A Lifetime Prediction Method for IGBT Modules Considering the Effects of Multi-Factors

With the engineering realization and further development of maglev trains, the lifetime prediction of its main power unit, i.e., insulated gate bipolar transistor (IGBT) module, attracts much popularity. However, the complex operating conditions of maglev trains cause the lifetime of IGBTs to be affected by a combination of factors. And the analysis of a single factor will lead to inaccurate lifetime prediction. To address this, for the IGBT of the maglev trains, this paper proposes a lifetime prediction model that considering the effect of changing practical conditions and device aging. In this method, the ambient temperature change of the maglev train in a year and the change of the on-resistance during the aging process is taken into account. Meanwhile, in the system perspective, the effects of changes in voltage levels and switching frequencies are included. And the coupling effects of multiple factors are thoughtfully analyzed and introduced into the lifetime prediction model, which improves the accuracy of the lifetime prediction. Finally, the results of the factor impact analysis and lifetime prediction are validated by power cycling experiments and real vehicle operation data.