A Large-Scale Identification Approach for Thermal Parameters of Multichips IGBT Modules Based on LLSO-SQP Algorithm
Cao Zhan, Lingyu Zhu, Weicheng Wang, Qianming Jiao, Yaxin Zhang, Shengchang Ji
Abstract
This article proposes a large-scale optimization approach for identifying thermal parameters of multichips insulated-gate bipolar transistor (IGBT) modules. State-space equation, in which the coefficient matrix comprises the thermal resistance and capacitance, is provided to represent the compact 3-D thermal network model. Then, the level-based learning swarm optimization (LLSO) algorithm is first utilized to identify large-scale thermal parameters. Additionally, to solve the inefficient convergence problem, the optimization results obtained from the LLSO are provided as the initial value of the sequential quadratic programming (SQP) algorithm to find the global optimal solution. Hence, the proposed LLSO-SQP algorithm can identify the large-scale thermal parameters efficiently and accurately. The training dataset for the algorithm is derived from the transient temperature response of a finite element model (FEM) of the IGBT module under power step excitation. Since only one-time simulation is in-demand, this approach needs less computational effort than others. The identified thermal network model is utilized to estimate junction temperature profiles taking a two-level inverter as a case study. In comparison to that of the experiment and FEM, the results validate the feasibility and accuracy of the junction temperature estimation method based on the compact 3-D thermal network model.