Multi-source Bidirectional Quasi-Z-source Inverter using Fractional Order PI Controller for Electric Traction System
Bibliographic record
Abstract
This paper proposes a new control configuration of hybrid energy storage system (HESS) using a battery pack and a supercapacitor for electric vehicle (EV). The HESS is designed based on bidirectional quasi-Z-source inverter (QZSI) and a DCDC converter. The HESS configuration with its modeling is presented in this paper. The operation modes of HESS for EV and its control scheme using fractional order PI controller (FOPI) are presented. FOPI controller is combined with a filtering technique to contribute to battery degradation mitigation. The fluxweakening method is applied to provide correct operation with the maximum available torque at any speed request within current and voltage limits. The simulation results verify the performance and effectiveness of the HESS topology. The results also point out the ability of the inverter to give a fast response to the mechanical load and provide a DC bus constant voltage over the powerdemand profile. The proposed HESS with these types of controller and converter can globally enhance the performance of the EV by allowing the efficient energy use of the battery for a longer distance coverage and extending its driving range.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".