Taking Second-life Batteries from Exhausted to Empowered using
  Experiments, Data Analysis, and Health Estimation
Xiaofan Cui Muhammad Aadil Khan Gabriele Pozzato Surinder Singh Ratnesh Sharma Simona Onori
The reuse of retired electric vehicle (EV) batteries in electric grid energy
storage emerges as a promising strategy to address environmental concerns and
boost economic value. This study concentrates on devising health monitoring
algorithms for retired batteries (BMS$_2$) deployed in grid storage
applications. Over 15 months of testing, we compile, analyze, and publicly
share a dataset of second-life (SL) batteries, implementing a cycling protocol
simulating grid energy storage load profiles within a 3 V-4 V voltage window.
Four machine learning-based health estimation models, relying on BMS$_2$
features and initial capacity, are developed and compared, with the selected
model achieving a Mean Absolute Percentage Error (MAPE) below 2.3% on test
data. Additionally, an adaptive online health estimation algorithm is proposed
by integrating a clustering-based method, limiting estimation errors during
online deployment. These results constitute an initial proof of concept,
showcasing the feasibility of repurposing retired batteries for second-life
applications. Based on obtained data and representative power demand, these SL
batteries exhibit the potential, under specific conditions, for over a decade
of grid energy storage use.