REMAINING USEFUL LIFE PREDICTION METHOD BASED ON PARALLEL NEURAL NETWORKS WITH MULTI-SCALE FEATURE FUSION

Yu Ping; Wang Haonian; Cao Jie

doi:10.19912/j.0254-0096.tynxb.2024-1676

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (1) : 785-796. DOI: 10.19912/j.0254-0096.tynxb.2024-1676

REMAINING USEFUL LIFE PREDICTION METHOD BASED ON PARALLEL NEURAL NETWORKS WITH MULTI-SCALE FEATURE FUSION

Yu Ping^1~3, Wang Haonian¹, Cao Jie¹

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Abstract

To achieve more accurate predictions of the remaining useful life （RUL） of lithium-ion batteries and enhance their reliability to ensure stable operation, this paper proposes a parallel neural network prediction method based on multi-scale feature fusion. First, multi-scale features of the lithium-ion batteryies are extracted using temporal convolutional network （TCN） at different scales, enhancing the capture of both local and global features. Then, a cross-attention mechanism is introduced to filter and fuse the features, focusing on key degradation information. Next, parallel Bi-LSTM and Bi-GRU networks are constructed to learn degradation features and establish long-term dependencies on the time scale, ultimately achieving RUL prediction. The proposed method is validated using the NASA and CALCE lithium battery datasets, demonstrating its effectiveness in various contexts.

Key words

multi-scale feature fusion / TCN / cross-attention mechanism / lithium battery / remaining useful life （RUL） prediction / paralled neural network

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Yu Ping, Wang Haonian, Cao Jie. REMAINING USEFUL LIFE PREDICTION METHOD BASED ON PARALLEL NEURAL NETWORKS WITH MULTI-SCALE FEATURE FUSION[J]. Acta Energiae Solaris Sinica. 2026, 47(1): 785-796 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1676

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