Energy Conversion and Management最新文献

{"title":"Adaptive model predictive control of a residential solar-air hybrid heat pump system","authors":"","doi":"10.1016/j.enconman.2024.119026","DOIUrl":"10.1016/j.enconman.2024.119026","url":null,"abstract":"<div><p>With the increasing adoption of renewable energy in the power grid, the future of building energy systems is transitioning toward a distributed and multi-source energy framework. To significantly alleviate strain on the power grid and enhance the integration of renewable energy sources, it is important to optimize the energy intergation and the use of energy storage. To realize decarbonization in domestic hot water supply, this paper proposed a new residential solar-air hybrid heat pumps water heating system based on a three-fluid heat exchanger. Furthermore, an adaptive model predictive control (AMPC) with online model-updating was developed to realize the demand response control for minimized power payment when the hot water requirement is well satisfied. As a result, the proposed hybrid solar-air heat pump system demonstrates superior performance and flexibility, which outperforms the parallel solar-assisted heat pump system with a 14.5% increase in energy savings and a 23.3% improvement in overall system performance. Moreover, the developed model predictive controller consistently demonstrates 52.5% energy saving and 15.5% cost savings in typical day compared to rule-based control strategies.</p></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capacity configuration of fuel cell hybrid vehicles using enhanced multi-objective particle swarm optimization with competitive mechanism","authors":"","doi":"10.1016/j.enconman.2024.119039","DOIUrl":"10.1016/j.enconman.2024.119039","url":null,"abstract":"<div><p>A well-designed hybrid powertrain is crucial for ensuring the safe, efficient, and durable operation of fuel cell hybrid vehicles. This paper introduces a modular design approach for powertrains, utilizing a Competitive mechanism-based Multi-Objective Particle Swarm Optimization (CMOPSO) algorithm integrated with nested dynamic programming. In this approach, the upper layer employs the CMOPSO algorithm to design the powertrain system, while the lower layer optimizes power coordination for each proposed design. This two-layer optimization framework considers factors such as vehicle economy and durability. Under WLTP conditions, the capacity configuration results are a fuel cell with a rated power of 22 kW, 100 batteries in series, and 7 batteries in parallel. Furthermore, the modular approach outperforms three other algorithms in terms of solution count, diversity, and overall performance metrics. The study also highlights that the vehicle’s power demand characteristics, influenced by different driving cycles, significantly affect capacity configuration results. Sensitivity analysis reveals that both the total operating cost and manufacturing cost of the vehicle are most sensitive to variations in the fuel cell rated power.</p></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental study on a 3 kWe free-piston Stirling engine-based combined heat and power system using a clean coal burner","authors":"","doi":"10.1016/j.enconman.2024.119014","DOIUrl":"10.1016/j.enconman.2024.119014","url":null,"abstract":"<div><p>Distributed energy systems have gained increased attention in recent years, due to their technical and economic benefits. Among them, the free-piston Stirling engine offers remarkable advantages such as compactness, high efficiency, and reliability. In this work, a distributed energy system comprising a 3 kWe free-piston Stirling generator and utilizing a clean coal burner as a heat source is proposed. The system is optimized based on one-dimensional steady state thermodynamic calculations using the software package of Engineering Equation Solver. Experiments are carried out to investigate the operational performance of the coal-fired free-piston Stirling engine based combined heat and power system. The field test results demonstrate a maximum power of 3200 We and a thermal efficiency of 67%. Overall, the maximum thermal-to-electric of the system is 16%. Additionally, the analysis of the internal energy flow showed that the maximum exergy efficiency of the system is 20%, revealing a potential for further optimization. The findings of this study demonstrate the feasibility of developing a cost-effective and efficient off-grid energy supply system based on free-piston Stirling engine technology while employing clean coal burners as the heat source.</p></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy, exergy, economic and environmental studies on a nonflammable eco-friendly mixture for efficient heating in cold regions","authors":"","doi":"10.1016/j.enconman.2024.119031","DOIUrl":"10.1016/j.enconman.2024.119031","url":null,"abstract":"<div><p>As a compelling alternative to fossil fuel combustion, air-source heat pumps face challenges in inefficiency, flammability, and pollution when operated at low ambient temperatures. To address the demand for safe, eco-friendly and efficient heating during wintertime, this work is devoted to exploring a novel refrigerant for air-source heat pumps. The zeotropic mixture has the potential to meet all the above demands at appropriate operating parameters, although specific feasible mixtures are still under investigation. In this work, a novel mixture of carbon dioxide and <em>trans</em>-1,1,1,4,4,4-hexafluoro-2-butene is proposed, with the heating performance being parametrically optimized based on a genetic algorithm. Energy analysis indicates a coefficient of performance improvement of up to 15.7 %, and thus an improvement in heating seasonal performance factor of more than 13.5 % for different cities, compared with traditional configurations. Exergy analysis shows that the low irreversibility of throttling is the main contributing factor to the improvement in energy efficiency. Meanwhile, economic and environmental analyses reveal a payback period of less than 7.5 years and an annual cost reduction of up to 14.5 %, as well as a carbon dioxide emission reduction of over 15.7 %. The sensitivity of operating parameters is analyzed, and other advantages of this concept are discussed as well. The results indicate a safe, efficient, environmentally friendly, and cost-effective air-source heat pump. This study contributes to: 1) providing an energy-efficient and environmentally friendly alternative refrigerant for heat pumps; and 2) promoting the sustainable development of low-carbon energy transformation technology.</p></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Multi-Objective optimization framework for Earth-to-Air heat Exchanger Systems: Enhancing thermal performance and economic viability in Moroccan climates","authors":"","doi":"10.1016/j.enconman.2024.119024","DOIUrl":"10.1016/j.enconman.2024.119024","url":null,"abstract":"<div><p>In this study, a multi-objective optimization framework is developed to provide a comprehensive approach to designing efficient and cost-effective Earth-to-Air Heat Exchanger systems (EAHE). By integrating sensitivity analysis, design of experiments, genetic algorithms, and multi-criteria decision-making, the framework addresses the complexities of balancing thermal performance and economic viability. Through an experimentally validated model of the exchanger, the study conducts sensitivity analyses to identify key design parameters and uses a fine-tuned genetic algorithm for optimization. The optimization focuses on minimizing the life cycle cost (LCC) and maximizing the cooling potential across three distinct Moroccan climates. Furthermore, multi-criteria decision-making methods were employed to determine an optimal solution from the multi-objective optimization results. Results indicate that the optimal exchanger configurations vary with location, highlighting the importance of site-specific design. For instance, the optimal design selected for Marrakech and Oujda is a pipe of 160 mm of diameter with 49 m of length and buried at 3 m, while for Errachidia it is a 160 mm pipe with 47 m of length and buried at 4 m since the location has a higher gradient of ground temperature. The EAHE gave a cooling potential of 1447 kWh/year, 1172 kWh/year and 1739 kWh/year with a LCC of 4122$, 4091$ and 4073$ over 50 years for Marrakech, Oujda and Errachidia, respectively. The normalized life cycle cost (NLCC) is the lowest for Errachidia (0.234$/kWh), followed by Marrakech (0.285$/kWh) then Oujda (0.349$/kWh).</p></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep reinforcement learning-based energy management strategy for fuel cell buses integrating future road information and cabin comfort control","authors":"","doi":"10.1016/j.enconman.2024.119032","DOIUrl":"10.1016/j.enconman.2024.119032","url":null,"abstract":"<div><p>Conventional energy management strategy (EMS) for fuel cell vehicles (FCVs) aims to optimize powertrain energy consumption while ignoring the air conditioning regulation, whereby the overall energy efficiency cannot be optimal. To enhance the cabin-powertrain holistic energy utilization without compromising energy storage system degradation and passenger temperature comfort, this paper proposes a novel energy management paradigm. The comprehensive control of cabin comfort and fuel cell/battery durability is achieved by comprehensively utilizing onboard sensors and vehicle-cloud infrastructure. Specifically, the vehicle energy- and thermal-coupled control problem is formulated by considering energy consumption, component ageing, and cabin’s dynamic thermal model. In addition to regular state space in energy management problems, future road information and environmental temperature are innovatively integrated into the energy management framework. A twin delayed deep deterministic policy gradient algorithm is used to solve the problem to enhance the overall energy efficiency. Simulation results indicate that, compared with rule-based EMSs, the proposed strategy achieves cabin comfort while extending the battery life by at least 3.79 % and reducing the overall vehicle operating cost by at least 2.71 %.</p></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning-based hierarchical cooperative eco-driving with traffic flow prediction for hybrid electric vehicles","authors":"","doi":"10.1016/j.enconman.2024.119000","DOIUrl":"10.1016/j.enconman.2024.119000","url":null,"abstract":"<div><p>The integration of autonomous driving and hybrid electric vehicle technologies presents a promising solution for achieving environmental sustainability. This paper introduces an innovative energy-efficient driving strategy for hybrid electric vehicles that incorporates real-time traffic flow prediction. The study delves into the impact of both lateral and longitudinal vehicle maneuvers on energy consumption within dynamic traffic environments, offering novel insights into optimizing energy utilization. Firstly, a multi-lane traffic flow state rolling predictor is constructed based on the Hankel dynamic mode decomposition algorithm. Subsequently, a vehicle longitudinal and lateral coordinated control strategy is established by integrating the prioritized experience replay double deep Q-network algorithm. Finally, a novel energy management strategy is proposed that leverages Simulink dynamic model and the deep deterministic policy gradient algorithm to address the vehicle dynamic decision-making planning results. Within a hierarchical cooperative optimization framework, this research comprehensively considers safety, comfort, traffic efficiency, and fuel economy. By introducing a novel hierarchical collaborative ecological driving framework, we have achieved a substantial improvement in environmental sustainability, with traffic efficiency increasing by 10.27%-14.41% and fuel economy rising by 9.44%-10.47%. Hardware-in-the-loop validation has confirmed the proposed approach’s real-time capabilities and promising practical applications.</p></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic study involving patent analysis and theoretical modeling of eco-friendly technologies for electric vehicles and power batteries to ease carbon emission from the transportation industry","authors":"","doi":"10.1016/j.enconman.2024.118996","DOIUrl":"10.1016/j.enconman.2024.118996","url":null,"abstract":"<div><p>Using natural and recycled materials in the manufacturing of electric vehicles (EVs) and power batteries (PBs) offers several environmental, economic, and technical compelling advantages. Recently, extensive study has been dedicated on the manufacturing of EVs and their power batteries to comprehensively address these advantages. This research analyzes 12,202 scientific patents from 1970 to 2021, evaluating eco-friendly materials for EVs and power batteries. The study identifies current status and gaps in research, mapping collaborations and networks, assessing core technologies, classification of innovative materials, future research directions; hence environmental and economic implications. To assess current development and forecast future technologies, hybrid autoregressive integrated moving average (ARIMA) time series model combined with an advanced machine learning logistic regression algorithm were employed. Patents analyses results unveil noteworthy insights: Dynamic analysis demonstrates a growing interest in eco-friendly EVs and PBs manufacturing from countries such as China (PF2/PF1 = 0.406), U.S.A (PF2/PF1 = 0.468), Germany (PF2/PF1 = 0.465), and Japan (PF2/PF1 = 0.427). Key companies including TOSHIBA, BYD, TOYOTA, and TESLA are leading the way in technology transfer related to the manufacturing of eco-friendly EVs and PBs. Latest technology update reveals that, synthetic “SofTex” leather has 85 % less CO₂ emissions than genuine leather during the processing, recycled aluminum production emits 97 % less CO₂ than new production. Ford’s aim is to reduce its carbon footprint by utilizing 1.2 billion plastic bottles from landfills waste per year for making vehicle parts, resulting in a noteworthy 50–60 % weight reduction and a 37 % decrease in CO₂ emissions in new vehicles compared to traditional counterparts. S-curve analysis further highlights a remarkable surge in patent filings for EVs and PBs since 2011. Notably, the patent CN-101013763-A holds significant influence in driving innovation route and utilization of eco-friendly materials such as bio-paint, bamboo, recycled plastic, and advanced steel in the manufacturing of EVs and PBs. In the predictable future, ongoing research will pinpoint opportunities for advancing technology to lead carbon reduction and sustainability in the EV industry.</p></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-criteria decision analysis of clean energy technologies for envisioning sustainable development goal 7 in Australia: Is solar energy a game-changer?","authors":"","doi":"10.1016/j.enconman.2024.119007","DOIUrl":"10.1016/j.enconman.2024.119007","url":null,"abstract":"<div><p>Achieving energy sustainability is cardinal to align the efforts of any country towards climate actions. Australia being blessed with high renewable energy potential has an energy mix currently dominated by fossil fuels. This projects Australia as an interesting case study to analyse the potential of commercial clean energy technologies to pursue the vision of Sustainable Development Goal 7 in Australia. The proposed study incorporates a hybrid quantitative–qualitative methodology to assess the potential of clean energy technologies and direct the current scenario to achieve energy sustainability. Initially, the prominent commercialised clean energy technologies in Australia are identified which include hydropower, wind energy, solar energy and bioenergy. The evaluation criteria are designed to illustrate the characteristics of energy sustainability from the dimensions of energy transition, geographic, economic, political, social and environmental criteria. A total of 16 sub-criteria are outlined to reflect the energy sustainability vision against which the alternatives are assessed. The weightage to each criterion is determined by using the Fuzzy Analytic Hierarchy Process (AHP) and Shannon’s Entropy models. The potential of clean energy alternatives is assessed and ranked with the aid of 7 multicriteria decision-making models to obtain robust and reliable results. The results imply that solar energy technology has the highest potential to support the energy sustainability vision of Australia with a score of 39.7 % followed by wind energy, hydropower and bioenergy technologies with a score of 29.8 %, 20.6 % and 9.9 %, respectively. Sensitive analysis is performed to analyse the impact of weightage and normalization methods. Spearman’s weighted rank correlation analysis and the rank reversal test has been performed to validate the results. The qualitative assessment is performed to investigate the hindering and supporting factors to promote clean energy technologies in Australia and to frame the strategies to achieve SDG 7. Policies to support rooftop PV and investments towards either grid integration or grid expansion scenarios have a huge role in influencing the progress towards Sustainable Development Goal 7 in Australia.</p></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0196890424009488/pdfft?md5=84331bdc8e0520c023c81f155b7a6bc5&pid=1-s2.0-S0196890424009488-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ammonia as a hydrogen carrier: An energy approach","authors":"","doi":"10.1016/j.enconman.2024.118998","DOIUrl":"10.1016/j.enconman.2024.118998","url":null,"abstract":"<div><p>The aim of this work was to evaluate the feasibility of ammonia as an energy carrier by simulating and analysing the energy consumption and production of an integrated ammonia-to-power system. It involves ammonia synthesis, purification, conditioning to meet the storage requirements, ammonia decomposition, membrane hydrogen separation, and the power generation through either a proton exchange membrane fuel cell or a hydrogen combustion process. The two different integrated systems were simulated using Aspen HYSYS® and Aspen Custom Modeler® to generate 105 kW of electrical power for a residential application (twenty homes). The energy feasibility was assessed by comparing the total energy consumption with the energy produced by the two integrated options. Due to the initially high energy consumption, <em>Aspen Energy Analyzer</em>^TM was employed to design a heat exchanger network for heat integration. The obtained results based on the heat integration network significantly improved energy efficiency for both alternatives of hydrogen exploitation, demonstrating the feasibility of a circular hydrogen economy.</p></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}