Optimisation of fractional‐order PI controller for bidirectional quasi‐Z‐source inverter used for electric traction system
Bibliographic record
Abstract
This study presents the optimisation of fractional‐order proportional–integral (FOPI) controllers for a bidirectional quasi‐Z‐source inverter (QZSI) in an electric vehicle (EV) off‐road application. An ant colony optimisation Nelder–Mead (ACO‐NM) algorithm is used for the optimisation of the controller parameters. This optimisation method is applied to enhance the performance of FOPI control for bidirectional QZSI. Ziegler–Nichols (ZN) with relay and the pole placement tuning method are also used for the FOPI controller design for comparison purposes. The modelling and the control design of bidirectional QZSI for an electric traction system are presented and discussed. Simulations are performed to verify the efficacy of the proposed controller structure with the bidirectional QZSI for two standardised driving cycles. The result shows that the FOPI controller designed with the ACO‐NM algorithm provides more suitable ageing performance index values for the battery. The ACO‐NM algorithm permits to reduce the root‐mean‐square value and the standard deviation by 2 and 5% of the battery current compared to the ZN tuning method and direct battery supply topology, respectively. The bidirectional QZSI with this type of controller can globally enhance the performance of EVs by optimising the electric power consumption 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".