Data-Driven Safety Envelope of Lithium-Ion Batteries for Electric Vehicles

In the accident scenarios of electric vehicles, the battery pack can be damaged catastrophically, resulting in the electric short circuit, thermal runaway, and possible fire and explosion. Therefore, it is important to investigate the range of conditions under which the safe operation of each individual cell is adequately controlled, known as the "safety envelope" The biggest challenge of developing such a safety envelope lies in the acquisition of a large data bank of battery failure tests. In this study, we overcome the challenge by establishing a high-accuracy detailed computational model of lithium-ion pouch cells, in which all the component materials are characterized by well-calibrated constitutive models. A large matrix of extreme mechanical loading conditions is simulated, and a data-driven safety envelope is obtained using the machine learning algorithm. This work is a demonstration of combining numerical data generation with data-driven modeling to predict the safety of energy storage systems.