魏婧雯 - 南京大学 - 工程管理学院

个人简介

2019年6月于中国科学技术大学自动化系获得博士学位，同年12月加入南京大学工程管理学院任助理研究员。已在IEEE Transactions on Industrial Electronics, Applied Energy, Journal of Power Sources等领域内顶级期刊上发表SCI期刊论文20余篇，授权专利2项。曾获2017年中国科学技术大学华为一等奖学金、 2018年博士研究生国家奖学金、 2019年中国科学院院长优秀奖以及中国科学技术大学优秀博士学位论文提名。

研究领域

主要的理论研究方向包括：（1）非线性参数辨识理论、状态估计与滤波、以及参数预测理论；（2）设备健康预后与管理（Prognositics and Health Management）理论方法；（3）模型驱动和数据驱动的系统异常检测与故障诊断理论方法；主要的应用研究包括：（1）电动汽车及智能电网储能系统行为建模；（2）储能系统关键运行参数的在线估计与预测；（3）储能系统故障诊断以及寿命预测；

近期论文

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[1] J. Wei, G. Dong, Z. Chen. Lyapunov-based thermal fault diagnosis of cylindrical lithium-ion batteries, IEEE Transactions on Industrial Electronics, 67(6), pp:4670-4679, Jun. 2020. [Link]. [2] J. Wei, G. Dong, Z. Chen. 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, Vol. 65(7), pp: 5634-5643, July. 2018. [Link] [3] J. Wei, G. Dong, Z. Chen. 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, Vol. 365, pp: 308- 319, 2017. [Link] [4] J. Wei, G. Dong, Z. Chen. System state estimation and optimal energy control framework for multicell lithium- ion battery system, Applied Energy, Vol. 187(0), pp: 37-49, Feb. 2017.[Link] [5] J. Wei, G. Dong, Z. Chen. Lyapunov-based state of charge diagnosis and health prognosis for lithium-ion batteries, Journal of Power Sources, Vol. 397, pp: 352-360. [Link] [6] C. Zhang, Y. Zhu, G. Dong, J. Wei*. Data-driven lithium-ion battery states estimation using neural networks and particle filtering, International Journal of Energy Research, Early access. [Link] Peer-Reviewd Conference Papers: [1] J. Wei, G. Dong, Z. Chen. Model-based fault diagnosis of Lithium-ion battery using strong tracking Extended Kalman Filter, the 10th International Conference on Applied Energy, ICAE 2018; Hong Kong; China. [2] J. Wei, C. Chen. State of Charge and Health Estimation For Lithium-Ion Batteries Using Recursive Least Squares, the 5th International Conference on Advanced Robotics and Mechatronics , ICARM 2020; Shenzhen; China.