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Optimization Control of Motor Torque for a Hybrid Electric Tractor under Plowing Conditions Based on DP-MPC

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Aiming at the problems of insufficient power control accuracy, poor stability of the electric drive system, low traction efficiency, and unsatisfactory energy economy in hybrid electric tractors during field operations, this paper proposes a DP-MPC multi-objective cooperative optimization control strategy that integrates improved Dynamic Programming (DP) and Model Predictive Control (MPC). Targeting an Output-Split Electrically Continuous Variable Transmission (OS-ECVT) configuration hybrid tractor, multiple subsystem models were established to construct a hierarchical control architecture. This architecture features DP-based multi-objective global optimization at the upper layer and improved MPC for torque tracking at the lower layer. The DP algorithm takes motor torque tracking error, average current fluctuation, wheel slip control error, and motor operating efficiency as comprehensive optimization objectives. For comparison, a rule-based MPC strategy, which combines upper-layer rule-based control with lower-layer MPC, is also established. Simulation and bench test results show that, compared with the Rule-MPC strategy, the proposed DP-MPC strategy reduces the average motor torque tracking error by 37% and 34.3%, decreases the average current fluctuation by 24.2% and 23.5%, lowers the wheel slip control error by 16.6% and 13.1%, and improves the average operating efficiency by 0.11 and 0.18 percentage points, respectively. The comprehensive evaluation scores are increased by 5.7 and 4.9 points, respectively. Bench test results verify the effectiveness of the DP-MPC hierarchical control strategy. This study demonstrates that the proposed DP-MPC strategy can effectively balance the dynamic control performance of the drive motor with global energy efficiency optimization under plowing operations, providing a theoretical basis and technical pathway for the control design and energy efficiency improvement of high-horsepower hybrid tractor powertrains.
Title: Optimization Control of Motor Torque for a Hybrid Electric Tractor under Plowing Conditions Based on DP-MPC
Description:
Aiming at the problems of insufficient power control accuracy, poor stability of the electric drive system, low traction efficiency, and unsatisfactory energy economy in hybrid electric tractors during field operations, this paper proposes a DP-MPC multi-objective cooperative optimization control strategy that integrates improved Dynamic Programming (DP) and Model Predictive Control (MPC).
Targeting an Output-Split Electrically Continuous Variable Transmission (OS-ECVT) configuration hybrid tractor, multiple subsystem models were established to construct a hierarchical control architecture.
This architecture features DP-based multi-objective global optimization at the upper layer and improved MPC for torque tracking at the lower layer.
The DP algorithm takes motor torque tracking error, average current fluctuation, wheel slip control error, and motor operating efficiency as comprehensive optimization objectives.
For comparison, a rule-based MPC strategy, which combines upper-layer rule-based control with lower-layer MPC, is also established.
Simulation and bench test results show that, compared with the Rule-MPC strategy, the proposed DP-MPC strategy reduces the average motor torque tracking error by 37% and 34.
3%, decreases the average current fluctuation by 24.
2% and 23.
5%, lowers the wheel slip control error by 16.
6% and 13.
1%, and improves the average operating efficiency by 0.
11 and 0.
18 percentage points, respectively.
The comprehensive evaluation scores are increased by 5.
7 and 4.
9 points, respectively.
Bench test results verify the effectiveness of the DP-MPC hierarchical control strategy.
This study demonstrates that the proposed DP-MPC strategy can effectively balance the dynamic control performance of the drive motor with global energy efficiency optimization under plowing operations, providing a theoretical basis and technical pathway for the control design and energy efficiency improvement of high-horsepower hybrid tractor powertrains.

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