REMAINING USEFUL LIFE PREDICTION OF LITHIUM-ION BATTERIES BASED ON CEEMDAN AND GA-BILSTM

Xu Peng; Ran Wenwen; Huang Yuan; Li Huijuan; Xiao Kelin; Wan Shibin

doi:10.19912/j.0254-0096.tynxb.2024-1019

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (11) : 35-43. DOI: 10.19912/j.0254-0096.tynxb.2024-1019

REMAINING USEFUL LIFE PREDICTION OF LITHIUM-ION BATTERIES BASED ON CEEMDAN AND GA-BILSTM

Xu Peng, Ran Wenwen, Huang Yuan, Li Huijuan, Xiao Kelin, Wan Shibin

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Abstract

In this paper, an RUL prediction method with complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） and a genetic algorithms （GA） optimized bi-directional long and short-term memory （BILSTM） neural network is proposed. In this method, CEEMDAN decomposes the battery capacity data and also performs differential analysis on the battery data to obtain the feature inputs, and subsequently, the GA is used to optimize the hyperparameters of BILSTM to build the RUL prediction model of GA-BILSTM. Finally, validation is performed on the NASA dataset, and the results show that the method can realize RUL prediction accurately and effectively.

Key words

lithium-ion batteries / genetic algorithms / long and short-term memory / bi-directional long and short-term memory neural network / remaining useful life / complete ensemble empirical mode decomposition with adaptive noise

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Xu Peng, Ran Wenwen, Huang Yuan, Li Huijuan, Xiao Kelin, Wan Shibin. REMAINING USEFUL LIFE PREDICTION OF LITHIUM-ION BATTERIES BASED ON CEEMDAN AND GA-BILSTM[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 35-43 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1019

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