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| 1 | Multi-physics-resolved digital twin of proton exchange membrane fuel cells with a data-driven surrogate model显示文摘The development of multi-physics-resolved digital twins of proton exchange membrane fuel cells(PEMFCs)is sig-nificant for the advancement of this technology.Here,to solve this scientific issue,a surrogate modelling method that combines a state-of-the-art three-dimensional PEMFC physical model and data-driven model is proposed.The surrogate modelling prediction results demonstrate that the test-set relative root mean square errors(rRMSEs)of the multi-physics fields range from 3.88%to 24.80%and can mirror the multi-physics field distribution charac-teristics well.In summary,for multi-physics field prediction,the data-driven surrogate model has a comparable accuracy to the comprehensive 3D physical model;however,it considerably reduces the cost of computation and time and achieves the efficient multi-physics-resolved digital-twin.Two model-based designs based on the as-developed digital twin framework,i.e.the PEMFC healthy operation envelope and the PEMFC state map,are demonstrated.This study highlights the potential of combining data-driven approaches and comprehensive physical models to develop the digital twin of complex systems,such as PEMFCs. | Bowen Wang Guobin Zhang Huizhi Wang Jin Xuan Kui Jiao | 2020 | Energy and AI2020，1，1： | 3 |
| 2 | Cooperation under uncertainty in distributed expert system显示文摘 | Zhang C | 1992 | AI1992，56，： | 2 |
| 3 | Explainable Artificial Intelligence (XAI) techniques for energy and powersystems: Review, challenges and opportunities显示文摘Despite widespread adoption and outstanding performance, machine learning models are considered as ‘‘blackboxes’’, since it is very difficult to understand how such models operate in practice. Therefore, in the powersystems field, which requires a high level of accountability, it is hard for experts to trust and justify decisionsand recommendations made by these models. Meanwhile, in the last couple of years, Explainable ArtificialIntelligence (XAI) techniques have been developed to improve the explainability of machine learning models,such that their output can be better understood. In this light, it is the purpose of this paper to highlight thepotential of using XAI for power system applications. We first present the common challenges of using XAI insuch applications and then review and analyze the recent works on this topic, and the on-going trends in theresearch community. We hope that this paper will trigger fruitful discussions and encourage further researchon this important emerging topic. | R.Machlev L.Heistrene M.Perl K.Y.Levy J.Belikov S.Mannor Y.Levron | 2022 | Energy and AI2022，9，3： | 2 |
| 4 | Two-stage capacity optimization approach of multi-energy system considering its optimal operation显示文摘With the depletion of fossil fuel and climate change,multi-energy systems have attracted widespread attention in buildings.Multi-energy systems,fuelled by renewable energy,including solar and biomass energy,are gain-ing increasing adoption in commercial buildings.Most of previous capacity design approaches are formulated based upon conventional operating schedules,which result in inappropriate design capacities and ineffective operating schedules of the multi-energy system.Therefore,a two-stage capacity optimization approach is pro-posed for the multi-energy system with its optimal operating schedule taken into consideration.To demonstrate the effectiveness of the proposed capacity optimization approach,it is tested on a renewable energy fuelled multi-energy system in a commercial building.The primary energy devices of the multi-energy system consist of biomass gasification-based power generation unit,heat recovery unit,heat exchanger,absorption chiller,elec-tric chiller,biomass boiler,building integrated photovoltaic and photovoltaic thermal hybrid solar collector.The variable efficiency owing to weather condition and part-load operation is also considered.Genetic algorithm is adopted to determine the optimal design capacity and operating capacity of energy devices for the first-stage and second-stage optimization,respectively.The two optimization stages are interrelated;thus,the optimal design and operation of the multi-energy system can be obtained simultaneously and effectively.With the adoption of the proposed novel capacity optimization approach,there is a 14%reduction of year-round biomass consumption compared to one with the conventional capacity design approach. | X.J.Luo Lukumon O.Oyedele Olugbenga O.Akinade Anuoluwapo O.Ajayi | 2020 | Energy and AI2020，1，1： | 2 |
| 5 | Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells显示文摘High-performance batteries greatly benefit from accurate,early predictions of future capacity loss,to advance the management of the battery and sustain desirable application-specific performance characteristics for as long as possible.Li-ion cells exhibit a slow capacity degradation up to a knee-point,after which the degradation ac-celerates rapidly until the cell’s End-of-Life.Using capacity degradation data,we propose a robust method to identify the knee-point within capacity fade curves.In a new approach to knee research,we propose the concept‘knee-onset’,marking the beginning of the nonlinear degradation,and provide a simple and robust identifica-tion mechanism for it.We link cycle life,knee-point and knee-onset,where predicting/identifying one promptly reveals the others.On data featuring continuous high C-rate cycling(1C–8C),we show that,on average,the knee-point occurs at 95%capacity under these conditions and the knee-onset at 97.1%capacity,with knee and its onset on average 108 cycles apart.After the critical identification step,we employ machine learning(ML)techniques for early prediction of the knee-point and knee-onset.Our models predict knee-point and knee-onset quantitatively with 9.4% error using only information from the first 50 cycles of the cells’life.Our models use the knee-point predictions to classify the cells’expected cycle lives as short,medium or long with 88–90% accuracy using only information from the first 3–5 cycles.Our accuracy levels are on par with existing literature for End-of-Life prediction(requiring information from 100-cycles),nonetheless,we address the more complex problem of knee prediction.All estimations are enriched with confidence/credibility metrics.The uncertainty regarding the ML model’s estimations is quantified through prediction intervals.These yield risk-criteria insurers and manufacturers of energy storage applications can use for battery warranties.Our classification model provides a tool for cell man-ufacturers to speed up the validation of cell production techniques. | Paula Fermin-Cueto Euan McTurk Michael Allerhand Encarni Medina-Lopez Miguel F.Anjos Joel Sylvester Goncalo dos Reis | 2020 | Energy and AI2020，1，1： | 2 |
| 6 | Review of dynamic performance and control strategy of supercritical CO_(2) Brayton cycle显示文摘In recent years,the supercritical carbon dioxide Brayton cycle(SCBC)has been regarded as a promising next generation power conversation system,owing to its high efficiency,compact components,applicability for various kinds of heat sources and so on.This paper makes a detailed review of the dynamic performance and control strategy of SCBC.The dynamic simulation model of SCBC is overviewed in detail including different modeling methods of the main component models and validation of system model.As the most inevitable approach to evaluate the dynamic performance of SCBC in practice,existing SCBC test benches concerning SCBC are well collected and presented.Based on these,the open loop dynamic system performances by changing different manipulated variables are reviewed and then various control methods for essential state parameters by different manipulated variables are summarized.Finally,various control strategies of load following and startup/shutdown are clearly presented.With the rapid development of artificial intelligence,combining the core mechanism model and key parameters identifi-cation based on experimental data and machine learning to obtain an accurate model within a wide range of working condition is a popular trend in modeling of SCBC.Moreover,deep reinforcement learning will be a potential method for the control strategy in SCBC. | Xuan Wang Rui Wang Xingyan Bian Jinwen Cai Hua Tian Gequn Shu Xinyu Li Zheng Qin | 2021 | Energy and AI2021，5，3： | 2 |
| 7 | Enabling Technology for Knowledge Sharing显示文摘 | R Neches R Fikes T Finin | 1991 | AI Magazine1991，12，3： | 2 |
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| 9 | Emergence of vowel systems through self-organisation显示文摘 | Bart de Boer | 2000 | AI Communications2000，，13： | 1 |
| 10 | Advances in cancer pmteomics study显示文摘 | Chen ZC | 2004 | Ai Zheng2004，23，2： | 1 |
| 11 | Machine learning research: four current di- rcctions显示文摘 | Dicttcrich Thomas G | 1997 | AI Magazine1997，18，4： | 1 |
| 12 | Creativity and Learning in a Case-based Explainer显示文摘 | Roger C S David B L | 1989 | AI1989，40，13： | 1 |
| 13 | Enabling Technology Knowledge Sharing显示文摘 | Neches R Fikes R E Gruber T R | 1991 | AI Magazine1991，12，3： | 1 |
| 14 | DERVISH: An officenavigating robot 显示文摘 | Nourbakhsh I R Powers R Birchfield S | 1995 | AI Magazine1995，16，2： | 1 |
| 15 | Case-based reasoning: foundational issues, methodological variations, and system approaches显示文摘 | AAMODT A PLAZA E | 1994 | AI Communications1994，7，1： | 1 |
| 16 | An Introduction to Case Based Reasoning显示文摘 | Barletta R | 1991 | AI Expert1991，，8： | 1 |
| 17 | Planning and reacting in uncertain and dynamic environment显示文摘 | Wilkins D Myers K Lowrance J | 1995 | Journal of Experiment and Theoretical AI1995，7，1： | 1 |
| 18 | Case-based reasoning: Foundational issues, methodological variations, and system approaches 显示文摘 | Aamodt A Plaza E | 1994 | AI communications1994，7，1： | 1 |
| 19 | The Regional Innovation System in Sweden: a Study of Regional Clusters for the Development of High Technology 显示文摘 | SANG C P LEE S K | 2004 | AI & SOCIETY2004，18，3： | 1 |
| 20 | Adapting open information extrac tion to domain-specific relations显示文摘 | Soderland S Roof B | 2010 | AI Magazine2010，31，3： | 1 |
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