Enhancing Validation Procedure of State of Health (SoH) Test Product for Electric Vehicles

Abstract

This thesis focuses on optimizing and validating the State of Health (SoH) measurement system testing for Electric Vehicle (EV) batteries. The system undergoes regular software updates and enhancements. Each update requires structured release cycles to deploy changes while ensuring accuracy and reliability. Accurate testing is essential to validate these updates. The study uses Hardware in Loop (HiL) testing and regression testing to achieve efficient and reliable validation. HiL testing provides real-time emulation and validation of signals, enabling precise comparisons between real-world and simulated data. Regression testing focuses on verifying only the altered functions, reducing redundancy and improving efficiency. The application of signal processing techniques such as resampling, interpolation, and time-series alignment address challenges like timelag discrepancies, amplitude mismatches, and failure mode detection. A key outcome is the reduction of testing duration from 53 days to 5 days, maintaining precision and reliability. This framework accelerates release cycles and improves software deployment efficiency for SoH measurement. The research establishes a robust validation approach and paves the way for future advancements, including dynamic failure mode detection and scalable testing methods.

Date of Award	2024
Original language	English
Supervisor	Christian Ertl (Supervisor)