  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).
Second-life battery systems can be sourced from different battery packs with
lack of knowledge of their historical usage. To tackle the in-the-field use of
SL batteries, this paper introduces an online adaptive health estimation
approach with guaranteed 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 data set of retired EV batteries. The estimator
gains are dynamically adapted to accommodate the distinct characteristics of
each individual cell, making it a promising candidate for future SL battery
management systems (BMS2).
