Cardiac disease is the leading cause of death and places significant strain on the

limited supply of donor hearts. Left Ventricular Assist Devices (LVADs) extend pa- tient survival as a bridge to transplant and, increasingly, as destination therapy;

however, current devices are challenged by driveline infections and inadequate re- sponsiveness to dynamic cardiovascular demands. Wireless charging offers promise

for mitigating driveline infections, but its success depends on reducing overall pump energy consumption. To address these issues, we’re developing an intelligent hybrid magnetic levitation LVAD system. In this study, we introduce a novel, automatic speed modulation algorithm that leverages the LVAD’s average magnetic levitation power consumption as a surrogate indicator of patient activity state. The algorithm, implemented via a finite state machine with threshold detection, dynamically adjusts pump speed to optimize hemodynamic performance while minimizing energy usage. The system was evaluated using a previously validated numerical mock circulatory loop (nMCL) coupled with a detailed LVAD model, with simulations conducted across activity states including sleep, rest, and light exercise. Comparative analysis demonstrated that the speed modulation algorithm improved circulatory support during exercise

while reducing energy consumption during rest. This framework promises enhanced energy efficiency and improved physiological compatibility, paving the way for future LVAD designs and clinical applications.