一、基本情况
郭永芳，女，工学博士，副教授。
二、硕导所属（含跨转）学科、专业学位类别
所属学科：计算机科学与技术、计算机技术、人工智能
主要研究方向：数据驱动与建模、图像处理与模式识别
三、主要主持、参与的科研
[1] 河北省科技支撑计划：自动导盲系统中多图像听觉显示关键技术研究2013.12-2015.12 (主持)
[2]天津市自然科学基金：基于可听化的电子行走辅助系统研究 2013.06-2016.06（第二）
[3] 河北省教育厅青年基金项目: 考虑迟滞效应的锂离子电池建模及在线荷电状态估计研究 2017.1-2018.12（主持）
[4] 河北省自然科学基金项目：基于工况鲁棒性的动力锂离子电池多尺度健康因子提取及健康状态预测方法研究:2019.1-2021.12 （第二）
[5] 中央引导地方科技发展资金项目：综合电-热性能退化识别的动力锂离子电池组健康状态估计方法研究:2023.01-2025.12 （第二）
[6] 天津市自然科学基金项目：基于不一致性演化跟踪的动力锂离子电池未来退化路径快速预测方法.2023.10-2026.09（第二）
四、近年来发表代表性论文情况（仅限第一作者或通讯作者）
[1] Health prognostics of lithium-ion batteries based on universal voltage range features mining and adaptive multi-Gaussian process regression with Harris Hawks Optimization algorithm[J]. Reliability Engineering & System Safety ,2024
[2] State of health estimation of lithium-ion batteries based on fine tuning or rebuilding transfer learning strategies combined with new features mining [J]. Energy. 2023
[3] Future degradation trajectory prediction of lithium-ion battery based on a three step similarity evaluation criterion for battery selection and transfer learning[J]. Journal of Energy Storage, 2023
[4] Co-estimation of maximum available capacity and state-of-charge for lithium-ion batteries in multi- operating mode with temperature and degradation state adaptivity[J]. Measurement, 2023
[5] State-of-health estimation for lithium-ion batteries based on historical dependency of charging data and ensemble SVR [J]. Electrochimica Acta, 2022
[6] Estimation of maximum available capacity of lithium-ion battery based on multi-view features extracted from reconstructed charging curve [J]. International Journal of Hydrogen Energy, 2022
[7] State-of-Health Estimation of Lithium-Ion Batteries Based on Thermal Characteristics Mining and Multi-Gaussian Process Regression Strategy [J]. Energy Technology, 2022
[8] Multi-task person re-identification via attribute and part-based learning [J]. Multimedia Tools and Applications, 2022
[9] Battery Modeling Considering Hysteresis Effect and Temperature Adaptability [J]. Energy Technology, 2023
[10] A state-of-health estimation method of lithium-ion batteries based on multi-feature extracted from constant current charging curve[J]. Journal of Energy Storage, 2021
[11] 基于短时搁置端电压压降的快速锂离子电池健康状态预测[J].电工技术学报. 2019, 34(19): 3968-3978.
[12] 基于信息反馈粒子群的高精度锂离子电池模型参数辨识 [J]. 电工技术学报, 2019, 34 (S1): 378-387.
[13] 基于数据预处理和长短期记忆神经网络的锂离子电池寿命预测 [J]. 电工技术学报, 2022, 37 (15): 3753-3766.
[14] 基于观测方程重构滤波算法的锂离子电池荷电状态估计 [J]. 电工技术学报, 2024, 39 (07): 2214-2224.
五、联系人：郭永芳：guoyongfang@hebut.edu.cn