Slip-Ratio-Aware Energy Management of a Hybrid Tractor Under Variable Plowing Loads Using a DP-Calibrated ECMS

To enhance the fuel economy and operational adaptability of hybrid tractors under variable plowing loads, this paper proposes a slip-ratio-aware equivalent consumption minimization strategy (ECMS) calibrated via dynamic programming (DP). A resistance–slip ratio prediction model was first identified using plowing resistance and slip ratio data collected from soil-bin tests. The predicted slip ratio was integrated into the demand power model to quantify slip-induced traction losses. Offline DP was subsequently applied to generate globally optimized power split trajectories and establish a baseline equivalence-factor map indexed by plowing resistance level and battery state of charge (SOC). For real-time operation, the equivalence factor is dynamically adjusted via SOC feedback and normalized slip ratio deviation, enabling coordinated power distribution among the engine, MG1, and MG2. Powertrain bench tests were conducted by reproducing variable plowing loads using a dynamometer. The equivalent plowing resistance was calculated from measured load torque, and the corresponding slip ratio was estimated using the identified prediction model. Compared with A-ECMS, the proposed strategy reduced equivalent fuel consumption by 14.02% in simulation and 7.33% in bench tests. The proportion of engine operation in the high-efficiency region increased from 61% to 80% in simulation and from 65% to 77% in the bench test, while the corresponding proportion for the electric motors increased from 87% to 92% and from 88% to 90%, respectively. The SOC deviation decreased from 3.03% to 2.26% in simulation and from 3.07% to 2.43% in the bench test. These results demonstrate that the proposed strategy improves fuel economy, SOC regulation, and component operating efficiency under variable plowing loads.