Diagnosis and prognosis of IGBT modules

An electric drive configured to drive an electrical machine, the electric drive comprising:

• an IGBT module;
• one or more temperature sensors configured to measure values of the dissipated power at the semiconductors of the IGBT module and an ambient temperature of the IGBT module;
• and
• a computing device having a processor, and a memory storing instructions that, when executed by the processor, cause the computing device to:
• maintain, in the memory, information on a computational model for modelling thermal behavior of layers of the IGBT module, wherein the computational model includes a plurality of inputs including at least dissipated power at semiconductors of the IGBT module and the ambient temperature of the IGBT module;
• receive measurements of values of the dissipated power at the semiconductors of the IGBT module and the ambient temperature from the one or more temperature sensors;
• determine one or more current values of one or more temperatures of the IGBT module, wherein the determining includes at least measuring a current value of a switching delay of the IGBT module and determining a current value of a junction temperature of the IGBT module based on the current value of the switching delay;
• and
• calculate a current estimate of a joint state-parameter space defined for the computational model using a Bayesian filter in combination with the computational model taking as inputs at least the measured values of the dissipated power and the ambient temperature, wherein the joint state-parameter space includes at least the one or more temperatures of the IGBT module, one or more thermal loss parameters quantifying thermal loss in one or more layers of the IGBT module and one or more wear parameters quantifying a change in the one or more thermal loss parameters over time due to unknown wear, the one or more wear parameters being defined via one or more random processes, the one or more current values of the one or more temperatures being used as observations in the Bayesian filter.