Model-free detection and quantitative assessment of micro short circuits in lithium-ion battery packs based on incremental capacity and unsupervised clustering

Abstract

Timely diagnosis of micro short circuit (MSC) faults is crucial for ensuring the safe operation of lithium-ion battery energy storage systems. Existing diagnostic methods face limitations such as high dependency on battery models, difficulty in determining accurate diagnostic thresholds, or low computational efficiency. This work presents a model-free approach for the detection and quantitative assessment of MSCs in lithium-ion battery packs, with incremental capacity (IC) and unsupervised clustering. First, the IC is extracted from charging voltage data to effectively characterize MSC faults in lithium-ion batteries. Next, principal component analysis is used to map the high-dimensional feature space onto a two-dimensional plane to facilitate fault detection and result visualization. Then, an unsupervised clustering algorithm is employed for anomaly detection to identify MSC cells within the battery pack. For the detected MSC cells, a method based on the maximum charging voltage difference between adjacent cycles is designed to estimate the MSC resistance, quantitatively assessing the severity and evolution stage of the MSC. Experimental results show that the accuracy of MSC detection is 99.17 % and the minimum relative error of short-circuit resistance estimation is 1.20 %, which demonstrates the effectiveness and feasibility of the proposed method.