Researchers at Wuhan University of Science and Technology in China have proposed a control method based on a speed loop structure-variable sliding mode controller (SMC) for interior permanent magnet synchronous motors in electric vehicles. The method combines maximum torque per ampere with a vector control strategy.
In an open access paper on their work is published in the journal Advances In Mechanical Engineering, the team reports that their method can improve motor performance with more stable speed and torque output.
A structure-variable sliding mode control is a kind of nonlinear control method which can self-adjust in accordance with the current state of the system. The researchers reported that, comparing with the conventional proportional–integral controller, their proposed sliding mode control algorithm has more reliable control performance.
In the electric vehicle driving system, the motor speed should closely follow a specified reference trajectory, regardless of any load disturbance, parameter variation, and model uncertainty. Additionally, a wide speed range covers the constant torque, and the constant power region is desired.
For electric vehicles, there exist inevitable interferences such as current coupling, friction force, parameter variation, as well as load disturbance during the running process. Due to the presence of these interferences, it is difficult to describe IPMSM with accurate mathematical model, and it is hard to quickly suppress these disturbances with a linear control method.
Structure-variable sliding mode control … does not require accurate mathematical model and can purposefully force the system to move according to the scheduled sliding mode trajectory. The sliding mode trajectory can be set artificially in advance, and it is irrelevant to the object parameters and disturbances.
—Liu et al.
Conventionally, when the motor runs below the base speed maximum torque per ampere (MTPA) is used for constant torque control. MTPA is often implemented using look-up table method or a curve fitting method; due to poor real-time performance of the former, the latter is often used in engineering practice, the researchers noted.
Also conventionally, when the motor runs above the base speed, the flux-weakening (FW) algorithm is usually employed to implement constant power control. Common methods for FW control strategy include formulation calculation method; gradient descent algorithm; look-up table method; and negative id current compensation method. Negative id current compensation method has been widely used in practice because it does not need motor parameters, and the algorithm is simple and reliable, the authors noted.
Based on the existing research results, a kind of FW control system combined with the rotor field-oriented vector control algorithm is proposed in this article. The combination of the MTPA algorithm and the FW algorithm improves the performance and efficiency of the electric vehicles by regulating the current of the motor in the whole running process.
To consider the constraint of the battery capacity of the electric vehicles and to improve the utilization rate of the direct current (DC) bus voltage of the inverter, a kind of space vector pulse-width modulation (SVPWM) over-modulation method is adopted. Meanwhile, a kind of feed-forward compensation method is introduced to realize the decoupling control and then improve the dynamic response of the current regulator. In order to depress motor’s internal and external interferences, and to achieve faster response, stronger adaptability, and higher control precision, a kind of SMC is designed to replace common proportional–integral (PI) controller. To prevent the voltage saturation of current controller, a kind of anti-windup integral controller is employed to improve the dynamic performance of the loop.
—Liu et al.
Bin Liu, Yue Zhao, Hui-Zhong Hu (2018) “Structure-variable sliding mode control of interior permanent magnet synchronous motor in electric vehicles with improved flux-weakening method” Advances in Mechanical Engineering doi: 10.1177/1687814017704355