Lifetime Extension Approach Based on the Levenberg–Marquardt Neural Network and Power Routing of DC–DC Converters
Jiusi Zhang, Jilun Tian, Abraham Marquez, José I. Leon, Sergio Vázquez, Leopoldo G. Franquelo, Hao Luo, Shen Yin
Abstract
The power conversion system based on the modular connection has widespread applications in various power electronic systems. To accurately estimate the state of health without recognizing the systematic mathematical model and to extend the lifetime, this article proposes a lifetime extension approach based on the Levenberg–Marquardt back propagation neural network (LM-BPNN) and power routing of interleaved dc–dc boost conversion systems. The LM-BPNN model is constructed based on the voltage, current, and temperature data generated by the system. On the basis of the trained LM-BPNN, the real-time cumulated damage estimation of each power cell in the conversion system can be accomplished. Applying the power routing concept, the dc–dc boost conversion system allocates different power to the cells according to the cumulated damage of each cell, thereby delaying the failure of cells with higher cumulated damage. Numerical simulation results show that the proposed lifetime extension approach can extend the overall system lifetime. Furthermore, an experimental setup of the interleaved dc–dc boost conversion is constructed to verify the proposed approach, which is of great significance for predictive maintenance in the industrial system.