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• Proposed a novel advanced solar-driven combined heat and power system. • Constructed the overall heat current model and exergy analysis expression. • Analyzed the exergy loss and efficiencies of each component and the system. • Analyzed the influences of various parameters on the system exergy efficiency. Conducting research on advanced cycle of distributed energy system and improving energy utilization efficiency is an important approach to promote the development of distributed energy systems. The paper proposed a novel solar-driven supercritical carbon dioxide-heat pump cogeneration system based on solar power tower system and performed the overall modeling of the system using the heat current method. The overall exergy loss calculation model of the system was established by applying the exergy analysis method incorporating the standard thermal resistance-based entropy generation rate expression. The calculation results present that under rated condition, irreversible loss mainly occurs in the solar energy conversion part, accounting for 73.83 %, and the system exergy efficiency is 34.69 %. Besides, the paper investigated the steady state performance of the system under different working conditions and the influence of the turbine inlet temperature and the intermediate pressure on the system performance. When the direct normal irradiance increases, the proportion of exergy loss in solar energy conversion part increases by 62.13 %, while the change of system exergy efficiency depends on the operating state of the system. The optimum intermediate pressure is 11.72 kPa, when the net power output and the exergy efficiency of the whole system reaches the maximum. Finally, the proposed system contributes to the improvement of cascade utilization of energy, and the modeling method is effective for solar-driven supercritical carbon dioxide cogeneration systems. [ABSTRACT FROM AUTHOR]

• The thermal, rheological, and optical properties of ferrofluids and how they change with magnetic fields were discussed. • The utilization of ferrofluids in solar energy systems was detailed in a state-of-the-art analysis. • The recommendations aimed to optimize the use of ferrofluids and magnetic fields in energy systems to enhance performance. Solar energy technologies play a crucial role in the transition to sustainable energy sources; however, there is an ongoing challenge in optimizing their efficiency and performance. This review paper thoroughly exposes the potential for enhancing solar systems by incorporating ferrofluids and utilizing magnetic fields. It provides a comprehensive examination of the rheological, thermal, and optical properties of ferrofluids, as well as their behavior when exposed to magnetic fields. The paper emphasizes the ability of ferrofluids to improve heat transfer efficiency, adapt to changing conditions, and enable precise control in the conversion of solar energy. Furthermore, the review addresses the obstacles and limitations associated with integrating ferrofluids into solar technologies, proposing potential solutions and outlining future research directions. [ABSTRACT FROM AUTHOR]

In their quest to enhance energy efficiency and carbon footprint management, many enterprises are turning their attention towards optimizing their facilities through amplified data monitoring and innovative control methods. In this context, this research highlights a practical solution in a commercial building located in the United States. It explains the creation, implementation, and costs of a universally applicable model predictive control (MPC) framework for the building's heating, ventilation, and air conditioning (HVAC) system. The paper elaborates on both the hardware and software used to accumulate pertinent data and the formulation of the MPC system. This approach leverages cloud-based microservices and can be seamlessly integrated into current building management systems. In this regard, this paper presents three innovative strategies: Proportional–integral (PI)+Preheating, MPC, and occupancy-based control. These scenarios were designed to improve energy efficiency and thermal comfort significantly by reducing temperature fluctuations and adhering more closely to the setpoint temperature. The implementation of these strategies resulted in substantial energy savings, with reductions in energy consumption of 12.83%, 19.21%, and 14.98%, respectively. • A novel cloud-based MPC scheme is proposed for controlling the educational building HVAC system. • Real-world case installation details are provided with a focus on cost and sensor information. • A comprehensive discussion on thermal modeling, occupants' comfort, and control performance. [ABSTRACT FROM AUTHOR]

[Display omitted] • Assessed concepts combine biogas- and electricity-based methanol production • Adding electricity-based hydrogen increases methanol production by 28 to 67% • Tri-reforming enables high biogas conversion for a total carbon utilization of 97% • Bi-reforming exceeds energy efficiency of tri-reforming by 5%pt. in overall concept • Methanol production costs are estimated at 1,300 € 2022 /t The limited potential of sustainably available biomass requires efficient conversion to defossilize future demands of carbon-based chemicals and energy carriers. Therefore, integrating electricity-derived hydrogen into biomass-based production concepts appears promising, maximizing carbon utilization while minimizing biomass requirement. However, specific process concepts and analyses are still lacking to quantify the potential technical and economic benefits of such hybrid production processes. Thus, this research paper investigates this novel approach for decentralized methanol production from biogas within a techno-economic analysis, considering two production configurations. One configuration (BiRef) utilizes electrically heated bi-reforming for syngas generation, while the other configuration (TriRef) uses autothermal tri-reforming. Based on stationary process simulations, in-depth knowledge about process behavior is gained, efficiencies and costs are determined, and influencing factors are assessed. Within the reference case, the BiRef configuration achieves a carbon efficiency of 93 %, while the TriRef configuration reaches 97 % due to its enhanced methane conversion. Energy efficiency is 5 %pt. higher within the BiRef configuration (74 %), primarily due to the lower hydrogen demand. Carbon and energy efficiency are crucially affected by reforming temperature and inert gas fraction in the syngas. The economic analysis shows methanol production costs of ca. 1,300 € 2022 /t for both configurations. Variations in economic parameters emphasize advantages for the TriRef configuration when low hydrogen costs (<6 € 2022 /kg) are achievable. The comparison with purely biogas-based methanol production shows that hydrogen addition increases production capacity by up to 67 %. Cost advantages result against purely electricity-based production, while cost parity is achieved with purely biogas-based production. This paper demonstrates the potential of hybrid methanol production for efficient biogas utilization and highlights decisive parameters influencing technical and economic efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • Sorting strategies based on source, physical and chemical properties were compared. • The gasification efficiency of sorted RMSW was 40.89% higher than that of unsorted. • The yield of syngas can be increased by 37.14% after waste sorting. • The emission of CO 2 can be reduced by 81 t·per year after waste sorting. • Elemental composition based sorting method benefited to efficient gasification. Gasification is a promising way to convert residual municipal solid waste (RMSW) to high-valued syngas. Waste sorting is helpful to alleviate the heterogeneity problem of waste on gasification. This paper aimed to search for an optimal sorting strategy and evaluate the influence of waste sorting on downstream gasification. A sorting strategy based on elemental composition was proposed and compared with common source and density based strategies. The results validated the enhancement of waste sorting on RMSW gasification, where the elemental composition based strategy showed optimal gasification. The yield of syngas, cold gas efficiency, the lower heating value of syngas and H 2 /CO were increased by 37.14 %, 38.95 %, 2.18 % and 8.81 %, respectively. It also showed a good effect on the improvement of air gasification compared with the literature, especially for H 2 /CO. This paper also discussed the economic and environmental potential of the elemental composition based sorting method. The waste sorting can help increase 29,250 RMB/year and reduce CO 2 emission by 81 t/year. This method has potential for industrial application. It is suggested that further fast elemental composition characterization technologies and intelligent sorting equipment can be developed. Limitations and future perspectives were discussed from gasification simulation models, more energy utilization technologies, the underlying logic of waste sorting and sustainability evaluation. It is hoped that this work can provide insightful fundamentals to their applications in RMSW sorting, thus benefiting resource recovery and circular economy. [ABSTRACT FROM AUTHOR]

The objective of this paper is to implement an automated system for the control of all energy resources and devices within smart buildings. The paper develops a Two-Layer Electric Water Heater Management (TL-EWHM) system tailored for efficient power distribution within a smart microgrid connected to both a photovoltaic (PV) system and the Electric Grid (EG). The TL-EWHM consists of two distinct layers: the Offline Optimization Layer (OFF-OL) and the Online Optimization Layer (ON-OL). Within the OFF-OL, an optimization algorithm known as Particle Swarm Optimization (PSO) is employed to determine the optimal power settings for Electric Water Heaters (EWH) based on forecasts of several factors including PV generation, ambient temperature (A T), water demand (W D), and the consumption of fixed appliances (F A C) for the upcoming 24 h. These parameters are forecasted using a novel prediction algorithm referred to as regressor stacking, which is developed based on actual data. Within the ON-OL, real-time data is effectively utilized, and an Extremum-Seeking (ES) Controller is employed to continuously adapt the EWH power settings in response to uncertainties in the predictions. Furthermore, a strategic approach is being devised to generate the water temperature setpoints (W T S) by considering dynamic data factors like P V power, total energy consumption (P L), and the water demand profile to ensure that user comfort is maintained. A comprehensive comparative analysis is carried out to evaluate the effectiveness of our strategy approach. This strategy is characterized by the inclusion of a Water Temperature Setpoint Generator (W T S G) and the integration of a two-layer optimization methodology that combines the OFF-OL and the ON-OL. In this comparative study, our strategy is assessed in comparison to other conventional approaches, with the objective of highlighting the distinctive advantages of our method in terms of cost reduction and the enhancement of user comfort. On the one hand, comparisons are made with strategies that give priority to offline optimization. On the other hand, the strategy that maintains a constant water temperature setpoint throughout the day. The results of this paper establish the effectiveness of the proposed strategy. In particular, the energy cost savings associated with the two-layer optimization approach is 13.73%. Even more noteworthy is the significant reduction in energy costs, which reaches an impressive 18.63% when the variation of water temperature setpoints is taken into account. These results underscore the significant benefits of using a two-stage optimization strategy over relying solely on offline optimization. They also underscore the value of incorporating a WTSG as opposed to maintaining WTSC. • Real-time optimization of a electric water heater (EWH) under model uncertainties. • The two-layer management approach enhances robustness to operating conditions. • This paper proposes a new strategy for variable water temperature setpoints. • The objective is to minimize energy costs while maximizing occupant comfort. • MLP stacking reduces overfitting, boosts flexibility by diverse dataset training. [ABSTRACT FROM AUTHOR]

• A new DPV-5G BS-ES communication and power mutual coupling system is developed. • A BSSCP model is proposed to minimize the number of 5G base station deployments. • The ISOM model is used to optimize the microgrid connection lines in 3D space. • Economically optimal simulation experiments are conducted for different PV systems. The emergence of ultra-dense 5G networks and a large number of connected devices will bring with them significant increases in energy consumption, operating costs, and CO 2 emissions. At the same time, the deployment of distributed photovoltaic (DPV) in megacities plays an important role in promoting the integration of "building-photovoltaic", adjusting the city's clean energy supply system, and building a green, and low-carbon smart city. The development of a new "DPV-5G Base Station-Energy Storage (DPV-5G BS-ES)" coupled DC microgrid system and its pre-deployment investment costs are fundamental factors to be considered when the problem of large-scale DPV and BS deployment in cities has to be addressed. There are almost no published papers on this issue in the literature, which only consider the problem of maximizing the deployment of 5G BS service coverage or only perform optimization analyses for the performance of microgrid systems without considering the economic optimality and low-carbon green issues in the construction of urban utilities. In order to solve the problem of 5G BS deployment and economic optimization of topology in the "DPV-5G BS-ES" coupled DC microgrid system, a novel deployment strategy is proposed in this paper. The strategy is divided into: (a) The Geographic Information System (GIS) for extracting and analyzing 3D data from all buildings in the area. (b) A GIS-based formulation of a mixed integer quadratic constrained planning model (BSSCP) under discrete fixed radius coverage constraints is used to delineate building clusters within the area and determine the optimal deployment location of 5G BS under each sub-cluster. (c) An Improved Self-Organizing Mapping (ISOM) model combines GIS and microgrid performance parameters to address the economically optimal connection of grid lines for DPV in 3D space. (d) Simulation experiments were conducted for the power output and profitability of eight different PV panels and tracking systems to calculate the economically optimal type of DPV generation hardware. The experimental results show that the BSSCP model proposed in this study reduced the 5G BS deployment cost input by up to 35.8974 % compared to that of the control experimental model. Meanwhile, the ISOM model obtained an average reduction of 52.3741 % in the cost of connecting cables for the optimized 25 DPV planning and DC microgrid topology. [ABSTRACT FROM AUTHOR]

• Linear planning models of two types of compressed air energy storage are proposed. • A novel electricity-gas coupled energy hub for cascade utilization is structured. • Comparisons between energy-based and capacity-based energy storage are analyzed. Large-scale long-duration energy storage (LDES), like compressed air energy storage (CAES) and liquid air energy storage (LAES), is promising for high-penetration renewable energy consumption in the city-scale integrated energy system (IES). Due to high installation costs and challenges in decentralized control of distributed energy storage, this paper proposes an optimal planning model for an urban electricity-gas coordination hub (UEGCH) with centralized energy storage using large-scale LDES. Firstly, the UEGCH with energy cascade utilization is designed, where process-based linearized models of advanced adiabatic compressed air energy storage (AACAES) and LAES are established to make a trade-off between solving accuracy and speed. Then, a city-scale integrated electricity and gas system (IEGS) is constructed with multiple regions considering line-packing. Finally, a multi-scenario planning method is proposed based on X-means for scenario reduction, and the global optimum is obtained through linearization. Results show that compared with traditional gas storage tank (GST) in the UEGCH, the utilization of AACAES and LAES requires as little as 7 % and 0.7 % of the construction volume with lower carbon emissions and can be cost-competitive in terms of future advancement. As for different EESs, in contrast to the energy-based energy storage (EBES) (i.e., battery (BT)), the levelized cost of energy (LCOE) of the capacity-based energy storage (CBES), which includes AACAES and LAES, can be reduced by nearly 0.1 $/kWh, bringing an annual economic benefit of $ 3 million. Besides, the discharge duration of CBES can be increased to 13 h, demonstrating the application prospect in 100 % renewable consumption and islanding operation. Compared with AACAES, LAES can reach 11.67 times the energy density, with a drop of 6.11 % in operating cost. The abilities of EBES and CBES to locally consume renewables are respectively weaker and stronger than that of power-to-gas (P2G) combined with line-packing. Meanwhile, this paper further analyzes the impacts of temperature, discharge duration, renewable penetration, energy tariff, and demand on the operation and sizing of centralized energy storage. [ABSTRACT FROM AUTHOR]

• A hierarchical convex optimization framework for HEV-PCC is proposed. • A TSI-MPC approach is designed to address the speed planning problem. • An energy management strategy that combines regression model with PMP is designed. Predictive Cruise Control (PCC) achieves automated driving with improved vehicle economy by incorporating road gradient information from high-precision maps. However, existing research on PCC has primarily centered around conventional gasoline vehicles. Hybrid Electric Vehicle (HEV) offer superior fuel efficiency compared to their traditional counterparts. To further enhance vehicle economy by integrating the PCC system with HEVs, this paper proposes a hierarchical control architecture for HEV-PCC based on convex optimization. Firstly, considering spatial slope information from the road ahead, this paper introduces a Time-Space Integration Model Predictive Control (TSI-MPC) based on the principles of kinetic energy. Secondly, the paper combines a current regression model with the Pontryagin Minimum Principle (PMP) to present a Regression Analysis - Pontryagin Minimum Principle (RA-PMP) approach for the direct analytical solution of the energy management problem. Finally, the strategy is validated through simulation analysis and Hardware-in-Loop (HIL) testing on various real road scenarios. The result indicate that, in comparison to the Equivalent Consumption Minimization Strategy (ECMS) with constant speed cruising (CC), this method achieves an average fuel economy improvement of 9.45%. [ABSTRACT FROM AUTHOR]

• TIP-EMS enhances EV energy efficiency by integrated prediction and energy management. • Significant driving economy boost; 1.6%-4.2% better than state-of-the-art PEMS. • TIP-EMS shows robust performance with varying prediction weight adjustments. To enhance the energy efficiency of electrified vehicles (EVs), developing effective energy management strategies (EMS) for hybrid storage systems is essential. Predictive EMS (PEMS) that foresee future vehicle speeds have demonstrated substantial potential in boosting EMS performance. However, traditional PEMS models, employing a sequential approach of speed prediction followed by energy allocation, are hampered by cumulative errors. These errors from the initial speed predss this issue, this paper introduces a novel solution: the trainable integrated preiction negatively impact the efficiency of subsequent energy management. To addrediction and energy management strategy (TIP-EMS). Contrasting with conventional sequential PEMS, TIP-EMS features a dual-branch, integrated neural network, which is fully trainable. This network processes driving status inputs via attention layers, with one branch dedicated to energy management objectives using a reinforcement learning (RL) algorithm, and the other to vehicle speed prediction. Both branches are trained simultaneously, but post-training, only the RL branch is activated for energy management. Implemented with a soft actor-critic RL algorithm, TIP-EMS is applied to a fuel cell EV for optimized energy management. The validation involved training TIP-EMS using 27 driving profiles, which developed its prediction and energy management capabilities, followed by tests in untrained scenarios. The results show that TIP-EMS surpasses conventional sequential PEMS by up to 4.2% in scenarios where prediction accuracies are comparable, highlighting the efficacy of the trainable integrated mechanism. In addition, TIP-EMS exhibits superior energy conservation compared to non-predictive RL strategies. Lastly, TIP-EMS exhibits robustness to adjustments in the weight given to the prediction objective, further confirming its practical applicability. [ABSTRACT FROM AUTHOR]

• Effects of structural factors on the new compressor performance are analysed. • Equivalent stroke diameter ratio is the key factor for power consumption. • Diameter ratio of pistons is the dominating factor for the mass delivered. • The optimal parameters for low specific energy consumption are obtained. • The specific energy consumption is 2328.49 kJ/kg under the optimal parameters. The ionic compressor has a prospective application potential for the conversion of mechanical energy into internal energy in high-pressure hydrogen as the ionic liquid can separate the metal piston and the hydrogen gas. The sloshing of the liquid inside is inevitable in such a liquid piston compressor, which has an impact on the specific energy consumption of the hydrogen compression. The thermofluid behaviour of this liquid sloshing can be effectively influenced by the baffle structure and piston trajectory. A novel concentric two-piston compressor is proposed in this paper, which has a unique structure that can act as a baffle inside and actively influences the fluid flow by adjusting the piston trajectory. The quantitative influence of key structural design factors on the compressor performance is investigated by mathematical modelling, based on which the optimal structural design was obtained by Taguchi and analysis of variance methods. Results indicate that the diameter ratio of Piston A to Piston B is the dominating factor for both the mass delivered and the specific energy consumption. With the optimal design in the structure, the specific energy consumption is found as 2328.49 kJ/kg, where the mass delivered and power consumption are observed as 21.42 kg/h and 13.86 kW, respectively. [ABSTRACT FROM AUTHOR]

• A Rankine-based pumped thermal energy storage concept with coordination design. • Evaluation on the potential of low-temperature heat integration is considered. • A systematic investigation into the proposed concept is conducted. • A round-trip efficiency of over 72 % is achieved under a 100 K temperature span. • Strong mutual promotion of the thermal integration is observed. Rankine-based pumped thermal energy storage (PTES) is a potential electricity storage technology for accelerating the integration of renewables. This paper provides a novel Rankine-based PTES concept based on cascade-charging, dual-expansion, and hybrid thermal storage, which enables large temperature span and the cooperative thermal matching of the heat pump, thermal storage, and heat engine. A system layout and the corresponding theoretical model for energy and exergy analysis are developed. The integration of low-temperature heat is considered, and multiple criteria are defined to evaluate the thermal energy utilization quantitatively. Afterward, a systematic investigation into its performance is conducted. The influence of key parameters is analyzed with a multiobjective optimization to unveil the trade-off relations between different performance indicators. Beyond that, the exergy loss distribution of the presented concept is revealed, and a comparison with the traditional concept is also performed. Results show the concept has promising comprehensive performance. A round-trip efficiency of over 72 % is achieved under a 100 K temperature span with the exergy efficiency of heat engine reaching about 70 %. Moreover, the strong mutual promotion of thermal integration is observed. The concept reported here can offer a meaningful reference for the coordination design of the Rankine-based PTES. [ABSTRACT FROM AUTHOR]

[Display omitted] • Solar-hydrogen hybrid system integration proposed for oil & gas industry. • Techno-economic-environmental optimization using novel Fick's Law Algorithm. • System sizing using Fick's Law Algorithm show an optimization in efficiency and cost. • Study shows 45% solar, 35% fuel cell, 20% gas turbine power contribution. • Renewable energy integration in oil & gas cuts CO2 emissions, boosts sustainability. The global demand for sustainable energy solutions in the oil and gas industry has stimulated interest in the integration of renewable energy sources. This paper investigates the techno-economic and environmental aspects of incorporating a solar-hydrogen-based hybrid renewable energy system (SHRES) into oil and gas processing facilities. The proposed SHRES, comprising Solar PV modules, hydrogen production and storage systems, and a gas turbine generator (PV-FC-EL-Tank-GT), aims to enhance energy efficiency and reduce the carbon footprint of energy-intensive operations. The study utilises a multidisciplinary approach, encompassing engineering, economics, and environmental analysis. The techno-economic evaluation employs detailed modelling and optimisation using Fick's Law Algorithm, ensuring efficient energy production and cost-effectiveness. Ecological assessments quantify potential reductions in greenhouse gas emissions and other environmental impacts. The technical and economic evaluation identifies the electrolyser system as the most significant cost contributor (57%). The optimised system balances energy sources with 45% from PV modules, 35% from fuel cells, and 20% from gas turbines, showcasing substantial reliance on renewable energy while maintaining required power generation. Addressing the urgent need for sustainable energy solutions in the oil and gas industry, the study highlights the potential of SHRES integration. By employing Fick's Law Algorithm, alongside with 80 % of renewable energy integration, the research optimises system components to optimise the annualised cost to be $57,539.85, decrease to zero the Loss of Power Supply Probability (LPSP), and mitigate CO2 emissions to a lowest value of 0.147 gCO2eq/kWh (by 80 %). The electrolyser system's pivotal role and the distribution of power generation sources are emphasised. This study presents a novel approach to integrating solar-hydrogen systems into oil and gas processing facilities, aiming for sustainability. The findings underscore the significance of the electrolyser system and the distribution of power generation sources in optimising the SHRES. Recommendations for renewable energy integration and potential reductions in CO2 emissions and costs are discussed, providing valuable insights for future energy policies. [ABSTRACT FROM AUTHOR]

• Develop an energy prediction framework - Vehicle Energy Conservation Equation (VECE) • VECE coefficients were correlated with vehicle type, speed, and acceleration. • VECE demonstrated low data classification requirements and strong data adaptability. • VECE achieved a minimum prediction error of 4.2% for cumulative vehicle energy. • VECE exhibited excellent usability, coefficient interpretability and extensibility. Predictive models for vehicle energy consumption are crucial for sustainable development in urban road traffic systems. This paper comprehensively reviews classic predictive models and develops a novel statistical-dynamical energy consumption prediction framework called Vehicle Energy Conservation Equation (VECE). VECE is constructed based on the principles of vehicle energy flow and regression analysis, employing a continuous and concise mathematical formulation. Its coefficients possess clear physical interpretations, allowing for application in various vehicle categories. To validate VECE, this study collected energy consumption data from 28 vehicles, including 9 diesel vehicles, 16 gasoline vehicles, 1 ethanol gasoline vehicle, and 2 battery electric vehicles. A rigorous data processing procedure was designed. Data analysis revealed that the VECE coefficients are correlated with vehicle type, speed, and acceleration. VECE's predictive performance is minimally impacted by the number of vehicle categories, effectively modeling energy consumption for vehicles of the same fuel type or size category. Comparative analysis with E-EcoGest, VT-Micro, PERE, and CMEM demonstrates the moderate accuracy of VECE in predicting instantaneous vehicle energy consumption while excelling in predicting cumulative energy consumption. The minimum relative percentage error for the cumulative predicted values is 4.2%. Overall, VECE demonstrates outstanding performance in computational simplicity, coefficient interpretability, adaptability, and extensibility, making it a crucial tool for achieving energy-efficient road transport systems. [ABSTRACT FROM AUTHOR]

• "Transition to sustainable multi-building energy systems can reduce greenhouse gas emissions by synergising the multiple renewable energy sources with specific structural characteristics and occupancy constraints of buildings." • "To ensuring energy flexibility and resilience, optimization strategies for multi-building energy systems vary significantly with the climatic context and potentials for integrating energy storage technologies, highlighting the necessity for adaptable designs considering future energy demands and the expansion of district-level systems." • "There is a pressing need for more user-friendly multi-building energy system simulation tools to facilitate effective long-term planning and to optimize the design and management of energy flows among multiple buildings, ensuring maximum efficiency and sustainability. The transition to clean and sustainable energy systems in the building sector is vital to reduce global greenhouse gas emissions. In this regard, shifting the focus from single buildings to clusters of buildings, such as at the district level, can provide more effective sustainable solutions. In this study, the term Multi Building Energy Systems (MBES) was introduced to represent various scales, from local communities to districts and larger urban areas. This approach reflects a more inclusive understanding of sustainability potentials for large-scale building blocks that goes beyond traditional boundaries. Such systems encompass the aggregated energy impacts of multiple buildings whether physically proximate or connected via smart grid technologies that enable two-way energy and information flows. This paper comprehensively reviewed case studies on MBES that have achieved zero-energy, nearly-zero-energy, and positive-energy performance. The literature review, facilitated by a combination of database searches and snowball search techniques, has yielded 67 relevant case studies. The findings revealed diverse pathways towards achieving optimal renewable energy production depending on the location and the climate. Integration of renewable energy sources and energy storage systems is pivotal in achieving economies of scale, reducing operational costs, and enhancing resilience and reliability of energy supply at the neighbourhood scale. Total energy use and emissions reduction could be achieved by considering the architectural features of the building stocks. The study highlighted the need for adaptive strategies in MBES implementation to address challenges such as power grid overloads and the complexities of spatial arrangement, to meet current demands, while ensuring long-term energy resilience and sustainability in evolving urban landscapes. The limited adoption of MBES simulation software suggests a need for accessible tailored software. Future research should focus on developing advanced personalised energy management and control strategies, while continuously assessing emerging technologies for improving MBES performance. [ABSTRACT FROM AUTHOR]

Competitive and changing frame conditions, such as the need for decarbonization of energy supply or the growing share of renewable electricity generation, are driving the need for flexibility in different systems, e.g., in power supply and industrial production. Flexibility is often included in mathematical optimization applications. However, integrating it in optimization may create conflicting understandings of flexibility due to the properties of relativity and goal orientation. Establishing a common understanding of flexibility in industrial systems among all stakeholders, and a general but adaptable formulation for inclusion in mathematical optimization could better exploit the potential for flexibility in the future and make a greater contribution to energy transition. In this paper, an overview of flexibility, the incentives for it, and its integration into the decision variables, constraints, and objectives of mathematical optimization is provided. This enables the authors to conceptualize industrial flexibility in optimization and provide the basis for the presented approach: a three-step mixed-integer linear programming optimization model for integrating flexibility in the evaluation of cost-optimal energy supply systems. The presented approach is generally suitable for various flexibility types but is applied in this study to assess energy-source flexibility and elaborate its cost implications in a use case from the industry. For the identified energy supply systems with full energy carrier flexibility, the total annual costs increased by 62 to 112% compared to the cost-optimal solution with all available energy carriers, of which a relevant share was due to the investment cost increase by a factor of up to four. The two main conclusions are that (i) the results of the presented use case reveal the order of magnitude in cost increase for energy-source flexible supply systems, and (ii) the presented approach should be further used and developed to evaluate different types of industrial flexibility e.g., analysis of cost increase for robust energy systems in a changing environment, a potential contribution of flexibility for energy markets or power grids, or the flexibility to allow production changes. [Display omitted] • Interfaces of flexibility incentives and optimization for industry presented. • Set up of adaptable optimization framework for different flexibility aspects. • Cost increase quantified for energy-source flexibility in industrial use case. [ABSTRACT FROM AUTHOR]

• Five propulsion systems and six energy carriers for regional trains are analyzed. • Dutch regional railway network with sixteen rail passenger services is considered. • Energy carrier production pathway is the main driver of energy use and emissions. • Battery-electric system running on green electricity offers net-zero emissions. • Hydrogen provides emissions reduction only if produced from green electricity. This paper presents a method for estimating Well-to-Wheel (WTW) energy use and greenhouse gas (GHG) emissions attributed to the advanced railway propulsion systems implemented in conjunction with different energy carriers and their production pathways. The analysis encompasses diesel-electric multiple unit vehicles converted to their hybrid-electric, plug-in hybrid-electric, fuel cell hybrid-electric or battery-electric counterparts, combined with biodiesel or hydrotreated vegetable oil (HVO) as the first and second generation biofuels, liquefied natural gas (LNG), hydrogen and/or electricity. The method is demonstrated using non-electrified regional railway network with heterogeneous vehicle fleet in the Netherlands as a case. Battery-electric system utilizing green electricity is identified as the only configuration leading to emission-free transport while offering the highest energy use reduction by 65–71% compared to the current diesel-powered hybrid-electric system. When using grey electricity based on the EU2030 production mix, these savings are reduced to about 27–39% in WTW energy use and around 68–73% in WTW GHG emissions. Significant reductions in overall energy use and emissions are obtained for the plug-in hybrid-electric concept when combining diesel, LNG, or waste cooking oil-based HVO with electricity. The remaining configurations that reduce energy use and GHG emissions are hybrid-electric systems running on LNG or HVO from waste cooking oil. The latter led to approximately 88% lower WTW emissions than the baseline for each vehicle type. When produced from natural gas or EU2030-mix-based electrolysis, hydrogen negatively affected both aspects, irrespective of the prime mover technology. However, when produced via green electricity, it offers a GHG reduction of approximately 90% for hybrid-electric and fuel cell hybrid-electric configurations, with a further reduction of up to 92–93% if combined with green electricity in plug-in hybrid-electric systems. The results indicate that HVO from waste cooking oil could be an effective and instantly implementable transition solution towards carbon–neutral regional trains, allowing for a smooth transition and development of supporting infrastructure required for more energy-efficient and environment-friendly technologies. [ABSTRACT FROM AUTHOR]

• A new energy balance model of outdoor swimming pools assesses their heating demand. • The proposed model was validated using the experimental outdoor data set. • Lowing the operating water temperature by 1 °C cuts energy consumption by about 11 % • Anti-evaporative cover usage reduces energy demand, with up to 30% savings. • Local wind conditions play a key role in outdoor pool energy consumption. In the Mediterranean region, outdoor swimming pools, despite having lower energy consumption compared to indoor pools, are nonetheless highly energy-intensive structures, offering significant opportunities for energy efficiency improvements. Although there are numerous studies in the literature, few of these works have been dedicated to estimating the energy consumption of outdoor pools while considering typical local meteorological conditions and their occupancy rates. This paper presents a novel energy balance model for outdoor pools that incorporates the latest correlations for calculating evaporation due to forced convection and sky temperature, a new phenomenological model for assessing the enhancement of evaporation as a function of occupancy rate, and an approach that takes into account atmospheric stability conditions in defining wind-related heat losses. The model successfully predicts the annual energy consumption data for outdoor Olympic swimming pools in Greece with a mean absolute percentage error of less than 12 %. Simulations of an outdoor swimming pool operating with water at 26.5 °C and in which the cover is not used present indicate a specific thermal energy consumption of about 2300 kWh/m2. A decrease in water temperature by 1 °C and the use of a cover result in approximately 11 % and 30 % reduction in consumption, respectively. [ABSTRACT FROM AUTHOR]

The simultaneous reduction of fuel consumption and pollutant emissions such as Nitrogen oxides (NO x) and soot remains the primary goal of modern engine development. In this context, Low Temperature Combustion (LTC) concepts are considered promising approaches. The so-called Reactivity Controlled Compression Ignition (RCCI) combustion process is one of these LTC concepts. RCCI combustion with two differently reactive fuels enables highly efficient combustion while reducing NO x and soot raw emissions compared to conventional combustion processes. RCCI combustion is significantly affected by the thermodynamical conditions in the combustion chamber (i.e., pressure and temperature), by the injection parameters (i.e., injection duration, timing, and offset), and by the reactivity of the fuels. It is very important to understand how these parameters affect the mixture formation in RCCI combustion and consequently the engine efficiency and emissions. This paper therefore focuses on the analysis of RCCI mixture formation and combustion. To this end, optical fuel injection investigations were performed in a High Pressure Chamber (HPC) under steady-state conditions to gain in-depth insights into the fuel spray propagation and mixture formation of the differently reactive fuels. Ethanol (low reactivity fuel) and diesel (high reactivity fuel) were used in the study. The results from the optical investigations showed that RCCI combustion is very sensitive to the chamber temperature and that a small increase in temperature change leads to a significant reduction in the Ignition Delay (ID) and results in early combustion. The reactivity gradients and concentrations are governed by the injection timing and duration of the low and high reactivity fuels, and also the offset between them. It was found that with increased chamber pressure, the Liquid Penetration Length (LPL) and the ID decrease. However, there is no impact on the Gaseous Penetration Length (GPL). The results from the optical HPC investigations were used to calibrate the spray model for the numerical simulations of RCCI combustion. The calibrated spray models agree well with the experimental data regarding LPL and GPL for ethanol and diesel fuel, indicating that they are well calibrated. • RCCI combustion in the High Pressure Chamber (HPC) was successfully achieved for diesel and ethanol fuel. • The optical studies showed that RCCI combustion is very sensitive to changes in the chamber temperature. • Variations in injection duration and injection offset have a significant effect on the reactivity of mixture inside the HPC. • The LPL and GPL data from the numerical 3D-CFD simulations matches well with the experimental data form HPC. [ABSTRACT FROM AUTHOR]

• It offers a hierarchical scheme solution for the optimal control of multi-zone HVAC systems. • A deep reinforcement learning-based method is proposed to optimize airflow demands. • A genetic algorithm-based method is proposed to accurately track multi-zone airflows. • It achieves energy saving and good IAQ under dynamic environmental changes. • It exhibits good generalization under different occupancy schemes and system topologies. Heating, ventilation, and air conditioning (HVAC) systems are designed to maintain a healthy indoor environment, where indoor air quality (IAQ) and energy use issues are of top concerns. This paper proposes a hierarchical optimal control method for multi-zone HVAC systems to improve IAQ while reducing fan energy consumption. The proposed hierarchical optimal control method consists of two levels. At the upper level, a virtual multi-zone HVAC environment is established and a soft actor-critic-based agent is trained under reinforcement learning framework to optimize the fan energy consumption while maintaining satisfactory IAQ in each zone. At the lower level, a "proportional balance + proportional recovery" strategy is devised to accurately track the terminal airflow via a hybrid search optimization method incorporating genetic algorithm and fmincon function. Compared with existing ventilation control methods, the proposed hierarchical optimal control method offers the following advantages: a) It can achieve good IAQ in multiple zones and low fan energy consumption by optimizing the demand airflow in response to the changes in real-time environment. b) It can accurately control the airflow with further energy saving, by optimizing the duct static pressure implicitly. c) Simulations demonstrate that the proposed method can achieve a maximum energy saving of 38.0 % and 43.6 % compared with two traditional methods, respectively. d) The proposed method exhibits good generalization ability under different occupancy scenarios and ventilation system topologies. [ABSTRACT FROM AUTHOR]

[Display omitted] • A station-network synergy planning framework for regional integrated energy system. • A two-stage stochastic programming model is used to address uncertainty optimization. • Exergy efficiency is incorporated into the multi-objective optimization model. • The effectiveness of the framework was validated through a case study. Regional integrated energy system (RIES) is conductive to integrating distributed renewable energy and represent a crucial form for constructing low-carbon energy systems in future cities. Addressing key issues in RIES planning such as station-network synergy optimization, uncertainty optimization, and multi-objective optimization, this paper proposes a synergy planning framework for RIES considering energy cascading utilization and uncertainty. Firstly, a dual-layer synergy planning model is established. In the upper layer, it resolves the station-network layout optimization problem considering user distribution and load complementarity. An improved P-median model is introduced to optimize the site selection and network layout of energy stations. In the lower layer, a two-stage stochastic programming model is proposed to address the configuration optimization problem for energy stations considering uncertainty. To enhance the energy utilization efficiency of RIES, exergy efficiency is incorporated into the multi-objective optimization model. A combination of ε - constrant methods and the TOPSIS method is employed for multi-objective solving and decision-making. The proposed models and methods are validated through a case study. The analysis of seven comparative experiments indicates that there is an optimal number of energy stations for the overall economic efficiency in RIES station-network layout optimization. Compared to single-objective optimization, multi-objective optimization considering both economic and exergy efficiency has advantages in improving energy utilization efficiency and reducing carbon emissions. When the load complementarity between multiple regions is higher, the interconnection gains between energy stations are greater. The research framework proposed in this study can provide essential references for designers of urban RIES. [ABSTRACT FROM AUTHOR]

• Combined cycle capable of offering three switchable energy supply modes was studied. • The set of the evaporator ensures the effective switching of different cycle modes. • Heat source in low temperature grade helps to desorb the work or basic solution. • Analysis was conducted from both thermodynamic and economic perspectives. • All cycle modes can maintain high comprehensive performance. The emergence of energy-related issues and the growing need for year-round energy supply has created a pressing demand for efficient cogeneration combined cycles. To this end, a new combined cycle using ammonia-water as the working fluid is proposed and studied from both thermodynamic and economic standpoints in this paper. The proposed cycle can effectively operate in power generation, power/refrigeration cogeneration and power/heating cogeneration modes. Parametric analysis and performance optimization of the proposed cycle are carried out, and the genetic algorithm method is employed for the optimization. Under the same initial conditions, the proposed cycle exhibits comprehensive power recovery efficiencies that are 58.11 %, 23.85 %, and 7 % relatively higher than those of the compared cogeneration cycles and Kalina cycle reported in the literature, respectively. The optimization results show that the maximum comprehensive power recovery efficiencies for the power/refrigeration, power/heating and power modes with heat source/cooling water temperature of 250℃/30℃, are 0.2271, 0.2658, and 0.1789, respectively. The associated power/refrigeration, power/heating, and power outputs are 221.1 kW/417.8 kW, 248.5 kW/650.9 kW, and 292.1 kW, respectively. Under these conditions, the overall annual unit cost of comprehensive power and the payback period of the cycle are 0.03947 $/kWh and 3.898 years. Results of the comprehensive analysis indicate that the proposed cycle can achieve satisfactory comprehensive performance for each mode, while also sufficiently recovering sensible heat source. [ABSTRACT FROM AUTHOR]

• A CCHP system based on solar energy and thermochemical energy storage is proposed. • Increasing the share of solar energy used in combined cooling, heating and power (CCHP) systems. • The 4E analysis and parameter study of the system are carried out. • The SPECO method is done and provides direction for the optimization of the system. • The effects of the main parameters on the system performance were evaluated. The combination of calcium looping and concentrating solar power (CSP) is a promising energy conversion technology that can greatly increase the share of solar energy used in combined cooling, heating and power (CCHP) systems. This paper designs a CCHP system based on solar energy and thermochemical energy storage. The system runs all day through day and night modes. Under basic working conditions, the energy and exergy efficiencies of the system could reach 56.92 % and 35.94 %, respectively. The system is evaluated by multiple approaches including parametric sensitivity analysis and 4E (energy, exergy, economy and environment)) analyses. The results demonstrate that the system could produce 2618.09 MW of electricity, 305.56 MW of heat and 523.88 MW of cooling capacity per day, respectively. At the same time, the reductions of CO 2 emissions and fossil fuel consumption could reach 2222.76 tons and 696.41 m3 per day, respectively. Furthermore, the increasing mass flow of main turbine inlet could increase both the system efficiency and the exergy efficiency. Thus, it is beneficial to reduce the environmental impact but with the increasing investment cost. In the meanwhile, the exergoeconomic factor and exergoenvironment factor of the system were 55.65 % and 0.139, respectively. Besides, as the extraction flow rate of the intermediate pressure turbine increases, the energy efficiency increases but the exergy efficiency descends. This is unfavorable to reducing the environmental impact, yet it could decrease the investment cost of the system. These results reveal that the hybrid system has the characteristics of high efficiency and emission reduction in energy storage, power generation, heating and cooling, and has great development potential. [ABSTRACT FROM AUTHOR]

• The study used CFD and machine learning to optimize TREC power device. • The trained network can forecast TREC device performance well. • The heat to electricity efficiency of TREC device can be up to 7.34%. Utilizing low grade heat and waste heat to generate electricity not only mitigates environmental impacts, meanwhile it enhances energy efficiency and lowers energy expenses. Thermal regenerative electrochemical cycles (TREC) can directly convert low grade heat to electricity, and it has a massive potential for low grade heat utilization. The type and design of a TREC system with practical and continuous power output are of paramount importance, and they can directly influence the performance and efficiency of low-grade heat recuperation systems. In this paper, a novel TREC system of a double layers rotating structure was proposed, and the novel optimal design approach employs CFD and machine learning for this system was presented. The results revealed that the height and number of TREC units had a significant impact on the device's performance. The heat to electricity efficiency of TREC device can be up to 7.34% (equivalent to 48.91% of the Carnot cycle efficiency) when the number of units was 12 and the height of unit was 5 mm. The results indicated that the MLP model was the most suitable, with RMSE values of 0.0077, 0.4415, and 0.5091 for heat to electricity efficiency, heat recuperation efficiency, and heat recuperation, respectively. This work established an effective approach for more rapid and precise estimation of the TREC device's performance metrics. The findings from prediction can serve as a practical guide for the design of TREC devices of various sizes. Furthermore, the method can envision the efficiency before the TREC device is manufactured in case of insufficient numerical calculations, reducing the computational and optimized costs. [ABSTRACT FROM AUTHOR]

• A combined cooling, heating, hydrogen and power multi-generation system is proposed; • Low-grade solar energy in long wavelength is converted to high-grade chemical energy; • Up to 18.8% energy storage ratio and high dispatchability are achieved； • Primary energy saving ratio is 81.78% when compared to the reference system. Solar photovoltaic/thermal (PV/T) hybrid systems are considered energy-efficient and eco-friendly. However, conventional PV/T systems generate electricity with high power fluctuations and low-grade thermal energy. In this paper, dry reforming of methane (DRM) is introduced to convert low-grade thermal energy through chemical reaction and enhance the dispatchability of the PV/T system. A combined cooling, heating, hydrogen and power (CCHHP) multi-generation system that integrates the PV/T, DRM and CCHP (combined cooling, heating and power) is proposed to use the full-spectrum solar energy. Energy and exergy analysis under design and variable working conditions are conducted to analyze the system performance. The results reveal that the proposed system can achieve a high energy conversion efficiency of 61.34 % and 18.8 % energy storage ratio at direct normal irradiance (DNI) of 800 W·m−2. In addition, the system can save up to 81.78 % primary energy when compared with the reference system. This study can facilitate the better design and performance evaluation of PV/T hybrid systems. [ABSTRACT FROM AUTHOR]

• HRES with PV-Wind and different types of energy storage systems. • Optimal size and efficient performance of HRES. • Bi-level mixed integer nonlinear programming optimization approach. • Real application of a stand-alone HRES in a real case study, Jordan. This paper aims to design and run a standalone hybrid renewable energy System (HRES) feeding residential building load. The suggested HRES encompasses photovoltaic panels and wind turbines as energy sources, combined with a hydrogen subsystem and a bank of batteries for energy storage. The meteorological input data includes irradiance, temperature, and wind speed for four seasonal weeks. These data sets are collected for a rural site situated in the town of Ras Munif, northern Jordan. The load profiles characterize a selected benchmark residential building. Consequently, a bi-level mixed integer nonlinear programming (BMINLP) problem is formulated to incorporate the designing problem with the energy management process. The upper level represents the sizing problem, while the lower level problem denotes the energy management strategy (EMS). Each problem has its independent objective function, constraints, and optimization variables. The EMS is an economic-based single objective optimization problem expressed as a mixed integer linear programming (MILP) problem. On the contrary, a mixed integer nonlinear programming (MINLP) approach frames the sizing problem to optimize a techno-economic-based multi-objective function. The interaction between the two problems is that the lower problem is an embedded constraint of the upper problem. Moreover, the candidate solutions from the upper problem are treated as fixed parameters in the lower problem. A multi-objective genetic algorithm is used to execute the overall problem by the MATLAB toolbox. The technical and economic performance parameters, including the investment cost, maintenance, operating costs, and others, are assessed based on the best findings. The key finding demonstrates that the summer week has the lowest total cost and energy loss factor, with around 132,000 $ and 0, respectively. Furthermore, the findings reveal that the entire system's energy loss factor for each sample period is less than 0.0009. This value is substantially less than the permitted limit of 0.01. [ABSTRACT FROM AUTHOR]

• We present a novel integration for reducing CO 2 emissions in blast furnaces. • It combines top gas recycling, oxy-fuel regime, power to gas and biomass pyrolysis. • The integration was modelled using the extended operating line methodology. • The best performance was found for pyrolysis at 700 °C. • The CO 2 emissions were reduced by 58% (9.8 MJ/kg CO2 energy consumption) The paper introduces a novel approach for mitigating CO 2 emissions in blast furnaces by integrating top gas recycling, an oxy-fuel regime, power to gas, and biomass pyrolysis. Various case studies were conducted, involving the adjustment of pyrolysis temperatures (300 °C, 500 °C, 700 °C, and 900 °C) and varying the quantity of blast furnace gas directed to methanation for carbon recycling. Pinus radiata, abundant and cost-effective in Chile and Spain, was chosen as the biomass source. The integration was modeled using the extended operating line methodology and evaluated through 12 key performance indicators, such as flame temperature, coke consumption, CO 2 emissions, and specific primary energy consumption per unit of CO 2 avoided. Optimal performance was observed with pyrolysis at 700 °C and no blast furnace gas recycled through methanation. This configuration achieved a 58 % reduction in CO 2 emissions, with an energy consumption of 9.8 MJ/kg CO2 , and obviated the need for geological storage. Comparing this innovative proposal with other oxygen blast furnace approaches from the literature revealed a 13 percentage point improvement in CO 2 reduction over the second-best alternative. Additionally, the required electrolysis capacity, influencing capital expenditure, was 57 % lower, and energy consumption was reduced by 44 %. [ABSTRACT FROM AUTHOR]

[Display omitted] • Feasibility of using organic wastewater as renewable energy source is verified. • ＞99% COD, TOC removal rate in the experimental platform. • Up to 78.75% of the energy recovery efficiency of the SCWO system. The degradation of high concentration organic wastewater and the recovery of energy using supercritical water oxidation (SCWO) technology have the potential to become viable in the future. High concentration organic wastewater contains a significant amount of energy. This paper focuses on utilizing high concentrated wastewater as a renewable energy source, from an energy system perspective, an experimental rig was designed to determine the optimal working conditions. The experiments were conducted with an oxidation coefficient of 1.1, a flow rate of high concentration organic wastewater of 9 ml/min, a heating temperature of 400 °C, and a reaction pressure of 25 MPa. Under these conditions, the COD and TOC removal rates of the high concentration organic wastewater were found to be 99.383 % and 99.876 % respectively. Additionally, the phenomenon of "Abnormalities in pressure sensitivity" observed during high concentration organic wastewater the experiment was further explained through additional experiments. Finally, by comparing the experimental energy recovery with the simulation results, an energy recovery efficiency of 78.95 % was achieved. The feasibility of the simulated energy recovery in practice was verified, confirming the potential to convert the previous energy-consuming wastewater treatment process into a productive one. This study provides a theoretical and experimental foundation for further related research. [ABSTRACT FROM AUTHOR]

[Display omitted] • A "production, storage and utilization" hydrogen integrated utilization system. • Steady-state, and dynamic analysis for proposed system. • Integrated heat recovery of electrolyzer and fuel cell. • Energy efficiency of electrolyzer and fuel cell improved by 6.3% and 24.5%. • Reduced electrolyzer voltage drop by 0.19 mV in 168 h of fluctuating operation. In integrated hydrogen energy utilization systems, due to the low efficiency of hydrogen/electricity conversion, coordination of energy management and efficient waste heat recovery is required to optimize performance. To address this challenge, this paper presents a comprehensive and sophisticated modeling and energy management strategy to enhance the off-grid energy utilization rate while prolonging the main components' lifetime. The developed model incorporates multiphase flow and heat transport balance for electricity and heat production, enabling a highly accurate representation of real-world behaviors of the system. The proposed off-grid operation strategy is complemented by a designed heat recovery scheme, ensuring the use of energy resources and waste heat. In addition, the proposed energy management strategy monitors the real-time status of each subsystem, actively reducing the number of harmful start-stop cycles of the hydrogen production system, thereby mitigating short-term power impacts and delaying its aging. Specifically, the voltage degradation of the reduction cell is reduced from 4.67 mV to 4.48 mV, the energy utilization rate is increased from 47.6 % to 53.9 %, and the energy efficiency of fuel cells significantly increases from 53.6 % to 78.1 %. [ABSTRACT FROM AUTHOR]

• The boundary of injection timing is affected by per-ignition and after-burning. • The boundary of the minimum excess air ratio limited by knock is established. • The boundary of burn duration is built by engine speed, excess air ratio, and ignition timing. • The boundary of turbocharging determined by intake and exhaust back pressure is established. In the process of hydrogen energy conversion and utilization, the direct-injection hydrogen internal combustion engine has been proven to produce zero carbon emissions with high thermal efficiency. However, the risk of abnormal combustion and high requirements of turbocharging limits the improvement of engine performance. This paper focuses on exploring the performance boundary of a 2.0 L turbocharged direct injection hydrogen engine with bench test under all working conditions. Applied variable geometry turbocharger can provide high intake pressure of 270 kPa with proper exhaust back pressure under all engine speed working conditions by changing the turbo opening angle. Delayed injection timing can enhance the performance, while improper injection timing may cause severe pre-ignition and afterburning. The burn duration ranges from 11 °CA to 29 °CA is significantly impacted by the engine speed and the excess air fuel ratio, whereas the load exerts less influence. Furthermore, the boundary of the minimum excess air fuel ratio limited by knock with different intake pressure, compression ratio and ignition timing are investigated and optimized. The minimum excess air fuel ratio limited by knock reaches 1.80 with a maximum torque of 325 Nm at the engine speed of 2500 rpm due to the limitation of the pre-ignition and super -knock. A high brake thermal efficiency of 43.02 % and a maximum power of 125 kW are achieved with the minimum excess air fuel ratio limited by knock of 2.05 and 1.4 respectively. [ABSTRACT FROM AUTHOR]

• A physics-informed data-driven model is presented for fuel consumption and battery degradation. • The commuting-oriented hybrid powertrain optimization with multiple objectives is carried out. • The commuting scenario is synthesized by a method combining Markov chain and neural network. • A novel hybrid powertrain with multiple drive modes is modeled based on vehicle bench test data. • The optimized solution can improve the vehicle performance under the commuting driving cycle. The physics-informed data-driven approach is instructive in powertrain modeling with high-fidelity, which can be adopted for powertrain optimization to further improve vehicle performances. In this paper, a plug-in hybrid electric vehicle (PHEV) equipped with a novel powertrain is considered as the research object, and a series of vehicle bench experiments are carried out for operative data acquisition. With the collected data, the physics-informed data-driven models for engine fuel consumption and battery state of health (SOH) loss are proposed. The accuracy and superiority of the proposed model are demonstrated compared with other approaches. In order to realize the commuting-oriented powertrain optimization, the driving cycle representing the commuting scenario is reconstructed based on the collected traffic data in Beijing. The synthesized driving cycle generated by the proposed method can describe the practical application scenario in Beijing within the allowable error range. In addition, the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to further optimize the parameters for the hybrid powertrain according to vehicle performances. The multi-objective optimization is completed with the engine fuel consumption and battery state of health (SOH) loss as targets. The results indicate that the optimized configuration parameters can reduce fuel consumption by 4.85% and battery capacity loss by 3.7% compared with the original vehicle. [ABSTRACT FROM AUTHOR]

• A separation-enhanced auto-cascade refrigeration cycle is proposed. • Energy, modified exergy, related economic and environmental analyses are conducted. • The theoretical performance of the proposed cycle is superior to two baseline cycles. • The modified exergy analysis method identifies the source of exergy destruction. • The total cost rate and environmental impact rate of the proposed cycle is reduced. Auto-cascade refrigeration technology has broad application prospects in the field of low-temperature refrigeration. The separation efficiency of refrigerants is one of the main factors affecting system performance. This paper proposes a separation-enhanced auto-cascade refrigeration cycle. An additional cascade heat exchanger and an auxiliary phase separator are introduced before the original cascade heat exchanger. The configuration enhances cooling capacity and cycle performance. The energy, modified exergy, exergo-economic and exergo-environmental performance of the cycle are evaluated through simulation and compared with the basic cycle and previously modified cycle. The results demonstrate that the proposed cycle outperforms both the basic cycle and the modified cycle in both energy and exergy efficiency. The maximum coefficient of performance and exergy efficiency of the separation-enhanced cycle is 44.4% and 44.3% higher than those of the basic cycle, 17.1% and 24.8% higher than those of the modified cycle, respectively. Furthermore, the modified exergy analysis method identifies the exergy destruction caused by the components and zeotropic refrigerant. Actually, the exergy destruction of refrigerant contributes to 39.7% of the overall exergy destruction under typical conditions. Moreover, the exergo-economic analysis indicates a 7.6% reduction in the total cost rate for the proposed cycle compared to the basic cycle and a 2.35% reduction compared to the modified cycle. The exergo-environmental analysis highlights a decrease of 25.4% in overall environmental impact for proposed cycle compared to basic cycle and 12.1% reduction compared to modified cycle. In general, the comparative analysis reveals the improvement potential of the proposed cycle in terms of energy-saving, economic and environmental performance. [ABSTRACT FROM AUTHOR]