Reliability evaluation of IGBT power module on electric vehicle using big data

Abstract

There are challenges to the reliability evaluation for insulated gate bipolar transistors (IGBT) on electric vehicles, such as junction temperature measurement, computational and storage resources. In this paper, a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed. The direct current (DC) voltage, operation current, switching frequency, negative thermal coefficient thermistor (NTC) temperature and IGBT lifetime are inputs. And the junction temperature (T j) is output. With the rain flow counting method, the classified irregular temperatures are brought into the life model for the failure cycles. The fatigue accumulation method is then used to calculate the IGBT lifetime. To solve the limited computational and storage resources of electric vehicle controllers, the operation of IGBT lifetime calculation is running on a big data platform. The lifetime is then transmitted wirelessly to electric vehicles as input for neural network. Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated. A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test. Subsequently, the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method. The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network, which improves the reliability evaluation of system.