This paper studies an economic driving control method for plug-in hybrid electric vehicles (PHEVs) on undulating roads. Existing energy management systems often rely on standard driving cycles or historical speed data, neglecting the real-time effects of road gradient and dynamic speed variation. To address this, an economic speed planning algorithm based on dynamic programming is proposed from an energy management perspective. The algorithm optimises the vehicle speed on undulating roads to improve powertrain efficiency and achieve economical driving. Simulation results demonstrate that the proposed method significantly improves energy saving, reduces fuel consumption, and lowers the final battery state of charge compared to other approaches. In summary, this study provides valuable insights for enhancing the energy economy of PHEVs on undulating roads.

Accurate forecasting of solar photovoltaic (PV) energy generation remains a challenging task due to the strong variability and nonlinear influence of meteorological conditions, which directly affects the reliability of energy management systems. This paper proposed the comparative framework of models for highly accurate forecasting of solar photovoltaic (PV) energy generation, utilizing state-of-the-art machine learning (ML) and deep learning (DL) approaches to substantially boost forecasting reliability under varying environmental conditions. All five models, Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) and families of models, gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) have been implemented and tested using realistic meteorological data and irradiance data directly retrieved from the GRIDouble project, supported in I-NERGY from the HORIZON 2020 program of the European Union. More specifically, the dataset consisted of hourly measurements of such variables as air temperature, cloud opacity, diffuse and direct irradiance, and global horizontal irradiance, coupled with solar energy production reading. To maximize the learning power of time-patterned data and to minimize its variance, a comprehensive pre-processing pipeline was implemented that included temporal features extraction, cyclical encoding, as well as min–max normalization. Experiments confirmed that XGBoost yielded the highest forecasting performance with R² = 0.9478, MSE = 3191.15, RMSE = 56.49, MSRE = 27.59. The remaining models, bidirectional LSTM and MLP, demonstrated R² = 0.8662 and R² = 0.8581, respectively, highlighting their substantial ability to model the temporal and nonlinear environment. In summary, XGBoost can be praised as the top model with the achieved the best performance in this experimental setting. It also presents incredible potential for smart-grid optimization and renewable energy forecasting.

Managing energy in urban microgrids is a major issue because of the high degree of variability of renewable energy sources and the dynamic nature of the urban demand especially in regions with arid climates that pose extreme temperatures that provide volatile cooling loads. Existing energy management systems (EMS) often relied on static or rule-based control systems, which lack the responsiveness needed to manage the variability inherent in renewable generation and fluctuating demands. This study presents a simulation-based and adaptive reinforcement learning (RL)-based energy management framework that addresses persistent inefficiencies in coordinating diverse energy sources within urban microgrids, particularly in arid regions, to bridge this gap. In this study, it was tried to develop a simulation-driven platform combining EnergyPlus with Python/TensorFlow RL agents to dynamically optimize the dispatch of solar, wind, diesel, and battery resources. Unlike prior approaches, the proposed system also combined inter-microgrid communication via MQTT protocols, enabling real-time energy sharing. The framework was validated in a case study reflecting Riyadh's climatic conditions, where it significantly improved operational and environmental performance. Simulation outcomes demonstrated high predictive accuracy for hourly and annual consumption (R² = 0.94 and 0.90, respectively). Compared to baseline methods, the RL-based approach reduced CO2 emissions by 14%, SO2 emissions by 13.6%, and primary energy consumption by 10%.

The article presents a comprehensive review of current trends and challenges related to the development of hybrid propulsion systems (HEV) in the automotive industry. The paper discusses the main types of hybrid systems, features of their architectures and principles of operation, with a focus on technical, environmental and economic aspects. Particular attention is paid to the analysis of battery technologies, energy management systems, and recycling issues. The authors emphasize the key challenges of the industry: battery degradation, high component costs, dependence on climatic conditions, and others. The review also contains a comparative analysis of solutions from the world's leading manufacturers. In addition, promising trends are discussed, including the development of solid-state batteries, the introduction of artificial intelligence, and the development of wireless charging. Special attention is paid to analyzing the value chain of hybrid installations. The uniqueness of this review lies in the systematic approach to analyzing the HEV market, combining technical, economic and regulatory perspectives. The paper can be useful for both transportation and energy professionals and researchers involved in sustainable development and implementation of innovative technologies in the automotive sector.

Energy management systems in microgrids and future prospects application in nuclear power plants: a review

Retraction notice to “A fully decentralized home energy management system for efficient energy management and photovoltaic with battery energy storage system sizing for grid connected home microgrid” [J. Clean. Product. 514 (2025) 145768

Experimental validation of real-time energy management for hybrid energy storage systems based on predictive wavelet transforms

A hierarchical energy management system for a cluster of buildings: Reinforcement learning and model predictive control (RL-MPC) approach

Comprehensive design and experimental validation of a hybrid energy management system controller for a residential microgrid using power hardware in-the-loop

Hierarchical energy management system for ships based on MPC-ECMS with multi-dimensional testing and validation

A novel modeling framework for demand response-based energy management systems in smart electricity markets, using optimization and multi-criteria decision making techniques

Design and implementation of a new dual-layer type 2 FLC-based energy management system for a fuel cell electric vehicle

Model predictive control (MPC) has become one of the most promising control strategies in the field of electric vehicle energy management due to its rolling optimization and explicit constraint processing capabilities. This study analyzes the modeling mechanism and implementation path of MPC in power allocation, regenerative braking and energy collaborative control, which elaborates on the improvement principle of energy efficiency and system stability through predictive modeling and dynamic optimization. The evolution of MPC application in hybrid power systems, vehicle dynamic stability control, and hierarchical optimization control is discussed. The synergistic effect of multi-objective optimization and health-conscious control in energy efficiency improvement and service life extension is analyzed. With the development of artificial intelligence technology, MPC is expanding from model-based deterministic control to the directions of intelligent learning and distributed adaptation. Model uncertainty, computational complexity, and real-time solving efficiency are the main challenges faced by MPC. Future research will focus on the deep integration of model simplification, rapid solving, and intelligent learning to achieve a more efficient and reliable intelligent energy management system.

Although renewable energy sources offer enormous potential to improve environmental sustainability, maximizing economic benefits inside microgrids requires resolving their intermittency and irregularity. A viable alternative is to combine energy storage with renewable energy technologies. This article introduced a energy management system for hybrid renewable power plants that includes fuel cells, wind turbines, solar cells, battery energy storage devices, and micro-turbines. Optimization problem is formulated as Hyper Learning Binary Dragonfly Algorithm (HLBDA) for optimizing economic benefits and with objectives of minimizing operating costs and pollutant gas emissions. Suggested model is compared with existing methods like Genetic Algorithms (GA), and Crayfish Optimization Algorithm (COA). Also, stochastic framework is considered suitable solution for achieving optimal operation point in microgrids to cope with uncertain parameters. According to the simulation results, suggested method proves reductions in overall system costs and pollutant gas emissions. The proposed system achieved significant superiority across all indicators. In the area of cost reduction, the algorithms demonstrated remarkable progress. The algorithms achieved significant improvements in cost reduction compared to genetic algorithm (GA). HLBDA algorithm achieved a 12.4% cost saving compared to GA, and the COA algorithm showed a 3.24% improvement in cost reduction. In the area of carbon emission reduction, the algorithms also showed significant progress: the HLBDA algorithm recorded the highest emission reduction rate at 9.54%, and the COA algorithm showed a 2.40% improvement in emission reduction.

Multi-energy systems are one of the main solutions to facilitate the integration of renewable energy resources in the smart energy system. To this end, this paper presents a comprehensive structure for the energy system that integrates the electrical, hydrogen, and water sections for sustainable management of modern energy systems. The presented model offers cooperative scheduling for neighbor multi-energy systems that provides the opportunity of local energy trading among them. Also, it focuses on the water system and seeks to supply potable water for the energy systems by a water well, desalination unit, and water storage tank. Besides, compressed air energy storage is developed to utilize the surplus generation of renewable energy to provide an efficient operation for the system. To control the uncertain nature of renewable generation, the energy systems can take part in the electrical and thermal demand-side programs to manage their consumption in response to the signal prices. The proposed model is tested on a standard case study, and the numerical results show that the cooperation among energy systems reduces their operating cost and unserved energy by $ 23.91 and 64.317 kWh compared to autonomous operation.

In provincial electricity markets where long-term contracts and spot trading coexist, multiple categories of imbalance funds arise from congestion, energy deviations and dual-track price differences, posing challenges to energy management systems (EMS) in terms of fair and robust settlement. This paper proposes an EMS-oriented framework to assess and diagnose alternative imbalance settlement mechanisms in a provincial dual-track market. First, a unified settlement model is developed that reconstructs key imbalance fund categories and allocates them to heterogeneous agents—thermal, renewable and storage units and different user groups—under a library of settlement rules. Second, a multi-scenario simulation platform is built, covering normal operation, tight supply and high-renewable-volatility conditions. Third, a multi-criteria evaluation scheme is designed to quantify economic efficiency, fairness, risk and renewable support for each mechanism–scenario combination. Finally, a category–agent two-dimensional diagnostic module is introduced to reveal misallocation patterns and the main money-transfer paths among fund categories and agent groups. A case study on a realistic provincial system shows that the proposed framework can distinguish mechanisms with better overall robustness, identify severe cross-subsidies in extreme scenarios and provide practical guidance for refining imbalance settlement parameters within EMS-driven market operations.

Solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid systems exhibit inherent system uncertainties and unmodeled dynamics during operation, which compromise the accuracy of predicting gas turbine power. This poses challenges for system operation analysis and energy management. To enhance the prediction accuracy and stability of gas turbine power in SOFC/GT hybrid systems, a power prediction method capable of incorporating total system disturbance information is investigated. This study constructs a high-fidelity simulation model of an SOFC/GT hybrid system to generate gas turbine power prediction datasets. With fuel utilization (FU) as the input and gas turbine power as the output, this system is assumed to be a first-order dynamic system. Building upon this foundation, an extended state observer (ESO) is employed to extract the total system disturbance (f) that affects the power output of the gas turbine, excluding fuel utilization. The total disturbance f and fuel utilization are used as inputs to a Backpropagation (BP) neural network to construct a disturbance-aware power prediction model. The predictive performance of the proposed method is evaluated by comparison with a BP neural network without disturbance estimation information and several benchmark models. Simulation results indicate that incorporating the disturbance term estimated by ESO enhances both the accuracy and stability of the BP neural network’s power prediction, particularly under operating conditions characterized by significant power fluctuations. Quantitatively, when comparing the predictive model with disturbance included to the model without disturbance, including the disturbance reduces the prediction error by approximately 89.33% (MSE) and 67.34% (RMSE), while the coefficient of determination R2 increases by 0.1132, demonstrating a substantial improvement in predictive performance under the same test conditions. The research findings indicate that incorporating disturbance information into data-driven prediction models represents a viable modeling approach, providing effective support for predicting gas turbine power in SOFC/GT hybrid systems.

This study proposes a smart energy management framework for a university campus microgrid aimed at reducing dependence on the main power grid and increasing the utilization of photovoltaic (PV) generation under dynamic load and environmental conditions. The core contribution is a two-stage approach that combines a genetic algorithm (GA) for static day-ahead optimization with a soft actor-critic (SAC) reinforcement learning (RL) agent performing adaptive supervisory management of microgrid active and reactive power flows via battery control. The GA provides an optimal reference schedule under forecasted conditions, while the SAC agent is trained on eight representative scenarios derived from measured PV generation and campus load data to adapt battery operation and grid exchange under uncertainty. The results show that the benefit of RL does not lie in reproducing the static GA solution, but in learning economically rationally adaptive behavior. In particular, the SAC agent exploits low-tariff periods and hedges against adverse PV conditions by proactively adjusting battery charging strategies in real time. This adaptive behavior addresses a key limitation of static optimization, which cannot respond to deviations from forecasted operation, and represents the main added value of the proposed framework. From a practical perspective, the GA-SAC architecture operates at a supervisory level with low computational requirements, making it suitable for scalable deployment in smart campus and smart city energy management systems.

Abstract - The rapid transition toward electric vehicles (EVs) is driven by the global need to reduce carbon emissions, improve energy efficiency, and achieve sustainable transportation systems. it is essential to not only review recent technological developments but also analyse their practical performance and limitations. This paper presents a comprehensive review and experimental-oriented discussion of sustainable electric vehicle technologies, focusing on recent advances in battery technology, electric motor design, and charging infrastructure. Lithium-ion battery improvements, solid-state batteries, and recycling approaches are reviewed alongside experimental electrochemical and thermal performance trends reported in recent literature. Electric motor technologies such as permanent magnet synchronous motors, induction motors, and emerging axial flux motors are examined with respect to efficiency, torque density, and material sustainability. Charging infrastructure developments, including fast charging, wireless charging, and smart grid integration, are analysed from both system-level and experimental perspectives. The paper also discusses current challenges, experimental gaps, and future research directions. The findings highlight that sustainable EV development requires an integrated approach combining advanced materials, efficient electromechanical design, and intelligent energy management systems. Key Words: Electric Vehicles, Sustainable Mobility, Battery Technology, Electric Motors, Charging Infrastructure, Experimental Analysis

The energy audit is a method for analyzing energy use patterns in a facility and identifying potential efficiency improvement opportunities. In this study, level 2 of the energy audit was conducted at one of Indonesia's fuel terminals. The energy performance indicator (ENPI) was calculated over three years. The ENPI of the fuel terminal increased from 2022 to 2024, indicating that energy consumption per unit of production (kL) rose year by year. The increase from 2022 to 2023 was 27%, while that from 2023 to 2024 was 14%. A higher ENPI value indicates greater energy consumption to produce each kiloliter of product. The implementation of energy conservation opportunities is expected to reduce energy consumption by an average of 5–10% per month, equivalent to approximately 35,784–48,585 kWh per month, and can also reduce CO? emissions by 298.14 TonCO?Eq using an emission factor of 0.83. In order to implement an energy management system, the organization must establish a supporting system, particularly an energy policy that defines the company’s general energy-related policies.