Implementation of Model Predictive Control (MPC) in electric vehicle testing simulator

Most physical systems in the industry have performance limitation which limits their performance regardless the input given. This performance limitation commonly occurs in the form of input saturation of the actuator. To overcome this problem, Model Predictive Control (MPC) is used due to its capability to compute the optimum control signal while handling input saturation. The optimal control signal is obtained by MPC by optimizing certain quadratic objective functions having constrained on the variables which is done at each time instants. This process is called quadratic programming (QP). QP having an upper level and a lower level constrained is equivalent to an algebraic loop involving diagonal upper and lower saturation. Hence, QP can be computed by iteratively solved that algebraic loop until converged. In this paper, the capability of MPC will be examined in the Electric Vehicle Testing Simulator (EVTS) as part of the EV testing phase. EV Testing Simulator employs two BLDC motors that are mechanically coupled to their axis. One motor simulates an electric motor that drives the EV and the other simulates the mechanical loads on the motor driving as frictional, drag, and gravitational forces in the downhill and uphill conditions. In this simulator, MPC will be implemented in testmotor control sub-module to be analyzed its capability to handle the load from load-motor control sub-module. From the results, it can be observed that MPC is capable of handling the input saturation of the test-motor control sub-module and capable of providing a stable response when the test-motor is loaded by the load motor control-sub module. Moreover, MPC is shown to be capable to be used in the fast dynamic system to control the driving motor of EVTS with small error.