Expert System for Electric Vehicle Charging Infrastructure Planning: A Scenario-Based Decision Support Framework for Two-Wheeler and Bus Fleets

Thea Fitri Astarani (Politeknik Negeri Medan)
Stephanie Christina Yolanda Pardede (Politeknik Negeri Medan)
Aprima Anugerah Matondang (Politeknik Negeri Medan)
Junaidi Junaidi (Politeknik Negeri Medan)

Jurnal Sains dan Teknologi
Vol. 5 No. 4 (2026): Agustus 2026

The rapid adoption of electric vehicles (EVs) presents significant challenges in charging infrastructure planning, particularly for diverse vehicle fleets in developing regions. This study proposes an expert system-based decision support framework for EV charging infrastructure planning, specifically addressing two-wheeler and bus fleet scenarios. The framework integrates scenario-based demand characterization with rule-based inference to provide location recommendations and capacity planning decisions. Using data from Indonesian urban transportation contexts, the expert system evaluates multiple criteria including demand patterns, infrastructure availability, and energy consumption profiles. Results indicate that the proposed framework successfully identifies optimal charging locations with 87.5% accuracy compared to expert validation, while reducing planning time by approximately 40% compared to conventional methods. The system provides decision-makers with actionable recommendations for prioritizing infrastructure investments across different fleet types. This research contributes to the growing body of knowledge on AI-based decision support systems for sustainable transportation infrastructure, with implications for policy formulation and urban planning in developing economies.