Early detection of Internal Short Circuits in series-connected battery packs based on nonlinear process monitoring

While the development of new materials in recent years has enabled an increase in energy density, power density and cycle life of batteries, safety remains a challenge. For electric vehicle applications, thermal runaway of a battery cell can lead to serious consequences. Thermal runaways are often caused by an Internal Short Circuit (ISC). This study aims to detect ISCs in the early latent phase, before extensive heat generation on the battery cell surface is measurable. To obtain high sensitivity, we apply a novel data-driven approach based on the cell voltage differences within the battery pack. Using the Kernel Principal Component Analysis (KPCA), a nonlinear data model is trained and applied for online detection of ISCs. By combining multiple kernel functions, fast detection and robust behavior is achieved for progressed ISCs while maintaining high sensitivity to soft ISCs. To demonstrate the applicability of the method in the presence of cell inconsistencies, the approach is experimentally validated on a calendar and cyclic aged module. A comparison with existing methods shows significant reduction in the detection time using the presented approach.