<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>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.</p>
</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.

Remaining Useful Life Prediction of a Lithium–Ion Battery Based on a Temporal Convolutional Network with Data Extension

Institute of Robotics and Intelligent Manufacturing

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Unmanned underwater vehicles are typically deployed in deep sea environments, which present unique working conditions. Lithium-ion power batteries are crucial...

Remaining Useful Life Prediction of a Lithium–Ion Battery Based on a Temporal Convolutional Network with Data Extension

Publié en ligne: 26 mars 2024

Pages: 105 - 117

Reçu: 15 mars 2023

Accepté: 13 nov. 2023

DOI: https://doi.org/10.61822/amcs-2024-0008

Mots clés
lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net

© 2024 Jing Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Remaining Useful Life Prediction of a Lithium–Ion Battery Based on a Temporal Convolutional Network with Data Extension

Publié en ligne: 26 mars 2024

Pages: 105 - 117

Reçu: 15 mars 2023

Accepté: 13 nov. 2023

DOI: https://doi.org/10.61822/amcs-2024-0008

Mots cléslithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net

© 2024 Jing Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net