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Paper details
Number 1 - March 2024
Volume 34 - 2024
Remaining useful life prediction of a lithium-ion battery based on a temporal convolutional network with data extension
Jing Zhao, Dayong Liu, Lingshuai Meng
Abstract
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.
Keywords
lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net