Real-Time Energy Management Based on Proximal Policy Optimization With Mask Layer for Hybrid Electric Mining Trucks
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Bibliographic record
Abstract
An effective energy management strategy (EMS) is crucial to improve the energy efficiency of hybrid vehicles, especially for heavy-duty mining trucks. An energy management strategy based on a proximal policy optimization algorithm with mask layer and novel reward functions (PPO-MASK-NR) is proposed for hybrid electric mining trucks (HEMTs) with multi-planetary systems. This algorithm fundamentally avoids irrational exploration by an intelligent agent by incorporating a real-time mask layer, and it accelerates learning efficiency by suppressing the backward propagation of gradients for irrational actions. A universally designed reward function is applied to ensure the achievement of the correct final state of charge (SOC) value and the expansion of the SOC's exploration range. Finally, the generalization performance of the proposed algorithm is validated through new driving cycles, and its authenticity is confirmed through hardware-in-the-loop (HiL) testing. The simulation results show that within the selected training cycles, the proposed algorithm achieves 98% compared with the dynamic programming algorithm (DP). The proposed algorithm has an improvement of 11% and 5% in online applications for a new driving cycle compared to a rule-based technique (RB) and the equivalent fuel consumption minimization approach (ECMS), respectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it