Adaptive Health Monitoring of Second-Life Batteries

As surplus of second-life (SL) batteries becomes available, one critical obstacle between their widespread adoption is the accurate estimation and monitoring of their state-of-health (SOH). Various retired battery packs with a lack of knowledge of their historical usage are used to set up new SL battery systems. The paper presents a new online adaptive health estimation method designed to address these practical challenges associated with SL battery systems. This method utilizes the real-time data obtained from the SL batteries used for grid energy storage systems. The proposed approach is validated on a laboratory-aged experimental data set of retired EV batteries. Through dynamic adjustment of estimator gains, the approach can effectively accommodate the unique characteristics of individual cells, enhancing its adaptability and robustness. We prove theoretically that this method has bounded-input-bounded-output (BIBO) stability.