Khaled I. Alsharif received the B.S. degree in mechanical engineering from Youngstown State University, in 2021, with a minor in electrical engineering, where he is currently pursuing the master’s degree in electrical engineering. He is also a Graduate Research Assistant at Youngstown State University. Additionally, he was an research intern at, Center for Automotive Research, The Ohio State University where he worked on DC Microgrid simulations, battery pack testing, and the dynamic load characterization of the Super Truck II program for PACCAR and e-school bus for Daimler Truck. He was the lead of the drivetrain team for the SAE Baja team. His current work focuses on the dynamic modeling and characterization of Lithium-Ion Batteries. He has published three journals at IEEE and MPDI as the lead author and three conference papers. His research interests include automotive engineering, heat transfer, energy conservation, dynamic system modeling, and control of dynamic systems.
Many electric vehicles (and drones, etc.,) use brushless DC (BLDC) motors because of their superior torque to weight ratio. However, because of the unique pole orientation, the BLDC motor puts a pulsing dynamic load on the battery pack of the system. This study integrates a mechanical analogy model of a battery to predict the dynamic voltage response under this pulsing load. The dynamic system is based on a modally decomposed three-degree-of-freedom, spring-mass-damper analogy is used to estimate the cells terminal voltage. The batteries utilized to construct the battery pack were tested following the hybrid pulse power characterization (HPPC) testing to determine the dynamic performance over the usable voltage ranges of the cell, which results in an accurate battery parameter identification to create robust model for simulation. Moreover, the modal parameters are determined by minimizing the error over the experimental and simulated time. After the battery model is calibrated, an experiment is conducted at which a battery pack is used to drive a benchtop BLDC motor with a magnetorheological brake as a programable load at varying running speeds. The voltage and current of the battery and the BLDC motor driver are recorded. Additionally, the speed and the torque of the motor are recorded. These data are compared to the predicted voltage of the battery pack using the mechanical analogy model.
This study explores a newly developed mechanical analogy battery dynamic model in a real-life application. The battery model is integrated with brushless DC motor and magnetorheological brake as a load. The main objective is to inform the attendees of the potential applications of the model.
