Applied Energy最新文献

{"title":"Optimization framework for multi-objective energy management strategy in hybrid electric vehicles integrating explainable artificial intelligence","authors":"Zhiyuan Lu ,&nbsp;Hu Wang ,&nbsp;Guanzhang He ,&nbsp;Yong Chen ,&nbsp;Zihou Li ,&nbsp;Zunqing Zheng ,&nbsp;Mingfa Yao ,&nbsp;Song Zhang ,&nbsp;Hao Wang","doi":"10.1016/j.apenergy.2025.126484","DOIUrl":"10.1016/j.apenergy.2025.126484","url":null,"abstract":"<div><div>This study presents a multi-objective optimization (MOO) and explainable artificial intelligence (XAI) integrated framework for establishing MOO strategies in hybrid electric vehicle (HEV) and analyzing the decision-making patterns of the system. Specifically, the study first establishes a power-split HEV model and incorporates fuel consumption, electricity consumption, battery degradation, and the number of engine start-stops into the system evaluation metrics. Subsequently, the dynamic programming (DP) algorithm is employed to develop an offline globally optimal multi-objective strategy based on the multi-indicator vehicle model, and the non-dominated sorting genetic algorithm-II (NSGA-II) is used to perform MOO of the strategy's cost function. By combining the cognition-driven analytical hierarchy process (AHP) decision-making method with the data-driven technique for order preference by similarity to ideal solution (TOPSIS) method, the AHP-TOPSIS decision-making method is used to select the optimal solution from the Pareto frontier. Tree-based XAI methods are introduced, employing mean decrease impurity (MDI) and partial dependence plots (PDP) to analyze the interaction mechanisms among the four objectives during the decision-making process. A double-layer random forest (RF) energy management strategy is constructed, combining a five-fold cross-validated RF pattern recognition model with an engine power prediction model based on the optimal solution dataset. The results demonstrate that the multi-objective strategy exhibits better overall performance compared to single-objective strategies. The proposed double-layer RF strategy reduces fuel consumption by 1.8 %, maintains similar electricity consumption, decreases battery degradation by 7.1 %, and reduces the number of engine start-stops by 82.1 % compared to a rule-based (RB) strategy, with minimal deviation from the DP strategy. This validates the superior performance of the strategy in multi-objective control processes.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126484"},"PeriodicalIF":10.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704196","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":"The optimal is not always the best: Life cycle impacts of near-optimal energy systems","authors":"Alexander de Tomás-Pascual ,&nbsp;Laura À. Pérez-Sánchez ,&nbsp;Miquel Sierra-Montoya ,&nbsp;Francesco Lombardi ,&nbsp;Stefan Pfenninger-Lee ,&nbsp;Inês Campos ,&nbsp;Cristina Madrid-López","doi":"10.1016/j.apenergy.2025.126487","DOIUrl":"10.1016/j.apenergy.2025.126487","url":null,"abstract":"<div><div>Energy system optimization models (ESOMs) can be used to guide long-term energy transitions but often overlook environmental impacts and the diversity of solutions close to the cost-optimal one. Here, we combine an ESOM using Modelling to Generate Alternatives (MGA) with Life Cycle Assessment (LCA) to evaluate 260 near-optimal and technologically diverse carbon-neutral energy system designs for Portugal in 2050 across five environmental indicators: climate change, land use, water use, ecotoxicity, and materials. Using the Calliope energy modelling framework and ENBIOS for environmental assessment, we find that system designs whose cost is within 10 % of the minimum feasible cost provide up to 50 % lower environmental impacts. Our results reveal a trade-off between technological diversity and environmental performance, showing that while diversity enhances resilience, this may come with a significant increase in environmental drawbacks. Solar photovoltaic and battery technologies dominate the environmental impacts, particularly in water consumption and critical material use. This study shows that traditional cost-optimal energy system designs may not be environmentally optimal. Exploring near-optimal alternatives reveals lower-impact solutions and supports more inclusive planning for energy transitions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126487"},"PeriodicalIF":10.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704200","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":"Facility-level energy-driven water footprint and scarcity implications of Chinese data centers: a bottom-up analysis and scenario-based projection","authors":"Feng Jiang ,&nbsp;Cuncun Duan ,&nbsp;Bin Chen","doi":"10.1016/j.apenergy.2025.126522","DOIUrl":"10.1016/j.apenergy.2025.126522","url":null,"abstract":"<div><div>Data centers have rapidly expanded to meet increasing computing demands. As energy-intensive infrastructures, data centers consume significant amounts of water to meet cooling requirements and support operations. Here, we present a bottom-up framework to estimate the facility-level water footprints (WFs) of data centers in China and project future pathways. First, we compile an inventory of Chinese data center operations and calculate both direct and indirect water consumption. Then, by mapping water scarcity levels, we assess the dependence of the data centers on limited water supplies. Finally, 21 scenarios are developed to model WFs trends and water scarcity implications from 2025 to 2050. The results show that in 2022, China's data centers consumed 15.7 billion m³ of water, accounting for 2.7 % of the nation's total. Notably, 72 % of the computing capacity is located in severe water-scarce regions. By 2050, simultaneous improvements in power usage efficiency, water usage efficiency, and grid decarbonization could reduce national WFs by 51.5–58.3 % compared with the baseness as usual scenario, alleviating the increased water consumption driven by the Eastern Data and Western Computing Project in water-scarce regions such as Gansu and Ningxia Provinces. This study fills a gap in understanding the water consumption of Chinese data centers, highlighting the urgent need to incorporate water resource limitations into planning for sustainable digital infrastructure.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126522"},"PeriodicalIF":10.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704199","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":"Modeling battery aging in linear energy system optimizations by applying convex hulls - accuracy benefits and computational costs","authors":"Petrin Geert Kristian,&nbsp;Arens Stefan,&nbsp;Schoenfeld Patrik,&nbsp;Klement Peter,&nbsp;Schlüters Sunke","doi":"10.1016/j.apenergy.2025.126492","DOIUrl":"10.1016/j.apenergy.2025.126492","url":null,"abstract":"<div><div>Linear energy system optimization plays an important role in the design of future energy systems, due to its availability and low computational requirements. While linear optimization offers many benefits, the rise in popularity of battery storage and its use cases represents a challenge, commonly causing oversimplification by ignoring aging and its causes. Thus, this paper aims to include battery aging and subsequently battery optimized operation, to lower aging, into linear system optimization. Two approaches are highlighted, namely a basic linear aging implementation, as well as a convex hull approximation. These are then compared against the generic implementation found in oemof.solph, an open source energy modeling tool, to assess the benefits and drawbacks of including aging. The chosen case study for evaluation models a buffer storage for an overhead line island to reduce peak loads, enabling connections to weaker grids. The results show up to 46.9 % lower battery aging for the convex approach, compared to the generic baseline. Furthermore, the amount of aging is precisely determined by the convex approach, with errors below 0.4 % and the computational times close to the same magnitude, due to the lack of mixed integer linear programming. The suggested approaches of integrating battery aging into linear optimization should be integrated into future models to better predict and reduce battery lifetimes, allowing for better cost calculations in such systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126492"},"PeriodicalIF":10.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704201","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":"Cost analysis comparison of reference and near-zero energy office building design in Indonesia: a life cycle approach and its sensitivity analysis","authors":"Anisah Andika Fajar,&nbsp;Koji Tokimatsu","doi":"10.1016/j.apenergy.2025.126496","DOIUrl":"10.1016/j.apenergy.2025.126496","url":null,"abstract":"<div><div>This study compares the life-cycle costs (LCC) of a conventional office building and a near-zero-energy building (NZEB) in Indonesia to assess the cost-effectiveness of NZEBs using a life-cycle approach. A hypothetical 10-storey office building model in Jakarta is used as a case study, with the reference case having an Energy Use Intensity (EUI) of 226.38 kWh/m2/year and the NZEB case incorporating passive design, active systems, and renewable energy technologies, resulting in an EUI of 120.18 kWh/m2/year. The LCC calculation considers the design and construction, operational, maintenance, and end-of-life costs over a 50-year lifetime. The results showed that the NZEB case had 11 % higher construction costs, 2.2 % higher maintenance costs, and 43 % higher end-of-life costs than the reference case. However, the NZEB case achieved a 47.6 % reduction in operational costs, leading to a 10.8 % lower LCC than the reference case. Sensitivity analysis revealed that lower inflation rates and longer building lifetimes are more beneficial for NZEBs and that variations in operation and maintenance costs have a more significant impact on the LCC than those in the construction or end-of-life phases. These findings highlight the economic advantages of NZEBs over conventional buildings in Indonesia, and emphasize the importance of considering the entire life cycle when evaluating the cost-effectiveness of sustainable construction practices.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126496"},"PeriodicalIF":10.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704197","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":"Techno-economic assessments of electrolyzers for hydrogen production","authors":"Efenwengbe Nicholas Aminaho ,&nbsp;Ndukaegho Sabastine Aminaho ,&nbsp;Faith Aminaho","doi":"10.1016/j.apenergy.2025.126515","DOIUrl":"10.1016/j.apenergy.2025.126515","url":null,"abstract":"<div><div>This review provides a comprehensive techno-economic assessment of four leading electrolyzer technologies such as the Alkaline Water Electrolyzers (AWE), Proton Exchange Membrane (PEM) electrolyzers, Solid Oxide Electrolyzer Cells (SOEC), and Anion Exchange Membrane (AEM) systems for green hydrogen production. Drawing on more than 40 peer-reviewed studies and real-world deployment scenarios, the analysis compares performance indicators such as levelised cost of hydrogen (LCOH), capital expenditure (CAPEX), operating expenditure (OPEX), efficiency, stack durability, and water treatment requirements. AWE is identified as the most cost-effective option for baseload power contexts, while PEM offers superior dynamic response and gas purity at a higher cost. SOECs, despite their high theoretical efficiency, remain limited by thermal cycling and material degradation. AEMs, though less mature, hold promise for low-cost, decentralized hydrogen production. Cost of electricity is more than 64 % of LCOH in all technologies, so it is important to match electrolyzers with stable or hybrid renewable energy resources such as geothermal, wind-solar, or Concentrated Solar Power (CSP). Optimisation methods such as genetic algorithms and GIS-based siting also enhance system performance and economic value. The report also considers regional and policy dimensions of deployment, underlining the need for site-specific solutions in the context of local energy portfolios, water supply, and infrastructure readiness. Recommendations are provided for advancing membrane longevity, integrating smart control systems, and optimizing techno-economic assessment models. This study is a policy decision-making tool for policymakers, investors, and researchers who are interested in accelerating the global scale-up of green hydrogen using context-relevant and economically viable electrolyzer technologies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126515"},"PeriodicalIF":10.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703822","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":"Reversible solid oxide electrolyser for energy storage: levelized cost of storage estimation","authors":"Jingjing Liang ,&nbsp;Yi Zhao ,&nbsp;Du Wen ,&nbsp;Ye Huang ,&nbsp;Minfang Han ,&nbsp;François Maréchal","doi":"10.1016/j.apenergy.2025.126431","DOIUrl":"10.1016/j.apenergy.2025.126431","url":null,"abstract":"<div><div>Reversible Solid Oxide electrolyser (rSOE) is a promising solution for long-duration energy storage due to its exceptional features such as inherent reversibility, co-electrolysis capability in electrolysis mode, high efficiency, adaptability to multiple fuels and the ability to concentrate CO₂ in fuel cell mode (SOFC) during the processing of carbon-based fuels. However, its economic viability in power-to-X-to-power (P2X2P) applications remains underexplored. This study evaluates the levelized cost of storage (LCOS) of a 250 MW rSOE across six energy storage forms, considering different discharging durations (168–1440 h) and varied annual charging–discharging cycles (1–30). H₂ stored in salt caverns, CH₄ stored in gas grids and CH₃OH stored in tanks are identified as the most viable storage options. The impact of stack lifetime and degradation on LCOS is also analyzed, revealing that associated benefits in terms of LCOS from extending the stack lifetime are marginal. Furthermore, Capital expenditure (Capex) thresholds for achieving an LCOS target of 0.2 $/kWh are calculated for rSOE scales ranging from 10 MW to 1000 MW under scenarios with discharging durations exceeding 700 h and 1–3 charging–discharging cycles per year. Results indicate that H₂ stored in salt caverns is optimal for smaller scales and shorter discharging durations, while CH₄ stored in gas grids becomes more cost-effective at larger scales with longer durations. Also, it is found that there is a minimum deployment scale for rSOE to meet the LCOS target of 0.2 $/kWh. The minimum deployment scale varies with different energy carriers and energy storage scenarios. Moreover, the Capex thresholds to achieve 0.2 $/kWh LCOS for Alkaline Water Electrolyser (AWE) and Proton Exchange Membrane Water Electrolyser (PEMWE) are also uncovered. The results revealed that rSOE outperforms AWE and PEMWE at MW-scale seasonal energy storage due to its reversibility. For carbon-based fuels (CH₄, CH₃OH), rSOE demonstrates unique advantages, as its reverse SOFC mode inherently enables CO₂ concentration, significantly reducing CO₂ capture costs.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126431"},"PeriodicalIF":10.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695400","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":"Adaptive ECMS for trip level energy management of HEVs considering vehicle and route parameter variations","authors":"Susenjit Ghosh,&nbsp;Siddhartha Mukhopadhyay","doi":"10.1016/j.apenergy.2025.126374","DOIUrl":"10.1016/j.apenergy.2025.126374","url":null,"abstract":"<div><div>Minimizing fuel consumption is critical to justify the additional investment in motors and batteries for Hybrid Electric Vehicles (HEVs). This requires a trip-level energy management (TEM) strategy that accounts for dynamic vehicle parameters, such as mass, rolling resistance, and powertrain efficiency, alongside future drive cycles influenced by traffic, vehicle loading, and driver behaviour. Conventional TEM approaches, assuming nominal parameters, compromise fuel economy and charge sustainability. This paper presents a hierarchical and computationally efficient TEM technique integrating real-time vehicle parameter estimation with personalized drive cycle prediction. The method utilizes dynamic vehicle parameter models, interactive multiple models, and multi-scale drive cycle analysis to capture individual driver behaviour and traffic evolution. Validation on standard and ViSSIM-generated drive cycles, along with Driver-in-the-Loop simulations, shows a 4 %–6 % fuel economy improvement compared to conventional TEM. Onboard implementation feasibility is demonstrated through Hardware-in-the-Loop testing on an industrial embedded platform.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126374"},"PeriodicalIF":10.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695414","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":"Global diffusion and key features of Energy Communities with a main focus on building loads modelling and management: A review","authors":"Elisa Belloni ,&nbsp;Tommaso Ferrucci ,&nbsp;Davide Fioriti ,&nbsp;Andrea Tumiati ,&nbsp;Davide Poli","doi":"10.1016/j.apenergy.2025.126461","DOIUrl":"10.1016/j.apenergy.2025.126461","url":null,"abstract":"<div><div>The whole world is moving towards energy communities and energy sharing, each country with its own history, resources, and needs that determine different legislations and infrastructures, but above all different application models. This paper reviews the literature on Energy Communities (ECs), with a focus on Renewable Energy Communities (RECs), in promoting decarbonization and enhancing the flexibility of energy systems through decentralized energy generation, storehouse, and demand-side management. A systematic review on the classification of aggregation forms and global worldwide development of ECs is also conducted. The paper also explores the integration of renewable energy sources (RES), energy storage systems (ESS) and smart technologies, demand response strategies applications able to improve the overall benefits of the communities, and the importance of prosumers behavior in achieving energy effectiveness. Case studies recently analyzed by prominent scholars and researchers are analyzed to show the design objectives and the key features in REC scenarios and their impact on energy systems and on the national transmission grid. The findings present the challenges and openings in RECs diffusion and their role in the broader energy transition scenario.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126461"},"PeriodicalIF":10.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695415","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":"Combining electrochemistry and data-sparse Gaussian process regression for lithium-ion battery hybrid modeling","authors":"Jackson Fogelquist,&nbsp;Xinfan Lin","doi":"10.1016/j.apenergy.2025.126458","DOIUrl":"10.1016/j.apenergy.2025.126458","url":null,"abstract":"<div><div>The widespread adoption of lithium-ion batteries is driving the concurrent development of advanced battery management systems, which seek to maximize safety and performance through state-of-the-art control, diagnostic, and prognostic techniques. To enable these capabilities, battery models must provide accurate predictions of output voltage and physical internal states, which is challenging due to the inevitable presence of system uncertainties and limited online computational resources. In response, a computationally-efficient hybrid modeling framework is proposed that integrates a physics-based electrochemical battery model with a Gaussian process regression (GPR) machine learning model to compensate for output prediction errors due to system uncertainties. A key feature of the framework is a proposed data sampling procedure that mitigates computational expense by leveraging the prediction capability of GPR under sparse data. The hybrid model was experimentally validated, yielding an average prediction root-mean-square error (RMSE) of 7.3 mV across six testing profiles, versus 119 mV for the standalone electrochemical model. The observed ratio of computation time to modeled time was 0.003, which is amply sufficient for online BMS applications. Finally, in a simulated BMS demonstration, the hybrid model was observed to reduce parameter estimation errors by one order of magnitude, the voltage prediction RMSE by 63 %, and the state estimation RMSE by 52 % when compared against the standalone electrochemical model.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126458"},"PeriodicalIF":10.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695425","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}