Hydrogen Carriers for Renewable Microgrid System Applications
Artificial Intelligence and Digital Twins for Bioclimatic Building Design: Innovations in Sustainability and Efficiency
Power-to-Heat and Seasonal Thermal Energy Storage: Pathways Toward a Low-Carbon Future for District Heating
Deep Neural Network-Based Optimal Power Flow for Active Distribution Systems with High Photovoltaic Penetration
Fuzzy Logic Estimation of Coincidence Factors for EV Fleet Charging Infrastructure Planning in Residential Buildings

Journal Description

Energies

Energies is a peer-reviewed, open access journal of related scientific research, technology development, engineering policy and management studies related to the general field of energy (from technologies of energy supply, conversion, dispatch and final use to the physical and chemical processes behind such technologies), and is published semimonthly online by MDPI. The European Biomass Industry Association (EUBIA), Association of European Renewable Energy Research Centres (EUREC), Institute of Energy and Fuel Processing Technology (ITPE), International Society for Porous Media (InterPore), CYTED and others are affiliated with Energies and their members receive discounts on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, RePEc, Inspec, CAPlus / SciFinder, and other databases.
Journal Rank: CiteScore - Q1 (Control and Optimization)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.2 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Sections: published in 41 topical sections.
Testimonials: See what our editors and authors say about Energies.
Companion journals for Energies include: Energy Storage and Applications and Bioresources and Bioproducts.
Journal Cluster of Energy and Fuels: Energies, Batteries, Hydrogen, Biomass, Electricity, Wind, Fuels, Gases, Solar, ESA and Methane.

Impact Factor: 3.2 (2024); 5-Year Impact Factor: 3.1 (2024)

Imprint Information Journal Flyer Open Access ISSN: 1996-1073

Latest Articles

17 pages, 1233 KB

Open AccessArticle

Consistency Testing Method for Energy Storage Systems with Time-Series Properties

by Nan Wang and Zhen Li

Energies 2026, 19(1), 46; https://doi.org/10.3390/en19010046 (registering DOI) - 21 Dec 2025

Abstract

As a cushion for the volatility of renewable energy, energy storage systems can achieve peak shaving and valley filling, thereby improving the operational efficiency and economic performance of the power grid. In addition, energy storage systems can absorb renewable energy production, thereby enhancing the safety and reliability of the electrical power system. Nowadays, energy storage systems are facing severe problems such as explosions that are caused by overcharging and discharging. The main reason for the overcharging and discharging of energy storage systems is the inconsistency in the state of the electric core in the charging and discharging process, which not only affects the safety of the electric core, but also influences the overall charging and discharging capacity of the energy storage system. To address this inconsistency of energy storage cores, this paper proposes an energy storage consistency monitoring method under the framework of clustering-classification, which adopts the Belief Peaks Evidential Clustering and Evidential K-Nearest Neighbors classification algorithm. This paper proposes a BPEC-EKNN-based method for battery inconsistency detection and localization. The proposed approach first constructs battery performance evaluation coefficients to characterize inter-cell behavioral differences, and then integrates an enhanced k-nearest neighbor strategy to identify abnormal cells. It also identifies and locates inconsistent battery cells by analyzing the magnitude of the confidence level m (Ω), without relying on predefined thresholds. Also, time-series data as opposed to the evaluation of voltage data at a singular point is engaged to realize the detection and localization of energy storage core consistency anomalies under the consideration of time-series data. The proposed algorithm is capable of identifying inconsistencies among energy storage batteries, with the parameter m (Ω) serving as an indicator of the likelihood of inconsistency. Experimental results on battery pack datasets demonstrate that the proposed method achieves higher detection accuracy and robustness compared with representative statistical threshold-based methods and machine learning approaches, and it can more accurately identify inconsistent battery cells. By applying perturbation analysis to real-time operational data, the algorithm proposed in this paper can detect inconsistencies in battery cells reliably. Full article

(This article belongs to the Section D: Energy Storage and Application)

22 pages, 3271 KB

Open AccessArticle

Thermal Characterization of a Stainless Steel Flat Pulsating Heat Pipe and Benchmarking Against Copper

by Larissa Krambeck, Kelvin Guessi Domiciano, Maria Eduarda Beé, Marco Marengo and Marcia Barbosa Henriques Mantelli

Energies 2026, 19(1), 45; https://doi.org/10.3390/en19010045 (registering DOI) - 21 Dec 2025

Abstract

Copper is widely used in two-phase devices for electronic cooling due to its ease of manufacture and high thermal conductivity. However, such high-heat conduction can limit the performance of pulsating heat pipes (PHPs) through transverse heat leakage. The use of lower-conductivity materials such as stainless steel enhances phase-change heat transfer by promoting stronger flow oscillations and reducing parasitic heat leakage, but it may be overall detrimental due to its poor thermal linkage between evaporator and condenser sections. Therefore, in this study, two main objectives are addressed: (i) experimentally characterizing the thermal behavior of a mini flat-plate PHP made of stainless steel (AISI 316L), and (ii) benchmarking its performance against a copper counterpart. Both devices were manufactured by diffusion bonding and tested under different orientations to evaluate operational robustness. The stainless steel PHP initiated oscillations at lower heat loads and showed larger temperature oscillations compared to the copper PHP, demonstrating effective phase-change heat transfer despite its lower thermal conductivity. A filling ratio of 71% of water provided the most stable operation, while orientation affected startup conditions and oscillation amplitude. Overall, stainless steel achieved comparable thermal performance to copper at low-to-moderate heat loads from 2.6 to 13.0 W/cm², with additional benefits including reduced mass (~11% lighter), higher mechanical strength, and corrosion resistance. These results indicate that stainless steel is a viable alternative to copper at least for miniature flat-plate PHPs, offering a balance between thermal efficiency, mechanical robustness, and operational reliability. Full article

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13 pages, 2422 KB

Open AccessArticle

Prediction of DC Breakdown Strength for Polymer Nanocomposite Based on Energy Depth of Trap

by Xiaohu Qi, Jian Guan, Xuri Xu, Zhen Zhang, Chuanyun Zhu, Chenyi Guo, Qifeng Shang and Yu Gao

Energies 2026, 19(1), 44; https://doi.org/10.3390/en19010044 (registering DOI) - 21 Dec 2025

Abstract

Understanding the role of carrier traps in the determination of dielectric breakdown of polymer nanocomposite would yield a novel method for the estimation of breakdown strength of the material. In this study, we propose a novel approach to predict the DC breakdown strength of polyethylene (PE) and its nanocomposite at room temperature via the bipolar charge transport (BCT) model based on trap energy estimated from isothermal surface potential decay (ISPD). Test specimens of polyethylene (PE) and its nanocomposites, with a thickness of 110 μm, were fabricated using the hot-pressing method by incorporating 20 nm SiO₂ particles as fillers. The distribution of carrier traps within these specimens was subsequently determined through ISPD measurements. The intrinsic breakdown strength of the sample was derived from the determined trap energy levels, by which the breakdown strength was predicted through the BCT model. Experimental DC breakdown tests were conducted on the specimens to validate the accuracy of the predictions. The results indicated that the DC breakdown strength predicted theoretically was in good agreement with that measured from the experiment. Such a prediction method provides a possible way to employ a non-destructive test to evaluate the DC breakdown strength of polymer nanocomposite. Full article

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25 pages, 5309 KB

Open AccessArticle

Wide Voltage Gain Range Bidirectional DC-DC Converter with Reduced Switches Count and Buck-Boost Characteristic in Both Power Flow Directions

by Victor Fernão Pires, Armando Cordeiro, Daniel Foito, Tito Amaral and José Fernando Silva

Energies 2026, 19(1), 43; https://doi.org/10.3390/en19010043 (registering DOI) - 21 Dec 2025

Abstract

Several applications require bidirectional power converters with high-voltage gain. While several topologies have been proposed, none of them exhibit Buck-Boost characteristics in both forward and reverse power transfer. Most proposals behave as a Boost converter in forward direction and as a Buck converter in the reverse direction. Therefore, this paper proposes a novel DC-DC bidirectional power converter that exhibits Buck-Boost characteristics in both power flow directions while providing very high wide voltage gain range. The proposed converter has, in addition, the ability to maintain continuous currents in the input and output. The theoretical analysis of the converter under bidirectional power flow conditions will be presented and examined, along with the design of its components. The validation of the characteristics and behavior of the proposed bidirectional power converter were tested in several laboratory experiments. The experimental results obtained from both power flow directions show agreement with the theoretical considerations. Full article

(This article belongs to the Special Issue Design and Control Strategies for Wide Input Range DC-DC Converters)

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23 pages, 3451 KB

Open AccessArticle

Load Flexibilities from Charging Processes by Electric Vehicles at the Workplace: A Case Study in Southern Germany

by Ronald Opoku and Patrick Jochem

Energies 2026, 19(1), 42; https://doi.org/10.3390/en19010042 (registering DOI) - 21 Dec 2025

Abstract

The workplace, as a promising location for Electric Vehicle Supply Equipment (EVSE), presents a particular challenge, as different user requirements (e.g., parking and charging durations) meet a spatially and quantitatively limited offer of EVSE. However, integrating electric vehicles synergistically into the energy system of the employer can increase the profitability of the system and, correspondingly, increase the number of EVSE. For this, a deep understanding of employees’ charging behavior is key. For providing some evidence of empirical charging patterns at the workplace, this work examined a dataset of 23.9 million observations on empirical charging processes at workplaces in 2023. To identify user groups, a probabilistic model (Gaussian Mixture Model) and a K-Means clustering approach were applied and the results compared. Eight groups were identified, including full-time and part-time employees, pool vehicle users, and opportunists. The group-specific probability distributions are used to publish a synthetic dataset of parking and charging patterns at workplaces. The openly provided dataset helps to identify the right composition of EVSE in the employee context and to optimize potential fields of action. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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23 pages, 1856 KB

Open AccessArticle

Influence of Photosynthetic Cathodes on Anodic Microbial Communities in Acetate-Fed Microbial Fuel Cells Pre-Enriched Under Applied Voltage

by Paulina Rusanowska, Łukasz Barczak, Adam Starowicz, Katarzyna Głowacka, Marcin Dębowski and Marcin Zieliński

Energies 2026, 19(1), 41; https://doi.org/10.3390/en19010041 (registering DOI) - 21 Dec 2025

Abstract

Electrical stimulation is increasingly explored as a strategy to accelerate the development of electroactive biofilms in microbial fuel cells (MFCs), yet its integration with photosynthetic MFCs (pMFCs) remains insufficiently understood. This study evaluated how short-term anodic stimulation (0.5–5 V, 4 days) affects biofilm formation and COD removal, and how subsequent operation with photosynthetic cathodes—Chlorella sp., Arthrospira platensis and Tetraselmis subcordiformis—modulates anodic microbial communities and functional potential. Stimulation at 1 V yielded the best activation effect, resulting in the highest voltage output, power density and fastest COD removal kinetics, whereas 5 V inhibited biofilm development. During pMFC operation, Chlorella produced the highest voltage (0.393 ± 0.064 V), current density (0.14 ± 0.02 mA·cm⁻²) and Coulombic efficiency (~19%). Arthrospira showed moderate performance, while Tetraselmis generated no current despite efficient COD removal. 16S rRNA sequencing revealed distinct cathode-driven community shifts: Chlorella enriched facultative electroactive taxa, Arthrospira promoted sulfur-cycling bacteria and Actinobacteria, and Tetraselmis induced strong methanogenic dominance. Functional prediction and qPCR confirmed these trends, with Chlorella showing increased pilA abundance and Tetraselmis displaying enriched methanogenic pathways. Overall, the combined use of optimal anodic stimulation and photosynthetic cathodes demonstrates that cathodic microalgae strongly influence anodic redox ecology and energy recovery, with Chlorella-based pMFCs offering the highest electrochemical performance. Full article

(This article belongs to the Special Issue Applications of Fuel Cell Systems)

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22 pages, 2174 KB

Open AccessArticle

Dynamic CO₂ Emission Differences Between E10 and E85 Fuels Based on Speed–Acceleration Mapping

by Piotr Laskowski, Edward Kozłowski, Magdalena Zimakowska-Laskowska, Piotr Wiśniowski, Jonas Matijošius, Stanisław Oszczak, Robertas Keršys, Marcin Krzysztof Wojs and Szymon Dowkontt

Energies 2026, 19(1), 40; https://doi.org/10.3390/en19010040 (registering DOI) - 21 Dec 2025

Abstract

This study compared CO₂ emissions during a WLTP (Worldwide Harmonized Light-Duty Vehicles Test Procedure) test performed on a chassis dynamometer for the same flex-fuel vehicle, fuelled sequentially with E10 gasoline and E85 fuel. Based on the test data, a CO₂ emissions map was created, describing its dependence on speed and acceleration. The use of a 3D surface enabled the visualisation of the whole dynamics of emissions as a function of engine load in the WLTP cycle, including the identification of distinct emission peaks in areas of high positive acceleration. Analysis of the emission surface enabled the identification of structural differences between the fuels. For E85, more pronounced emission increases are observed in areas of intense acceleration, a consequence of the higher fuel demand resulting from the lower calorific value of bioethanol. In steady-state and moderate-load driving, CO₂ emissions for both fuels are similar. The results confirm that the main differences between E10 and E85 are not simply a shift in emission levels per se, but stem from variations in engine load during the dynamic cycle. Although E85 emits measurable CO₂ emissions, its carbon is not of fossil origin, highlighting the importance of biofuels in the context of greenhouse gas emission reduction strategies and the pursuit of climate neutrality. The presented methodology, combining chassis dynamometer tests with analysis of the speed-acceleration emission map, provides a tool for clearly identifying emission zones and can serve as a basis for further optimisation of engine control strategies and assessing the impact of fuel composition on emissions under dynamic conditions. Full article

(This article belongs to the Special Issue Combustion and Application of Carbon Neutral Fuel in Internal Combustion Engine)

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31 pages, 4974 KB

Open AccessArticle

Evaluation of Machine Learning Approaches for Hydration Heat Prediction in Energy-Efficient Cement Composites

by Barbara Klemczak, Dawid Bąba and Rafat Siddique

Energies 2026, 19(1), 39; https://doi.org/10.3390/en19010039 (registering DOI) - 21 Dec 2025

Abstract

Accurate prediction of the heat of hydration is essential for designing low-emission, durable mortars and concretes with controlled thermal behavior, as the partial replacement of Portland cement clinker with supplementary cementitious materials (SCMs) fundamentally alters hydration kinetics. Although hydration heat can be measured experimentally, such tests are often time-consuming and labor-intensive. Machine learning (ML)-based prediction methods offer a promising alternative, but identifying the most effective model is necessary before practical application. This study evaluates the performance of three ML algorithms, CatBoost, ExtraTrees, and XGBoost, in predicting the heat of hydration in energy-efficient cementitious composites containing SCMs. A dataset of 51 experimental samples was analyzed, comprising mix composition parameters (temperature, slag, fly ash content, and water-to-binder ratio) and four output variables: heat release rate and total heat released after 12, 72, and 168 h. Model performance was assessed using cross-validation and performance metrics (MAE, RMSE, MAPE, R²). All tested models showed a high level of fit (R² > 0.9 for short-term predictions). ExtraTrees demonstrated the most consistent performance, particularly for hydration heat and heat rate estimation, while XGBoost showed superior accuracy for early-age heat evolution. Residual analyses confirmed model stability and minimal bias. The results indicate that ML-based modeling can significantly reduce laboratory workload and enhance understanding of hydration behavior in low-carbon cementitious systems. Full article

(This article belongs to the Special Issue Energy-Efficient Building Materials: Innovations, Enhancements, Testing Methods, and Predictive Modelling)

76 pages, 6350 KB

Open AccessReview

Collaborative Mechanisms of Fixed and Mobile Resources: A Review on Enhancing the Full-Cycle Resilience of Integrated Energy Cyber-Physical Systems Against Cyber-Attacks

by Tianlei Zang, Kewei He, Chuangzhi Li, Lan Yu, Libo Ran, Siting Li, Rui Su and Buxiang Zhou

Energies 2026, 19(1), 38; https://doi.org/10.3390/en19010038 (registering DOI) - 21 Dec 2025

Abstract

Integrated energy cyber-physical systems (IECPS) face escalating cyber-attack threats due to their deep cyber-physical coupling, while traditional resilience models relying solely on fixed resources exhibit rigidity and limited adaptability. This review investigates IECPS attack mechanisms through the lens of the confidentiality, integrity, and availability framework, revealing their cross-layer propagation characteristics. We explicitly distinguish between fixed and mobile resources. Fixed resources include energy sources, transmission and distribution network facilities, coupling and conversion devices, fixed energy storage systems, and communication and control infrastructure. Mobile resources are grouped into five categories: mobile electricity resources, mobile gas resources, mobile heat resources, mobile hydrogen resources, and mobile communication resources. Fixed resources provide geographically anchored capacity and structural redundancy, and they offer static operational flexibility. Mobile resources, in contrast, provide spatially reconfigurable and rapidly deployable support for sensing, temporary multi-energy supply, and emergency communications. Building on this distinction, this review proposes a full-cycle resilience enhancement framework that encompasses pre-event prevention, in-progress response, and post-event recovery, with a particular focus on collaborative mechanisms between fixed and mobile resources. Furthermore, this review examines the foundational theories and key supporting technologies for such coordination, including fixed-mobile resource scheduling, intelligent perception and data fusion, communication security, and collaborative scheduling optimization. Key technical gaps and challenges in fixed-mobile resource collaboration are identified. Ultimately, this review aims to provide theoretical insights and practical guidance for developing resilient, adaptive, and secure integrated energy systems in the face of evolving cyber-physical threats. Full article

(This article belongs to the Section F1: Electrical Power System)

18 pages, 4234 KB

Open AccessArticle

Simulation and Optimization of Biomass Gasification Process in Fluidized Bed Coupled with Entrained-Flow Bed

by Jingjing Wang, Zhen Liu, Huimin Zhang, Xin Huang, Baozai Peng, Liang Chang, Ruihan Yang and Weiwei Li

Energies 2026, 19(1), 37; https://doi.org/10.3390/en19010037 (registering DOI) - 21 Dec 2025

Abstract

Biomass gasification serves as a key carbon-neutral technology. To effectively address the challenge of tar treatment during biomass gasification, the National Institute of Clean and low-carbon Energy developed a fluidized bed coupled with an entrained-flow bed. A steady-state Aspen Plus V12 model was designed to assess the compatibility between the two beds and optimize operating parameters. The model divides the process into three main zones: fluidized bed gasification, entrained-flow bed gasification, and bottom slag treatment, employing a reaction-restricted equilibrium assumption. Simulation results indicate that an increase in pressure leads to a reduction in the concentration of syngas components (CO and H₂), an insignificant rise in gas low heating value (LHV), and a notable decline in cold gas efficiency (η). A higher equivalence ratio (ER) results in decreased syngas components, along with a significant reduction in both LHV and η. The introduction of carbon dioxide reduces syngas components and lowers LHV. Similarly, the addition of steam reduces the CO content of the syngas and decreases its LHV. When the fluidized bed temperature exceeds 900 °C, changes in LHV and gas yield become negligible, while variations remain minimal when the entrained-flow bed temperature exceeds 1200 °C. Full article

(This article belongs to the Special Issue Thermochemical Conversion of Biomass and Organic Solid Wastes)

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31 pages, 3606 KB

Open AccessArticle

Experimental Investigation of Energy Efficiency, SOC Estimation, and Real-Time Speed Control of a 2.2 kW BLDC Motor with Planetary Gearbox Under Variable Load Conditions

by Ayman Ibrahim Abouseda, Reşat Doruk, Ali Emin and Jose Manuel Lopez-Guede

Energies 2026, 19(1), 36; https://doi.org/10.3390/en19010036 (registering DOI) - 21 Dec 2025

Abstract

This study presents a comprehensive experimental investigation of a 2.2 kW brushless DC (BLDC) motor integrated with a three-shaft planetary gearbox, focusing on overall energy efficiency, battery state of charge (SOC) estimation, and real-time speed control under variable load conditions. In the first stage, the gearbox transmission ratio was experimentally verified to establish the kinematic relationship between the BLDC motor and the eddy current dynamometer shafts. In the second stage, the motor was operated in open loop mode at fixed reference speeds while variable load torques ranging from 1 to 7 N.m were applied using an AVL dynamometer. Electrical voltage, current, and rotational speed were measured in real time through precision transducers and a data acquisition interface, enabling computation of overall efficiency and SOC via the Coulomb counting method. The open loop results demonstrated that maximum efficiency occurred in the intermediate-to-high-speed region (2000 to 2800 rpm) and at higher load torques (5 to 7 N.m) while locking the third gearbox shaft produced negligible parasitic losses. In the third stage, a proportional–integral–derivative (PID) controller was implemented in closed loop configuration to regulate motor speed under the same variable load scenarios. The closed loop operation improved the overall efficiency by approximately 8–20 percentage points within the effective operating range of 1600–2500 rpm, reduced speed droop, and ensured precise tracking with minimal overshoot and steady-state error. The proposed methodology provides an integrated experimental framework for evaluating the dynamic performance, energy efficiency, and battery utilization of BLDC motor planetary gearbox systems, offering valuable insights for electric vehicle and hybrid electric vehicle (HEV) drive applications. Full article

20 pages, 3294 KB

Open AccessArticle

Voltage Rise Mitigation in 0.4 kV Power Distribution Lines Powered by Multiple Residential Photovoltaic Solar Plants

by Gytis Petrauskas and Gytis Svinkunas

Energies 2026, 19(1), 35; https://doi.org/10.3390/en19010035 (registering DOI) - 20 Dec 2025

Abstract

This research addresses the challenge of a voltage increase on 0.4 kV power distribution lines with high photovoltaic solar plants penetration. Connecting a large number of photovoltaic solar plants to power distribution lines causes a significant voltage rise, which leads to the automatic shutdown of these plants in emergency mode. This seriously hinders the use of solar energy. PV solar plant inverters use reactive power generation or consumption technology to regulate the voltage level, but it is effective only when the line X/R ratio is sufficient. This publication proposes an innovative and easily implemented method for voltage rise mitigation, based on the connection of additional inductive chokes to the power distribution line. The method will expand and fully implement existing voltage regulation measures in already installed PV solar plant inverters, which other methods do not. A methodology for selecting the additional inductance value is presented. Reactive power control methods are compared, and recommendations for the use of methods are presented. An equation is proposed for calculating the coupling point voltage of a PV solar plant inverter using the Q = f(U) diagram. The effectiveness of this method is based on mathematical calculations, simulation, and experimental verification, using typical Lithuanian power distribution lines configurations. Full article

(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)

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33 pages, 3160 KB

Open AccessArticle

A Unified Optimization Approach for Heat Transfer Systems Using the BxR and MO-BxR Algorithms

by Ravipudi Venkata Rao, Jan Taler, Dawid Taler and Jaya Lakshmi

Energies 2026, 19(1), 34; https://doi.org/10.3390/en19010034 (registering DOI) - 20 Dec 2025

Abstract

In this work, three novel optimization algorithms—collectively referred to as the BxR algorithms—and their multi-objective versions, referred to as the MO-BxR algorithms, are applied to diverse heat transfer systems. Five representative case studies are presented: two single-objective problems involving a heat exchanger network and a jet-plate solar air heater; a two-objective optimization of Y-type fins in phase-change thermal energy storage units; and two three-objective problems involving TPMS–fin three-fluid heat exchangers and Tesla-valve evaporative cold plates for LiFePO₄ battery modules. The proposed algorithms are compared with leading evolutionary optimizers, including IUDE, εMAgES, iL-SHADEε, COLSHADE, and EnMODE, as well as NSGA-II, NSGA-III, and NSWOA. The results demonstrated improved convergence characteristics, better Pareto front diversity, and reduced computational burden. A decision-making framework is also incorporated to identify balanced, practically feasible, and engineering-preferred solutions from the Pareto sets. Overall, the results demonstrated that the BxR and MO-BxR algorithms are capable of effectively handling diverse thermal system designs and enhancing heat transfer performance. Full article

(This article belongs to the Section J1: Heat and Mass Transfer)

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24 pages, 13810 KB

Open AccessArticle

Multi-Channel Gliding Arc Plasma Cracking of RP-3 Kerosene Spray

by Shengfang Huang, Yun Wu, Shunhua Yang, Di Jin and Yinghong Li

Energies 2026, 19(1), 33; https://doi.org/10.3390/en19010033 (registering DOI) - 20 Dec 2025

Abstract

To address the issues of narrow ignition limits and low combustion efficiency in ramjet combustors under low-temperature and low-pressure conditions, caused by low reactivity of liquid fuel and slow chemical reaction rates, a multi-channel gliding arc plasma excitation activation method for fuel–air mixtures is proposed. This method generates gaseous small molecules and highly active radicals. Focusing on the vaporizing flame holder of a subsonic ramjet combustor, this study investigates the fuel–air activation characteristics under different carrier gas flow rates, fuel flow rates, and numbers of discharge channels. The mechanism by which multi-channel gliding arc discharge plasma enhances fuel–air activation, ignition, and combustion performance is revealed. Full article

(This article belongs to the Special Issue Fuel and Energy: Unlocking the Potential of Combustion Research Techniques)

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22 pages, 5978 KB

Open AccessArticle

Robust Control of an Electrical Drive with a Flexible Joint Using PI Controllers Based on Torsional Torque Derivative Feedback

by Amanuel Haftu Kahsay, Piotr Stanisław Derugo, Kosuke Shikata, Seiichiro Katsura and Krzysztof Szabat

Energies 2026, 19(1), 32; https://doi.org/10.3390/en19010032 (registering DOI) - 20 Dec 2025

Abstract

Electric drives are responsible for torque generation in most industrial processes and devices, including electromobility. The operational performance of industrial drives is often limited by torsional vibrations and time-varying load inertia. To address these challenges, this paper proposes and compares three robust control structures based on PI controllers augmented with additional feedback from the first and second derivatives of torsional torque. A higher-order integral disturbance observer (IDO) is employed to estimate the unmeasurable derivatives of torsional torque, enabling accurate compensation. Furthermore, a bio-inspired optimization algorithm (Britch-inspired Optimization Algorithm, BiOA) is utilized to determine the optimal gain coefficients for the PI controller and the additional feedback loops. The proposed methodologies are validated through simulations and experimental testing on a physical setup, demonstrating improved reference-trajectory tracking and enhanced torsional vibration suppression under varying load conditions. Full article

(This article belongs to the Special Issue Drive System and Control Strategy of Electric Vehicle)

12 pages, 3507 KB

Open AccessArticle

Characteristics and Impact of Fouling from Copper Production on the Operation of a Waste Heat Recovery Boiler

by Roksana Urbaniak, Beata Hadała and Marcin Kacperski

Energies 2026, 19(1), 31; https://doi.org/10.3390/en19010031 (registering DOI) - 20 Dec 2025

Abstract

The paper focuses on the characteristics of fouling from copper production on the tube surface in a waste heat recovery boiler during the transfer of heat from the flash furnace process gas. The likely mechanism of deposit formation on the tubes is described, and the morphology and chemical composition of the bound deposit taken from the radiation zone of the waste heat recovery boiler are reviewed. In addition, the impact of the presence of bound and loose deposits on the tube’s surface on the increase in the deposit surface temperature and the decrease in the heat transferred at the inner side of the tube is evaluated. Changes in the chemical, mineralogical, and phase constitutions along the thickness of the build-up were established on the basis of XRF, SEM, and XRD quantitative analyses. The heat exchanger tube temperature distribution was computed with the finite element method using an axi-symmetrical solution of the heat conductivity equation. Computing was carried out for a clean tube surface as well as for a case with loose and bound deposits present on the surface, with thicknesses of 0.5 cm, 1 cm, and 2 cm. The boundary conditions at the deposit side varied. For loose deposits with a thickness of 0.5 cm, the decline in the heat transferred was similar to the values obtained for a bound deposit with a thickness of 2 cm. It was established that, for a deposit with a thickness of 20 mm, there was an approximately 80% decline in the energy transferred by the walls compared to the clean tube surface. This study represents a novel approach by integrating mineralogical and phase analyses with finite element modelling to comprehensively assess the impact of both bound and loose deposits on heat transfer efficiency in waste heat recovery boilers from copper production. Full article

(This article belongs to the Special Issue Advanced Bioenergy, Biomass and Waste Conversion Technologies: 2nd Edition)

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18 pages, 2031 KB

Open AccessArticle

A Missing Data Imputation Method for Distribution Network Data Based on TGAN-GP

by Li Huang, Meng Wang, Lingyun Wang and Jinglin Cao

Energies 2026, 19(1), 30; https://doi.org/10.3390/en19010030 (registering DOI) - 20 Dec 2025

Abstract

Distribution network data may encounter random missing data caused by abnormal conditions and continuous missing data caused by natural disasters during gathering, transmission, and conversion. To address these problems, this paper proposes a missing data imputation method based on the Temporal Generative Adversarial Network with Gradient Penalty (TGAN-GP), which takes the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) as its basic framework, replaces traditional fully connected layers with a Temporal Convolutional Network (TCN) in the generator’s core, leverages causal dilated convolution to efficiently capture the long-range temporal dependencies and periodicity of measurement data, and integrates residual connections to mitigate gradient vanishing and network degradation during deep training. For the discriminator, the method adopts a Long Short-Term Memory (LSTM) network, which enhances the evaluation of the temporal rationality of generated data and thereby further improves imputation accuracy. Finally, simulations were conducted on the IEEE 33-bus distribution network test system. Results show that under the random missing scenario (10% missing rate), the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) of node voltage magnitude imputation are as low as 0.00062 and 0.00051, those of node injected active power imputation are 0.00081 and 0.00065, and those of node injected reactive power imputation are 0.00082 and 0.00076. Under the continuous missing scenario, the RMSE and MAE of node voltage magnitude imputation are 0.00147 and 0.00122, those of node injected active power imputation are 0.00373 and 0.00268, and those of node injected reactive power imputation are 0.00314 and 0.00226. The imputation errors of all three data types are significantly lower than the comparison methods’. Full article

(This article belongs to the Special Issue Approaches and Experiences of Monitoring and Control of Electrical Distribution Grids)

24 pages, 3158 KB

Open AccessArticle

Ultra-Short-Term Multi-Step Photovoltaic Power Forecasting Based on Similarity-Based Daily Clustering

by Yongcheng Jin, Zhichao Sun, Dongliang Lv, Weicheng Gao, Fengze Liu and Qinghua Yu

Energies 2026, 19(1), 29; https://doi.org/10.3390/en19010029 (registering DOI) - 20 Dec 2025

Abstract

Photovoltaic (PV) power generation is inherently intermittent and volatile, complicating power system operation and control. Accurate forecasting is crucial for proactive grid responses and optimal energy resource scheduling. This study proposes a novel hybrid forecasting model that achieves high-precision PV power forecasting by integrating similar-day clustering, generating extreme weather samples, and optimizing the Bidirectional Temporal Convolutional Network (BiTCN) and Bidirectional Gated Recurrent Unit (BiGRU) model via the Animated Oat Optimization (AOO) algorithm. The proposed method outperforms other models in the three evaluation metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The innovations lie in the integration of similar-day clustering with deep learning and the application of AOO for hyperparameter optimization, which significantly enhances forecasting accuracy and robustness. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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15 pages, 3189 KB

Open AccessArticle

Multi-Layer Nonlinear Extended Kalman Filters for Two-Mass Drive System Parameters Identification

by Karol Wróbel, Kacper Krzysztof Śleszycki and Piotr Majdański

Energies 2026, 19(1), 28; https://doi.org/10.3390/en19010028 (registering DOI) - 20 Dec 2025

Abstract

In this paper, the concept of an estimator of the parameters of the two-mass drive system is presented. Significant changes in parameters, such as the load-side time constant, can lead to large estimation errors and compromised closed-loop performance in drives with an elastic shaft. The proposed multi-layer estimators use multiple internal nonlinear extended Kalman filters (EKF) to enhance the accuracy of state estimation. After a theoretical introduction, different structures were studied with simulation tests conducted in MATLAB/Simulink (2010a) and the effect of the number of filters was presented. The results show that the use of multi-layer observers can improve state estimation and accelerate online identification compared to classical methods. The results are confirmed by experimental validation carried out using dSPACE 1103. Full article

(This article belongs to the Special Issue Drive System and Control Strategy of Electric Vehicle)

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25 pages, 21291 KB

Open AccessArticle

Lithium-Ion Battery Open-Circuit Voltage Analysis for Extreme Temperature Applications

by Nick Nguyen and Balakumar Balasingam

Energies 2026, 19(1), 27; https://doi.org/10.3390/en19010027 (registering DOI) - 20 Dec 2025

Abstract

Accurate estimation of the open-circuit voltage (OCV) as a function of state of charge (SOC) is critical for reliable battery-management system (BMS) design in lithium-ion battery applications. However, at low temperatures, polarization effects distort the measured OCV–SOC profile due to premature voltage cutoffs during low-rate testing. This paper presents an offsetting-based correction method that reconstructs the truncated portions of the OCV curve by extrapolating the charge/discharge data beyond the cutoff points using simple voltage offsets. The approach is applied entirely in post-processing, requiring no modification to standard test protocols. Experimental validation using Samsung EB575152 Li-ion cells across a wide temperature range (−25 °C to 50 °C) demonstrates that the method restores the full OCV span, reduces apparent capacity loss, and improves consistency across cells and temperatures. The proposed technique offers a practical and effective enhancement to standard OCV testing procedures for temperature-aware SOC modeling. Full article

(This article belongs to the Section E: Electric Vehicles)

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