In order to effectively achieve torque demand in electric vehicles (EVs), this paper presents a torque control strategy based on model predictive control (MPC) for permanent magnet synchronous motor (PMSM) drive...In order to effectively achieve torque demand in electric vehicles (EVs), this paper presents a torque control strategy based on model predictive control (MPC) for permanent magnet synchronous motor (PMSM) driven by a two-level three-phase inverter. A centralized control strategy is established in the MPC framework to track the torque demand and reduce energy loss, by directly optimizing the switch states of inverter. To fast determine the optimal control sequence in predictive process, a searching tree is built to look for optimal inputs by dynamic programming (DP) algorithm on the basis of the principle of optimality. Then we design a pruning method to check the candidate inputs that can enter the next predictive loop in order to decrease the computational burden of evaluation of input sequences. Finally, the simulation results on different conditions indicate that the proposed strategy can achieve a tradeoff between control performance and computational efficiency.展开更多
基金This work was supported by the NSFC Projects of International Cooperation and Exchanges (No. 61520106008), the National Natural Science Foundation of China (Nos. 61503149, U1564207) and the Graduate Innovation Fund of Jilin University (No. 2016093).
文摘In order to effectively achieve torque demand in electric vehicles (EVs), this paper presents a torque control strategy based on model predictive control (MPC) for permanent magnet synchronous motor (PMSM) driven by a two-level three-phase inverter. A centralized control strategy is established in the MPC framework to track the torque demand and reduce energy loss, by directly optimizing the switch states of inverter. To fast determine the optimal control sequence in predictive process, a searching tree is built to look for optimal inputs by dynamic programming (DP) algorithm on the basis of the principle of optimality. Then we design a pruning method to check the candidate inputs that can enter the next predictive loop in order to decrease the computational burden of evaluation of input sequences. Finally, the simulation results on different conditions indicate that the proposed strategy can achieve a tradeoff between control performance and computational efficiency.