Electric motorcycle retrofitting has emerged as a potentially cost-effective pathway for accelerating transport electrification, particularly in motorcycle-dependent regions where the replacement of existing internal combustion engine fleets remains economically challenging. However, current retrofitting practices are often manual, fragmented, and weakly supported by data-driven decision tools. This paper presents a structured review of artificial intelligence applications relevant to electric motorcycle retrofitting, with emphasis on battery management, predictive maintenance, component selection, energy optimization, conversational decision support, and data-scarcity mitigation. The review synthesizes a fragmented body of literature across electric vehicle systems, machine learning, optimization, and intelligent service platforms to identify the technical and practical gaps that limit the deployment of AI-assisted retrofit workflows. Based on this synthesis, the paper proposes an evidence-grounded conceptual Retrofit-as-a-Service (RaaS) framework for motorcycle conversion, integrating structured data management, AI-assisted compatibility analysis, performance prediction, and technician-oriented chatbot support. Rather than claiming full technical validation, the framework is presented as a literature-derived architecture intended to guide future implementation and evaluation. The study highlights the relevance of motorcycle-specific constraints, including packaging limitations, weight sensitivity, workshop capability, supply-chain variability, and regional regulatory differences. By positioning AI not as a standalone solution but as an enabling layer for more consistent and scalable retrofit planning, this review contributes a clearer foundation for future prototype development, validation studies, and policy-aligned deployment of electric motorcycle retrofitting systems.
Alqedra et al. (Mon,) studied this question.