  Carbon fiber composite can be a potential candidate for replacing metal-based
battery enclosures of current electric vehicles (E.V.s) owing to its better
strength-to-weight ratio and corrosion resistance. However, the strength of
carbon fiber-based structures depends on several parameters that should be
carefully chosen. In this work, we implemented high throughput finite element
analysis (FEA) based thermoforming simulation to virtually manufacture the
battery enclosure using different design and processing parameters.
Subsequently, we performed virtual crash simulations to mimic a side pole crash
to evaluate the crashworthiness of the battery enclosures. This high throughput
crash simulation dataset was utilized to build predictive models to understand
the crashworthiness of an unknown set. Our machine learning (ML) models showed
excellent performance (R2 > 0.97) in predicting the crashworthiness metrics,
i.e., crush load efficiency, absorbed energy, intrusion, and maximum
deceleration during a crash. We believe that this FEA-ML work framework will be
helpful in down select process parameters for carbon fiber-based component
design and can be transferrable to other manufacturing technologies.

Machine Learning;Signal Processing