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

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.

Contact fatigue life prediction of a bevel gear under spectrum loading

Rolling contact fatigue(RCF)issues,such as pitting,might occur on bevel gears because load fluctuation induces considerable subsurface stress amplitudes.Such issues can dramatically affect the service life of associated machines.An accurate geometry model of a hypoid gear utilized in the main reducer of a heavy-duty vehicle is developed in this study with the commercial gear design software MASTA.Multiaxial stress–strain states are simulated with the finite element method,and the RCF life is predicted using the Brown–Miller–Morrow fatigue criterion.The patterns of fatigue life on the tooth surface are simulated under various loading levels,and the RCF S–N curve is numerically generated.Moreover,a typical torque–time history on the driven axle is described,followed by the construction of program load spectrum with the rain flow method and the Goodman mean stress equation.The effects of various fatigue damage accumulation rules on fatigue life are compared and discussed in detail.Predicted results reveal that the Miner linear rule provides the most optimistic result among the three selected rules,and the Manson bilinear rule produces the most conservative result.