Reduced-Scale Hardware-in-the-Loop Platform for Dual-Source Off-Road Electric Vehicle using Energetic Macroscopic Representation
Bibliographic record
Abstract
This paper explores the transition from full-scale simulation to reduced-scale hardware-in-the-loop testing using Energetic Macroscopic Representation (EMR) for dual-source off-road electric vehicles. A comprehensive EMR model is developed, incorporating two energy sources (batteries and super capacitors). Full-scale simulation is established for further vehicle performance optimization. Reduced-scale hardware-in-the-loop testing creates a physical prototype, integrated with the EMR simulation platform for real-time interaction. This approach offers cost-effective validation of EMR-based strategies development, evaluating performance and energy management indicators. It enables iterative design improvements and rapid prototyping. Integration of the reduced-scale prototype with EMR simulation software facilitates seamless data exchange and control signal interaction. Simulations on the prototype verify the accuracy and effectiveness of EMR-based strategies. Results are compared with full-scale simulation, establishing the reliability of the EMS model. This transition enables the development and validation of strategies for our off-road electric vehicle, ensuring accurate performance evaluation and optimization. The research demonstrates the potential of EMR as a valuable tool for advanced electric vehicle system design and development.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".