  The multi-source electromechanical coupling makes the energy management of
fuel cell electric vehicles (FCEVs) relatively nonlinear and complex especially
in the types of 4-wheel-drive (4WD) FCEVs. Accurate state observing for
complicated nonlinear system is the basis for fantastic energy managing in
FCEVs. Aiming at releasing the energy-saving potential of FCEVs, a novel
learning-based robust model predictive control (LRMPC) strategy is proposed for
a 4WD FCEV, contributing to suitable power distribution among multiple energy
sources. The well-designed strategy based on machine learning (ML) translates
the knowledge of the nonlinear system to the explicit controlling scheme with
superior robust performance. To start with, ML methods with high regression
accuracy and superior generalization ability are trained offline to establish
the precise state observer for SOC. Then, explicit data tables for SOC
generated by state observer are used for grabbing accurate state changing,
whose input features include the vehicle status and the states of vehicle
components. To be specific, the vehicle velocity estimation for providing
future speed reference is constructed by deep forest. Next, the components
including explicit data tables and vehicle velocity estimation are combined
with model predictive control (MPC) to release the state-of-the-art
energy-saving ability for the multi-freedom system in FCEVs, whose name is
LRMPC. At last, the detailed assessment is performed in simulation test to
validate the advancing performance of LRMPC. The corresponding results
highlight the optimal control effect in energy-saving potential and strong
real-time application ability of LRMPC.
