EIS Capable Hardware for BEV On-Board Battery Internal Temperature Estimation

The growing demand for efficient and reliable Electric Vehicle (EV) powertrains has made accurate battery monitoring and thermal management essential for safety and performance. A key challenge is estimating internal cell temperature, which cannot be measured directly and is usually inferred from surface sensors and models. These methods risk overlooking internal thermal behavior critical for detecting thermal run-away. This thesis explores electrochemical impedance spectroscopy (EIS) as a method for internal temperature estimation using next-generation Battery Management System (BMS) hardware with built-in EIS capability. A Python-based framework was developed to automate frequency sweeps and collect data across a controlled temperature range. The hardware was tested on a commercial lithium-ion cell in a thermal chamber and validated against a laboratory-grade frequency response analyzer (FRA). Results show that the hardware reliably measured EIS data with the expected inverse relationship between impedance and temperature. These findings confirm the feasibility of EIS-based temperature estimation in EVs, though further refinement is needed to improve robustness and reduce data requirements.