REMAINING USEFUL LIFE PREDICTION OF LITHIUM-ION BATTERIES BASED ON DIFFERENTIAL VOLTAGE AND ICS-ELMAN NEURAL NETWORKS

Li Lianbing; Zhu Le; Li Sijia; Liu Hanmin; Wang Yang; Zhao Jianhua

doi:10.19912/j.0254-0096.tynxb.2022-1357

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (12) : 433-443. DOI: 10.19912/j.0254-0096.tynxb.2022-1357

REMAINING USEFUL LIFE PREDICTION OF LITHIUM-ION BATTERIES BASED ON DIFFERENTIAL VOLTAGE AND ICS-ELMAN NEURAL NETWORKS

Li Lianbing^1,2, Zhu Le¹, Li Sijia¹, Liu Hanmin³, Wang Yang³, Zhao Jianhua³

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Abstract

Lithium-ion batteries are widely used in equipment supporting new energy grid connection, and their remaining useful life （RUL） prediction is very important for equipment operation and maintenance management. This paper presents a method for predicting the remaining service life of lithium-ion batteries based on differential voltage and improved cuckoo search algorithm （ICS） -Elman neural network. Firstly, the internal electrochemical reaction and external data characteristics of the battery were analyzed, and the differential voltage curve combined with the internal and external characteristics of the battery was selected as the feature extraction object, and the relevant features were selected from the charge differential voltage curve and discharge differential voltage curve. Considering the phenomenon of battery capacity regeneration, a battery capacity prediction model based on Elman neural networks is established. In order to improve the prediction accuracy, the improved cuckoo search algorithm is used to optimize the initial weights and thresholds of the network. The cuckoo search is improved by three methods： improving the probability formula, increasing the diffusion factor and chaos initialization to form the ICS-Elman prediction method. Finally, the ICS-Elman method is validated by using NASA dataset and self-test dataset. The results show that the ICS-Elman method can predict the RUL of lithium-ion battery more accurately and effectively compared with the CS-Elman and Elman models.

Key words

lithium-ion batteries / Elman neural networks / remaining useful life / improved cuckoo search algorithm / curves of differential voltage

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Li Lianbing, Zhu Le, Li Sijia, Liu Hanmin, Wang Yang, Zhao Jianhua. REMAINING USEFUL LIFE PREDICTION OF LITHIUM-ION BATTERIES BASED ON DIFFERENTIAL VOLTAGE AND ICS-ELMAN NEURAL NETWORKS[J]. Acta Energiae Solaris Sinica. 2023, 44(12): 433-443 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1357

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