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
The existing research of the acceleration control mainly focuses on an optimization of the velocity trajectory with respect to a criterion formulation that weights acceleration time and fuel consumption. The minimum-f...The existing research of the acceleration control mainly focuses on an optimization of the velocity trajectory with respect to a criterion formulation that weights acceleration time and fuel consumption. The minimum-fuel acceleration problem in conventional vehicle has been solved by Pontryagin＇s maximum principle and dynamic programming algorithm, respectively. The acceleration control with minimum energy consumption for battery electric vehicle（EV） has not been reported. In this paper, the permanent magnet synchronous motor（PMSM） is controlled by the field oriented control（FOC） method and the electric drive system for the EV（including the PMSM, the inverter and the battery） is modeled to favor over a detailed consumption map. The analytical algorithm is proposed to analyze the optimal acceleration control and the optimal torque versus speed curve in the acceleration process is obtained. Considering the acceleration time, a penalty function is introduced to realize a fast vehicle speed tracking. The optimal acceleration control is also addressed with dynamic programming（DP）. This method can solve the optimal acceleration problem with precise time constraint, but it consumes a large amount of computation time. The EV used in simulation and experiment is a four-wheel hub motor drive electric vehicle. The simulation and experimental results show that the required battery energy has little difference between the acceleration control solved by analytical algorithm and that solved by DP, and is greatly reduced comparing with the constant pedal opening acceleration. The proposed analytical and DP algorithms can minimize the energy consumption in EV＇s acceleration process and the analytical algorithm is easy to be implemented in real-time control.展开更多
永磁同步电机因其结构紧凑、噪声较少、功耗较少、运行速度快、操作稳定,已被普遍采用。针对永磁同步电机弱磁控制过程中,转速环参数选取采用传统PI(proportional-integral)控制方法,依靠经验整定参数,外界抗干扰能力较差、难以保证在...永磁同步电机因其结构紧凑、噪声较少、功耗较少、运行速度快、操作稳定,已被普遍采用。针对永磁同步电机弱磁控制过程中,转速环参数选取采用传统PI(proportional-integral)控制方法,依靠经验整定参数,外界抗干扰能力较差、难以保证在各运行区间具有优良性能等问题,提出了一种基于减法平均优化算法的永磁同步电机的弱磁和MTPA(maximum torque per ampere)控制的宽运行范围方法。将智能寻优算法、MTPA控制、弱磁控制三者相结合,利用减法平均优化算法优化PI控制器的参数,提高了系统的响应性能和抗干扰能力;工作电压未超过电压极限圆使用MTPA控制策略运行;工作电压超过电压极限圆利用电压闭环反馈,进行弱磁控制。使用MATLAB/Simulink构建的永磁同步电机弱磁控制仿真模拟,通过PI控制器和减法平均优化算法优化后的PI控制器性能对比,从仿真结果得到控制器方法的有效性。实验有效证明了该控制方法能够解决各种运行工况下控制器参数的优化整定问题,提高电机控制精度。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.
基金supported by US-China Clean Energy Research Collaboration:Collaboration on Cutting-edge Technology Development of Electric Vehicle(Program of International S&T Cooperation,Grant No.2010DFA72760)
文摘The existing research of the acceleration control mainly focuses on an optimization of the velocity trajectory with respect to a criterion formulation that weights acceleration time and fuel consumption. The minimum-fuel acceleration problem in conventional vehicle has been solved by Pontryagin＇s maximum principle and dynamic programming algorithm, respectively. The acceleration control with minimum energy consumption for battery electric vehicle（EV） has not been reported. In this paper, the permanent magnet synchronous motor（PMSM） is controlled by the field oriented control（FOC） method and the electric drive system for the EV（including the PMSM, the inverter and the battery） is modeled to favor over a detailed consumption map. The analytical algorithm is proposed to analyze the optimal acceleration control and the optimal torque versus speed curve in the acceleration process is obtained. Considering the acceleration time, a penalty function is introduced to realize a fast vehicle speed tracking. The optimal acceleration control is also addressed with dynamic programming（DP）. This method can solve the optimal acceleration problem with precise time constraint, but it consumes a large amount of computation time. The EV used in simulation and experiment is a four-wheel hub motor drive electric vehicle. The simulation and experimental results show that the required battery energy has little difference between the acceleration control solved by analytical algorithm and that solved by DP, and is greatly reduced comparing with the constant pedal opening acceleration. The proposed analytical and DP algorithms can minimize the energy consumption in EV＇s acceleration process and the analytical algorithm is easy to be implemented in real-time control.
文摘永磁同步电机因其结构紧凑、噪声较少、功耗较少、运行速度快、操作稳定,已被普遍采用。针对永磁同步电机弱磁控制过程中,转速环参数选取采用传统PI(proportional-integral)控制方法,依靠经验整定参数,外界抗干扰能力较差、难以保证在各运行区间具有优良性能等问题,提出了一种基于减法平均优化算法的永磁同步电机的弱磁和MTPA(maximum torque per ampere)控制的宽运行范围方法。将智能寻优算法、MTPA控制、弱磁控制三者相结合,利用减法平均优化算法优化PI控制器的参数,提高了系统的响应性能和抗干扰能力;工作电压未超过电压极限圆使用MTPA控制策略运行;工作电压超过电压极限圆利用电压闭环反馈,进行弱磁控制。使用MATLAB/Simulink构建的永磁同步电机弱磁控制仿真模拟,通过PI控制器和减法平均优化算法优化后的PI控制器性能对比,从仿真结果得到控制器方法的有效性。实验有效证明了该控制方法能够解决各种运行工况下控制器参数的优化整定问题,提高电机控制精度。
