State of Health Estimation for Second-Life Lithium-Ion Batteries in Energy Storage System With Partial Charging-Discharging Workloads
Yiyue Jiang, Yuqi Ke, Fangfang Yang, Jinchen Ji, Weiwen Peng
Abstract
Echelon utilization in energy storage systems (ESSs) has emerged as one of the predominant solutions for addressing large-scale retired lithium-ion batteries from electrical vehicles. However, high unit-to-unit health variability and partial charging-discharging workloads render the state of health (SOH) estimation of these second-life lithium-ion batteries (SL-LIBs) in ESSs a crucial and challenging issue. Existing SOH estimation methods are commonly focused on new batteries with consistent health state and complete charging-discharging workloads, while the estimation methods for SL-LIBs have been rarely developed. To fill this gap, this article proposes a novel SOH estimation method with specially designed features and calibrated uncertainty quantification for SL-LIBs. Joint features are first introduced for extracting useful SOH information from the cycling data of SL-LIBs under partial charging-discharging workloads. Then, Bayesian neural network with uncertainty calibration is used to generate SOH estimation results, which can quantify the estimation uncertainty caused by unit-to-unit health variability. To demonstrate the proposed method, a total of 36 retired NCM-18650 power batteries are cycled under 9 different partial charging-discharging workloads. A case study on this SL-LIB lab-test dataset reaches the best results of 2.13% mean absolute percentage error and 0.0178 root-mean-squared error, as well as well-calibrated estimation uncertainty.