Ensuring service continuity in electric vehicles with vector control and linear quadratic regulator for dual star induction motors

Introduction. In this paper, the use of a Linear Quadratic Regulator (LQR) to control a Dual Star Induction Motor (DSIM) powered by dual three-level neutral point clamped inverters in electric vehicle (EV) propulsion systems is explored. Purpose. Ensuring both high performance against parameter sensitivity and service continuity in the event of faults is challenging in EV propulsion systems. The aim is to maximize both system performance and service continuity through the optimal design of the controller. Methods. DSIM is controlled by a LQR, which is replaced the traditional PI controller in the field-oriented control (FOC) system for speed regulation. Starting with FOC the optimal regulator is designed by introducing a minimization criterion into the Ricatti equation. The LQR control law is then employed as a speed regulator to ensure precise regulation and optimize DSIM operation under various load and speed conditions. The avoidance of linearization of the DSIM facilitates the exploitation of its true nonlinear dynamics. Novelty. Three tests are conducted to evaluate system performance. A precision test by varying the reference speed and analyzing speed response, settling time, precision and overshoot, a robustness test against parameter variations, assessing system robustness against changes in stator and rotor resistances and moment of inertia, and a fault robustness test evaluating system robustness against faults such as phase faults while maintaining load torque. The results show that this approach can keep the motor running smoothly even under parameter variations or degraded conditions. The precision and adaptability of the LQR technique enhance the overall efficiency and stability of the DSIM, making it a highly viable solution for modern EVs. This robust performance against parameter variations and loads is essential in ensuring the reliability and longevity of EV propulsion systems. Practical value. This approach holds significant potential for advancing EV technology, promising improved performance and reliability in real-world applications. References 44, tables 2, figures 15.