LITHIUM-ION BATTERY REMAINING USEFUL LIFE PREDICTION METHOD BASED ON IMPROVED HEAP OPTIMIZATION ALGORITHM AND TIMESNET NEURAL NETWORK

Zhang Chu; Wang Yiwei; Zhang Yue; Chen Jialei; Peng Tian

doi:10.19912/j.0254-0096.tynxb.2024-1219

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (11) : 98-108. DOI: 10.19912/j.0254-0096.tynxb.2024-1219

LITHIUM-ION BATTERY REMAINING USEFUL LIFE PREDICTION METHOD BASED ON IMPROVED HEAP OPTIMIZATION ALGORITHM AND TIMESNET NEURAL NETWORK

Zhang Chu^1,2, Wang Yiwei¹, Zhang Yue¹, Chen Jialei¹, Peng Tian^1,2

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Abstract

This paper proposes a method for predicting the remaining useful life （RUL） of lithium-ion batteries based on an improved heap-based optimizer （HBO） and a TimesNet neural network. Firstly, kernel principal component analysis （KPCA） is used to perform dimensionality reduction and redundancy removal on the raw data to extract key features. Subsequently, a deep learning model based on TimesNet is constructed to evaluate the RUL of the battery. To further improve the performance of the model, the hyperparameters of the TimesNet model are optimized using the improved heap-based optimizer （IHBO）. In addition, an error correction mechanism is introduced to enhance the prediction accuracy. Experimental results show that this method has high accuracy in predicting the RUL of lithium-ion batteries, with a root mean square error（RMSE） of 1.9937 time, which is superior to traditional time-series models.

Key words

lithium-ion battery / remaining useful life / machine learning / heap-based optimizer algorithm / TimesNet / error correction

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Zhang Chu, Wang Yiwei, Zhang Yue, Chen Jialei, Peng Tian. LITHIUM-ION BATTERY REMAINING USEFUL LIFE PREDICTION METHOD BASED ON IMPROVED HEAP OPTIMIZATION ALGORITHM AND TIMESNET NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 98-108 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1219

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