Taking Second-life Batteries from Exhausted to Empowered using Experiments, Data Analysis, and Health Estimation
This work addresses the need for reliable health estimation in second-life batteries for grid storage applications, offering incremental improvements in monitoring algorithms.
This study tackled the problem of health monitoring for retired electric vehicle batteries reused in grid energy storage by developing machine-learning-based estimation models, achieving a mean absolute percentage error below 2.3% on test data and demonstrating potential for over a decade of use.
The reuse of retired electric vehicle batteries in grid energy storage offers environmental and economic benefits. This study concentrates on health monitoring algorithms for retired batteries deployed in grid storage. Over 15 months of testing, we collect, analyze, and publicize a dataset of second-life batteries, implementing a cycling protocol simulating grid energy storage load profiles within a 3-4 V voltage window. Four machine-learning-based health estimation models, relying on online-accessible features and initial capacity, are compared, with the selected model achieving a mean absolute percentage error below 2.3% on test data. Additionally, an adaptive online health estimation algorithm is proposed by integrating a clustering-based method, thus limiting estimation errors during online deployment. These results showcase the feasibility of repurposing retired batteries for second-life applications. Based on obtained data and power demand, these second-life batteries exhibit potential for over a decade of grid energy storage use.