[1] 印学浩, 宋宇晨, 刘旺, 等. 基于多时间尺度的锂离子电池状态联合估计[J]. 仪器仪表学报, 2018, 39(8): 119-123.

Yin Xuehao, Song Yuchen, Liu Wang, et al. Multi-scale state joint estimation for lithium-ion battery [J]. Chinese Journal of Scientific Instrument, 2018, 39(8): 119-123. (in Chinese)
[2] 王振新, 秦鹏, 康健强, 等. 基于衰退机理的三元锂离子电池SOH的诊断与估算[J]. 电子测量技术, 2020, 43(10): 7-13.

Wang Zhenxin, Qin Peng, Kang Jianqiang, et al. SOH diagnosis and estimation for NCM lithium-ion batteries based on degradation mechanism [J]. Eletronic Measurement Technologu, 2020, 43(10): 7-13. (in Chinese)
[3] Xiong R, Li L, Tian J. Towards a smarter battery management system: A critical review on battery state of health monitoring methods [J]. Journal of Power Sources, 2018, 405(30): 18-29.
[4] 刘波, 许廷发, 李相民, 等. 自适应上下文感知相关滤波跟踪[J]. 中国光学, 2019, 12(2): 20191202.0265. doi:  10.3788/co.20191202.0265

Liu Bo, Xu Tingfa, Li Xiangmin, et al. Adaptive context aware correlation filter tracking [J]. Chinese Optics, 2019, 12(2): 20191202.0265. (in Chinese) doi:  10.3788/co.20191202.0265
[5] Andre D, Appel C, Soczka-Guth T. Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries [J]. Journal of Power Sources, 2013, 224: 20-27. doi:  10.1016/j.jpowsour.2012.10.001
[6] Yang D, Zhang X, Pan R, et al. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve [J]. Journal of Power Sources, 2018, 384: 387-395. doi:  10.1016/j.jpowsour.2018.03.015
[7] Chaoui H, Ibe-Ekeocha C C. State of charge and state of health estimation for lithium batteries using recurrent neural networks [J]. IEEE Transactions on Vehicular Technology, 2017, 66(10): 8773-8783. doi:  10.1109/TVT.2017.2715333
[8] Lin H T, Liang T J, Chen S M. Estimation of battery state of health using probabilistic neural network [J]. IEEE Transaction on Industry Information, 2013, 9: 679-685. doi:  10.1109/TII.2012.2222650
[9] Wu J, Wang Y, Zhang X, Chen Z. A novel state of health estimation method of Li-ion battery using group method of data handling[J]. Journal of Power Sources, 2016, 327: 457-464.
[10] 杨楠, 南琳, 张丁一, 等. 基于深度学习的图像描述研究[J]. 红外与激光工程, 2018, 47(2): 0203002. doi:  10.3788/IRLA201847.0203002

Yang Nan, Nan Lin, Zhang Dingyi, et al. Depth estimation technique of sequence image based on deep learning [J]. Infrared and Laser Engineering, 2018, 47(2): 0203002. (in Chinese) doi:  10.3788/IRLA201847.0203002
[11] 周宏强, 黄玲玲, 王涌天. 深度学习算法及其在光学的应用[J]. 红外与激光工程, 2019, 48(12): 1226004. doi:  10.3788/IRLA201948.1226004

Zhou Hongqiang, Huang Lingling, Wang Yongtian. Deep learning algorithm and its application in optics [J]. Infrared and Laser Engineering, 2019, 48(12): 1226004. (in Chinese) doi:  10.3788/IRLA201948.1226004
[12] 范丽丽, 赵宏伟, 赵浩宇, 等. 基于深度卷积神经网络的目标检测研究综述[J]. 光学 精密工程, 2020, 28(5): 20202805.1152. doi:  10.3788/OPE.20202805.1152

Fan Lili, Zhao Hongwei, Zhao Haoyu, et al. Survey of target detection based on deep convolutional neural networks [J]. Optics and Precision Engineering, 2020, 28(5): 20202805.1152. (in Chinese) doi:  10.3788/OPE.20202805.1152
[13] 黄乐弘, 曹立华, 李宁. 深度学习的空间红外弱小目标状态感知方法[J]. 中国光学, 2020, 13(3): 2019-0120. doi:  10.3788/CO.2019-0120

Cao Lehong, Cao Lihua, Li Ning. A state perception method for infrared dim and small targets with deep learning [J]. Chinese Optics, 2020, 13(3): 2019-0120. (in Chinese) doi:  10.3788/CO.2019-0120
[14] 周旭峰, 王醒策, 武仲科, 等. 基于组合RNN网络的EMG信号手势识别[J]. 光学 精密工程, 2020, 28(2): 20202802.0424. doi:  10.3788/OPE.20202802.0424

Zhou Xufeng, Wang Xingce, Wu Zhongke, et al. Gesture recognition with EMG signals based on ensemble RNN [J]. Optics and Precision Engineering, 2020, 28(2): 20202802.0424. (in Chinese) doi:  10.3788/OPE.20202802.0424
[15] Li C, Xiao F, Fan Y. An approach to state of charge estimation of lithium-ion batteries based on recurrent neural networks with gated recurrent unit [J]. Energies, 2019, 12(9): 1592. doi:  10.3390/en12091592
[16] Jiao M, Wang D, Qiu J. A GRU-RNN based momentum optimized algorithm for SOC estimation [J]. Journal of Power Sources, 2020, 59(31): 228051.
[17] Song Y, Li L, Peng Y, et al. Lithium-ion battery remaining useful life prediction based on GRU-RNN[C]//2018 12th International Conference on Reliability, 2018.
[18] Bole B, Kulkarni C S, Daigle M. Adaptation of an electrochemistry-based Li-ion battery model to account for deterioration observed under randomized use[C]//Conference of the Prognostics & Health Management Society, 2014.
[19] Birkl C R. Diagnosis and Prognosis of Degradation in Lithium-ion Batteries[D]. Oxford: University of Oxford, 2017.