The high-speed motorized spindle, as the key component of machining centers and other high-end CNC machine tools, has performance directly affecting machining accuracy. According to the internal motor character- istic...The high-speed motorized spindle, as the key component of machining centers and other high-end CNC machine tools, has performance directly affecting machining accuracy. According to the internal motor character- istics of the high speed motorized spindle in the paper, two major heat sources are analyzed and quantity of heat is calculated. The finite element analysis model of motorized spindle thermal characteristics is built through ap- plying the ANSYS Workbench. The thermal steady state, heat-structure coupling characteristics is carried out based on the cooling coefficient of thermal boundary conditions, and taking heating value of the bearing and mo- tor as thermal load, the temperature field distribution and thermal deformation of the spindle system are obtained, which prepare fox＂ the next thermal error modeling展开更多
Braking energy recovery(BER)aims to recover the vehicle's kinetic energy by coordinating the motor and mechanical braking torque to extend the driving range of the electric vehicle(EV).To achieve this goal,the mot...Braking energy recovery(BER)aims to recover the vehicle's kinetic energy by coordinating the motor and mechanical braking torque to extend the driving range of the electric vehicle(EV).To achieve this goal,the motor/generator mode requires frequent switching and prolonged operation during driving.In this case,the motor temperature will unavoidably rise,potentially triggering motor thermal protection(MTP).Activating MTP increases the risk of motor component failure,and the EV typically disables the BER function.Thus,maximizing BER while reducing the risk of motor overheating is a challenging problem.To address this issue,this article proposes a predictive BER strategy with MTP using the non-smooth Pontryagin Minimum Principle(NSPMP)for EVs.Firstly,a Markov long short-term memory(MLSTM)model is designed to obtain future velocity information.Secondly,the BER problem with MTP in the studied EV is embedded in a model predictive control(MPC)framework.Then,under the MPC framework,the NSPMP strategy is proposed to resolve the problem of MTP.Finally,the performance of the proposed strategy is verified through simulation and a hardware-in-loop test.The results show that in two real-world driving cycles,compared to the rule-based strategy,the proposed strategy reduced power consumption by 1.24%and0.96%,respectively,and effectively limited motor temperature.Additionally,under global cycle conditions,this strategy demonstrated better MTP control performance compared to other benchmark strategies.展开更多
文摘The high-speed motorized spindle, as the key component of machining centers and other high-end CNC machine tools, has performance directly affecting machining accuracy. According to the internal motor character- istics of the high speed motorized spindle in the paper, two major heat sources are analyzed and quantity of heat is calculated. The finite element analysis model of motorized spindle thermal characteristics is built through ap- plying the ANSYS Workbench. The thermal steady state, heat-structure coupling characteristics is carried out based on the cooling coefficient of thermal boundary conditions, and taking heating value of the bearing and mo- tor as thermal load, the temperature field distribution and thermal deformation of the spindle system are obtained, which prepare fox＂ the next thermal error modeling
基金supported by the National Natural Science Foundation of China(Grant Nos.52275047,51975048)。
文摘Braking energy recovery(BER)aims to recover the vehicle's kinetic energy by coordinating the motor and mechanical braking torque to extend the driving range of the electric vehicle(EV).To achieve this goal,the motor/generator mode requires frequent switching and prolonged operation during driving.In this case,the motor temperature will unavoidably rise,potentially triggering motor thermal protection(MTP).Activating MTP increases the risk of motor component failure,and the EV typically disables the BER function.Thus,maximizing BER while reducing the risk of motor overheating is a challenging problem.To address this issue,this article proposes a predictive BER strategy with MTP using the non-smooth Pontryagin Minimum Principle(NSPMP)for EVs.Firstly,a Markov long short-term memory(MLSTM)model is designed to obtain future velocity information.Secondly,the BER problem with MTP in the studied EV is embedded in a model predictive control(MPC)framework.Then,under the MPC framework,the NSPMP strategy is proposed to resolve the problem of MTP.Finally,the performance of the proposed strategy is verified through simulation and a hardware-in-loop test.The results show that in two real-world driving cycles,compared to the rule-based strategy,the proposed strategy reduced power consumption by 1.24%and0.96%,respectively,and effectively limited motor temperature.Additionally,under global cycle conditions,this strategy demonstrated better MTP control performance compared to other benchmark strategies.
