目前，本人研究方向是机器学习、强化学习及其在储能系统健康评估、故障诊断与优化控制中的应用。具体而言：主要包括以下几个方向：

• 基于数据挖掘和深度学习的储能电池故障诊断、健康状态评估与剩余寿命预测；

• 基于强化学习和迁移学习的储能电池控制与优化；

• 数据与信息不确定表达量化、强化学习安全与鲁棒训练的相关理论探究；

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。曾获中国科学院院长优秀奖，入选2021年度全球前2%顶尖科学家榜单“年度科学影响力榜”（World's Top 2% Scientists2021）

本人研究方向是机器学习、强化学习及其在储能系统健康评估、故障诊断与优化控制中的应用。具体而言：主要包括以下几个方向：

1. 基于数据挖掘和深度学习的储能电池故障诊断、健康状态评估与剩余寿命预测；

2. 基于强化学习和迁移学习的储能电池控制与优化；

3. 数据与信息不确定表达量化、强化学习安全与鲁棒训练的相关理论探究；

欢迎控制、自动化、电气、电力电子以及人工智能领域内的学生加入本人团队。

Peer-Reviewd Journal Papers:(First-Authored or Corresponding Authored)

[13] Y. Yang, J. He, C. Chen and J. Wei*, Balancing Awareness Fast Charging Control for Lithium-Ion Battery Pack Using Deep Reinforcement Learning, IEEE Transactions on Industrial Electronics, Early Access, 2023, DOI:10.1109/TIE.2023.3274853.

[12] J. Wei, C. Chen and G. Dong, Global Sensitivity Analysis for Impedance Spectrum Identification of Lithium-Ion Batteries Using Time-Domain Response, IEEE Transactions on Industrial Electronics, 70(4), pp:3825-3835, Jun. 2022.

[11] Y. Zhou, G. Dong, Q. Tan, X. Han, C. Chen, J. Wei*, State of health estimation for lithium-ion batteries using geometric impedance spectrum features and recurrent Gaussian process regression, Energy, 2022, 125514.

[10] G. Dong, Y. Feng, Y. Wang, J.Wei*. Remaining discharge time prediction of lithium-ion batteries via arobust observer and statistical characterization of future loading profiles, InternationalJournal of Energy Storage, 59(2023)106488.

[9] J. Wei, C. Chen, A multi-timescale framework for state monitoring and lifetime prognosis of lithium-ion batteries, Energy, Volume 229, 2021, 120684.

[8] G. Dong, J. Wei*, A physics-based aging model for lithium-ion battery with coupled chemical/mechanical degradation mechanisms, Electrochimica Acta, volume 395, 2021, 139133.

[7] G. Dong, J. Wei*, Determination of the load capability for a lithium-ion battery pack using two time-scale filtering, Journal of Power Sources, volume 480, 2020, 229056.

[6] 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.

[5] 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.

[4] 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.

[3] 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.

[2] 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.

[1] 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,43(14), pp8230-8241,2019.

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.

[3] Y. Yang, J. Wei* and C. Chen, Health-aware Fast-charging Control of Lithium-Ion Battery Based on Reinforcement Learning, 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC), 2021, pp. 1-6, doi: 10.1109/ICNSC52481.2021.9702172.

1. 国家自然科学基金，青年项目 “基于安全鲁棒强化学习的电池健康感知快充控制策略研究”，（30万元）

2. 江苏省自然科学基金，青年项目 “基于多尺度模型的锂电池系统故障检测与诊断方法研究 “，（20万元）

3. 中央高校基本科研业务费原创交叉项目，“强化学习在储能系统中的应用研究  “，（60万元）联合主持