杨芳芳

杨芳芳

中山大学 智能工程学院

简介

杨芳芳，副教授，中山大学 智能工程学院。
2021.07 – 至今， 中山大学，智能工程学院，副教授
2019.08 – 2021.07，香港城市大学，数据科学学院，副研究员
2017.11 – 2019.07，香港城市大学，系统工程与工程管理系，博士后研究员
2013.09 – 2017.10，香港城市大学，系统工程与工程管理，博士
2009.09 – 2013.06，中国科学技术大学，自动化，学士

近五年在新能源汽车锂电池性能预测和健康管理、轴承故障诊断和寿命预测、深度学习目标检测等研究领域发表SCI期刊论文40余篇(第一/通信作者20篇), 论文被引超过2500次。

研究方向

主要从事新能源汽车动力电池的电量估计、寿命预测、退化建模、和健康管理，以及统计建模、数据挖掘、深度学习应用等方向的研究。
欢迎对以上方向感兴趣的研究生、本科生加入我们研究小组。

科研项目

  1. 2023-2025 国家自然科学基金青年项目，在研，主持
  2. 2021-2024 广东省粤深联合项目，在研，主持
  3. 2022-2022 中央高校基本科研业务费项目，在研，主持
  4. 2021-2023 中山大学百人计划启动项目，在研，主持
  5. 2020-2022 香港研究资助局GRF项目，结题，主持

论著专利

专著： —- [1] K. L. Tsui*, C. P. Chen, W. Jiang, F. Yang, C. Kan. Data Mining Methods and Applications, Springer Handbook of Engineering Statistics, 2nd ed.

代表性论文(通讯作者*)：

[1] Meng, F., Yang, F., Yang, J., & Xie, M. (2023). A power model considering initial battery state for remaining useful life prediction of lithium-ion batteries. Reliability Engineering & System Safety, 237, 109361. [Link]

[2] Fei, Z., Zhang, Z., Yang, F., & Tsui, K. L. (2023). A deep attention-assisted and memory-augmented temporal convolutional network based model for rapid lithium-ion battery remaining useful life predictions with limited data. Journal of Energy Storage, 62, 106903. [Link]

[3] Tsui, KL., Chen, V., Jiang, W., Yang, F., Kan, C. (2023). Data Mining Methods and Applications. In: Pham, H. (eds) Springer Handbook of Engineering Statistics. Springer Handbooks. Springer, London. https://doi.org/10.1007/978-1-4471-7503-2_38. [Link]

[4] Wang, Z., Yang, F., Xu, Q., Wang, Y., Yan, H., & Xie, M. (2023). Capacity estimation of lithium-ion batteries based on data aggregation and feature fusion via graph neural network. Applied Energy, 336, 120808. [Link]

[5] Jiang, Y., Chen, Y., Yang, F., & Peng, W. (2023). State of health estimation of lithium-ion battery with automatic feature extraction and self-attention learning mechanism. Journal of Power Sources, 556, 232466. [Link]

[6] Huang, Z., Xu, F., & Yang, F. (2023). State of health prediction of lithium-ion batteries based on autoregression with exogenous variables model. Energy, 262, 125497.[Link]

[7] Chen, G., Jiang, S., Xie, M., & Yang, F. (2022, October). A hybrid DNN-KF model for real-time SOC estimation of lithium-ion batteries under different ambient temperatures. In 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) (pp. 1-5). IEEE.

[8] Lu, Z., Wang, B., Tan, X., & Yang, F. (2022, October). Battery Capacity Estimation based on Convolutional Neural Network. In 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) (pp. 1-6). IEEE.

[9] Fei, Z., Zhang, Z., Yang, F., Tsui, K. L., & Li, L. (2022). Early-stage lifetime prediction for lithium-ion batteries: A deep learning framework jointly considering machine-learned and handcrafted data features. Journal of Energy Storage, 52, 104936.

[10] Yang, F., Tan, X., Wang, Z., Lu, Z., & He, T. (2022). A geometric approach for real-time forward kinematics of the general Stewart platform. Sensors, 22(13), 4829.

[11] Palayangoda, L. K., Butler, R. W., Ng, H. K. T., Yang, F., & Tsui, K. L. (2022). Evaluation of mean-time-to-failure based on nonlinear degradation data with applications. IISE Transactions, 54(3), 286-302.

[12] Wang, T., Zhang, Z., Yang, F., & Tsui, K. L. (2021). Automatic rail component detection based on AttnConv-net. IEEE Sensors Journal, 22(3), 2379-2388.

[13] Lin, C. P., Ling, M. H., Cabrera, J., Yang, F., Yu, D. Y. W., & Tsui, K. L. (2021). Prognostics for lithium-ion batteries using a two-phase gamma degradation process model. Reliability engineering & system safety, 214, 107797.

[14] Z. Fei, F. Yang*, K. L. Tsui, L. Li, Z. Zhang (2021). Early prediction of battery lifetime via a machine learning based framework. Energy, 225, 120205.

[15] F. Xu, F. Yang*, Z. Fei, Z. Huang, K. L. Tsui (2020). Life prediction of lithium-ion batteries based on stacked denoising autoencoders. Reliability Engineering & System Safety, 107396.

[16] F. Yang, D. Wang, F. Xu*, Z. Huang, K. L. Tsui (2020). Lifespan Prediction of Lithium-ion Batteries based on Various Extracted Features and Gradient Boosting Regression Tree Model. Journal of Power Sources, 476, 228654.

[17] F. Yang, S. Zhang, W. Li, Q. Miao* (2020). State-of-charge estimation of lithium-ion batteries using LSTM and UKF. Energy, 117664.

[18] G. Dong, F. Yang*, Z. Wei, J. Wei, K. L. Tsui (2020). Data-driven Battery Health Prognosis Using Adaptive Brownian Motion Model. IEEE Transactions on Industrial Informatics, 16(7), 4736-4746.

[19] G. Dong, F. Yang*, K. L. Tsui, C. Zou (2020). Active Balancing of Lithium-Ion Batteries Using Graph Theory and A-Star Search Algorithm. IEEE Transactions on Industrial Informatics.

[20] T. Wang, F. Yang*, K. L. Tsui (2020). Real-time Visual Inspection of Railway Track Component Based on YOLO Series Models. Sensors, 15, 4325.

[21] F. Yang, W. Li, C. Li, Q. Miao* (2019). State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network. Energy, 88, 137-144. (ESI highly cited)

[22] F. Yang, X. Song*, G. Dong, K. L. Tsui (2019). A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries. Energy, 171, 1173-1182.

[23] F. Yang*, D. Wang, Y. Zhao, K. L. Tsui, S. J. Bae (2018). A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries. Energy, 145: 486-495. (ESI highly cited)

[24] F. Yang*, D. Wang, Y. Xing, K. L. Tsui (2017). Prognostics of Li(NiMnCo)O2-based 18650 lithium-ion batteries using a novel degradation model. Microelectronics Reliability, 70: 70-78.

[25] D. Wang, F. Yang*, Y. Zhao, K. L. Tsui (2017). Battery remaining useful life prediction at different discharge rates. Microelectronics Reliability, 78: 212-219.

[26] D. Wang, F. Yang*, K. L. Tsui, Q. Zhou, S. J. Bae (2016). Remaining useful life prediction of lithium-ion batteries based on spherical cubature particle filter. IEEE Transactions on Instrumentation and Measurement, 65(6): 1282-1291.

[27] F. Yang*, Y. Xing, D. Wang, K. L. Tsui (2016). A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile. Applied Energy, 164: 387-399. (ESI highly cited)

[28] C. P. Lin*, M.H. Ling, J. Cabrera, F. Yang, Y. Yu, K.L. Tsui (2021). Prognostics for lithium-ion batteries using a two-phase gamma degradation process model. Reliability Engineering & System Safety, 107797.

[29] J. Kong, F. Yang, X. Zhang, E. Pan, Z. Peng, D. Wang* (2021). Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries. Energy, 223, 120114.

[30] L. K. Palayangoda, R. W. Butler, H. K. T. Ng*, F. Yang, K. L. Tsui (2021). Evaluation of mean-time-to-failure based on nonlinear degradation data with applications. IISE Transactions, 1-18.

[31] F. Xu, Z. Huang*, F. Yang, D. Wang, K. L. Tsui (2020). Constructing a health indicator for roller bearings by using a stacked auto-encoder with an exponential function to eliminate concussion. Applied Soft Computing, 89, 106119.

[32] C. P. Lin*, J. Cabrera, K.L. Tsui, F. Yang, M.H. Ling (2020). Battery state of health modeling and remaining useful life prediction through time series model. Applied Energy, 275, 115338.

[33] C. P. Lin*, J. Cabrera, Y. Yu, F. Yang, K.L. Tsui (2020). SOH estimation and SOC recalibration of lithium-ion battery with incremental capacity analysis & cubic smoothing spline. Journal of the Electrochemical Society, 167(9), 090537.

[34] J. Wang, J. Miao, J. Wang, F. Yang, K. Tsui, M. Qiang* (2020). Fault Diagnosis of Electrohydraulic Actuator Based on Multiple Source Signals: An Experimental Investigation. Neurocomputing, 417, 224-238.

[35] A. Yang, Y. Wang, F. Yang, D. Wang, Y. Zi*, K. L. Tsui, B. Zhang (2019). A comprehensive investigation of lithium-ion battery degradation performance at different discharge rates. Journal of Power Sources, 443, 227108.

[36] D. Wang, Y. Zhao*, F. Yang, K. L. Tsui (2017). Nonlinear-Drifted Brownian motion with multiple hidden states and its application to prognostics of lithium-Ion batteries. Mechanical Systems and Signal Processing, 93: 531-544.

主要兼职

担任IISE Transaction、IEEE Transactions on Cybernetics、IEEE Transactions on Industrial Electronics、IEEE Transactions on Industrial Informatics、IEEE Transactions on Vehicular Technology、Mechanical Systems and Signal Processing、Applied Energy、Energy、Journal of Power Sources、Reliability Engineering and System Safety等国际期刊审稿人。