Efficiency Optimization Control For Interior Permanent Magnet Synchronous Machines In Green Transportation Application

In order to address the increasingly severe global issues of climate change and energy shortage,the transportation equipment is witnessing a trend towards green,low-carbon,and electrically intelligent solutions.With the continuous expansion of the electric vehicles’ market,the green transportation equipment of our country is gradually shifting its focus to a new landscape that primarily involves the application of electric ships,vehicles,and aircrafts in the sea,land,and air domains.This trend has led to an increase in the carrying capacity of green transportation equipment.As a result,the green transportation equipment may face more complex operating conditions and more severe range anxiety.At the same time,the green transportation equipment is also showing a trend towards consumer electronics,including diversification of product types and faster development iterations.Such new development situation poses technical challenges for the motor drive system of green transportation equipment,including complex and variable motor parameters,strong requirement for efficiency optimization control and rapid product iteration.Interior permanent magnet synchronous machines(IPMSMs)are widely used in the motor drive systems of green transportation equipment,due to their advantages of high power density,high torque density,and high operating efficiency.However,the existing research on efficiency optimization control algorithms for IPMSMs has several problems,such as neglecting material characteristics of IPMSMs,requiring a wide range of experimental conditions,low automation of testing,and constrained parameter identification accuracy.These problems make the existing efficiency optimization control algorithms difficult to meet the requirements of the development in green transportation equipment.In this regard,based on an overview of the main research content of efficiency optimization control algorithms,the key issues pertaining to the efficiency optimization control algorithms for IPMSMs are investigated in this thesis,with a focus on achieving maximizing efficiency per torque(MEPT)as the control objective.The research is approached from three main aspects: the equivalent circuit model,online look-up table(LUT)control,and online search control.Firstly,regarding the unresolved rank-deficiency issue,caused by neglecting the characteristics of ferromagnetic material,during the solution of equivalent circuit model(ECM)parameters,an improved electromagnetic resistance incremental model for IPMSMs is derived considering the saturation and loss variation characteristics of ferromagnetic materials.This model reduces the number of parameters by using an equivalent electromagnetic-loss resistance,thus resolving the rank-deficiency issue in parameters solving.Besides,it utilizes the change rate of resistance with current to represent the variation characteristic of electromagnetic loss,thereby improving the descriptive performance of the model for motor characteristics.Additionally,this paper proposes an improved Jiles-Atherton(JA)model that accurately describes the transient characteristics of ferromagnetic materials.Based on the improved JA model,the numerical differences among different physically defined inductance parameters are revealed,and then,the shortcomings of existing ECMs in resolving the rank-deficiency issue can be clarified.Secondly,regarding the time-consuming experiments,due to the extensive range of operating conditions and low degree of testing automation,in the development of online LUT control,an improved LUT control with reduced number of experimental conditions,along with an automated calibration platform,are developed in this thesis.Specifically,this paper utilizes the Hoeffding’s inequality to characterize the relationship between the number of training samples and the descriptive accuracy of neural networks(NNs)for the time-varying model parameters,and then,a parameter calibration strategy is designed to achieve an optimal balance between the number of experimental conditions and the accuracy of parameter description.Subsequently,the trained NN is solved using gradient descent algorithm to determine the MEPT currents with reduced experimental time cost.Furthermore,a highly reliable automated calibration platform is developed according to the practical needs of industrial users.This platform automates the time-consuming tasks such as device control and data recording,thus reducing the experimental time required for each calibration condition.Finally,regarding the challenge of achieving high torque accuracy in MEPT online search control,which is hindered by the limited accuracy of existing parameter identification methods,this thesis proposes an accuracy-improved parameter identification method,and then develops a corresponding MEPT online search control,achieving accurate torque control during the online search process.The proposed identification method uses the DC components of reactive powers to construct the identification matrix,avoiding the adverse effects of loss variation characteristics on identification complexity and improving identification accuracy.Additionally,to rapidly obtain the DC components required by the proposed identification method,this paper introduces a DC component calculation method based on the properties of the Vandermonde matrix.This calculation method can eliminate the mechanical harmonics caused by mechanical misalignment,while importantly,it has a shorter signal processing delay compared to traditional notch filters.The prosed online LUT control and online search control each have their own advantages.In practical applications,these two control algorithms can be combined effectively: the online LUT control can be utilized as open-loop control to achieve fast dynamic response,while the online search control can be used as closed-loop control to adaptively adjust for unexpected variations in IPMSMs characteristics,thereby further enhancing the control performance.The effectiveness and accuracy of the research findings presented in this thesis are validated through experiments conducted on an IPMSM prototype.