A Multitime-scale Deep Learning Model for Lithium-ion Battery Health Assessment Using Soft Parameter-sharing Mechanism

Lulu Wang; Kun Zheng; Yijing Li; Zhipeng Yang; Feifan Zhou; Jia Guo; Jinhao Meng

doi:10.23919/CJEE.2024.000085

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A Multitime-scale Deep Learning Model for Lithium-ion Battery Health Assessment Using Soft Parameter-sharing Mechanism

Lulu Wang^¹, Kun Zheng^², Yijing Li^³, Zhipeng Yang^⁴, Feifan Zhou^⁴, Jia Guo^⁵, Jinhao Meng^{³^,⁴}()

North China Electric Power Test and Research Institute of China Datang Group Science and Technology Research Institute Co., Ltd., Beijing 100040, China

School of Future Technology, Xi’an Jiaotong University, Xi’an 710049, China

National Innovation Platform (Center) for Industry-Education Integration of Energy Storage Technology, Xi’an Jiaotong University, Xi’an 710049, China

School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Department of Mechanical Engineering, Imperial College London, London SW7 2AZ, UK

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Graphical Abstract

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\frac{d Q}{d V}

, and

\frac{d T}{d V}

are extracted to form a four-channel image, from which the RCN can automatically extract the features and the GRU can capture the temporal features. By designing a soft parameter-sharing mechanism, the model can seamlessly predict the capacity and remaining useful life (RUL) on a dual time scale. The proposed method is validated on a large MIT-Stanford dataset comprising 124 cells, showing a high accuracy in terms of mean absolute errors of 0.00477 for capacity and 83 for RUL. Furthermore, studying the partial voltage fragment reveals the promising performance of the proposed method across various voltage ranges. Specifically, in the partial voltage segment of 2.8-3.2 V, root mean square errors of 0.0107 for capacity and 140 for RUL are achieved.

Abstract

Efficient assessment of battery degradation is important to effectively utilize and maintain battery management systems. This study introduces an innovative residual convolutional network (RCN)-gated recurrent unit (GRU) model to accurately assess health of lithium-ion batteries on multiple time scales. The model employs a soft parameter-sharing mechanism to identify both short- and long-term degradation patterns. The continuously looped Q(V), T(V), $\frac{d Q}{d V}$ , and $\frac{d T}{d V}$ are extracted to form a four-channel image, from which the RCN can automatically extract the features and the GRU can capture the temporal features. By designing a soft parameter-sharing mechanism, the model can seamlessly predict the capacity and remaining useful life (RUL) on a dual time scale. The proposed method is validated on a large MIT-Stanford dataset comprising 124 cells, showing a high accuracy in terms of mean absolute errors of 0.00477 for capacity and 83 for RUL. Furthermore, studying the partial voltage fragment reveals the promising performance of the proposed method across various voltage ranges. Specifically, in the partial voltage segment of 2.8-3.2 V, root mean square errors of 0.0107 for capacity and 140 for RUL are achieved.

Keywords

Residual convolutional network-gated recurrent unit capacity estimation soft parameter sharing remaining useful life prediction

References

[1]

E T Sayed, A G Olabi, A H Alami, et al. Renewable energy and energy storage systems. Energies, 2023, 16(3): 1415.