Against the backdrop of the gradual transition of traditional tractors toward new energy sources, this study proposes an energy management strategy combining Model Predictive Control and Dynamic Programming algorithms (MPC-DP) for a series-hybrid tractor, aiming to improve the energy utilization efficiency of hybrid tractors. Firstly, a simulation and test model for the powertrain of the hybrid tractor was established, and a rule-based energy management strategy was adopted to conduct and verify preliminary simulation tests. Secondly, a working condition prediction model for the tractor was built based on the Radial Basis Function (RBF) neural network algorithm, and its validity was verified using field-collected data. Finally, the Model Predictive Control (MPC) and Dynamic Programming (DP) algorithms were combined to obtain the optimal output control sequence for the tractor within a short-term future time domain. Simulation results show that the proposed energy management strategy can make full use of both the engine and the battery, thereby improving energy utilization efficiency. Compared with the rule-based strategy, it reduces the total fuel consumption by 4.62% under the tractor’s plowing working condition.
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