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The multi-source electromechanical coupling makes the energy management offuel cell electric vehicles (FCEVs) relatively nonlinear and complex especiallyin the types of 4-wheel-drive (4WD) FCEVs. Accurate state observing forcomplicated nonlinear system is the basis for fantastic energy managing inFCEVs. Aiming at releasing the energy-saving potential of FCEVs, a novellearning-based robust model predictive control (LRMPC) strategy is proposed fora 4WD FCEV, contributing to suitable power distribution among multiple energysources. The well-designed strategy based on machine learning (ML) translatesthe knowledge of the nonlinear system to the explicit controlling scheme withsuperior robust performance. To start with, ML methods with high regressionaccuracy and superior generalization ability are trained offline to establishthe precise state observer for SOC. Then, explicit data tables for SOCgenerated by state observer are used for grabbing accurate state changing,whose input features include the vehicle status and the states of vehiclecomponents. To be specific, the vehicle velocity estimation for providingfuture speed reference is constructed by deep forest. Next, the componentsincluding explicit data tables and vehicle velocity estimation are combinedwith model predictive control (MPC) to release the state-of-the-artenergy-saving ability for the multi-freedom system in FCEVs, whose name isLRMPC. At last, the detailed assessment is performed in simulation test tovalidate the advancing performance of LRMPC. The corresponding resultshighlight the optimal control effect in energy-saving potential and strongreal-time application ability of LRMPC.

A novel learning-based robust model predictive control energy management strategy for fuel cell electric vehicles