State of Charge Estimation of Battery for Electric Vehicle Based on VMD‐Basisformer Model

ABSTRACT Accurate State of Charge (SOC) estimation is critical for the safe and efficient operation of batteries in electric vehicles (EVs). While deep learning models like Transformers have shown promise, they often struggle with sensor noise and complex temporal dynamics. Similarly, hybrid approaches like VMD‐Transformer rely on fixed basis functions that lack adaptability. To address challenges such as sensor noise, nonlinear dynamics, and complex temporal dependencies, this study proposes a novel VMD‐Basisformer model that integrates Variational Mode Decomposition (VMD) with an enhanced Basisformer neural network. The key innovations of our approach include: (1) a battery‐optimized VMD process for noise reduction and multi‐scale feature extraction; (2) a dual‐path basis generation mechanism tailored to battery temporal dynamics; (3) a hierarchical attention architecture for capturing both local and global temporal dependencies. Experimental results under Dynamic Stress Test (DST) and Urban Dynamometer Driving Schedule (UDDS) conditions show that the proposed model significantly outperforms existing methods such as Transformer, Basisformer, and VMD‐Transformer. Experimental validation on LiFePO 4 battery and supercapacitor datasets under DST and UDDS conditions shows that the VMD‐Basisformer outperforms benchmark models (Transformer, Basisformer, VMD‐Transformer) in accuracy and robustness. Ablation studies confirm the critical role of InfoNCE loss in ensuring temporal consistency.