Hybrid energy storage system (HESS) is an effective solution to address power imbalance problems caused by variability of renewable energy resources and load fluctuations in DC microgrids. The goal of HESS is to efficiently utilize different types of energy storage systems

each with its unique characteristics. Normally

the energy management of HESS relies on centralized control methods

which have limitations in flexibility

scalability

and reliability. This paper proposes an innovative artificial neural network (ANN) based model predictive control (MPC) method

integrated with a decentralized power-sharing strategy for HESS. In the proposed technique

MPC is employed as an expert to provide data to train the ANN. Once the ANN is finely tuned

it is directly utilized to control the DC-DC converters

eliminating the need for the extensive computations typically required by conventional MPC. In the proposed control scheme

virtual resistance droop control for fuel cell (FC) and virtual capacitance droop control for battery are designed in a decentralized manner to achieve power-sharing

enhance lifespan

and ensure HESS stability. As a result

the FC is able to support steady state loads

while the battery handles rapid load variations. Simulation results using Matlab/Simulink demonstrate the effective performance of the proposed controller under different loads and input variations

showcasing improved performance compared to conventional MPC.