Remaining useful life prediction of a lithium-ion battery based on a temporal convolutional network with data extension

• Autorzy: Zhao Jing , Liu Dayong , Meng Lingshuai

Identyfikatory

DOI

10.61822/amcs-2024-0008

• Języki publikacji: EN

Abstrakty

EN

Unmanned underwater vehicles are typically deployed in deep sea environments, which present unique working conditions. Lithium-ion power batteries are crucial for powering underwater vehicles, and it is vital to accurately predict their remaining useful life (RUL) to maintain system reliability and safety. We propose a residual life prediction model framework based on complete ensemble empirical mode decomposition with an adaptive noise-temporal convolutional net (CEEMDAN-TCN), which utilizes dilated causal convolutions to improve the model’s ability to capture local capacity regeneration and enhance the overall prediction accuracy. CEEMDAN is employed to denoise the data and prevent RUL prediction errors caused by local regeneration, and feature expansion is utilized to extend the temporal dimension of the original data. The NASA and CALCE battery capacity datasets are used as input to train the network framework. The output is the current predicted residual capacity, which is compared with the real residual battery capacity. The MAE, RMSE and RE are used as the evaluation indexes of the RUL prediction performance. The proposed network model is verified on the NASA and CACLE datasets. The evaluation results show that our method has better life prediction performance. At the same time, it is proved that both feature expansion and modal decomposition can improve the generalization ability of the model, which is very useful in industrial scenarios.

Słowa kluczowe

EN

lithium ion battery; remaining useful life; adaptive noise; temporal convolutional network

PL

bateria litowo jonowa; pozostały okres użytkowania; sieć konwolucyjna

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2024
• Tom: Vol. 34, no. 1
• Strony: 105--117
• Opis fizyczny: Bibliogr. 22 poz., rys., tab., wykr.

Twórcy

autor

Zhao Jing

• Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
• Institute of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China

autor

Liu Dayong

• Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
• University of the Chinese Academy of Sciences, Beijing 100049, China

autor

Meng Lingshuai

• Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
• University of the Chinese Academy of Sciences, Beijing 100049, China

Bibliografia

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• [17] 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.
• [18] 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.
• [19] Ye, R. and Dai, Q. (2018). A novel transfer learning framework for time series forecasting, Knowledge-Based Systems 156: 74-99.
• [20] 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.
• [21] 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.
• [22] 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.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).