基于优化的VMD-SSA-PatchTST算法的锂离子电池RUL预测。

IF 3.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Pei Tang, Zetao Qiu, Zhongran Yao, Jiahao Pan, Dashuai Cheng, Xiaoyong Gu, Changcheng Sun
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引用次数: 0

摘要

准确预测锂离子电池的剩余使用寿命(RUL)对系统的可靠性和安全性至关重要。本文提出了一种将模态分解与先进的PatchTST模型相融合的预测框架。最初,使用Spearman相关系数来识别与电池容量密切相关的特征。然后使用变分模态分解(VMD)方法将原始容量序列分解为一组固有模态函数。为了提高分解质量,鲸鱼优化算法(WOA)通过最小化平均包络熵来优化模式K和惩罚因子α的数量。选择的特征和分解的组件随后被输入到PatchTST网络中,该网络的超参数通过麻雀搜索算法(SSA)进行调整,以预测电池RUL。在NASA电池数据集和NASA随机电池使用数据集上的实验验证表明,所提出的WOA-VMD-SSA-PatchTST模型始终优于基线模型,包括CNN、GRU和PatchTST,具有优越的预测精度和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm.

Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm.

Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm.

Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm.

Accurate prediction of lithium-ion batteries' remaining useful life (RUL) is critical for system reliability and safety. This study proposes a novel forecasting framework that fuses modal decomposition with the advanced PatchTST model. Initially, the Spearman correlation coefficient is employed to identify features strongly associated with battery capacity. The Variational Mode Decomposition (VMD) method is then used to break down the raw capacity sequence into a set of intrinsic mode functions. To enhance decomposition quality, the Whale Optimization Algorithm (WOA) optimizes the number of modes K and penalty factor α by minimizing mean envelope entropy. The selected features and decomposed components are subsequently input into a PatchTST network, whose hyperparameters are tuned via the Sparrow Search Algorithm (SSA), to predict battery RUL. Experimental validation on the NASA Battery dataset and NASA Randomized Battery Usage Dataset demonstrates that the proposed WOA-VMD-SSA-PatchTST model consistently outperforms baseline models, including CNN, GRU and PatchTST, achieving superior prediction accuracy and robustness.

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来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
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