ACCESSIBLE PREDICTIVE MAINTENANCE IN HIGH VOLTAGE: AN ELECTRICAL MONITORING MODEL FOR INDEPENDENT WORKSHOPS OF ELECTRIC AND HYBRID VEHICLES

Abstract

This paper proposes a simple, scalable predictive-maintenance model for high-voltage (HV) systems in electric and hybrid vehicles, tailored to the constraints of independent workshops and training centers. The framework integrates three complementary pillars: (i) structured electronic observability—leveraging what the vehicle reports (e.g., BMS/inverter telemetry via standard diagnostics); (ii) essential, safety-compliant electrical verification—confirming what the electricity proves through de-energized insulation checks and non-invasive measurements; and (iii) low-cost degradation indicators—anticipating failures with features derived from battery aging, power-electronics stress, and thermal behavior. The model translates safety and charging standards and current academic evidence into reproducible shop-floor routines that prioritize risk mitigation, technician safety, and instrument affordability. It outlines data acquisition, feature extraction, and rule-based/ lightweight decision stages to support proactive scheduling, consistent technical judgments, and higher service quality without reliance on proprietary or sophisticated equipment. The approach also provides a didactic pathway for workforce development, enabling systematic training aligned with best practices and compliance requirements.