{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:55:43Z","timestamp":1792173343796,"version":"4.4.0"},"reference-count":38,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chongqing Municipal Commission of Agriculture and Rural Affairs","award":["NYNCBNJYTH-CQ202503"],"award-info":[{"award-number":["NYNCBNJYTH-CQ202503"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>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\u201395.59% of the DP performance over cycles C1\u2013C3, exceeding MPC by 1.41\u20133.19 percentage points and adaptive equivalent-consumption minimization by 3.62\u20134.99 percentage points. Mean execution time was 0.128\u20130.191 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0360955","type":"journal-article","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:40:20Z","timestamp":1792172420000},"page":"e0360955","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive-feedforward energy management for a hybrid agricultural machine operating in mountainous orchards"],"prefix":"10.1371","volume":"21","author":[{"given":"Zuojin","family":"Li","sequence":"first","affiliation":[{"name":"Chongqing University of Science and Technology","place":["Chongqing, China"],"department":["College of Electronics and Electrical 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