An Energy Management Strategy for Fuel-Cell Hybrid Commercial Vehicles Based on Adaptive Model Prediction

Abstract

Fuel-cell hybrid electric vehicles have the advantages of zero pollution and high efficiency and are extensively applied in commerce. An energy management strategy (EMS) directly impacts the fuel consumption and performance. Moreover, model prediction control (MPC) is synchronous and has been a research hotspot of EMS in recent years. The existing MPC’s low-speed prediction accuracy, which results in considerable instability in EMS allocation, is solved by the proposed energy management strategy based on adaptive model prediction. Dynamic programming (DP) is used as the solver, improved condition recognition and a radial basis neural network (RBFNN) are used as the speed predictor, and hydrogen consumption and the state of charge (SOC) are used as the objective function. According to the simulation results, using a 5 s speed prediction improves the forecast accuracy by 9.75%, and compared with employing a rule-based energy management strategy, this strategy reduces hydrogen consumption and the power cell fluctuation frequency by 3.50%.