Research On Parameter Identification Of Vehicle Induction Motor Based On Model Design

Induction motor is one of the most commonly used driving motors for electric vehicles.It has good dynamic and steady state performance under the vector control,but its control effect extremely depend on the precision of parameters.In order to precisely control the vehicle induction motor,this paper studies the parameter identification of the motor.In addition,the model-based design idea is introduced into the algorithm development,which can use the advantages of early verification and automatic code generation to shorten the development cycle and improve development efficiency.Firstly,the mathematical model of induction motor and the rationale of space vector pulse width modulation are introduced,and the model of induction motor electric driving system of electric vehicle with torque closed-loop control was established.The influence of parameters on the control performance of the motor was also verified by changing the motor parameter variables in the control algorithm one by one.The results showed that any parameters variation can affect the torque output and rotor flux accuracy of the motor,and the rotor resistance and rotor leakage inductance have the most obvious influence.Secondly,the parameter offline identification of induction motor was introduced.The Inverse-? type equivalent circuit was chosen as the circuit model for parameter identification,the motor stator resistance was identified by single-phase dc experiment,and the rotor resistance,mutual inductance and leakage inductance were identified by two single-phase ac experiments.Since there is no voltage sensor in the vehicle controller,the motor stator voltage was obtained by PWM wave duty ratio reconstruction and compensation.Based on this,the single-phase experiment model was built and verified by simulation,the results showed that the single-phase experiment can realize the parameter self-tuning of induction motor.Considering that the parameters of the induction motor may change during operation,the self-tuning parameters cannot guarantee the accuracy of the vector control,an artificial neural network model for online parameter identification of induction motor was constructed based on the state equation of induction motor.The initial value of the weight matrix of the neural network was calculated by the parameter value of offline identification,the stator current,voltage and speed variable of the motor were collected as the input and output of the neural network,the weight matrix was corrected by the neural network gradient descent algorithm,and then,the parameters of the motor were updated by the weight matrix calculation.In the control algorithm module including parameter identification input and output acquisition,the idea of model design was introduced,and after discretization processing,the algorithm module was coded according to the process of generating embedded code according to the model,and the code generation report was obtained.Finally,the AVL electric drive system performance test bench was used as the experimental platform.The single-phase experiment of the vehicle motor is performed to complete the parameter self-tuning,and then the code generated by the control algorithm module including the online parameter identification input and output acquisition was transplanted into the motor control algorithm,the initial value of the weight matrix in the neural network algorithm was obtained by the self-tuning parameter calculation result,then the vector control of the induction motor was verified on the experimental bench,the collected current waveform and torque output prove the validity of the parameter online identification.