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

Zhao Jing, Liu Dayong, Meng Lingshuai

International Journal of Applied Mathematics and Computer Science

2024

Vol. 34, no. 1

105--117

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.

Analysis of marine risers subjected to shoal/deep water in the installation process

Wang Yikun, Luo Songxiang, Yang Mo, Qin Tao, Zhao Jing, Yu Gang

Polish Maritime Research

2022

nr 2

43--54

The dynamics of the installation process of marine risers subjected to shoal/deep seawater is studied. The riser is assumed to be a cantilevered Euler‒Bernoulli beam. The upper end of the riser is clamped on the vessel or the drilling platform. The lower end of the riser is connected to the Blowout Preventer Stack (BOPs) and Lower Marine Risers Package (LMRP). The lateral fluid forces induced by the sea wave and sea current are introduced into the governing equations of motion. The lateral displacement and stress distributions of the riser are obtained by solving the governing equation of the riser via Galerkin’s discretisation scheme and a fourth-order Runge‒Kutta algorithm. The results indicate that the riser exhibits different behaviours under various depths because of the different distributions of the flow velocity ranging from the sea surface to the seabed. In the case of shoal water, the dynamics of the riser are dominated by the sea wave, while in the case of deep water it is affected mainly by the sea current velocity and sea surface wind velocity.