Heun’s Method-Based Predictive Control With Reduced Power Ripple and Enhanced Efficiency for an Off-Board EV Battery Charger
Bibliographic record
Abstract
The use of the Euler approach in the formulation of predictive control methods for off-board electric vehicle (EV) battery chargers significantly affects the prediction accuracy of control variables, which further leads to high switching frequency operation and higher power ripples on both the grid and battery sides of the charger. To address the aforementioned challenges, Heun’s approach is proposed to formulate the predictive power control (PPC) for ac–dc converter stage and predictive current control (PCC) for dc–dc converter stage in an off-board EV battery charger in this article. In addition, an inherent relationship between the ac and dc-side powers of the charger is developed, thereby eliminating the need for external proportional-integral controller and weighting factor dependent cost function to regulate the dc-link capacitor voltage with the proposed approach. Furthermore, the proposed Heun’s approach is designed with predictor and corrector stages, leading to an enhanced accuracy in the prediction process of control variables. This further results in superior harmonic performance and low power ripples on grid and battery-sides of the charger. Also, the use of Heun’s approach in the formulation of PPC and PCC methods reduce the switching frequency of the ac–dc and dc–dc converters, resulting in lower power losses and higher efficiency of the charger. The efficacy of the proposed Heun’s method-based PPC and PCC is investigated on a scaled-down laboratory prototype of an off-board EV battery charger with dSPACE-DS1202 control platform. Finally, the performance of the proposed method is benchmarked with the Euler-based predictive control methods for a battery charger.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".