Besides the performance and range requirements, the breakthrough of electromobility depends crucially on the safety of battery systems. Cell faults that can lead to thermal runaway of the energy storage reduce customer acceptance. Thermal runaways are often preceded by an Internal Short Circuit (ISC). Thus, there is a necessity for a method that is able to detect the formation of ISCs, before a significant temperature rise is observed and the risk of thermal propagation arises. To achieve this, a data-driven method that enables early detection of soft ISCs is presented. The unsupervised machine-learning method is based on linear Principal Component Analysis (PCA) and nonlinear Kernel PCA (KPCA). Since the method only requires fault-free voltage measurements for training, it is directly applicable in conventional battery systems. The nonlinear KPCA is thoroughly compared with the linear PCA using experimental data. The data originates from a module consisting of twelve automotive cells. While the linear method has advantages in computational complexity, the nonlinear method detects ISCs earlier due to its high sensitivity and specificity.     «

Besides the performance and range requirements, the breakthrough of electromobility depends crucially on the safety of battery systems. Cell faults that can lead to thermal runaway of the energy storage reduce customer acceptance. Thermal runaways are often preceded by an Internal Short Circuit (ISC). Thus, there is a necessity for a method that is able to detect the formation of ISCs, before a significant temperature rise is observed and the risk of thermal propagation arises. To achieve this,...     »