<p>Electric vehicles (EVs) are one of the sustainable modes of transportation that can reduce the air pollution caused by fossil fuel-powered automobiles. The literature overlooks non-technical criteria, including environmental considerations and social responsibility in the EV selection process, which may navigate the adoption of EVs. This study addresses the EV selection process in the hesitant bi-fuzzy context, considering technical and non-technical EV features. A multi-criteria group decision-making approach is developed to evaluate EVs utilizing a modified hesitant bi-fuzzy COmbinative Distance-based Assessment technique. This study proposes a novel hesitant bi-fuzzy Dombi weighted average operator to integrate the decision maker’s opinions with their weightage. The proposed approach incorporates the importance of the criteria evaluated by the method based on the removal effects of criteria and hesitant information in Euclidean and Taxicab metrics to produce the ranking sequence of EVs. The Python code for the suggested group decision-making model is provided to rationalize the computational process. This study highlights the relative importance of charging time, price, and range compared to the remaining features in selecting EVs. The study’s findings indicate that the technical features of an EV are still a primary consideration for the customers. These findings will assist policymakers and EV industry stakeholders in making decisions to focus on EV modification. A comparative analysis with the existing decision-making models encompasses the applicability of the proposed technique. In contrast, the sensitivity analysis demonstrates the suggested approach’s robustness through the alteration in decision makers’ s preferences, internal parameters, and criteria importance to assess their impact on the final rankings of EVs.</p>