A novel platform for power train model of electric cars with experimental validation using real-time hardware in-the-loop (HIL): A case study of GM Chevrolet Volt 2<sup>nd</sup> generation
Bibliographic record
Abstract
This paper presents a novel platform for accurate mathematical modeling of electric cars' propulsion system. It provides, for the first time, a Hardware in-the-Loop (HIL) real-time experimental verification for a case study of GM Chevrolet Volt for both power and control parts in addition to the mechanical part. The novelty of this work can be split into three steps; first, each component of the power-train is accurately modeled taking transient dynamics of all parts of the electric vehicle (EV) into consideration. Secondly, a PSIM simulation platform is consequently developed, to demonstrate the validity of this mathematical modeling. Finally, the Typhoon HIL is used to provide the experimental verification of the proposed model in real-time, which precisely validate the viability of the model. The HIL technology is used to prototype and test the control proposed system while simulating the power circuit on the HIL module platform. The Permanent Magnet Synchronous Motor (PMSM) and the Power Electronics hardware components are simulated in real-time at which the parameters can be changed while the simulation is running. However, the control algorithm is generated as a C code and downloaded to the TI controller that exists on a Digital Signal Processing (DSP) board. The results from the simulation based on PSIM environment and hardware validations using HIL are in agreement, which validates the developed model. The performance has been investigated under different load operating conditions in real-time to verify its robustness. The case study can be extended for any electric car as it provides a generic platform for modeling any propulsion system.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".