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学者姓名：金涛
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Abstract ：
With the growing adoption of electric vehicles, demand for charging infrastructure has increased significantly, highlighting the need for timely maintenance and fault diagnosis of charging piles. To effectively leverage multi-scale features in charging pile fault signals, this paper proposes a fault information fusion diagnosis method for vehicle-to-grid (V2G) charging piles with open-circuit switching tubes, based on a multi-scale convolutional neural network (CNN) and dual-attention mechanism. The approach builds upon CNNs by integrating a self-attention mechanism to emphasize critical fault signal features. Simultaneously, max pooling and average pooling layers process fault signals to extract complementary multi-scale information. Additionally, a channel attention mechanism is incorporated to enhance model performance by weighting different channel features. Fault classification is performed using a Softmax classifier. Simulation results demonstrate the method's superiority over other algorithms in convergence speed, overfitting suppression, and diagnostic accuracy, while exhibiting strong noise robustness—effectively handling noise interference in fault signals. Experimental tests show the method achieves 96.67% accuracy in locating open-circuit faults in switching tubes, providing an effective solution for diagnosing such faults in charging piles. ©2025 Chin.Soc.for Elec.Eng.
Keyword ：
Convolutional neural networks Convolutional neural networks Data fusion Data fusion Electric fault location Electric fault location Fault detection Fault detection Multilayer neural networks Multilayer neural networks Scales (weighing instruments) Scales (weighing instruments) Vehicle-to-grid Vehicle-to-grid
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| GB/T 7714 | Xu, Yuzhen , Zou, Zhonghua , Liu, Yulong et al. Information Fusion Diagnosis of Switching Tube Open-circuit Fault in V2G Charging Piles Based on Multi-scale Convolutional Neural Network and Dual-attention Mechanism [J]. | Proceedings of the Chinese Society of Electrical Engineering , 2025 , 45 (8) : 2992-3002 . |
| MLA | Xu, Yuzhen et al. "Information Fusion Diagnosis of Switching Tube Open-circuit Fault in V2G Charging Piles Based on Multi-scale Convolutional Neural Network and Dual-attention Mechanism" . | Proceedings of the Chinese Society of Electrical Engineering 45 . 8 (2025) : 2992-3002 . |
| APA | Xu, Yuzhen , Zou, Zhonghua , Liu, Yulong , Zeng, Ziyang , Wen, Yun , Jin, Tao . Information Fusion Diagnosis of Switching Tube Open-circuit Fault in V2G Charging Piles Based on Multi-scale Convolutional Neural Network and Dual-attention Mechanism . | Proceedings of the Chinese Society of Electrical Engineering , 2025 , 45 (8) , 2992-3002 . |
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To support higher voltage onboard power supply system and electric vehicle (EV) endurance scheme, reconfiguration on novel unbalance levels strategy is developed and adopted in a three-level bidirectional inductor-inductor-capacitor (LLC) resonant converter for wider voltage charging between supercapacitor energy storage (SCES) and battery energy storage (BES) in hybrid energy storage system (HESS), which the proposed converter is composed of an LLC resonant tank module (RTM) and two three-level coupling cascaded neutral point clamping active bridge (3L-CCNPC). Also, the definition of each voltage gain mode in forward and backward workings is established by hybrid modulation strategy with flexible multilevel output ability of novel active bridge. Besides, a wider range of the unified bidirectional voltage gain is achieved by switching among multiple voltage gain modes, which can be implemented by moving among unified bidirectional voltage gain points. Based on the optimization objectives, such as narrowing the pulse frequency modulation (PFM) variation range, the parameters of LLC RTM are designed. Finally, the results from built experimental prototype can verify that the obtained voltage gains are all close to the theoretical design values, and the key waveforms visually reflect the characteristics of novel unbalance levels strategy adopted in bidirectional voltage gain modes of each gain point.
Keyword ：
Bidirectional voltage gain modes Bidirectional voltage gain modes Bridge circuits Bridge circuits Clamps Clamps Couplings Couplings hybrid energy storage system (HESS) hybrid energy storage system (HESS) Inductance Inductance inductor-inductor-capacitor (LLC) resonant tank module (RTM) inductor-inductor-capacitor (LLC) resonant tank module (RTM) Modulation Modulation novel active bridge novel active bridge Resonant converters Resonant converters Switches Switches Topology Topology unbalance levels strategy unbalance levels strategy Voltage Voltage Voltage control Voltage control
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| GB/T 7714 | Zhang, Zhongyi , Xu, Yi , Yuan, Yisheng et al. Reconfiguration on Novel Unbalance Levels Strategy Adopted in a Three-Level Bidirectional LLC Resonant Converter in HESS to EV Endurance Scheme [J]. | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2025 , 11 (1) : 4906-4919 . |
| MLA | Zhang, Zhongyi et al. "Reconfiguration on Novel Unbalance Levels Strategy Adopted in a Three-Level Bidirectional LLC Resonant Converter in HESS to EV Endurance Scheme" . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 11 . 1 (2025) : 4906-4919 . |
| APA | Zhang, Zhongyi , Xu, Yi , Yuan, Yisheng , Cao, Hui , Liu, Peng , Jin, Tao . Reconfiguration on Novel Unbalance Levels Strategy Adopted in a Three-Level Bidirectional LLC Resonant Converter in HESS to EV Endurance Scheme . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2025 , 11 (1) , 4906-4919 . |
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Abstract ：
With electric vehicles' popularity, a surge has been created in demand for charging infrastructure. As a result, the maintenance of charging piles has become a critical issue that requires attention. To effectively utilize the fault features of the front and back circuits in case of the charging pile fails, a multifeature fusion model is proposed in this article. First, use the front- and back-stage feature information fusion module to fuse the collected front-stage fault feature quantity signals and the back-stage fault feature quantity signals. Then, the spatial and temporal feature extraction modules are used to mine the spatial and temporal high-dimensional features in parallel. Finally, through the spatiotemporal feature fusion classification module, the spatial and temporal features are fused and classified to achieve the purpose of fault diagnosis. The proposed method employs deep learning techniques, which avoids the cumbersome steps involved in graphical input and the errors arising from manually selecting features in traditional deep learning algorithms and gives full play to the parallel diagnostic performance of deep learning. The simulation results demonstrate that the proposed method outperforms other comparative algorithms in terms of diagnostic accuracy, convergence speed, and overfitting suppression, and has excellent noise immunity, which can cope with the noisy situation of charging piles. In the experimental test, the fault diagnosis accuracy of this method reached 96.36%, and its recognition sensitivity for most fault categories was higher than that of the comparison model, which further verified the superiority and robustness of this method.
Keyword ：
Capacitors Capacitors Charging pile Charging pile Circuit faults Circuit faults data fusion data fusion deep learning deep learning Deep learning Deep learning fault diagnosis fault diagnosis Fault diagnosis Fault diagnosis Feature extraction Feature extraction Integrated circuit modeling Integrated circuit modeling Rectifiers Rectifiers spatiotemporal features spatiotemporal features
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| GB/T 7714 | Xu, Yuzhen , Zou, Zhonghua , Liu, Yulong et al. Deep Learning-Based Multifeature Fusion Model for Accurate Open-Circuit Fault Diagnosis in Electric Vehicle DC Charging Piles [J]. | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2025 , 11 (1) : 2243-2254 . |
| MLA | Xu, Yuzhen et al. "Deep Learning-Based Multifeature Fusion Model for Accurate Open-Circuit Fault Diagnosis in Electric Vehicle DC Charging Piles" . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 11 . 1 (2025) : 2243-2254 . |
| APA | Xu, Yuzhen , Zou, Zhonghua , Liu, Yulong , Zeng, Ziyang , Zhou, Sheng , Jin, Tao . Deep Learning-Based Multifeature Fusion Model for Accurate Open-Circuit Fault Diagnosis in Electric Vehicle DC Charging Piles . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2025 , 11 (1) , 2243-2254 . |
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Although deep convolutional neural network (CNN) has been widely used in the breast cancer detection based on thermal imaging technology, this scenario still did not receive enough attention in the mobile devices with limited resource. In addition, there still exists challenge on how to assist front view thermal imaging by side one during breast cancer detection. This study proposes a multi-input lightweight CNN named Multi-light Net in order to achieve more accurate early detection for breast cancer, which combines the thermal image from multiple perspectives with the lightweight CNN on the basis of model performance and scale. In addition, a new weighted label smoothing regularization (WLSR) is proposed for the Multi-light Net with the purpose of increasing the network's generalization ability and classification accuracy. The experimental results demonstrate that the proposed approach by combining front view with side view can achieve more significant results than the common one using only front view during breast cancer detection, and the proposed Multi-light Net also exhibits an excellent performance with respect to the currently popular lightweight CNN. Furthermore, the proposed WLSR loss function can also lead to both faster convergence rate and more stable training process during network training and ultimately higher diagnostic accuracy for breast cancer.
Keyword ：
Breast cancer Breast cancer CNN CNN Lightweight Lightweight Multi-input Multi-input Thermography Thermography
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| GB/T 7714 | Tang, Yundong , Zhou, Depei , Flesch, Rodolfo C. C. et al. A multi-input lightweight convolutional neural network for breast cancer detection considering infrared thermography [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 263 . |
| MLA | Tang, Yundong et al. "A multi-input lightweight convolutional neural network for breast cancer detection considering infrared thermography" . | EXPERT SYSTEMS WITH APPLICATIONS 263 (2025) . |
| APA | Tang, Yundong , Zhou, Depei , Flesch, Rodolfo C. C. , Jin, Tao . A multi-input lightweight convolutional neural network for breast cancer detection considering infrared thermography . | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 263 . |
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A high-boost interleaved DC-DC converter that utilizes coupled inductors and voltage multiplier cells (VMC) is proposed in this paper. The input power supply connects to switches through the primary sides of two coupling inductors with an interleaved structure, which reduces the voltage stresses of the switches and lowers the input current ripple. Two capacitors and a diode are placed in series on the secondary side of the coupled inductors to enhance the high boost capability. The implementation of maximum power point tracking (MPPT) is facilitated by the simplification of the control system through common ground. To verify the effectiveness of the proposed converter, an experimental platform and a prototype based on a turns ratio of 1 are presented. The test results show that the voltage stresses on the switches are only 1/8 of the output voltage. The operating principle and design guidelines of the proposed converter are described in detail. The experimental results show that the converter is efficient and stable over a wide power range.
Keyword ：
Capacitors Capacitors Control systems Control systems coupled inductor coupled inductor DC-DC converter DC-DC converter DC-DC power converters DC-DC power converters High voltage gain High voltage gain High-voltage techniques High-voltage techniques Inductors Inductors low voltage stress low voltage stress Maximum power point trackers Maximum power point trackers Power system stability Power system stability Renewable energy sources Renewable energy sources Stress Stress Voltage control Voltage control
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| GB/T 7714 | Chen, Yin , Li, Haibin , Jin, Tao . A Novel High-Boost Interleaved DC-DC Converter for Renewable Energy Systems [J]. | PROTECTION AND CONTROL OF MODERN POWER SYSTEMS , 2025 , 10 (1) : 132-147 . |
| MLA | Chen, Yin et al. "A Novel High-Boost Interleaved DC-DC Converter for Renewable Energy Systems" . | PROTECTION AND CONTROL OF MODERN POWER SYSTEMS 10 . 1 (2025) : 132-147 . |
| APA | Chen, Yin , Li, Haibin , Jin, Tao . A Novel High-Boost Interleaved DC-DC Converter for Renewable Energy Systems . | PROTECTION AND CONTROL OF MODERN POWER SYSTEMS , 2025 , 10 (1) , 132-147 . |
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Magnetic nanoparticles (MNPs) used for magnetic hyperthermia can not only damage tumor cells after elevating to a specific temperature but also provide the temperature required for thermosensitive liposomes (TSL) to release doxorubicin (DOX). MNPs injected into tumor will generate heat under an alternating magnetic field, so the MNPs distribution can determine temperature distribution and further affect the DOX concentration used for tumor therapy. This study proposes an asynchronous injection strategy for this combination therapy in order to improve the DOX concentration value for drug therapy, in which the MNPs are injected into tumor after a certain lagging of TSL injection in order to increase the TSL concentration inside tumor. In addition, the evaluation of treatment effect for this combination therapy is implemented by considering two different MNPs concentration distributions and two biological heat transfer models. The simulation results demonstrate that the treatment effect for combination therapy can be significantly improved after considering the proposed asynchronous injection strategy, which can mainly attribute to the improvement of DOX concentration. The DOX concentration difference during therapy is generally relevant to both the lagging time of different injections and the local temperature distribution due to MNPs concentration distribution.
Keyword ：
Heat transfer Heat transfer Heat transfer model Heat transfer model Magnetic hyperthermia Magnetic hyperthermia Targeted drug delivery Targeted drug delivery Temperature-sensitive liposomes Temperature-sensitive liposomes
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| GB/T 7714 | Tang, Yundong , Zhu, Jiajia , Flesch, Rodolfo C. C. et al. Effect of proposed asynchronous injection strategy on the combination therapy of magnetic hyperthermia and thermosensitive liposome [J]. | JOURNAL OF THERMAL BIOLOGY , 2025 , 127 . |
| MLA | Tang, Yundong et al. "Effect of proposed asynchronous injection strategy on the combination therapy of magnetic hyperthermia and thermosensitive liposome" . | JOURNAL OF THERMAL BIOLOGY 127 (2025) . |
| APA | Tang, Yundong , Zhu, Jiajia , Flesch, Rodolfo C. C. , Jin, Tao . Effect of proposed asynchronous injection strategy on the combination therapy of magnetic hyperthermia and thermosensitive liposome . | JOURNAL OF THERMAL BIOLOGY , 2025 , 127 . |
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为研究温度对油纸绝缘频域介电谱的影响,并探索高效的频温归一化策略以消除不同环境因素带来的测试温度误差,该文提出了基于极化复电容实部一阶微分解谱的多弛豫分解方法.首先,利用微分图谱特征划分出低频弛豫、中低频多弛豫、高频弛豫三类不同弛豫区间进行频温介电机理推演,发现各弛豫过程温度特性差异显著;其次,以Arrhenius衍生方程计算不同弛豫的活化能,基于该频温特性参量提取介质中多类贡献分量的频温频移因子,还原标准温度下的介电图谱;最后,利用不同温度及不同老化程度的试样验证该方法.实验分析表明,该方法很好地解决了传统频温归一法所存在的偏差,且对于不同老化程度的介质具有较好的适用性,可为现场测试提供可靠的理论支撑.
Keyword ：
弛豫活化能 弛豫活化能 微分解谱 微分解谱 油纸绝缘 油纸绝缘 频域介电法 频域介电法 频温归一化 频温归一化
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| GB/T 7714 | 邹阳 , 黄煜 , 方梦泓 et al. 基于微分解谱的油纸绝缘多弛豫频温机理与归一化研究 [J]. | 电工技术学报 , 2025 , 40 (5) : 1575-1586 . |
| MLA | 邹阳 et al. "基于微分解谱的油纸绝缘多弛豫频温机理与归一化研究" . | 电工技术学报 40 . 5 (2025) : 1575-1586 . |
| APA | 邹阳 , 黄煜 , 方梦泓 , 姚雨佳 , 金涛 . 基于微分解谱的油纸绝缘多弛豫频温机理与归一化研究 . | 电工技术学报 , 2025 , 40 (5) , 1575-1586 . |
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为解决传统有限控制集模型预测控制存在开关频率不固定的缺点,该文提出一种基于离散虚拟电压矢量的最优开关序列模型预测控制策略.所提策略采用开关序列的方式来固定开关频率,利用离散空间矢量的原理预定义虚拟电压矢量,引入一个查找表来描述虚拟电压矢量的开关序列占空比,并通过一种有效的寻优算法来减少控制策略的计算负担.仿真结果表明:所提控制策略在固定逆变器开关频率的同时,避免繁琐的权重系数整定过程,直流侧电容电压偏移控制在3%以内.
Keyword ：
三相三电平逆变器 三相三电平逆变器 开关频率 开关频率 模型预测控制 模型预测控制
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| GB/T 7714 | 朱敏龙 , 张煌辉 , 张杰梁 et al. 三电平逆变器固定开关频率的模型预测开关序列控制 [J]. | 中国测试 , 2025 , 51 (2) : 97-105 . |
| MLA | 朱敏龙 et al. "三电平逆变器固定开关频率的模型预测开关序列控制" . | 中国测试 51 . 2 (2025) : 97-105 . |
| APA | 朱敏龙 , 张煌辉 , 张杰梁 , 金涛 . 三电平逆变器固定开关频率的模型预测开关序列控制 . | 中国测试 , 2025 , 51 (2) , 97-105 . |
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随着电动汽车的普及,充电基础设施需求急剧上升,迫切需要对充电桩进行维护和故障诊断.为有效利用不同尺度下的充电桩故障信号特征,该文提出一种基于多尺度卷积神经网络和双注意力机制的 V2G(vehicle-to-grid)充电桩开关管开路故障信息融合诊断方法.该方法基于卷积神经网络,引入自注意力机制突出故障信号中的重要特征.同时,使用最大池化层和平均池化层处理故障信号,提供不同尺度的互补信息;此外,引入通道注意力机制关注不同通道特征,可提高模型性能;最后,采用Softmax分类器进行分类和识别.仿真结果表明,该方法在多个方面优于其他对比算法,包括收敛速度、抑制过拟合以及诊断准确率等,并且表现出卓越的抗噪性能,能够有效应对充电桩故障信号中的噪声.在实际测试中,该方法实现了开关管开路故障位置的准确定位,其准确率达 96.67％.结果为充电桩开关管开路故障的诊断提供了可行的解决方案.
Keyword ：
信息融合 信息融合 充电桩 充电桩 故障诊断 故障诊断 注意力机制 注意力机制 深度学习 深度学习
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| GB/T 7714 | 徐玉珍 , 邹中华 , 刘宇龙 et al. 基于多尺度卷积神经网络和双注意力机制的V2G充电桩开关管开路故障信息融合诊断 [J]. | 中国电机工程学报 , 2025 , 45 (8) : 2992-3002,中插12 . |
| MLA | 徐玉珍 et al. "基于多尺度卷积神经网络和双注意力机制的V2G充电桩开关管开路故障信息融合诊断" . | 中国电机工程学报 45 . 8 (2025) : 2992-3002,中插12 . |
| APA | 徐玉珍 , 邹中华 , 刘宇龙 , 曾梓洋 , 文云 , 金涛 . 基于多尺度卷积神经网络和双注意力机制的V2G充电桩开关管开路故障信息融合诊断 . | 中国电机工程学报 , 2025 , 45 (8) , 2992-3002,中插12 . |
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提出一种新型的高升压DC-DC变换器.该变换器输入侧采用Boost结构,因此继承了输入电流连续的优点,适合用于可再生能源应用.变换器后级利用三绕组耦合电感和倍压单元进行集成,从而能够使用较小总匝比的耦合电感获得高电压增益,且提高电压增益的调节自由度.开关管电压应力低,可使用低耐压器件.此外,钳位支路回收储存在耦合电感漏感中的能量,从而提高效率.该文对变换器的工作模态进行详细讨论,并与其他变换器的性能进行对比.最后,搭建一台实验样机进行验证,实验获取的数据结果与理论层面的分析高度契合.
Keyword ：
DC-DC变换器 DC-DC变换器 可再生能源 可再生能源 增益调节 增益调节 耦合电路 耦合电路 连续输入电流 连续输入电流
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| GB/T 7714 | 杨明发 , 翁雨森 , 李海滨 et al. 集成三绕组耦合电感和倍压单元的高增益连续输入电流DC-DC变换器 [J]. | 太阳能学报 , 2025 , 46 (4) : 133-142 . |
| MLA | 杨明发 et al. "集成三绕组耦合电感和倍压单元的高增益连续输入电流DC-DC变换器" . | 太阳能学报 46 . 4 (2025) : 133-142 . |
| APA | 杨明发 , 翁雨森 , 李海滨 , 颜胥 , 林佳奇 , 金涛 . 集成三绕组耦合电感和倍压单元的高增益连续输入电流DC-DC变换器 . | 太阳能学报 , 2025 , 46 (4) , 133-142 . |
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