杨朵

作者: 时间:2023-09-13 点击数:

       


杨朵

副教授,硕士生导师

电子邮箱:

yangduo@zzu.edu.cn

办公室:

电气学院1408

研究方向:

新能源系统优化控制、电池管理系统、人工智能应用

教育背景

2016/09-2021/06,中国科学技术大学,信息科学技术学院,博士

2012/09-2016/06,中国科学技术大学,信息科学技术学院,学士

工作经历

Ø2021/08-2022/01,郑州大学,电气工程学院,讲师

Ø2022/01-2024.12,郑州大学,电气与信息工程学院,讲师

Ø2025.01-今,    郑州大学,电气与信息工程学院,副教授

学术兼职

Ø中国仿真学会仿真技术应用专委会、中国自动化学会仿真技术专业委员会委员

ØEnergy、Energy Conversion and Management、IEEE Transactions on Industrial Electronics等期刊审稿专家

ØGreen Energy and Intelligent Transportation期刊青年编委

奖励与荣誉

Ø2022年,中国仿真学会,优秀博士学位论文

Ø2024、2025年,河南省教育厅优秀科技论文一等奖

Ø2024、2025年,全球2%顶尖科学家榜单“年度影响力排行榜”

Ø2022年,中国科学技术大学优秀毕业生

Ø2016、2017、2020年研究生国家奖学金

科研项目

Ø(主持)国家自然科学基金,面上项目,2027.01-2030.12.

Ø(主持)国家自然科学基金,青年基金,2024.01-2026.12.

Ø(主持)中国博士后科学基金,面上资助,2022.06-2024.05.

Ø(主持)河南省科技攻关项目,2024.01-2025.12.

Ø(主持)河南省科协青年人才托举工程,2025.01-2026.12.

Ø(主持)安徽省人机共融国家重点实验室开放课题,2026.01-2026.12.

Ø(主持)横向课题,湖南省银峰新能源有限公司,2023.08-2024.07.

Ø(主持)横向课题,湖北省电力规划设计研究院,2026.08-2026.12

Ø(主持)横向课题,河南中烟工业有限责任公司,2026.04-2028.04.


代表文章

[1]Yang D, Lv H, Yan Y, et al. A hybrid deep reinforcement-supervised learning framework for energy management of fuel cell-battery hybrid vehicles[J]. IEEE Transactions on Transportation Electrification, 2026, 12(3), 4196 - 4206.(中科院一区TOP)

[2]Yang D, Lv H, et al. CycleDegrade: An Explicit Periodicity Modeling Approach for Long-term Fuel Cell Voltage Degradation Forecasting[J]. IEEE Transactions on Industrial Informatics, 2026, accepted.(中科院一区TOP)

[3]Pan R, Li, J, Yang D*, et al. A fuzzy RL-Based energy management strategy for battery/supercapacitor hybrid energy storage system with road condition recognition[J]. IEEE Transactions on Transportation Electrification, 2026, 12(2): 2887 - 2898.(中科院一区TOP)

[4]Yan Y, Han X, Xu K, Yang D*, et al.Real-Time Energy Management Method for Fuel Cell Digital Railway Trams Based on Closed-Form Pontryagin’s Minimum Principle[J].IEEE Transactions on Industrial Electronics, 2026.(中科院一区TOP)

[5]Yang D, Liu X, et al. Multi-agent switching energy management strategy for fuel cell hybrid vehicles using an enhanced deep reinforcement learning algorithm[J]. Engineering Applications of Artificial Intelligence, 2026.(中科院一区TOP)

[6]Liao Y, Yang Z, Yang D*, et al. Power Sharing Control Strategy of ISOP LLC-DAB Hybrid Bidirectional Converters Based on Multiagent Consensus Theory[J]. IEEE Transactions on Industrial Electronics, 2025, 72(10): 10290-10300.(中科院一区TOP)

[7]杨朵,吕浩然,李民策,等. 基于双向长短期记忆网络和注意力机制的质子交换膜燃料电池衰退行为预测[J]. 电工技术学报, 2026, 2.

[8]Yang D, Yan F, Wang S, et al. Genetic programming-based energy management strategy for fuel cell vehicles considering aging factors[J]. IEEE Transactions on Transportation Electrification, 2025, 11(5): 12184-12196.(中科院一区TOP)

[9]Yang D, Wang L, et al. "A reinforcement learning-based energy management strategy for fuel cell hybrid vehicle considering real-time velocity prediction." Energy Conversion and Management 274 (2022): 116453. (中科院一区TOP)

[10]Yang D, Wang S, Liao Y, et al. "An Online Energy Management Strategy for Fuel Cell Vehicles Based on Fuzzy Q-Learning and Road Condition Recognition." IEEE Transactions on Intelligent Transportation Systems, 25(9) (2024): 12120-12130. (中科院一区TOP)

[11]Fu H, Yang D*, et al. A novel online energy management strategy for fuel cell vehicles based on improved random forest regression in multi road modes. Energy Conversion and Management 305 (2024): 118261. (中科院一区TOP)

[12]Wang S, Yang D*, Yan F, et al. Comparison of deep reinforcement learning-based energy management strategies for fuel cell vehicles considering economics, durability and adaptability. Energy 307 (2024): 132771. (中科院一区TOP)

[13]Pan R, …, Yang D*, et al. State of health estimation for lithium-ion batteries based on two-stage features extraction and gradient boosting decision tree. Energy 285 (2023): 129460. (中科院一区TOP)

[14]Yu K, Zhang K, Zhong Y, Yang D*, et al. Fractional-order modeling and parameter estimation of lithium-ion battery via multi-strategy and mean factor differential evolution[J]. Measurement, 2025, 246: 116721.(中科院二区Top)

[15]Yang D, Wang Y, Pan R, Chen R, Chen Z. State-of-health estimation for the lithium-ion battery based on support vector regression. Applied Energy, 2018, 227: 273-283.(中科院一区Top)

[16]Yang D, Zhang X, Pan R, et al. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve. Journal of Power Sources, 2018, 384: 387-395.(中科院二区Top,ESI高被引)

[17]Yang D, Pan R, Wang Y, Chen Z. Modeling and control of PEMFC air supply system based on T-S fuzzy theory and predictive control. Energy, 2019, 116078.(中科院一区Top)

[18]Yang D, Wang Y, Chen Z. Robust fault diagnosis and fault tolerant control for PEMFC system based on an augmented LPV observer. International Journal of Hydrogen Energy, 2020, 56: 23508-13522. (中科院二区)

[19]Yang D, Fu H, Li J, et al. A multivariable sliding mode predictive control method for the air management system of automotive fuel cells[J]. Measurement and Control, 2024, 57(2): 139-151.(中科院四区)

[20]Yan Y, Yang R, Li J, Xu K, Liu C, Yang D*, Yu K*. Capacity Parameter Configuration Method for Fuel Cell Hybrid Digital Rail Tram Considering Energy Management Coupling[J]. IEEE Transactions on Industrial Electronics, 2026, 73(2): 2276-2287. (中科院一区Top)

专利

[1]杨朵,王思雨,闫福慧,等.一种基于双 Q 学习与实时速度预测的氢-电混动系统能量管理方法、系统、设备及存储介质, 202410722254.8 (发明,授权)

[2]杨朵,吕浩然,陈鑫,等. 一种基于周期残差建模的燃料电池电压退化预测方法、系统、设备及介质. 202611365622.3 (发明,受理)

[3]于坤杰,杨朵,钟雅哲,梁静,等. 锂电池功率状态估计方法、系统、存储介质及设备. 2024102555313.5.(发明,公开)

[4]陈宗海,杨朵,潘瑞,汪玉洁,杨晓宇.一种燃料电池系统. 授权公告号:CN 209312921 U[实用新型]

[5]陈宗海,杨朵,潘瑞,汪玉洁,杨晓宇. 一种燃料电池系统及控制系统,CN109830711A.(发明,公开)

[6]廖粤峰,梁静,杨正颖,韩笑,杨朵,等. 一种多模块变换器的功率均分控制方法,2024114865636.6 (发明,公开)

其他信息

Ø课题组聚焦国家前沿热点需求,软硬件结合,具有良好的就业和深造前景,欢迎对新能源系统能量管理、深度学习与大模型应用、前沿AI算法在智慧能源中的交叉应用、电动汽车节能优化控制、电力系统优化调度等方向感兴趣的研究生和博士生报考。

Ø欢迎具有推免资格的本科生联系,欢迎大二、大三的同学提前加入实验室学习。



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