Nonlinear optimal control of dual-PMSMs for the traction of electric vehicles

The dual-PMSM EV traction scheme finds use in heavy-duty electric vehicles needing high power and torque. The article treats the problem of nonlinear optimal control of dual-PMSMs for the traction of electric vehicles. The traction system of the considered vehicles consists of two inverter-driven permanent magnet synchronous motors (PMSM) which are connected to the main shaft of the electric vehicle (EV) through gears, and which are synchronized by the proposed nonlinear optimal control scheme to make the shaft rotate at the desirable speed. It is proven that the dynamic model of the dual-PMSM traction system of EVs is differentially flat. To apply the proposed nonlinear optimal control method, the state-space model of the dual-PMSM EV traction system undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization takes place at each sampling instance around a temporary operating point which is defined by the present value of the system’s state vector and by the last sampled value of the control inputs vector. For the approximately linearized model of the system an H-infinity (optimal) feedback controller is designed. To compute the feedback gains of this controller an algebraic Riccati equation is solved repetitively at each time-step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis. The nonlinear optimal control scheme achieves fast and precise tracking of setpoints by the state variables of the dual-PMSM EV traction system under moderate variations of the control inputs. To apply state estimation-based control of the dual-PMSM EV traction system the H-infinity Kalman Filter is used as a robust state observer.