Remaining useful life indirect prediction of lithium-ion batteries using CNN-BiGRU fusion model and TPE optimization

Xiaoyu Zheng; Dewang Chen; Yusheng Wang; Liping Zhuang; Xiaoyu Zheng; Dewang Chen; Yusheng Wang; Liping Zhuang

doi:10.3934/energy.2023043

AIMS Energy

2023, Volume 11, Issue 5: 896-917. doi: 10.3934/energy.2023043

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Remaining useful life indirect prediction of lithium-ion batteries using CNN-BiGRU fusion model and TPE optimization

1.
School of Transportation, Fujian University of Technology, 69-1 Xuefu South Road, Fuzhou, 350118, China
2.
Intelligent Transportation System Research Center, Fujian University of Technology, 69‑1 Xuefu South Road, Fuzhou 350118, China
3.
College of Mathematics and Computer Science, Fuzhou University, 2 Xueyuan Road, Fuzhou, 350108, China
4.
School of Electronics, Electrical Engineering and Physics, Fujian University of Technology, 69 Xuefu South Road, Fuzhou, 350118, China

Received: 01 August 2023 Revised: 11 September 2023 Accepted: 14 September 2023 Published: 22 September 2023

The performance of lithium-ion batteries declines rapidly over time, inducing anxiety in their usage. Ascertaining the capacity of these batteries is difficult to measure directly during online remaining useful life (RUL) prediction, and a single deep learning model falls short of accuracy and applicability in RUL predictive analysis. Hence, this study proposes a lithium-ion battery RUL indirect prediction model, fusing convolutional neural networks and bidirectional gated recurrent units (CNN-BiGRU). The analysis of characteristic parameters of battery life status reveals the selection of pressure discharge time, average discharge voltage and average temperature as health factors of lithium-ion batteries. Following this, a CNN-BiGRU model for lithium-ion battery RUL indirect prediction is established, and the Tree-structured Parzen Estimator (TPE) adaptive hyperparameter optimization method is used for CNN-BiGRU model hyperparameter optimization. Overall, comparison experiments on single-model and other fusion models demonstrate our proposed model's superiority in the prediction of RUL in terms of stability and accuracy.
- lithium-ion battery,
- RUL,
- CNN-BiGRU,
- indirect prediction,
- deep learning
Citation: Xiaoyu Zheng, Dewang Chen, Yusheng Wang, Liping Zhuang. Remaining useful life indirect prediction of lithium-ion batteries using CNN-BiGRU fusion model and TPE optimization[J]. AIMS Energy, 2023, 11(5): 896-917. doi: 10.3934/energy.2023043

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Abstract

The performance of lithium-ion batteries declines rapidly over time, inducing anxiety in their usage. Ascertaining the capacity of these batteries is difficult to measure directly during online remaining useful life (RUL) prediction, and a single deep learning model falls short of accuracy and applicability in RUL predictive analysis. Hence, this study proposes a lithium-ion battery RUL indirect prediction model, fusing convolutional neural networks and bidirectional gated recurrent units (CNN-BiGRU). The analysis of characteristic parameters of battery life status reveals the selection of pressure discharge time, average discharge voltage and average temperature as health factors of lithium-ion batteries. Following this, a CNN-BiGRU model for lithium-ion battery RUL indirect prediction is established, and the Tree-structured Parzen Estimator (TPE) adaptive hyperparameter optimization method is used for CNN-BiGRU model hyperparameter optimization. Overall, comparison experiments on single-model and other fusion models demonstrate our proposed model's superiority in the prediction of RUL in terms of stability and accuracy.

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