Machine Learning Based Optimal Energy Storage Devices Selection Assistance for Vehicle Propulsion Systems
This study investigates the use of machine learning methods for the selection of energy storage devices in military electrified vehicles. Powertrain electrification relies on proper selection of energy storage devices, in terms of chemistry, size, energy density, and power density, etc. Military vehicles largely vary in terms of weight, acceleration requirements, operating road environment, mission, etc. This study aims to assist the energy storage device selection for military vehicles using the data-drive approach. We use Machine Learning models to extract relationships between vehicle characteristics and requirements and the corresponding energy storage devices. After the training, the machine learning models can predict the ideal energy storage devices given the target vehicles design parameters as inputs. The predicted ideal energy storage devices can be treated as the initial design and modifications to that are made based on the validation results. In the training phase, 80% of vehicle’s data borrowed from the literature were used, and the remaining 20% was used for validation. Results obtained from the proposed design predict the battery size and peak power with mean errors of 3.14% and 8.17%, respectively.