TY  - JOUR
AB  - 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.
AU  - Jiang, Y
AU  - Ke, Y
AU  - Yang, F
AU  - Ji, J
AU  - Peng, W
DA  - 2024/01/01
DO  - 10.1109/TIE.2023.3344825
EP  - 13188
JO  - IEEE Transactions on Industrial Electronics
PB  - IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PY  - 2024/01/01
SP  - 13178
TI  - State of Health Estimation for Second-Life Lithium-Ion Batteries in Energy Storage System With Partial Charging-Discharging Workloads
VL  - 71
Y1  - 2024/01/01
Y2  - 2026/10/21
ER  -