Cyber-Physical Control for Energy Management of Off-Road Vehicles With Hybrid Energy Storage Systems
Bibliographic record
Abstract
The main purpose of this article is to develop the energy management for off-road hybrid vehicle with cyber-physical approach. Three main endeavor make this paper different from the existing relevant literature. First, a hybrid energy storage system (HESS) is utilized in an off-road hybrid vehicle to study its potential benefits over conventional use of only a battery or ultracapacitor (UC) packs. The main potential advantages of coupling a UC and a battery pack are in reducing cost, space, and weight. Second, an average power based model predictive control is proposed to solve the energy management of the vehicle with multiple energy sources without any priori information of the drive cycles. After modeling, the optimization problem with multiobjective that takes battery life into account is introduced. Finally, a comparison study is presented to analyze the benefits of the HESS in off-road hybrid vehicles over vehicles equipped with only a UC or a battery pack. The results show that using an HESS in off-road hybrid vehicles reduces the total cost, space, and weight. More specifically, the cost of the energy storage system (ESS) is decreased by 36% and 32.5% compared to only battery and only UC ESS, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".