However, the primary merit of the LMS filter lies in its computational throughput and superior cost-effectiveness. By eliminating the heavy matrix inversions and continuous covariance updates inherent to Kalman and RLS filters, the LMS algorithm requires only 0.000 ms of total execution time (0.0000 $mu$s per sample). Consequently, LMS runs approximately 0 times faster than RLS and 00 to 00 times faster than LKF and EKF. Given that its estimation error remains well within highly acceptable thresholds, the exceptional processing speed and minimal computational burden render the LMS filter particularly well suited for resource-constrained, high-speed DSP and MCU embedded motor controllers in EV powertrains.