EV Powertrain Systems Diagnostics & Prognostics utilizing AI & ML (LLM) based approach

This study introduces a novel Large Language Model (LLM)-driven approach for comprehensive diagnosis and prognostics of vehicle faults, leveraging Diagnostic Trouble Codes (DTCs) in line with industry-standard automation protocols. The proposed model represents a significant advancement in automotive diagnostics by not only identifying fault codes but also reasoning through the root causes behind them, thereby enhancing fault interpretability and maintenance efficiency. The LLM is trained on extensive service manuals, sensor datasets, historical fault logs, and OEM-specific DTC definitions, enabling context-aware understanding and correlation of faults. Analytical validation has been performed using real-world vehicle datasets from multi-brand platforms, demonstrating the model’s ability to detect complex fault chains and accurately predict probable root causes. By utilizing time series based projection of vehicle pattern, model could also predict the potential future faults basis the current driving condition of the vehicle. Key contributions of this work include: (1) a modular diagnostic framework that can be seamlessly integrated into different electronic control unit (ECU) architectures, (2) cross-platform compatibility allowing usage across varied vehicle makes and models, and (3) a user-friendly interface that eliminates the need for technical expertise by translating DTCs and sensor data into simple, actionable insights. The model was benchmarked against traditional rule-based diagnostic tools and showed around 70% reduction in troubleshooting time for Root cause analysis (RCA). In the prognosis front, model could predict upcoming possible faults in the Battery behavior with significant accuracy. The framework also supports continuous learning by integrating new fault patterns, ensuring adaptability over time. This paper establishes the potential of integrating advanced language models into the automotive diagnostics pipeline and provides a scalable, intelligent, and intuitive solution for next-generation vehicle fault management.