Optimal energy management strategy based on neural network algorithm for fuel cell hybrid vehicle considering fuel cell lifetime and fuel consumption

This paper proposes a new design method of energy management strategy (EMS) with adaptive super-twisting sliding mode control (ASTSMC) for fuel cell/battery/supercapacitor hybrid vehicle (FCHEV). The main objective of the proposed EMS is to improve power performance, fuel cell lifetime, and fuel consumption while considering the regulation of the DC-bus voltage. The proposed EMS is designed based on a frequency-decoupling technique using an adaptive low-pass filter, Harr wavelet transform (HWT), and FLC to decouple the required power into low, medium, and high-frequency components for fuel cell, battery, and supercapacitor, respectively. The presented frequency-decoupling-based strategy can improve the power performance of the vehicle as well as reduce load stress and power fluctuation in the fuel cell. Nevertheless, the neural network optimization algorithm (NNOA) is employed to optimize the membership functions of FLCs while considering the hydrogen consumption and constraints on the state of charge (SOC) of the battery and supercapacitor. To achieve robustness and high precision control, the ASTSMC is developed based on a nonlinear disturbance observer (NDOB) to stabilize the DC-bus voltage and currents of the energy sources, ensuring that the fuel cell, battery, and supercapacitor track their obtained reference values. The FCHEV system with the proposed EMS is modeled on MATLAB/Simulink, and three typical driving cycles such as HWFET, UDDS, and WLTP driving schedules are used for evaluation. The findings exhibit that the proposed EMS can effectively improve the fuel economy, reduce power fluctuation in the fuel cell, and prolong its lifetime compared to other existing methods such as the equivalent consumption minimization strategy (ECMS), state machine (SM), and FLC-based EMSs.