Hardware-in-the-Loop Validation of Different Power Train Topologies’ Models in Electric Vehicles: A Plug-and-Play Capability
Bibliographic record
Abstract
This study presents a novel modelling methodology for electric vehicle power train. This covers both Front Wheel and all-wheel drives. The simulation is built in PSIM and verified in Typhoons hardware in the loop (HIL) solution. HIL technology is used for real time verification. The approach is highly attractive due to characteristics of rapid prototyping which allows quick and easy adjustment in simulation in real time. Thus, avoiding the high costs associated with physical prototypes. The paper presents results (dynamic responses of various vehicle components) and the effects of adjusting various vehicle parameters. The results obtained from simulation is successfully verified in the HIL platform. The result from this study proves the robustness of the simulation and HIL model and control algorithms. This methodology saves costs and lead time to build and design a physical hardware. Additionally, the results from the modeled vehicles agree with data provided by the manufacturer. Finally, the design is simulated in a modular way such that they can be used for various propulsion setups and schemes
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".