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）

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

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

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）

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

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