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Bai, S., Kolter, J.Z. and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, arXiv 1803.01271.Search in Google Scholar
Cao, J., Li, Z. and Li, J. (2019). Financial time series forecasting model based on CEEMDAN and LSTM, Physica A: Statistical Mechanics and Its Applications 519: 127–139.Search in Google Scholar
Cao, Y., Ding, Y., Jia, M. and Tian, R. (2021). A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings, Reliability Engineering & System Safety 215: 107813.Search in Google Scholar
Chen, D., Hong, W. and Zhou, X. (2022). Transformer network for remaining useful life prediction of lithium-ion batteries, IEEE Access 10: 19621–19628.Search in Google Scholar
He, K., Zhang, X., Ren, S. and Sun, J. (2016). Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 770–778.Search in Google Scholar
Hong, S., Yue, T. and Liu, H. (2022). Vehicle energy system active defense: A health assessment of lithium-ion batteries, International Journal of Intelligent Systems 37(12): 10081–10099.Search in Google Scholar
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C. and Liu, H.H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 454(1971): 903–995.Search in Google Scholar
Li, L., Li, Y., Mao, R., Li, L., Hua, W. and Zhang, J. (2023). Remaining useful life prediction for lithium-ion batteries with a hybrid model based on TCN-GRU-DNN and dual attention mechanism, IEEE Transactions on Transportation Electrification 9(3): 4726–4740.Search in Google Scholar
Li, X., Zhang, L., Wang, Z. and Dong, P. (2019). Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks, Journal of Energy Storage 21: 510–518.Search in Google Scholar
Park, K., Choi, Y., Choi, W.J., Ryu, H.-Y. and Kim, H. (2020). LSTM-based battery remaining useful life prediction with multi-channel charging profiles, IEEE Access 8: 20786–20798.Search in Google Scholar
Pecht, M. (2017). Battery data set: CALCE, Battery Research Data, CALCE Battery Research Group, University of Maryland, College Park, https://calce.umd.edu/data#CS2.Search in Google Scholar
Ren, L., Zhao, L., Hong, S., Zhao, S., Wang, H. and Zhang, L. (2018). Remaining useful life prediction for lithium-ion battery: A deep learning approach, IEEE Access 6: 50587–50598.Search in Google Scholar
Saha, B. and Goebel, K. (2007). Battery data set: NASA, NASA Ames Prognostics Data Repository, NASA Ames Research Center, Moffett Field, https://www.nasa.gov/intelligent-systems-division/#battery.Search in Google Scholar
Salimans, T. and Kingma, D.P. (2016). Weight normalization: A simple reparameterization to accelerate training of deep neural networks, Advances in Neural Information Processing Systems 29(9): 901–909.Search in Google Scholar
Seybold, L., Witczak, M., Majdzik, P. and Stetter, R. (2015). Towards robust predictive fault-tolerant control for a battery assembly system, International Journal of Applied Mathematics and Computer Science 25(4): 849–862, DOI: 10.1515/amcs-2015-0061.Search in Google Scholar
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research 15(1): 1929–1958.Search in Google Scholar
Torres, M.E., Colominas, M.A., Schlotthauer, G. and Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, pp. 4144–4147.Search in Google Scholar
Wu, Z. and Huang, N.E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method, Advances in Adaptive Data Analysis 1(01): 1–41.Search in Google Scholar
Ye, R. and Dai, Q. (2018). A novel transfer learning framework for time series forecasting, Knowledge-Based Systems 156: 74–99.Search in Google Scholar
Zhang, Y., Xiong, R., He, H. and Pecht, M.G. (2018). Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries, IEEE Transactions on Vehicular Technology 67(7): 5695–5705.Search in Google Scholar
Zhou, B., Cheng, C., Ma, G. and Zhang, Y. (2020). Remaining useful life prediction of lithium-ion battery based on attention mechanism with positional encoding, IOP Conference Series: Materials Science and Engineering, 895: 012006.Search in Google Scholar
Zhou, Y. and Huang, M. (2016). Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model, Microelectronics Reliability 65: 265–273.Search in Google Scholar
