Considering the huge influence of personal driving habits on the actual driving mileage of electric vehicles (EVs), together with the weak interpretability of most existing evaluation models, this research combines logistic regression and scorecard methods to build a new assessment model that focuses on energy consumption and driving behavior, and a complete closed-loop technical framework has been set up in the research process, covering key steps such as raw data preprocessing, feature mining and screening, model design and construction, as well as final score output. Taking real collected vehicle operation data as the basic research material, we extracted 17 feature indicators from three major dimensions, including driver pedal operation habits, real-time vehicle operating state, and actual energy consumption performance, and adopted the mutual information (MI) calculation method to select eight core features with strong differentiation for 100-kilometer power consumption. Since obvious nonlinear correlations exist between these core indicators and vehicle energy consumption, we adopted decision tree-based grouping processing, weight of evidence (WOE) encoding, and information value (IV) quantitative analysis to realize feature discretization and optimization, and then constructed a scoring model relying on a logistic regression algorithm. The verification results demonstrate that the proposed model has an Area Under the Curve (AUC) value of 0.83 when identifying driving behaviors with high or low energy consumption, proving its excellent differentiation ability. This model turns complicated driving behaviors into intuitive quantitative scores, effectively making up for the defects of traditional black-box models that cannot offer practical guidance for energy-saving optimization, and it can also provide solid theoretical reference and practical application support for promoting energy-saving driving behavior among EV users.