Dynamic Drive Control Scheme for Energy-Efficient Electric Drive Systems

The broader impact/commercial potential of this I-Corps project is the development of software for electric powertrains. An electric powertrain powers electronic vehicles and removes the need for an internal combustion engine. Currently, the performance of an electric powertrain in extreme working conditions, such as extreme heat or cold, is compromised and suffers from torque derating, a decrease in efficiency, and a shortened lifespan as its parameters change due to the change in operating and ambient temperatures. The proposed technology is designed to address electric powertrain performance degradation using a dynamic drive control scheme that facilitates the control of multiphase electric machines. The proposed dynamic drive control scheme has the potential to be employed in most large-scale industries including automotive, aerospace/defense, rail, marine, and manufacturing industries that need to enhance their overall product life and efficiency. The implementation of this proposed technology may lessen performance degradation, rapid aging, and poor efficiency. This I-Corps project is based on the development of a learning-based control algorithm to facilitate control of electric drive systems (electric powertrains) in multiphase electric machines. The proposed technology is designed to address electric machine performance degradation due to harsh environment and operating conditions using dynamic drive control. The proposed dynamic drive control scheme facilitates the control of multiphase electric machines to overcome the problem of thermally derated torque, minimizing the loss of lifetime, and improving energy conversion efficiency. The technology allows the controller to determine the schedule based on changes observed in the parameters of the electric drive system to address the issue of thermally derated torque. It utilizes the machine’s losses to compute the optimal flux demand to ensure improvements in the energy conversion efficiency of the machine and develop a dynamic relationship between road load demands and flux. The optimal cost function is formulated based on the operating voltage and current constraints to minimize aging. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.