Sodium-ion batteries(SIBs)are gaining traction as a cost-effective and sustainable alternative to lithium-ion batteries for large-scale energy storage,due to sodium’s abundance,low cost,and safety advantages.However,...Sodium-ion batteries(SIBs)are gaining traction as a cost-effective and sustainable alternative to lithium-ion batteries for large-scale energy storage,due to sodium’s abundance,low cost,and safety advantages.However,the discovery of high-performance electrode materials for SIBs remains a significant challenge because of the complex interactions between compositional and structural features that govern key properties such as specific capacity,average voltage,and volume change.In this study,we present an artificial intelligence(AI)-driven framework that integrates machine learning and multi-objective optimization to accelerate the design of sodium-ion battery electrodes.Four predictive models,namely Decision Tree,Random Forest,Support Vector Machine(SVM),and Deep Neural Network(DNN),were trained on a feature-rich dataset derived from high-throughput computational databases.The DNN model achieved the highest predictive accuracy,with R2 values up to 0.97 and mean absolute errors(MAE)below 0.11 for the target properties.To support material selection,the DNN was coupled with the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)to identify Pareto-optimal materials that maximize specific capacity while minimizing volume expansion.The resulting candidates exhibit balanced electrochemical performance and potential for practical SIB applications.This study demonstrates the power of combining deep learning and optimization to guide the discovery of next-generation energy storage materials with high efficiency and reduced experimental overhead.展开更多
Plug-in electric vehicle(PEV)load modeling is very important in the operation and planning studies of modern power system nowadays.Several parameters and considerations should be taken into account in PEV load modelin...Plug-in electric vehicle(PEV)load modeling is very important in the operation and planning studies of modern power system nowadays.Several parameters and considerations should be taken into account in PEV load modeling,making it a complex problem that should be solved using appropriate techniques.Different techniques have been introduced for PEV load modeling and each of them has individual specifications and features.In this paper,the most popular techniques for PEV load modeling are reviewed and their capabilities are evaluated.Both deterministic and probabilistic methods are investigated and some practical and theoretical hints are presented.Moreover,the characteristics of all techniques are compared with each other and suitable methods for unique applications are proposed.Finally,some potential research areas are presented for future works.展开更多
基金supported by Khalifa University of Science and Technology through the project number RC2-2018-024。
文摘Sodium-ion batteries(SIBs)are gaining traction as a cost-effective and sustainable alternative to lithium-ion batteries for large-scale energy storage,due to sodium’s abundance,low cost,and safety advantages.However,the discovery of high-performance electrode materials for SIBs remains a significant challenge because of the complex interactions between compositional and structural features that govern key properties such as specific capacity,average voltage,and volume change.In this study,we present an artificial intelligence(AI)-driven framework that integrates machine learning and multi-objective optimization to accelerate the design of sodium-ion battery electrodes.Four predictive models,namely Decision Tree,Random Forest,Support Vector Machine(SVM),and Deep Neural Network(DNN),were trained on a feature-rich dataset derived from high-throughput computational databases.The DNN model achieved the highest predictive accuracy,with R2 values up to 0.97 and mean absolute errors(MAE)below 0.11 for the target properties.To support material selection,the DNN was coupled with the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)to identify Pareto-optimal materials that maximize specific capacity while minimizing volume expansion.The resulting candidates exhibit balanced electrochemical performance and potential for practical SIB applications.This study demonstrates the power of combining deep learning and optimization to guide the discovery of next-generation energy storage materials with high efficiency and reduced experimental overhead.
文摘Plug-in electric vehicle(PEV)load modeling is very important in the operation and planning studies of modern power system nowadays.Several parameters and considerations should be taken into account in PEV load modeling,making it a complex problem that should be solved using appropriate techniques.Different techniques have been introduced for PEV load modeling and each of them has individual specifications and features.In this paper,the most popular techniques for PEV load modeling are reviewed and their capabilities are evaluated.Both deterministic and probabilistic methods are investigated and some practical and theoretical hints are presented.Moreover,the characteristics of all techniques are compared with each other and suitable methods for unique applications are proposed.Finally,some potential research areas are presented for future works.
