As industries increasingly rely on batteries for electric vehicles, robotics, and industrial automation, the demand for more insightful and predictive battery diagnostics has grown. Traditional Battery Management Systems (BMS) rely on external parameters such as voltage, current, and temperature, which do not fully capture the internal electrochemical changes within the battery. This thesis investigated the feasibility of integrating Electrochemical Impedance Spectroscopy (EIS) into BMS as a complementary technique to provide deeper insights into internal electrochemical behavior.

The study examined how traditional BMS works, highlighting its limitations, and explored the principles of EIS, focusing on impedance changes under varying conditions, such as temperature, state of charge (SoC), and charge/discharge rates (C-rates). Laboratory tests helped assess how EIS could provide additional insights into battery health beyond what traditional methods can detect.

While no direct comparisons with real-world BMS were made, the study showed that EIS can detect internal battery changes not visible through standard measurements. The thesis also considered key integration factors, such as signal injection, frequency control, and addressing disturbances like wiring or external noise that can impact accuracy.

Overall, the findings support the technical feasibility of integrating EIS into BMS, providing a foundation for future development. Although further testing is required to confirm its real-world diagnostic advantages, this work contributes to the goal of enabling smarter, more reliable battery systems, benefiting researchers, engineers, and industries aiming for safer and more efficient energy storage solutions.