An Intelligent Deformation-Based Approach to the State of Health Estimation of Collided Lithium-Ion Batteries for Facilitating Battery Module Safety Evaluation

With the increasing trend of electric vehicles sales, the awareness for safety issues in energy storage systems has seen a rapid growth. In this context, literature predominantly focuses on the assessment of lithium-ion battery cell State of Health (SoH) under regular operating conditions. An interesting gap is highlighted by scarce contributions with reference to the SoH in case of collision impacts. This paper proposes a method for estimating the battery cell SoH from collision deformation features via finite element (FE) simulation. An experimental series of collision impact tests is performed on battery cells to study deformation features. A 3D scanning procedure is performed on the cells to retrieve the geometrical features. The cells damage evaluation is carried out by considering physical and electrical performances. The SoH classification is modeled as a machine learning–based pattern recognition enabled by Artificial Neural Network (ANN) trained with deformation features and classifying cells into safe, latent danger, and unsafe cells respectively. Utilizing such pattern classifier, a FE approach is used to conduct an impact simulation on a battery module. In this respect, the deformation features obtained via simulation are input into the ANN pattern classifier experimentally trained to predict the cells SoH within the module.