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一、個(gè)人簡(jiǎn)介
楊闊，男， 1995年出生，漢，籍貫：河南信陽(yáng)；博士，上海電機(jī)學(xué)院機(jī)械學(xué)院講師。
二、主要學(xué)習(xí)與工作經(jīng)歷
主要學(xué)習(xí)經(jīng)歷：
2014.9-2018.7 上海電機(jī)學(xué)院 車輛工程專業(yè) 本科
2018.9–2023.8 上海大學(xué) 機(jī)械電子工程專業(yè) 博士研究生
2019.9-2019.12 法國(guó)特魯瓦科技大學(xué) 項(xiàng)目訪學(xué)
主要工作經(jīng)歷：
2023.9-至今，上海電機(jī)學(xué)院 教師
三、主要科研工作與成績(jī)
近年來(lái)在Energy、Renewable and Sustainable Energy Reviews等國(guó)內(nèi)外期刊發(fā)表了論文24篇，其中SCI收錄15篇，中科院一區(qū)9篇，高被引論文一篇，19年至今總被引超過(guò)900余次，單篇被引最高190余次,擔(dān)任International Journal of Electrical Power and Energy，Journal of Energy Chemistry等多個(gè)期刊審稿人。
近年主要代表論文：
[1]Liu Zixi, Guanqiang Ruan, Yupeng Tian, Xing Hu, An Zhongxun, Kuo Yang*.Application of a Transformer Network Based on Multi-Scale Branches and Fast Fourier Gating Mechanism in the State of Charge Prediction for Sodium-Ion Batteries，Expert Systems With Applications,2025（通訊作者，SCI一區(qū)，IF=7.5)
[2]Kuo Yang, Cai Y, Cheng J. A deep learning model based on multi-attention mechanism and gated recurrent unit network for photovoltaic power forecasting[J]. Computers and Electrical Engineering, 2025, 123: 110250.（SCI三區(qū)，IF=7.5)
[3] Liu Zixi, Guanqiang Ruan, Yupeng Tian, Xing Hu, Yan Rong, Kuo Yang*. A real-world battery state of charge prediction method based on a lightweight mixer architecture. Energy, 2024, 311: 133434. （通訊作者，SCI一區(qū)，IF=9)
[4] Kuo Yang, Yugui Tang, Shujing Zhang, Zhen Zhang*. A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism[J]. Energy, 2022, 244: 123233.(SCI一區(qū)，IF=9，ESI高被引論文)
[5] Kuo Yang, Yanyu Wang, Yugui Tang, Zhen Zhang*. Atemporalconvolutionandgatedrecurrentunitnetworkwithattentionforstateofchargeestimationoflithium-ionbatteries[J]. Journal of Energy Storage, 2023, 72: 108774.(SCI二區(qū)，IF=8.9)
[6] Ruan Guanqiang, Liu Zixi., Cheng Jinrun, Hu Xing, Chen Song, Liu Shiwen, Yong Guo, Kuo Yang*. A deep learning model for predicting the state of energy in lithium-ion batteries based on magnetic field effects. Energy, 2024, 304, 132161.(通訊作者，SCI一區(qū)，IF=9，引用次數(shù)4)
[7] Kuo Yang, Yugui Tang, Zhen Zhang*. Parameter identification and state-of-charge estimation for lithium-ion batteries using separated time scales and extended Kalman filter[J]. Energies, 2021, 14(4). (SCI四區(qū)，IF=4)
[8] Kuo Yang, Zhen Zhang. Real-timepatternrecognitionforhandgesturebasedon ANN andsurfaceEMG[J]. Proceedingsof2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2019, 2019: 799–802.(EI收錄)
[9] Yugui Tang, Kuo Yang, Zhang S. Wind power forecasting: A temporal domain generalization approach incorporating hybrid model and adversarial relationship-based training[J]. Applied Energy, 2024, 355: 122266.(SCI一區(qū)，IF=10.1)
[10] Yugui Tang, Kuo Yang, Yichu Zheng, Li Ma, Shujing Zhang, Zhen Zhang. Wind power forecasting: A transfer learning approach incorporating temporal convolution and adversarial training[J]. Renewable Energy, 2024: 12.(SCI一區(qū)，IF=9)
[11] Yugui Tang, Kuo Yang, Shujing Zhang, Zhen Zhang. Photovoltaic power forecasting: A dual-attention gated recurrent unit framework incorporating weather clustering and transfer learning strategy[J]. Engineering Applications of Artificial Intelligence, 2024, 130: 107691.(SCI二區(qū)，IF=8)
[12] Yugui Tang, Kuo Yang, Shujing Zhang, Zhen Zhang*. Wind power forecasting:A hybrid forecasting model and multi-task learning-based framework[J]. Energy, 2023: 127864.(SCI一區(qū)，IF=9)
[13] Yugui Tang, Kuo Yang, Shujing Zhang, Zhen Zhang. Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy[J]. Renewable and Sustainable Energy Reviews, 2022, 162(4): 112473.(SCI一區(qū)，IF=16.3)
[14] Yugui Tang, Kuo Yang, Shujing Zhang, Zhen Zhang. Early prediction of lithium-ion battery lifetime via a hybrid deep learning model[J]. Measurement, 2022, 199(6): 111530.(SCI二區(qū)，IF=5.2)
近五年主持和參與的主要科研項(xiàng)目：
（1）項(xiàng)目來(lái)源：上海市科技創(chuàng)新行動(dòng)計(jì)劃啟明星（揚(yáng)帆專項(xiàng)），極限工況下基于數(shù)據(jù)驅(qū)動(dòng)的鈉離子動(dòng)力電池退化機(jī)理和壽命預(yù)測(cè)方法研究,2024-2027,項(xiàng)目負(fù)責(zé)人
（2）項(xiàng)目來(lái)源：上海市基礎(chǔ)研究重大項(xiàng)目,多源融合神經(jīng)信號(hào)傳感器件新結(jié)構(gòu)設(shè)計(jì)與感知機(jī)理研究,2018-2020,主要參與人
（3）項(xiàng)目來(lái)源：國(guó)網(wǎng)山東省電力公司電力科學(xué)研究院，規(guī)模化分布式發(fā)電并網(wǎng)狀態(tài)評(píng)估技術(shù)研究,2020-2022,主要參與人。
四、主要研究方向
動(dòng)力電池管理系統(tǒng)，深度學(xué)習(xí)在儲(chǔ)能領(lǐng)域應(yīng)用、電池荷電狀態(tài)及壽命狀態(tài)預(yù)測(cè)，時(shí)序預(yù)測(cè)算法。
五、招生專業(yè)領(lǐng)域
085500機(jī)械（專碩）
五、聯(lián)系方式
郵箱：yangkuo@sdju.edu.cn