魏婧雯现为南京大学机器人与自动化学院准聘助理教授（特任研究员）、博士生导师。获中国科学技术大学控制科学与工程博士学位。

承担《传感器原理与技术》、《自动控制原理》、《智能微网》等本科生、研究生课程。课题组每年招收博士、硕士研究生若干名，欢迎联系！

详情见个人主页：jwwei-nju.github.io

主要研究方向聚焦于时序大模型 (Time-series foundation models) 与具身智能 (Embodied intelligence)，致力于构建面向装备健康管理与机器人能源自治的智能诊断与决策系统，涵盖装备故障诊断与寿命预测 (Prognostics and health management)、安全鲁棒强化学习 (Safe reinforcement learning)、电池储能系统建模与智能管控 (Battery modeling & intelligent management) 等前沿方向。

以一作/通作身份在 IEEE TIE/TII/TTE, Applied Energy, Journal of Power Sources, Energy 等国际高水平期刊发表论文 20 余篇，总引用 2700 余次，H-index 24，1 篇入选 ESI 高被引论文。担任 IEEE ICNSC、ICCSI、IEEE IAS I&CPS Asia 等国际会议分会主席 (Session chair)，以及 IEEE TIE/TII/TTE、Applied Energy 等多个国际高水平期刊审稿人。

主持国家自然科学基金青年项目、江苏省自然科学基金青年项目、国家市场监督管理总局重点实验室开放课题等项目，作为骨干参与多项国家级、省部级科研项目。获得江苏省科学技术进步奖三等奖、江苏省自动化学会青年科技奖、南京大学青年教师教学竞赛特等奖等荣誉，入选江苏省青年科技人才托举工程，连续入选斯坦福-爱思唯尔全球前 2% 顶尖科学家榜单 (World's Top 2% Scientists)。

已发表期刊论文(# 通讯作者与第一作者)

J1. He. J, Wei, J.# (2025). User Preference-Adaptive Battery Fast Charging Strategy Considering Temperature Rising Based on Transfer Reinforcement Learning. IEEE Transactions on Industrial Electronics, 72(10), 10327-10337.

J2. Xie, L., Wei, J.# (2025) Deep Learning-Enabled Fault Diagnosis of Lithium-Ion Batteries Using Real-World Vehicle Data With Gramian Angular Difference Fields, IEEE Transactions on Industrial Informatics, 21(7), 5622 – 5632.

J3. He, J., Wei, J.# (2024) A Data-driven Reinforcement Learning Enabled Battery Fast Charging Optimization Using Real-world Experimental Data. IEEE Transactions on Industrial Electronics, , 72(1), 430-438.

J4. Yang, Y., Wei, J.#. (2023)Balancing Awareness Fast Charging Control for Lithium-Ion Battery Pack Using Deep Reinforcement Learning. IEEE Transactions on Industrial Electronics, 71(4), 718-3727.

J5. Zhou, Y., Wei, J.#.(2023). State of health estimation for lithium-ion batteries using geometric impedance spectrum features and recurrent gaussian process regression. Energy,262, 125514.

J6. Dong, G., Wei, J.# (2023)Data-Driven Fast Charging Optimization for Lithium-Ion Battery Using Bayesian Optimization with Fast Convergence. IEEE Transactions on Transportation Electrification, Accepted.

J7. Dong, G., Wei, J.# (2023). Probabilistic dischargeable time forecasting of power batteries via statistical characterization of future loading profiles. Journal of Energy Storage,59, 106488.

J8. Wei, J. (2022). Global sensitivity analysis for impedance spectrum identification of lithium-ion batteries using time-domain response. IEEE Transactions on Industrial Electronics, 70(4), 3825-3835.

J9. Dong, G., Wei, J.#. (2021). A physics-based aging model for lithium-ion battery with coupled chemical/mechanical degradation mechanisms. Electrochimica Acta, 395, 139133.

J10. Wei, J. (2021). A multi-timescale framework for state monitoring and lifetime prognosis of lithium-ion batteries. Energy, 229, 120684.

J11. Dong, G., Wei, J.#. (2020). Determination of the load capability for a lithium-ion battery pack using two time-scale filtering. Journal of Power Sources, 480, 229056.

J12. Wei, J. (2020). Lyapunov-based thermal fault diagnosis of cylindrical lithium-ion batteries. IEEE Transactions on Industrial Electronics, 67(6), 4670–4679.

J13. Zhang, C., Wei, J.# (2019). Data-driven lithium-ion battery states estimation using neural networks and particle filtering. International Journal of Energy Research, 43(14), 8230–8241.

J14. Wei, J. (2018). Lyapunov-based state of charge diagnosis and health prognosis for lithium-ion batteries. Journal of Power Sources,397, 352–360.

J15. Wei, J. (2018). Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression. IEEE Transactions on Industrial Electronics,65(7), 5634–5643.

J16. Wei, J. (2017). On-board adaptive model for state of charge estimation of lithium-ion batteries based on Kalman filter with proportional integral-based error adjustment. Journal of Power Sources,365, 308–319.

J17. Wei, J. (2017). System state estimation and optimal energy control framework for multicell lithium-ion battery system. Applied Energy, 187, 37–49.