With the increasing adoption of lithium-ion batteries in energy storage systems, accurately monitoring the State of Health (SoH) of retired batteries has become a pivotal technology for ensuring their safe utilization and maximizing their economic value. In response to this need, this paper presents a highly efficient estimation model based on the multi-input metabolic gated recurrent unit (MM-GRU). The model leverages constant-current charging time, charging current area, and the 1800 s voltage drop as input features and dynamically updates these features through a metabolic mechanism. It requires only four cycles of historical data to reliably predict the SoH of subsequent cycles. Experimental validation conducted on retired Samsung and Panasonic battery cells and packs under constant-current and dynamic operating conditions demonstrates that the MM-GRU model effectively tracks SoH degradation trajectories, achieving a root mean square error of less than 1.2% and a mean absolute error of less than 1%. Compared to traditional machine learning algorithms such as SVM, BPNN, and GRU, the MM-GRU model delivers superior estimation accuracy and generalization performance. The findings suggest that the MM-GRU model not only significantly enhances the breadth and precision of SoH monitoring for retired batteries but also offers robust technical support for their safe deployment and asset optimization in energy storage systems.

This paper addresses the challenges of accurately estimating the state of health (SOH) of retired batteries, where factors such as limited historical data, non-linear degradation, and unstable parameters complicate the process. We propose a novel SOH estimation model based on an Integrated Hierarchical Extreme Learning Machine (I-HELM). The model minimizes reliance on historical data and reduces computational complexity by introducing health indicators derived from constant charging time and charging current area. The hierarchical structure of the Extreme Learning Machine (HELM) effectively captures the non-linear relationship between health indicators and battery capacity, improving estimation accuracy and learning efficiency. Additionally, integrating multiple HELM models enhances the stability and robustness of the results, making the approach more reliable across varying operational conditions. The proposed model is validated on experimental datasets collected from two Samsung battery packs, four Samsung single cells, and two Panasonic retired batteries under both constant-current and dynamic conditions. Experimental results demonstrate the superior performance of the model: the maximum error for Samsung battery cells and packs does not exceed 2.2% and 2.6%, respectively, with root mean square errors (RMSEs) below 1%. For Panasonic retired batteries, the maximum error remains under 3%.