Towards a BMS2 Design Framework: Adaptive Data-driven State-of-health Estimation for Second-Life Batteries with BIBO Stability Guarantees

TL;DR

This paper presents an online adaptive method for estimating the health of second-life EV batteries, ensuring stability and tailored to individual battery characteristics, facilitating their use in second-life applications.

Contribution

It introduces a novel adaptive health estimation approach with BIBO stability guarantees, tailored for real-time operation on diverse second-life batteries.

Findings

Effective on laboratory aged EV battery data

Dynamic adaptation of estimator gains

Potential for integration into second-life BMS

Abstract

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).