Predictive-feedforward energy management for a hybrid agricultural machine operating in mountainous orchards
- 1College of Electronics and Electrical Engineering, Chongqing University of Science and Technology, Chongqing, China
- 2Institute of Agricultural Science and Technology Information, Chongqing Academy of Agricultural Sciences, Chongqing, China
Abstract
Hybrid agricultural machines in mountainous orchards operate under low-speed, high-load conditions with coupled gradient and time-varying rolling-resistance disturbances. Their power demand is therefore nonstationary and may change abruptly. Rule-based and instantaneous strategies provide limited preview, whereas model predictive control (MPC) can transmit prediction errors directly to actuator commands and imposes substantial online computation. To address these limitations, a forward model was developed for a power-split hybrid agricultural machine comprising an engine, two electric machines, a traction battery, and a planetary gear set. A predictive-feedforward, multi-objective energy-management strategy was proposed. Short-horizon speed and gradient predictions are normalized, weighted, and saturated to form a bounded load-risk indicator. Rather than entering low-level torque calculation directly, this indicator modifies the dynamic state-of-charge (SOC) safety boundary, power-source mode, and transmission-ratio switching threshold, thereby softly decoupling prediction and execution. Engine high-efficiency operation, two transmission ratios, and actuator constraints are then coordinated for computationally efficient online power allocation. With offline dynamic programming (DP) as a common benchmark, the strategy achieved 93.29–95.59% of the DP performance over cycles C1–C3, exceeding MPC by 1.41–3.19 percentage points and adaptive equivalent-consumption minimization by 3.62–4.99 percentage points. Mean execution time was 0.128–0.191 ms per step, 93.54% lower than that of MPC, while terminal SOC remained near 0.600. No SOC, battery-current, or power-source torque constraint was violated under prediction noise or abrupt equivalent rolling-resistance disturbances. The conclusions apply to maintained, firm orchard lanes with low track slip; soft-soil operation involving high traction, severe sinkage, or substantial slip requires further validation. Bounded load-risk feedforward thus retained short-horizon preview while limiting direct error propagation, providing a practical real-time approach for hybrid agricultural machines with resource-constrained embedded controllers.
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BibTeX
@article{li2026predictivefeedforward,
title = {{Predictive-feedforward energy management for a hybrid agricultural machine operating in mountainous orchards}},
author = {Zuojin Li and Hao Huang and Linlu Dong and Xianfeng Zhang and Rui Zhou and Bo Li and Bao Yu},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0360955},
url = {https://doi.org/10.1371/journal.pone.0360955},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Predictive-feedforward energy management for a hybrid agricultural machine operating in mountainous orchards
AU - Zuojin Li
AU - Hao Huang
AU - Linlu Dong
AU - Xianfeng Zhang
AU - Rui Zhou
AU - Bo Li
AU - Bao Yu
PY - 2026
DA - 2026/10/16
JO - PLOS One
DO - 10.1371/journal.pone.0360955
UR - https://doi.org/10.1371/journal.pone.0360955
AB - Hybrid agricultural machines in mountainous orchards operate under low-speed, high-load conditions with coupled gradient and time-varying rolling-resistance disturbances. Their power demand is therefore nonstationary and may change abruptly. Rule-based and instantaneous strategies provide limited preview, whereas model predictive control (MPC) can transmit prediction errors directly to actuator commands and imposes substantial online computation. To address these limitations, a forward model was developed for a power-split hybrid agricultural machine comprising an engine, two electric machines, a traction battery, and a planetary gear set. A predictive-feedforward, multi-objective energy-management strategy was proposed. Short-horizon speed and gradient predictions are normalized, weighted, and saturated to form a bounded load-risk indicator. Rather than entering low-level torque calculation directly, this indicator modifies the dynamic state-of-charge (SOC) safety boundary, power-source mode, and transmission-ratio switching threshold, thereby softly decoupling prediction and execution. Engine high-efficiency operation, two transmission ratios, and actuator constraints are then coordinated for computationally efficient online power allocation. With offline dynamic programming (DP) as a common benchmark, the strategy achieved 93.29–95.59% of the DP performance over cycles C1–C3, exceeding MPC by 1.41–3.19 percentage points and adaptive equivalent-consumption minimization by 3.62–4.99 percentage points. Mean execution time was 0.128–0.191 ms per step, 93.54% lower than that of MPC, while terminal SOC remained near 0.600. No SOC, battery-current, or power-source torque constraint was violated under prediction noise or abrupt equivalent rolling-resistance disturbances. The conclusions apply to maintained, firm orchard lanes with low track slip; soft-soil operation involving high traction, severe sinkage, or substantial slip requires further validation. Bounded load-risk feedforward thus retained short-horizon preview while limiting direct error propagation, providing a practical real-time approach for hybrid agricultural machines with resource-constrained embedded controllers.
SN - 1932-6203
ER -