nernst-like term, and the model parameters are estimated based on the manufacturer discharge curves. The results show a good level of model accuracy in the case of three different commercial batteries considered by the study: Panasonic CGR18650AF, Panasonic NCR18650B and Tesla 4680. In particular, accurate description of the voltage curves versus the state of charge at different constant currents and during charging/discharging cycles is achieved. A possible model reduction is also addressed, and the effect of the equivalent internal resistance in improving the model predictions near fully depleted conditions is highlighted. [ABSTRACT FROM AUTHOR]

The telecommunication industry relies heavily on a reliable and continuous power supply. Traditional power sources like diesel generators have long been the backbone of telecom infrastructure. However, the growing demand for sustainable and eco-friendly solutions has spurred interest in renewable energy sources. Proton exchange membrane (PEM) fuel cell-based systems, integrated with solar and wind energy, offer a promising alternative. This review explores the potential of these hybrid systems in stationary telecom applications, providing a comprehensive overview of their architecture, energy management, and storage solutions. As the demand for telecommunication services grows, so does the need for a reliable power supply. Diesel generators are linked with high operational costs, noise pollution, and significant greenhouse gas emissions, prompting a search for more sustainable alternatives. This review analyzes the current state of PEM fuel cell systems in telecom applications, examines the architecture of microgrids incorporating renewable energy sources, and discusses optimization methods, challenges, and future directions for energy storage systems. Critical findings and recommendations are presented, highlighting objectives and constraints for future developments. Leveraging these technologies can help the telecom industry reduce fossil fuel reliance, lower operational costs, minimize environmental impact, and increase system reliability. [ABSTRACT FROM AUTHOR]

Microgrids have increased in popularity thanks to both the integration of renewable energy resources and their energy distribution capability for remote locations. Moreover, the microgrids, mainly using multiple generators connected in parallel, acquire additional advantages by using both Hot-Swap and Current-Sharing techniques. This paper presents the development of a Hardware in the Loop platform to test Current-Sharing algorithms. It is reinforced that the use of a real-time simulation based on Hardware in the Loop is a viable and cost-effective alternative in the validation of controllers. The platform was developed in a graphical programming environment (LabVIEW 2015) and implemented with NI MyRIO 1900 (National Instruments Corp., Austin, TX, USA) development boards for easier reproducibility. The entire code project is openly available and provided in this paper. A system of photovoltaic energy generators was used to evaluate the performance of the HIL platform. As a result, the platform was able to reproduce a similar behavior to the photovoltaic generator, presenting average mean errors of 0.4 V and 0.2 A in its voltage and current, respectively. Additionally, the platform showed its capability to test Current-Sharing algorithms in the occurrence of Hot-Swap events. This work contributes with a validation tool for energy management systems applied to microgrids. [ABSTRACT FROM AUTHOR]

The traditional energy market is being transformed with modern communication technology, with an interactive generation-load communication topology for energy management in a domestic utility connected over a micro smart grid (MSG). In the traditional energy market, the consumer's role is limited to power consumption. Modern microgrids allow the user to interact with the grid and have increased the energy efficiency of domestic utilities. It is very much necessary to study the role of distributed generation in demand-side management for a proper energy management system in MSG. This paper highlights the interactive areas for the user with the grid to minimize the losses and improve the grid efficiency, thereby minimizing the cost for the user. An agent-based system for the identification of priority and non-priority loads is discussed in this paper, along with an Adaptive Whale Optimization Algorithm (AWOA) for load switching based on priority. Simulation is conducted in MATLAB to validate the proposed technique. The effectiveness of the proposed framework is assessed by comparing it to the Whale optimization Algorithm (WOA) and cases without scheduling. By implementing AWOA, the following improvements were observed: the total electricity cost decreased by an average of 1.33% for 6 residential buildings. In the similar manner, the lad deviation of 1.36% is achieved by implementing AWOA compared to AWO Algorithm. [ABSTRACT FROM AUTHOR]

Copyright of e & i Elektrotechnik und Informationstechnik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, Electric Vehicles (EVs) powered by a Proton Exchange Membrane Fuel Cell (PEMFC) which has high energy density and efficiency have become an attractive solution for the automotive industry. However, since the power density of the PEMFC is not high enough for different road conditions such as accelerating and going uphill, a supercapacitor (SC) is preferred as the auxiliary energy source for high power density in EVs. Thus, in this paper, a hybrid power system consisting of the Fuel Cell (FC) and SC for a Four-Wheel Drive Electric Vehicle (4WDEV) is proposed. Since energy requirement of the EV depends on the travel route, an effective Energy Management System can be designed by using a Geographic Information System (GIS). According to the road trajectory, the power requirement of the EV is estimated and thus, energy consumption is reduced. The proposed system is validated by using the Matlab Simscape Toolbox. • A hybrid power system consisting of a PEM Fuel Cell and SC for a 4WDEV is proposed. • An effective energy management system is designed using a GIS based on road route. • The high power demand of the EV is met from the SC in the sloping roads. • The energy consumption and the stresses of the power converters are reduced. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources in the European power system is one of the main goals set by the European Union. In order to ease this integration, in recent years, Renewable Energy Communities (RECs) have been introduced that aim to increase the exploitation of renewable energy at the local level. This paper presents an Energy Management System (EMS) for an industrial microgrid owned and operated by a greentech company located in the north of Italy. The company is a member of an REC. The microgrid is made of interconnected busbars, integrating photovoltaic power plants, a fleet of electric vehicles, including company cars and delivery trucks supporting Vehicle-to-Grid (V2G), dedicated charging stations, and a centralized battery energy storage system. The industrial site includes two warehouses, an office building, and a connection to the external medium-voltage network. The EMS is designed to optimize the operation of the microgrid and minimize the operating costs related to the sale and purchase of energy from the external network. Furthermore, as the company is a member of an REC, the EMS must try to follow a desired power exchange profile with the grid, suggested by the REC manager, with the purpose of maximizing the energy that is shared within the community and incentivized. The results demonstrate that, when minimizing only costs, local self-consumption is favored, leading to a Self-Sufficiency Rate (SSR) of 65.37%. On the other hand, when only the adherence to the REC manager's desired power exchange profile is considered in the objective function, the SSR decreases to 56.43%, net operating costs increase, and the energy shared within the REC is maximized. [ABSTRACT FROM AUTHOR]

This manuscript proposes a hybrid technique for optimal energy management (EM) of fuel cell (FC) and ultra-capacitor (UC) hybrid electric vehicles (FCHEVs) under uncertainty. The proposed method is a joint execution of the Slime Mould Algorithm (SMA) and the Adaptive Neuro-fuzzy Inference System (ANFIS), otherwise called the SMA-ANFIS system. The main aim of the proposed system is to achieve and regulate the steady state of DC bus voltage with minimal steady-state error (SSE) under different load conditions by using the proposed SMA-ANFIS technique. FC supplies power during vehicular operation and it is used to regulate DC bus voltage to the chosen value and recharge, and UC supplies current optimally through the proposed technique. The optimal EMS performs and controls the power flows through the associated power converters to accomplish some power allocation and energy efficiency levels. Finally, the proposed method is done in the MATLAB platform, and the performance is compared with other methods. [ABSTRACT FROM AUTHOR]

In recent years, the adoption of renewable energy sources has significantly increased due to their numerous advantages, which include environmental sustainability and economic viability. However, the management of electric microgrids presents complex challenges, particularly in the orchestration of energy production and consumption under the uncertainty of fluctuating meteorological conditions. This study aims to enhance decision-making processes within energy management systems specifically designed for microgrids that are interconnected with primary grids, addressing the stochastic and dynamic nature of energy generation and consumption patterns among microgrid users. The research incorporates stochastic models for energy pricing in transactions with the main grid and probabilistic representations of energy generation and demand. This comprehensive methodology allows for an accurate depiction of the volatile dynamics prevalent in the energy markets, which are critical in influencing microgrid operational performance. The application of the Stochastic Dual Dynamic Programming (SDDP) algorithm within a multi-stage adaptive framework for microgrids is evaluated for its effectiveness compared to deterministic approaches. The SDDP algorithm is utilized to develop robust strategies for managing the energy requirements of 1, 2, and 12 prosumers over a 24 h planning horizon. A comparative analysis against the precise solutions obtained from dynamic programming via Monte Carlo simulations indicates a strong congruence between the strategies proposed by the SDDP algorithm and the optimal solutions. The results provide significant insights into the optimization of energy management systems in microgrid settings, emphasizing improvements in operational performance and cost reduction. [ABSTRACT FROM AUTHOR]

A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS. [ABSTRACT FROM AUTHOR]

This study aims to peak power shaving and reduce the cost of energy by using improved energy management system (EMS) in a microgrid. This study has three scenarios. In the first scenario, the EV charging station and the wind turbine operate standalone. In the second scenario, a microgrid structure including an energy storage system (ESS) and an EMS is established. The threshold algorithm has been used for the energy management. In the last scenario, fuzzy logic-based EMS is used for energy management. The impact of EV charge station and wind turbine on the grid and the cost of energy are analyzed for three scenarios. The results show that the use of microgrid-connected ESS significantly reduces the cost of energy and creates a more balanced load for the grid side. It is also proved that the EMS affects the energy losses. It is revealed that when the proposed fuzzy logic-based EMS is used, the energy cost is reduced by 78% and the peak power is reduced by 70%. Thus, it is seen that the proposed EMS successfully reduces both the cost of energy and the negative impact of EVs on the grid by balancing grid side load. [ABSTRACT FROM AUTHOR]

Due to their small size and isolated energy systems, islands face a significant energy supply challenge. To develop sustainable energy systems, Hybrid Renewable Energy Systems (HRES) help in the generation of electricity in island zones, as they are a clean and inexhaustible source of energy. The purpose of this study is to optimize the allocation of Renewable Energy Sources (RES) on an island in Tunisia. To ensure efficient management between the total power generation and the total community load demand, an Energy Management System (EMS) is required. This paper presents the integration of an optimal EMS using Particle Swarm Optimization (PSO) to directly allocate and optimize the energy generated by an HRES. In addition, the PSO algorithm is applied to regulate energy production, consumption, and storage to maximize the utilization of the available renewable sources while satisfying load requirements. The results exhibit that this approach is effective for the dynamic optimization of energy management in an HRES, contributing to a more efficient and sustainable utilization of energy resources. [ABSTRACT FROM AUTHOR]

Hybrid electric fuel cell vehicles (FCHEVs) represent a promising pathway for sustainable transportation by integrating fuel cells, batteries, and supercapacitors to power their propulsion systems. However, ensuring the efficiency and longevity of FCHEV components remains a significant challenge due to stress on batteries and fuel cells, as well as fluctuations in the DC bus voltage. This paper presents an innovative approach to enhance the effectiveness and lifespan of FCHEV storage elements through an energy management system (EMS) based on a multi-objective genetic optimization algorithm that accounts for road gradient variations. The proposed methodology dynamically allocates energy among the fuel cell, battery, and supercapacitor, optimizing three key objectives simultaneously: minimizing fluctuations in fuel cell and battery power, reducing variations in DC bus voltage, and balancing performance, efficiency, and storage system longevity. Extensive simulations demonstrate that simultaneous optimization of these three objectives yields the best results. Under the NEDC cycle, the optimized EMS resulted in a 12 % increase in fuel cell stress but a significant 73 % decrease in battery stress, along with a 39 % reduction in DC bus voltage variations. For the UDDS cycle, there was a 37 % decrease in fuel cell stress, a 69 % decrease in battery stress, and a 29 % reduction in DC bus voltage variations. During the HWFET cycle, the optimized EMS achieved an 8 % decrease in fuel cell stress, a 49 % decrease in battery stress, and an 8 % reduction in DC bus voltage variations. By incorporating road gradient considerations, the EMS aims to maintain the battery state of charge and hydrogen consumption within acceptable ranges while enhancing FCHEV efficiency under various road gradient conditions. This study advances FCHEV technology, offering insights into the design and operation of more sustainable and efficient transportation solutions.

Trucks consume a lot of energy. Hybrid technology maintains a long range while realizing energy savings. Hybrid is therefore an effective energy-saving technology for trucks. Recovery of engine waste heat through the organic Rankine cycle further enhances engine efficiency and provides effective thermal management. However, the powertrain greatly increases the complexity of energy management system. In order to design an energy management system with high efficiency and robustness, this study proposes a deep reinforcement learning embedded rule-based energy management system. This method optimises the key parameters of the rule-based energy management system by inserting deep reinforcement learning into it. Therefore, this scheme combines the good optimization effect of deep reinforcement learning and the excellent robustness of rule. In order to verify the feasibility of this scheme, this study builds the system dynamic model and carries out a simulation study. Subsequently, a hybrid powertrain semi physical experimental bench was constructed and a rapid control prototype experimental study was carried out. The simulation results show that the deep reinforcement learning embedded rule-based energy management system can reduce the energy consumption by 4.31 % compared with the rule-based energy management system under the C-WTVC driving cycle. In addition, energy saving and safe operation can also be achieved under other unfamiliar untrained driving cycles. The rapid control prototype experimental study shows that the deep reinforcement learning embedded rule-based energy management system has good agreement in experiment and simulation, which demonstrates the potential for real vehicle engineering applications and promotes the engineering application of deep reinforcement learning.

This article presents the complete design of a nonlinear control system for multiple stationary bikes connected to a mechanical energy conversion system equipped with squirrel cage induction generators (SIGs) linked to AC/DC converters. The primary objective of this study is to investigate the feasibility of powering multiple treadmills using the DC bus via DC/AC converters. The novelty of our approach lies in the integration of a new strategy of control system to achieve multiple control objectives within a gym microgrid environment, and an energy management algorithm to ensure the energy flow between intermittent generation and the considered, somewhat random, demand. The system is composed of several subsystems: i) stationary bikes connected to the DC bus via inverters acting as intermittent power sources; ii) a Li-ion battery-based energy storage system interfaced through a Buck-Boost converter; iii) electromechanical loads, including treadmills, powered by DC/AC converters, in addition to other DC loads. The main control objectives are as follows: a) each stationary bike extracts energy from the DC network by regulating the torque applied by the athlete, ensuring that the torque follows the reference torque; b) the treadmill’s speed must follow the reference generated based on the athlete’s condition; c) ensuring the protection of the energy storage system by monitoring its current and voltage; d) all mentioned objectives are achieved while maintaining the DC bus voltage at a reference value. To achieve these objectives, a nonlinear control approach based on Lyapunov theory is used to design the controllers. A formal analysis is conducted to assess the performance of the studied system. The system’s performance is demonstrated using the MATLAB/Simulink environment. Numerous simulations demonstrate that every control objective is achieved. Specifically, the system maintained the DC bus voltage at 500 V with a maximum deviation of 1 V, and the treadmill speed followed the reference within a margin of 0.1 m/s.