Design Optimization of Home Electric Vehicle Chargers Based on User Review Mining and Explainable Machine Learning

As electric vehicles become widespread, home EV chargers have emerged as a key interface between household energy use and daily mobility. However, their design optimization remains insufficiently informed by large-scale user feedback. This study develops a review-driven, interpretable machine learning framework to identify design priorities for home EV chargers. Of the 26,763 reviews collected from the JD e-commerce platform, 23,893 were retained after cleaning. BERTopic extracted raw topics, which were consolidated into ten design dimensions through independent coding, inter-coder agreement assessment, and consensus adjudication. A structured large language model protocol then transformed the reviews into evidence-constrained, aspect-level semantic proxy variables representing evaluative direction and intensity. Coding reliability was evaluated against dual-coder annotations, while a matched absence-as-zero specification examined sensitivity to the treatment of unmentioned dimensions. Platform ratings were subsequently introduced as the prediction target, and repeated data partitions and cross-model SHAP comparisons were used to assess partition- and model-level stability. Charging Performance, Operational Stability, Perceived Product Quality, and Operational Convenience and Portability consistently ranked as the most important factors associated with platform-rated satisfaction. In contrast, Installation Friendliness and After-sales Service showed asymmetric attribution patterns characterized by stronger low-value penalties than high-value gains. The framework supports translating online review evidence into product-level design priorities, while emphasizing that SHAP identifies predictive associations rather than causal effects.