Research on High-Efficient Low-Degradation Energy Management Control Strategy for Hydrogen Fuel Cell Tractors Based on Deep Reinforcement Learning During Row-Crop Seeding Operations

To solve the problems of low energy utilization efficiency, high equivalent hydrogen consumption, and significant power battery capacity degradation in hydrogen fuel cell tractors under row-crop seeding operations, a High-Efficient Low-Degradation Energy Management Control Strategy based on the Deep Deterministic Policy Gradient (HELD-DDPG) was proposed in this paper. Firstly, a tractor dynamics model was established based on the row-crop seeding operations. Subsequently, a power battery model was established based on the equivalent internal resistance theory to evaluate the state and energy consumption of the power battery, and a battery life model was established that accounts for the impact of capacity degradation on long-term system operation. A hydrogen fuel cell model was constructed according to the characteristics of the hydrogen fuel cell. To achieve a unified quantitative assessment of energy consumption across different energy sources in the entire power system, an equivalent hydrogen consumption model was established. On this basis, the HELD-DDPG was proposed with the objectives of minimizing the equivalent hydrogen consumption of the whole machine, stabilizing the SOC of the power battery, and minimizing battery capacity degradation, subject to the battery discharge current constraint. Finally, a rule-based energy management control strategy was selected as a comparative strategy. Simulation experiments on the HELD-DDPG were conducted using the MATLAB platform, and the effectiveness of the strategy was further validated through the hardware-in-the-loop (HIL) test platform. The simulation results showed that, compared with the comparative strategy, the HELD-DDPG significantly improved energy utilization efficiency of the tractor. Specifically, the chemical energy consumption of the hydrogen fuel cell was reduced by 45.43%, the cumulative energy throughput of the power battery was reduced by 32.92%, the total equivalent hydrogen consumption of the power system was reduced by 15.57%, and the battery capacity degradation was reduced by 19.76%. Meanwhile, the real-time implement ability of the HELD-DDPG was evaluated through the HIL test.