Neural learning fault-tolerant control of fuel cell–battery–ultracapacitor-based hybrid electric vehicle

This article investigates the energy management and control of a fuel cell hybrid electric vehicle (FCHEV) with parametric uncertainties and sensor faults. The FCHEV in this study consists of a fuel cell, battery, and ultracapacitor connected to the DC-bus via DC–DC power converters, while the DC-bus is connected to the AC motor which drives the electric vehicle via a DC–AC converter. To control the power transfer from the energy sources to the drivetrain, a finite-time fractional-order control is proposed to coordinate the DC–DC power converters. A radial basis function neural network (RBFNN) is employed to estimate and compensate for sensor faults and parametric uncertainties. A minimum learning parameter scheme is used to minimize the computational burden on the RBFNN. The main tasks of the proposed scheme are; tolerating faults and parametric uncertainties, delivering the required power to the load, voltage regulation of the DC-bus, tracking the reference currents for the battery, fuel cell, and ultracapacitor in a short finite time. The closed-loop system is guaranteed to be stable in finite time using the Lyapunov function. The simulation results highlight the validity of the presented control.