杨芳芳

教师简介

杨芳芳博士，副教授，博士生导师，入选深圳市海外高层次人才，广东省高层次人才。

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动力电池老化分析、特征提取、状态监测、寿命预测等研究领域发表SCI期刊论文40余篇（唯一一作和通讯20余篇，中科院一区22篇，二区14篇），论文被引超过5000次。课题组经费充足，欢迎自动化、计算机、电气等相关专业优秀同学保送、报考研究生，欢迎积极上进的本科生参与团队工作。感兴趣的同学请邮件联系。

个人主页：wimy2019.github.io/

学术主页：https://scholar.google.com/citations?user=bR3RVOUAAAAJ&hl=zh-CN

研究之门：https://www.researchgate.net/profile/Fangfang-Yang-4

学科方向

1. 新能源汽车动力电池电量估计、退化建模、寿命预测研究

2. 统计建模、数据挖掘、深度学习算法及应用研究

3. 轴承故障诊断和寿命预测研究

4. 深度学习目标检测研究

科研项目

1. 2023-2025 国家自然科学基金青年项目，在研，主持

2. 2024-2025 企业横向，在研，主持

3. 2021-2024 广东省粤深联合项目，结题，主持

4. 2022-2022 中央高校基本科研业务费项目，结题，主持

5. 2021-2023 中山大学百人计划启动项目, 结题，主持

6. 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. London: Springer London, 2023, 797-816.

代表性论文（通讯作者*）：

[1] N. He, Q. Wang, Z. Lu, Y. Chai, F. Yang*. (2024). Early prediction of battery lifetime based on graphical features and convolutional neural networks. Applied Energy, 353, 122048. 

[2] Z. Lu, Z. Fei, B. Wang, F. Yang *. (2024). A feature fusion-based convolutional neural network for battery state-of-health estimation with mining of partial voltage curve. Energy, 288, 129690. 

[3] Q. Wang, M. Xie, F. Yang*. (2024). Early battery lifetime prediction based on statistical health features and box-cox transformation. Journal of Energy Storage, 96, 112594.

[4] G. Chen, W. Peng, F. Yang*. (2024). An LSTM-SA model for SOC estimation of lithium-ion batteries under various temperatures and aging levels. Journal of Energy Storage, 84, 110906.

[5] F. Yang, Z. Lu, X. Tan, K. L. Tsui, D. Wang*. (2024). Battery prognostics using statistical features from partial voltage information. Mechanical Systems and Signal Processing, 210, 111140.

[6] G. Chen, F. Yang, W. Peng, Y. Fan, X. Lyu. (2024). State-of-health estimation for lithium-ion batteries based on Kullback–Leibler divergence and a retentive network. Applied Energy, 376, 124266.

[7] 何宁, 杨芳芳*. 考虑能量和温度特征的锂离子电池早期寿命预测. 储能科学与技术，2024, 13(9), 3016.

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

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

[10] 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. (ESI highly cited)

[11] 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. 

[12] 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.

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

[14] 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.

[15] 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. 

[16] 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)

[17] 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. 

[18] 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)

[19] 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. 

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

[21] 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. (ESI highly cited)

[22] 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)

[23] 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.

[24] 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. 

[25] 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. 

[26] 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. 

[27] 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. 

[28] 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.

详见谷歌学术主页：https://scholar.google.com/citations?user=bR3RVOUAAAAJ&hl=zh-CN

部分发明专利

[1] 杨芳芳, 周石润. 基于相对位置矩阵和残差网络的电池早期寿命预测方法, 专利号：ZL202411001806.2 

[2] 杨芳芳, 何宁, 柴艺柯, 李弈霆, 郭烨年. 一种基于三维电压特征的锂离子电池早期寿命预测方法, 专利号：ZL202310193881.2 

[3] 杨芳芳，陈冠旭. 基于KL散度和保留网络的电池健康状态估计方法及装置.（申请号：202410368120.0, 受理审查中）

[4] 何宁, 柴艺柯, 李弈霆, 郭烨年, 杨芳芳. 一种基于卷积神经网络的电池早期寿命预测方法及系统.（申请号：202310109921.0, 受理审查中）

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。

联系方式

电子邮箱：yangff7@mail.sysu.edu.cn、fangfyang2-c@my.cityu.edu.hk