Investigation of Robust Controllers and Model Uncertainty on Nonideal Boost Converter Lifetime in Hybrid Electric Vehicle

Electric vehicles (EVs) have caught significant attention in recent years due to their potential to reduce greenhouse gas emissions and dependency on fossil fuels. The reliability analysis of power electronic (PE) converters in EVs is crucial to improve their performance, cost-effectiveness, and long-term viability. In this paper, the lifetime estimation of IGBT in a hybrid EV unidirectional converter is evaluated based on control system impacts and statistical model uncertainties. For this purpose, a closed-loop model of a hybrid EV is developed in MATLAB using the Artemis mission profile to simulate the unidirectional converter output power. In the next step, the average model of the nonideal boost converter with Kharitonov's controller is employed to calculate the IGBT losses. The robust controller is able to maintain converter model stability during long-term output power mission profile simulation. By applying the thermal impedance, the junction temperature profile of the switch is obtained, enabling lifetime analysis via rain flow (RF) and Miner's rule methods. The results show that the controller selection considerably affects total consumed lifetime (TCL). Each controller can have a different TCL compared to other choices. Since the model coefficient for solder joint and bond wire failure mechanisms have been obtained based on the accelerated test results in the empirical method, considering the parameter statistical distribution and utilizing the Monte Carlo (MC) method can create a better view in the selection of IGBT and the converter design. Furthermore, based on the statistical results and the probability density function, it is feasible to determine how many percent of the IGBTs in the statistical community are damaged after a certain time. The B10 parameter for the failure mechanisms of bond wire and solder is 11.2 and 450 years, respectively. This approach provides insights into risk assessment and design optimization.