分解
计算机科学
电池(电)
国家(计算机科学)
降维
降噪
还原(数学)
噪音(视频)
概化理论
健康状况
特征(语言学)
维数(图论)
数据挖掘
算法
分解法(排队论)
均方误差
高效能源利用
能量(信号处理)
粒子群优化
稳健性(进化)
数学优化
水准点(测量)
人工神经网络
人工智能
特征向量
最优化问题
支持向量机
QR分解
标识
DOI:10.1016/j.est.2026.120353
摘要
With lithium-ion batteries (LIBs) serving as a cornerstone in modern energy storage systems, accurate state of health (SOH) prediction is vital for ensuring operational safety, optimizing performance, and extending service life. However, the highly nonlinear, time-varying, and noise-contaminated nature of battery degradation trajectories poses substantial challenges to achieving both accurate and robust SOH prediction. To address these issues, this study proposes a dual-stage synergistic enhancement framework for LIBs SOH prediction. A multi-strategy sand cat swarm optimization (MSCSO) algorithm is developed to enhance exploration–exploitation balance, which simultaneously drives entropy-guided variational mode decomposition for noise suppression and fine-tunes a neuro-fuzzy random vector functional link (NF-RVFL) network. The NF-RVFL network takes the extracted features from the denoised IMF components and concatenates them along the feature dimension with the health index (HI) to form the input vector for each sample, and outputs the predicted SOH. Finally, the proposed framework was rigorously evaluated on capacity data from two public LIBs datasets and benchmarked against state-of-the-art algorithms. The proposed algorithm achieves an average RMSE reduction of over 90 % compared with NF-RVFL on the NASA and CALCE datasets. The results demonstrate that our approach achieves superior accuracy, robustness, and generalizability in SOH prediction, confirming its significant promise for practical battery health monitoring systems. • A dual-stage synergistic framework is proposed for accurate SOH estimation of LIBs. • The multi-strategy SCSO algorithm enhances search efficiency and robustness. • Entropy-guided decomposition achieves denoising and feature extraction. • MSCSO fine-grained tuning of NF-RVFL parameters to boost prediction accuracy • Exhibits excellent results on two datasets with strong application potential
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