Model-based Predictive Maintenance for Li-Ion Battery Systems – Model Design and Experimental Data Generation

Methods of predictive maintenance for large-scale battery systems allow the early detection of fault potentials and the consequent replacement or repair of faulty components before severe compromises to system health, safety or performance must be made. While numerable methods exist for the detection of short-term faults such as thermal runaways or low-ohmic short circuits, predicting long-term errors, which develop and intensify over the scope of multiple months or years still proves difficult.

This work presents an electro-thermal battery system model suited for the model-based prediction of such long-term faults. It incorporates the effects of cell-to-cell variations (CtCVs) and therefore allows the calculation of voltage and temperature distributions within the system. In addition to the appropriate model, extensive testing and validation against experimental data of systems in faulty condition is required. Since the required data basis cannot be provided by productive battery systems in the field, a modular battery system test bench was designed. The test bench consists of 21700-type cylindrical battery cells in 6s3p configuration with individual voltage, current and temperature measurement. The system allows the fast and simple exchange of individual cells as well as the insertion of parallel and serial resistances. By this, the effects of accelerated aging of individual cells, increased contact resistances and high-ohmic internal or external short-circuits can be evaluated. The test bench was used to determine the electrical behavior resulting from the named fault scenarios for different fault intensities (e.g. resistances of the parallel resistor) during a load profile for stationary grid support applications. By using and further expanding the resulting data set, new methods for predictive maintenance can be methodically developed and evaluated.