Accurate state-of-charge (SOC) prediction is critical for estimating driving range and ensuring the reliability of electric vehicle (EV) battery management systems. Although machine learning-based SOC prediction models achieve high accuracy, their complex nonlinear structures limit interpretability and hinder practical deployment. This study proposes an automated interpretation framework that integrates a multimodal large language model (MLLM) with Shapley interaction quantification (SHAP-IQ) to explain SOC prediction results. An XGBoost-based SOC prediction model is developed, and SHAP-IQ is employed to analyze both main effects of individual input variables (order 1) and pairwise feature interactions (order 2). SHAP-IQ visualizations and attribution values are provided as inputs to MLLM, which generates instance-level natural language explanations, while cross-validation and aggregation procedures ensure consistency. Experiments using real-world driving data collected from a BMW i3 show that XGBoost outperforms benchmark models in SOC prediction accuracy. The results indicate that, for the analyzed instances, SOC predictions are primarily governed by electrical variables such as battery voltage and current, whereas driving and environmental variables mainly affect the prediction through interaction effects. The proposed framework demonstrates the potential to improve the interpretability of SOC prediction models and can be extended to other energy systems in EVs employing complex machine learning models.
Lee et al. (Tue,) studied this question.