Accurate Multi-Step State of Charge Prediction for Electric Vehicle Batteries Using the Wavelet-Guided Temporal Feature Enhanced Informer

The state of charge (SOC) serves as a critical indicator for evaluating the remaining driving range of electric vehicles (EVs), and its prediction is of significance for alleviating range anxiety and promoting the development of the EVs industry. This study addresses two key challenges in current SOC prediction technologies: (1) the scarcity of multi-step prediction research based on real driving conditions and (2) the poor performance in multi-scale temporal feature extraction. We innovatively propose the Wavelet-Guided Temporal Feature Enhanced Informer (WG-TFE-Informer) prediction model with two core innovations: a wavelet-guided convolutional embedding layer that significantly enhances anti-interference capability through joint time-frequency analysis and a temporal edge enhancement (TEE) module that achieves the collaborative modeling of local microscopic features and macroscopic temporal evolution patterns based on sparse attention mechanisms. Building upon this model, we establish a multidimensional SOC energy consumption prediction system incorporating battery characteristics, driving behavior, and environmental terrain factors. Experimental validation with real-world operating data demonstrates outstanding performance: 1-min SOC prediction accuracy achieves a mean relative error (MRE) of 0.21% and 20-min SOC prediction exhibits merely 0.62% error fluctuation. Ablation experiments confirm model effectiveness with a 72.1% performance improvement over baseline (MRE of 3.06%) at 20-min SOC prediction, achieving a final MRE of 0.89%.