Applied Energy最新文献

{"title":"Review of machine learning techniques for optimal power flow","authors":"Hooman Khaloie,&nbsp;Mihály Dolányi,&nbsp;Jean-François Toubeau,&nbsp;François Vallée","doi":"10.1016/j.apenergy.2025.125637","DOIUrl":"10.1016/j.apenergy.2025.125637","url":null,"abstract":"<div><div>The Optimal Power Flow (OPF) problem is the cornerstone of power systems operations, providing generators’ most economical dispatch for power demands by fulfilling technical and physical constraints across the power network. To ensure safe and reliable operation of power systems, grid operators must steadily solve the nonconvex nonlinear OPF problem for immense power networks in (near) real-time, which poses tremendous computational challenges. The enormous amount of available data created by power systems digitalization and recent breakthroughs in machine learning have opened up new opportunities for grid operators to build shortcuts to predict or solve the OPF problem close to real-time. This survey overviews recent attempts at leveraging machine learning algorithms to solve the transmission-level OPF problem. On this basis, the groundwork is laid for commonly employed machine learning approaches leveraged to address the OPF problem. Subsequently, the frequently used performance evaluation metrics in learning-based OPFs are delineated to judge efficiency from diverse aspects (e.g., optimality in terms of the dispatched cost, feasibility concerning technical constraints, and computational efficiency) compared to conventional approaches. Next, the trend and progress of recently developed algorithms are discussed. Finally, the challenges and open problems at the interface of machine learning and OPF problems are highlighted.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"388 ","pages":"Article 125637"},"PeriodicalIF":10.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563614","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":"Life-cycle prediction and optimization of sequestration performance in CO2 mixture huff-n-puff development for tight hydrocarbon reservoirs","authors":"Xinyu Zhuang ,&nbsp;Wendong Wang ,&nbsp;Yuliang Su ,&nbsp;Menghe Shi ,&nbsp;Zhenxue Dai","doi":"10.1016/j.apenergy.2025.125618","DOIUrl":"10.1016/j.apenergy.2025.125618","url":null,"abstract":"<div><div>The surge in CO₂ levels in the atmosphere is responsible for the greenhouse effect. Injecting substantial quantities of CO₂ into underground sequestration has emerged as a prominent topic in recent years. Unconventional reservoirs, owing to their complex geological structures, offer secure locations for CO₂ sequestration and enhance the efficiency of hydrocarbon extraction from these intricate subsurface formations. Tight hydrocarbon (such as tight oil and gas) is one of the most representative unconventional resources and has extraordinary development potential. Given its complex pore structure and extremely low permeability, CO₂ huff-n-puff is one of the effective tertiary methods for sequestering CO₂ underground while also enhancing overall cumulative hydrocarbon recovery. As commonly-used gas solvents for increasing the production of subsurface hydrocarbons, CO₂, CH₄ and N₂ show their excellent capabilities when used individually. Their mixture can effectively re-energize reservoirs and securely store large amounts of CO₂ underground, often yielding better results than single gas huff-n-puff. However, comprehensively accounting for the synergistic effects of different gas mixture composition and huff-n-puff operations on CO₂ sequestration and hydrocarbon recovery remains a significant challenge. In this study, a promising AI-based hybrid workflow that incorporates various CO₂ sequestration mechanisms is proposed for life-cycle prediction and multi-objective co-optimization of sequestration performance during the CO₂ mixture huff-n-puff process. A field-scale reservoir numerical simulation model was established to account for the CO₂ sequestration mechanisms involved in the CO₂ mixture huff-n-puff process. Based on the complex, high-precision simulation model, the workflow integrates Temporal Fusion Transformers (TFT) with non-dominated sorting genetic algorithm III (NSGA-III) to achieve efficient proxy-based optimization. This improves the prediction accuracy of CO₂ sequestration volume, oil recovery and NPV while reducing the multi-objective optimization cost. Different optimization schemes are proposed from the perspectives of sequestration scale, productivity, and economic benefits. Compared with the CO₂ sequestration volume, oil recovery, and NPV of baseline, the optimized scheme increased by 15.06 %, 14.52 %, and 3.57 % respectively. This study aims to reduce sequestration costs while maintaining efficient energy extraction and conversion by developing an innovative and extensible workflow for evaluating CO₂ sequestration performance, providing operational guidelines for long-term CO₂ mixture huff-n-puff development.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"388 ","pages":"Article 125618"},"PeriodicalIF":10.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563612","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":"Effect of the number of parallel batteries on thermal runaway evolution in LiFePO4 battery","authors":"Zhizuan Zhou ,&nbsp;Maoyu Li ,&nbsp;Xiaodong Zhou ,&nbsp;Xiaoyu Ju ,&nbsp;Lizhong Yang","doi":"10.1016/j.apenergy.2025.125651","DOIUrl":"10.1016/j.apenergy.2025.125651","url":null,"abstract":"<div><div>With the increasing demand for longer drive range, lithium-ion batteries (LIBs) are connected in parallel and in series to meet the power requirement of electric vehicles. In contrast to series connection, the presence of parallel connection may exacerbate thermal runaway (TR) issues of LIBs owing to the possible electricity transfer between batteries. However, the complex electricity and heat interactions between parallel-connected LIBs challenge the in-depth understanding of the effects of parallel connection on TR evolution. In this study, detailed effects of the number of parallel-connected batteries on TR evolution mechanisms are investigated by removing the heat conduction between batteries. Differing from the conventional belief that the electricity transfer is interrupted when the electrochemical system inside battery is damaged in the process of TR, it has been observed that the continuous electricity transfer occurs in the batteries connected in parallel with more than two units. Increasing the number of parallel-connected batteries facilitates the occurrence of continuous electricity transfer. The occurrence of TR is significantly advanced and the corresponding onset temperature decreases from more than 200 °C to less than 180 °C when the number of parallel batteries exceeds two, and the transferred electrical energy between batteries is determined as the dominant cause of the advanced TR. Particularly, parallel-connected batteries with more numbers exhibit a higher risk of fire during TR because of the ignition role of transferred electrical energy. This work reveals the detailed effects of the number of parallel batteries on TR evolution and triggering mechanisms, which contributes to sufficient evidence for reliable early warning and safety design of energy systems containing parallel-connected batteries.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"388 ","pages":"Article 125651"},"PeriodicalIF":10.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563613","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":"Equitably allocating wildfire resilience investments for power grids — The curse of aggregation and vulnerability indices","authors":"Madeleine Pollack ,&nbsp;Ryan Piansky ,&nbsp;Swati Gupta ,&nbsp;Daniel Molzahn","doi":"10.1016/j.apenergy.2025.125511","DOIUrl":"10.1016/j.apenergy.2025.125511","url":null,"abstract":"<div><div>Social vulnerability indices have increased traction for guiding infrastructure investment decisions to prioritize communities that need these investments most. One such plan is the Biden-Harris Justice40 initiative, which aims to guide equitable infrastructure investments by ensuring that disadvantaged communities defined by the Climate &amp; Economic Justice Screening Tool (CEJST) receive 40% of the total benefit realized by the investment. However, there is limited research on the practicality of applying vulnerability indices like the CEJST to real-world decision-making for policy outcomes. In this paper, we study this gap by examining the effectiveness of vulnerability indices in a case study focused on power shutoff and undergrounding decisions in wildfire-prone regions. Using a mixed-integer program and a high-fidelity synthetic transmission network in Texas, we model resource allocation policies inspired by Justice40 and evaluate their impact on reducing power outages and mitigating wildfire risk for vulnerable groups. Our analysis reveals that the Justice40 framework may fail to protect certain communities facing high wildfire risk. In our case study, we show that Indigenous groups are particularly impacted. We posit that this outcome is likely due to information losses from data aggregation and the use of generalized vulnerability indices. By incorporating explicit group-level protections, we illustrate the potential for improving outcomes for the most disproportionately affected communities.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"388 ","pages":"Article 125511"},"PeriodicalIF":10.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563889","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":"Flexible design and operation of off-grid green ammonia systems with gravity energy storage under long-term renewable power uncertainty","authors":"Jiahui Zhou,&nbsp;Bing Tong,&nbsp;Haiming Wang,&nbsp;Gang Xu,&nbsp;Runzhi Zhang,&nbsp;Wentao Zhang","doi":"10.1016/j.apenergy.2025.125629","DOIUrl":"10.1016/j.apenergy.2025.125629","url":null,"abstract":"<div><div>The conventional ammonia production process heavily depends on fossil fuels, making it urgent to redesign the synthesis process to reduce greenhouse gas emissions and address the challenges of depleting resources. Off-grid ammonia synthesis powered by renewable energy offers a feasible pathway to producing carbon-free ammonia. However, a significant challenge for off-grid green ammonia plants is ensuring the reliable operation of the relatively inflexible ammonia synthesis units under intermittent and unpredictable wind and photovoltaic power conditions. To address this challenge, this study proposes a novel off-grid green ammonia system and a discrete multi-stable flexible control strategy for ammonia synthesis. For the first time, gravity energy storage is integrated into a large-scale green ammonia project to ensure a continuous power supply to the ammonia synthesis reactor under limited flexible operation. The optimal design and operation of the proposed system are modeled as a mixed-integer nonlinear problem, which is reformulated into a linear version through piecewise linearization. Additionally, the stochastic nature of renewable energy generation is fully considered. A scenario generation framework based on Copula function theory and Markov stochastic processes is developed to accommodate the long-term simulation needs of chemical production. The effectiveness of the proposed method is validated through a case study. A sensitivity evaluation is also carried out to analyze the impact of wind and photovoltaic power configurations, load adjustment periods, and system component costs on system revenue and module capacity. This study provides new insights into the system configuration and flexible operation of green ammonia systems and is expected to guide the construction and operation of practical green ammonia plants.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"388 ","pages":"Article 125629"},"PeriodicalIF":10.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563610","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":"Developing deep learning-based model for silicon-based solar cells in concentrator photovoltaic systems: A real-time prediction for efficient application-oriented performance","authors":"Mohamed M. Elsabahy ,&nbsp;Mohamed Emam ,&nbsp;Sameh A. Nada","doi":"10.1016/j.apenergy.2025.125644","DOIUrl":"10.1016/j.apenergy.2025.125644","url":null,"abstract":"<div><div>Concentrator photovoltaic (CPV) technology harnesses intense incident solar radiation, offering the potential for simultaneous electrical power generation and thermal utilization via compact, cost-effective heat sinks. However, maximizing the concentration ratio necessitates intensive cooling, resulting in low-grade heat generation. On the other hand, to achieve the demanded temperature of this low-grade heat generation for thermally driven applications, several operational and design parameters, including concentration ratio and heat sink characteristics, need to be harmonized. This can be numerically revealed using the conventional finite volume method (FVM) through optimization techniques/intensive parametric studies for wide-range concentration ratios under different cooling techniques which needs a prohibited computational cost and time. Addressing this challenge, the present work develops a deep learning-based model as a computationally efficient alternative for real-time performance prediction of silicon-based solar cells. The model is trained and validated using extensive datasets from a numerically and experimentally validated 3D thermal-fluid FVM model. These datasets cover wide variations in concentration ratios, heatsink heat transfer coefficients, meteorological conditions (ambient temperature and wind speed), cell reference characteristics (reference efficiency and temperature coefficient), and cell structure providing a comprehensive input-output mapping. The optimized neural network demonstrates high accuracy and reliability with a minimal mean square error and a coefficient of determination approaching unity. Furthermore, a user-friendly software with a graphical user interface (GUI) is developed, enabling two modes of analysis: real-time performance optimization through dynamic design parameter adjustments and real-time solutions for massive parametric studies. This novel workflow significantly reduces computational costs and processing times, facilitating instantaneous generation of characteristic performance maps (CPMAPs). The proposed approach accelerates decision-making for CPV applications and can be extended to other energy-related technologies, offering a transformative tool for both industry and research communities.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"388 ","pages":"Article 125644"},"PeriodicalIF":10.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563611","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 comprehensive Thermoeconomic assessment of liquid air and compressed air energy storage with solid/liquid/hybrid thermal energy storage (TES): Addressing air and TES material storage cost impacts","authors":"Heidar Jafarizadeh ,&nbsp;M. Soltani ,&nbsp;Walied Alfraidi","doi":"10.1016/j.apenergy.2025.125615","DOIUrl":"10.1016/j.apenergy.2025.125615","url":null,"abstract":"<div><div>Present study undertakes a comprehensive thermoeconomic evaluation of Liquid Air Energy Storage (LAES) and Compressed Air Energy Storage (CAES), with a focus on cost implications concerning exergy and energy storage, material containment, and TES units. By addressing previous uncertainties, we aim to enable informed decision-making in the energy sector.</div><div>The investigation unveils a significant spatial disparity between CAES and LAES systems. CAES allocates considerable space to Compressed Air Storage (CAS), while LAES dedicates a similar volume to TES unit containment as Liquid Air Tanks (LAT). When considering all spatial factors affecting energy density, LAES demonstrates an impressive energy density advantage, surpassing CAES by a factor of 6.9. In the assessment of air storage, we emphasized the importance of cushion gas analysis for underground storage (UG), highlighting the cost-effectiveness of salt caverns, especially for short-term power generation in CAES. In contrast, our analysis of LAES shows that costs increase significantly in systems with lower power capacities, particularly those below 400 MW. Regarding the cost impact of various TES materials, our findings reveal distinct patterns. For CAES, solid TES materials are cost-effective for pressures below 110 bar, while liquid TES materials are more suitable beyond this threshold. In LAES, the dynamics differ. Solid TES materials exhibit considerably higher costs, making hybrid TES, especially at charging and discharging pressures of 150/90 bar, an attractive option, offering a 14 % cost reduction.</div><div>Our comprehensive evaluation highlights substantially higher storage costs for LAES due to extensive TES material and air storage requirements. Economic analysis indicates that non-storage equipment costs are similar for both technologies, but LAES faces a 3.1 times higher cost for material containment, resulting in a 65 % higher total cost than CAES. The choice between CAES and LAES depends on project-specific needs and budget constraints, with LAES showing exceptional potential, particularly in areas where geological limitations affect CAES feasibility.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"388 ","pages":"Article 125615"},"PeriodicalIF":10.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563615","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":"Combined assessment of material and energy supply risks in the energy transition: A multi-objective energy system optimization approach","authors":"Gianvito Colucci ,&nbsp;Jonas Finke ,&nbsp;Valentin Bertsch ,&nbsp;Valeria Di Cosmo ,&nbsp;Laura Savoldi","doi":"10.1016/j.apenergy.2025.125647","DOIUrl":"10.1016/j.apenergy.2025.125647","url":null,"abstract":"<div><div>This paper proposes a novel framework to study the trade-off between different energy transition supply risks through multi-objective energy system optimization. While the increasing use of clean energy technologies reduces reliance on fossil fuels imports and hence energy supply risks, these technologies depend heavily on critical raw materials, the supply chains of which present high geographical concentration and political instability. Current energy system planning lacks endogenous evaluations (e.g., minimization) of such supply risks. To address this gap, two consistent supply risk functions are derived considering concentration, import reliance, and political stability of supply chains of critical raw materials on the one hand and energy commodities on the other hand. We enhance the open-source energy system modeling framework TEMOA by multi-objective optimization using the AUGMECON method to consider these functions endogenously as objectives and demonstrate the capabilities of this new approach for the Italian power sector decarbonization by 2050. First, total system cost and CO₂ emissions are minimized to establish a baseline. Then, four multi-objective optimizations between material and energy supply risks are conducted, each allowing for increasing total system cost. This approach allows the underlying energy system to adapt to minimize supply risks. Results highlight a significant trade-off between the two risks. Minimizing the material supply risk increases energy supply risk by reducing investments in wind turbines and batteries. These technologies are replaced by solar PV and natural gas plants with CCS, which raises gas imports and energy supply risk. Higher costs lead to wind energy disappearance, replaced mainly by natural gas plants, increasing reliance on CCS and imports. These findings emphasize the importance of balancing material and energy supply risks in energy system planning.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"388 ","pages":"Article 125647"},"PeriodicalIF":10.1,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563606","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":"Sensitivity of energy storage system optimization program to the source of renewable energy in the presence of demand side management: A behind-the-meter case study","authors":"Yogesh Manoharan ,&nbsp;Keith Olson ,&nbsp;Alexander John Headley","doi":"10.1016/j.apenergy.2025.125557","DOIUrl":"10.1016/j.apenergy.2025.125557","url":null,"abstract":"<div><div>The trend of installing renewable energy in behind-the-meter (BTM) has increased to support energy transition. Energy-saving methodologies, such as energy storage systems (ESSs) and demand side management (DSM), are available to augment renewable energies and maximize their benefits. This research examines the impact of different renewable energy sources on energy-saving strategies, system installations, and long-term planning decisions. This study presents an optimization framework to determine the Energy Storage Systems (ESS) capacity and Demand Side Management (DSM) strategies. The effect of solar and wind renewable energies on the optimization program results was compared to aid the selection of suitable renewable energy and energy-saving methodologies for an energy load profile. Renewable energy and its penetration were studied based on its impact on energy purchase, system installation size, savings, renewable curtailment, and levelized cost of storage (LCOS). ESS and DSM options are compared, and their synergies are explored to minimize energy purchases and control peak power. Furthermore, the ongoing changes in influential factors, such as electricity prices, battery costs, and roundtrip efficiency, underscore their importance in system installation planning and to maintain a realistic perspective on these parameters. Therefore, an analysis of these influential factors is conducted, serving as a sensitivity analysis for the proposed model. This shows that system estimates can triple, highlighting the necessity of realistically considering these factors to avert potential failure or loss. This study is performed on the case of the Natural Energy Laboratory of Hawaii Authority (NELHA), the world's largest multizone seawater utility. The proposed optimization framework selects suitable renewable energy and energy-saving methodologies for the considered case and is validated with the state-of-the-art tool. The proposed optimization framework in this study will support water utilities interested in installing renewable energy and energy storage systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"388 ","pages":"Article 125557"},"PeriodicalIF":10.1,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563609","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":"Fault-tolerant method of open-cathode PEMFC based on adaptive strong tracking Kalman filter combined with Hampel algorithm","authors":"Caizhi Zhang ,&nbsp;Xingzi Yu ,&nbsp;Hossain Md Rubel ,&nbsp;Qi Li ,&nbsp;Yuhui Sun ,&nbsp;Shangfeng Jiang ,&nbsp;Gucheng Wang","doi":"10.1016/j.apenergy.2025.125570","DOIUrl":"10.1016/j.apenergy.2025.125570","url":null,"abstract":"<div><div>Control systems of open-cathode proton exchange membrane fuel cell (PEMFC) rely heavily on the stability of sensors, especially temperature control. Few targeted solutions have been developed for problems such as sensor data anomalies caused by sudden current and voltage changes during current pulses. This can cause the tracking trajectory in the temperature control system to deviate from the correct target trajectory and can easily lead to thermal runaway and severe damage to the stack. In this paper, a temperature fault-tolerant method based on Adaptive Strong Tracking Kalman Filter (ASTKF) is proposed, considering a series of problems such as the erroneous control caused by sensor failure and the strong nonlinearity of the system caused by external current loading. Combined with Hampel algorithm, the stability of the control system is improved effectively. The experimental data is used to verify that the sudden-change data and slow-change data caused by current pulses during system operation are well processed. Finally, the practical application scheme of the proposed fault-tolerant method is given. The proposed method can improve the accuracy, stability and real-time performance of the control system. The noise reduction and anti-interference ability of sensor data are significantly improved.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"388 ","pages":"Article 125570"},"PeriodicalIF":10.1,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563687","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}