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.

这篇研究论文主要讨论了回收利用报废的电动车电池用于电网能量存储的策略，重点研究了用于电网存储应用的报废电池的健康监测算法，并通过机器学习模型和初始容量开发和比较了四种健康估计模型，其中选定的模型在测试数据上实现了低于 2.3% 的平均绝对百分比误差（MAPE）。此外，该论文还提出了一种自适应在线健康估计算法，通过集成基于聚类的方法，限制了在线部署过程中的估计误差。这些结果初步证明了报废电池再利用于第二生命应用的可行性，根据获得的数据和代表性功率需求，这些报废电池在特定条件下具有十余年的电网能量存储使用潜力。