中科大先研院研究生校内导师简历
汪玉洁 博士/副教授
姓名 | 汪玉洁 |
学位/职称 | 博士/副教授 |
所属单位 | 中国科学技术大学 信息科学技术学院 自动化系 |
办公室电话 | 0551-63601514 |
wangyujie@ustc.edu.cn | |
教育背景 |
2017年博士毕业于中国科学技术大学自动化系,获控制科学与工程博士学位(获中科院院长特别奖、中科院优博论文) |
研究领域 |
节能与新能源汽车技术、电池安全管理、综合能源系统管控、数字孪生、AI在能源系统中的应用等 |
任职经历 |
2017.07-2020.01 中国科学技术大学 电子工程与信息科学系 博士后 2020.02-2023.01 中国科学技术大学 自动化系 副研究员 2023.02-迄今 中国科学技术大学 自动化系 副教授 |
获得荣誉、奖项 |
中国自动化学会自然科学一等奖 (2018.12) 中国仿真学会自然科学一等奖 (2022.12) 教育部技术发明二等奖 (2019.12) 安徽省科学技术二等奖 (2019.03) 中国科学院院长特别奖 (2017.08) 中国科学院优博论文 (2019.09) 中国自动化学会优秀科技工作者 (2021.12) 中国仿真学会优秀科技工作者 (2019.12) 第34届世界电动汽车大会 Excellent Paper Award (2021.06) |
主持、参与项目 |
基于虚拟电厂的电动汽车充放电行为双向导引机制研究 安徽省自然科学基金能源互联网联合基金/安徽省科技厅 2022.08-2025.08 主持 基于模型驱动的设计/制造/服务全域一体化技术研发 国家重点研发计划课题/科技部 2020.11-2023.10 主持 基于贝叶斯理论的动力电池系统参数与状态联合估计方法研究 国家自然科学基金青年基金/基金委 2019.01-2021.12 主持 5G 智能电池管理系统的研发与产业化 安徽省高校协同创新项目/安徽省教育厅 2019.11-2021.10 主持 动力能源系统的建模、状态估计与优化管理关键技术 支持“率先行动”计划中科院和中国博士后科学基金会联合资助优秀博士后项目/中科院、中国博士后科学基金会 2018.01-2019.12 主持 混合储能系统的建模与能量管理优化方法研究 中国博士后科学基金面上项目/中国博士后科学基金会 2018.01-2019.12 主持 基于云端辅助计算的智能电池管理关键技术研究 中国科学技术大学青年创新重点基金/中国科学技术大学 2019.11-2021.10 主持 面向电动汽车的混合储能系统功率分配方法研究 中国科学技术大学青年创新基金/中国科学技术大学 2019.01-2020.12 主持 |
论文、著作、成果 |
出版英文专著2部,教材1部,发表SCI论文80余篇,申请专利20余项。 专著、教材: [1]. K. Liu, Y. Wang, X. Lai. Data Science-Based Full-Lifespan Management of Lithium-Ion Battery: Manufacturing, Operation and Reutilization. 2022, Springer. https://doi.org/10.1007/978-3-031-01340-9 [2]. S. Wang, K. Liu, Y. Wang, D. Stroe, C. Fernandez, J. M. Guerrero. Multidimensional Lithium-Ion Battery Status Monitoring. 2022, CRC Press. https://doi.org/10.1201/9781003333791 [3]. 陈宗海, 杨晓宇, 汪玉洁编著. 计算机控制工程(第2版). 中国科学技术大学出版社, 2021. 期刊论文: [1]. Y. Wang*, G. Zhao. A comparative study of different fractional-order models for lithium-ion batteries, Control Engineering Practice, 133(2023), 105451. https://doi.org/10.1016/j.conengprac.2023.105451 [2]. Y. Wang*, K. Li, P. Peng, Z. Chen. Health diagnosis for lithium-ion battery by combining partial incremental capacity and deep belief network during insufficient discharge profile, IEEE Transactions on Industrial Electronics, early access. https://doi.org/10.1109/TIE.2022.3224201 [3]. G. Zhao, Y. Wang*, Z. Chen. Health-aware multi-stage charging strategy for lithium-ion batteries based on whale optimization algorithm, Journal of Energy Storage, 55(2022), 105620. https://doi.org/10.1016/j.est.2022.105620 [4]. K. Li, Y. Wang*, Z. Chen. A comparative study of battery state-of-health estimation based on empirical mode decomposition and neural network, Journal of Energy Storage, 54(2022), 105333. https://doi.org/10.1016/j.est.2022.105333 [5]. Y. Wang*, G. Zhao, C. Zhou, M. Li, Z. Chen. Lithium-ion battery optimal charging strategy using moth-flame optimization algorithm and fractional-order model, IEEE Transactions on Transportation Electrification, early access. https://doi.org/10.1109/TTE.2022.3192174 [6]. Y. Wang*, K. Li, Z. Chen. Battery full life cycle management and health prognosis based on cloud service and broad learning, IEEE/CAA Journal of Automatica Sinica, vol. 9, no 8, 2022, 1540-1542. https://doi.org/10.1109/JAS.2022.105779 [7]. Y. Wang*, X. Kang, Z. Chen. A survey of digital twin techniques in smart manufacturing and management of energy applications, Green Energy and Intelligent Transportation, 1(2022), 100014. https://doi.org/10.1016/j.geits.2022.100014 [8]. Y. Wang*, X. Zhang, Z. Chen. Low temperature preheating techniques for lithium-ion batteries: recent advances and future challenges, Applied Energy, 313(2022), 118832. https://doi.org/10.1016/j.apenergy.2022.118832 [9]. Y. Wang*, C. Zhou, G. Zhao, Z. Chen. A framework for battery internal temperature and state of charge estimation based on fractional-order thermoelectric model, Transactions of the Institute of Measurement and Control, 2022. https://doi.org/10.1177/01423312211067293 [10]. Y. Wang*, C. Zhou, Z. Chen. Optimization of battery charging strategy based on nonlinear model predictive control, Energy, 241(2022), 122877. https://doi.org/10.1016/j.energy.2021.122877 [11]. Y. Wang, R. Xu, C. Zhou, X. Kang, Z. Chen. Digital twin and cloud-side-end collaboration for intelligent battery management system, Journal of Manufacturing Systems, 62(2022), 124-134. https://doi.org/10.1016/j.jmsy.2021.11.006 [12]. 周才杰, 汪玉洁*, 李凯铨, 陈宗海. 基于灰色关联度分析-长短期记忆神经网络的锂离子电池健康状态估计. 电工技术学报, 2022, 37(23):6065-6073. https://doi.org/10.19595/j.cnki.1000-6753.tces.211366 [13]. Y. Wang, C. Zhou, Z. Chen. An enhanced approach for load behavior and battery residual capacity prediction using Markov chain and Monte Carlo method, IEEE Journal of Emerging and Selected Topics in Industrial Electronics, early access. https://doi.org/10.1109/JESTIE.2021.3115468 [14]. X. Tang, Y. Wang*, Q. Liu, F. Gao*. Reconstruction of the incremental capacity trajectories from current-varying profiles for lithium-ion batteries, iScience, 24(10)(2021), 103103. https://doi.org/10.1016/j.isci.2021.103103 [15]. Y. Wang, M. Li, Z. Chen. Experimental study of fractional-order models for lithium-ion battery and ultra-capacitor: modeling, system Identification, and validation, Applied Energy, 278(2020), 115736. https://doi.org/10.1016/j.apenergy.2020.115736 [16]. Y. Wang, J. Tian, Z. Sun, L. Wang, R. Xu, M. Li, Z. Chen. A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems, Renewable & Sustainable Energy Reviews, 131(2020), 110015. https://doi.org/10.1016/j.rser.2020.110015 [17]. Y. Wang, L. Wang, M. Li, Z. Chen. A review of key issues for control and management in battery and ultra-capacitor hybrid energy storage systems, eTransportation, 4(2020), 100064. https://doi.org/10.1016/j.etran.2020.100064 [18]. Y. Wang*, G. Gao, X. Li, Z. Chen. A fractional-order model-based state estimation approach for lithium-ion battery and ultra-capacitor hybrid power source system considering load trajectory, Journal of Power Sources, 449(2020), 227543. https://doi.org/10.1016/j.jpowsour.2019.227543 [19]. Y. Wang, Z. Chen. A framework for state-of-charge and remaining discharge time prediction using unscented particle filter, Applied Energy, 260(2020), 114324. https://doi.org/10.1016/j.apenergy.2019.114324 [20]. Y. Wang, Z. Sun, X. Li, X. Yang, Z. Chen. A comparative study of power allocation strategies used in fuel cell and ultracapacitor hybrid systems, Energy, 189(2019), 116142. https://doi.org/10.1016/j.energy.2019.116142 [21]. X. Tang, Y. Wang*, K. Yao, Z. He, F. Gao*. Model migration based battery power capability evaluation considering uncertainties of temperature and aging, Journal of Power Sources, 440(2019), 227141. https://doi.org/10.1016/j.jpowsour.2019.227141 [22]. Y. Wang*, X. Li, Li Wang, Z. Sun. Multiple-grained velocity prediction and energy management strategy for hybrid propulsion systems, Journal of Energy Storage, 26(2019), 100950. https://doi.org/10.1016/j.est.2019.100950 [23]. Y. Wang, Z. Sun, Z. Chen. Energy management strategy for battery/ supercapacitor/ fuel cell hybrid source vehicles based on finite state machine, Applied Energy, 254(2019), 113707. https://doi.org/10.1016/j.apenergy.2019.113707 [24]. Y. Wang, Z. Sun, Z. Chen. Development of energy management system based on a rule-based power distribution strategy for hybrid power sources, Energy, 175(2019), 1055-1066. https://doi.org/10.1016/j.energy.2019.03.155 [25]. X. Tang, Y. Wang*, C. Zou, K. Yao, Y. Xia, F. Gao*. A novel framework for Lithium-ion battery modeling considering uncertainties of temperature and aging, Energy Conversion and Management, 180(2019), 162-170. https://doi.org/10.1016/j.enconman.2018.10.082 [26]. Y. Wang, J. Tian, Z. Chen, X. Liu. Model based insulation fault diagnosis for lithium-ion battery pack in electric vehicles, Measurement, 131(2019), 443-451. https://doi.org/10.1016/j.measurement.2018.09.007 [27]. Y. Wang, X. Zhang, C. Liu, R. Pan, Z. Chen. Multi-timescale power and energy assessment of lithium-ion battery and supercapacitor hybrid system using extended Kalman filter, Journal of Power Sources, 389(2018), 93-105. https://doi.org/10.1016/j.jpowsour.2018.04.012 [28]. Y. Wang, R. Pan, C. Liu, Z. Chen, Q. Ling. Power capability evaluation for lithium iron phosphate batteries based on multi-parameter constraints estimation, Journal of Power Sources, 374(2018), 12-23. https://doi.org/10.1016/j.jpowsour.2017.11.019 [29]. Y. Wang, Z. Chen, C. Zhang. On-line remaining energy prediction: a case study in embedded battery management system, Applied Energy, 194(2017), 688-695. https://doi.org/10.1016/j.apenergy.2016.05.081 [30]. Y. Wang, C. Liu, R. Pan, Z. Chen. Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator, Energy, 121(2017), 739-750. https://doi.org/10.1016/j.energy.2017.01.044 [31]. Y. Wang, C. Zhang, Z. Chen. On-line battery state-of-charge estimation based on an integrated estimator, Applied Energy, 185(2017), 2026-2032. https://doi.org/10.1016/j.apenergy.2015.09.015 [32]. Y. Wang, D. Yang, X. Zhang, Z. Chen. Probability based remaining capacity estimation using data-driven and neural network model, Journal of Power Sources, 315(2016), 199-208. https://doi.org/10.1016/j.jpowsour.2016.03.054 [33]. Y. Wang, C. Zhang, Z. Chen. An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles, Journal of Power Sources, 305(2016), 80-88. https://doi.org/10.1016/j.jpowsour.2015.11.087 [34]. Y. Wang, C. Zhang, Z. Chen, J. Xie, X. Zhang. A novel active equalization method for lithium-ion batteries in electric vehicles, Applied Energy, 145(2015), 36-42. https://doi.org/10.1016/j.apenergy.2015.01.127 [35]. Y. Wang, C. Zhang, Z. Chen. A method for state-of-charge estimation of LiFePO4 batteries at dynamic currents and temperatures using particle filter, Journal of Power Sources, 279(2015), 306-311. https://doi.org/10.1016/j.jpowsour.2015.01.005 [36]. Y. Wang, C. Zhang, Z. Chen. A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy, Applied Energy, 137(2015), 427-434. https://doi.org/10.1016/j.apenergy.2014.10.034 [37]. Y. Wang, C. Zhang, Z. Chen. A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries, Applied Energy, 135(2014), 81-87. https://doi.org/10.1016/j.apenergy.2014.08.081 |
编辑:徐若兰 2023-02-15 13:03:12