Toward a BMS2 Design Framework: Adaptive Data-Driven State-of-Health Estimation for Second-Life Batteries With BIBO Stability Guarantees

A key challenge that is currently hindering the widespread use of retired electric vehicle (EV) batteries for second-life (SL) applications is the ability to accurately estimate and monitor their state of health (SOH). SL battery systems can be sourced from different battery packs with a lack of knowledge of their historical usage. Accurate SOH estimation is critical because it enables reliable performance, safety, and optimal utilization of SL batteries, ensuring they meet the requirements of various applications including grid energy storage and backup power. In this work, for in-the-field use of SL batteries, we introduce an online adaptive health estimation approach with the guarantees of bounded-input, bounded-output (BIBO) stability. This method relies exclusively on operational data that can be accessed in real-time from SL batteries. The effectiveness of the proposed approach is shown on a laboratory-aged experimental dataset of retired EV batteries.