This paper proposes an approach to estimate the state of health of DC-DC converters that
feed the electrical system of an electric vehicle. They have an important role in providing a smooth
and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status
and predict the future performance of the converter and specifically of the electrolytic capacitors,
in order to avoid malfunctioning and failures, since it is known they have the highest failure rates
among power converter components. To this end, accelerated aging tests of the electrolytic capacitors
are performed by applying an electrical overstress. The gathered data are used to train a CNN-LSTM
model that is capable of predicting the future values of the capacitance and the equivalent series
resistance (ESR) of the electrolytic capacitor. This model can be used to estimate the remaining useful
life of the device, thus, increasing the reliability of the system and ensuring an adequate operating
condition of the electric motor.Peer ReviewedPostprint (published version
