charging

This policy brief summarizing findings from the study which investigated fuel cell vehicle buyers in California and compared them to battery electric vehicle owners, with the hope of understanding why some households choose a FCV rather than a BEV.

This study aims to develop a prediction framework based on a Bidirectional Long Short-Term Memory Network model to suggest when battery electric vehicle drivers should charge their vehicles, in order to to provide a more accurate and precise method of predicting charging events than conventional machine learning models.

This project explored how locating charging facilities at California’s highway rest stops might impact electricity demand, grid operation, and integration of renewables like solar and wind into California’s energy mix.