AI-Enabled Vehicle Telematics for Predictive Maintenance, EV Safety, and Secure Connected Mobility: A Conceptual Review and Framework

This preprint presents a conceptual review and integrated framework for next-generation vehicle telematics in the context of artificial intelligence, green mobility, electric and hybrid vehicle safety, secure data governance, and connected transportation systems. Building upon an earlier 2017 telematics concept, the paper repositions vehicle telematics within the current mobility landscape shaped by predictive maintenance, edge AI, blockchain-enabled data governance, vehicle-to-everything communication, smart-city integration, and EV battery safety. The proposed framework integrates vehicle data capture, preprocessing, AI-based failure prediction, root-cause analysis, service automation, fleet learning repositories, and feedback loops for design and maintenance improvement. Special attention is given to EV and hybrid vehicle risks, including battery health monitoring, thermal safety, charging behavior, degradation prediction, and emergency response coordination. The paper is positioned as a conceptual framework and research agenda rather than an experimental validation study. It identifies key technology gaps, implementation challenges, and potential project opportunities for emerging economies such as India, where scalable, cost-effective, and safety-oriented telematics systems may support future smart and sustainable mobility.