Optimal Design of IPT Bipolar Power Pad for Roadway-Powered EV Charging Systems
Bibliographic record
Abstract
This article proposes a multiobjective design optimization of rectangular bipolar power pads (BPP) for dynamic inductive power transfer (IPT) with application in electric vehicles. Minimization of IPT's design cost, loss that includes core and winding, and maximization of the IPT system's tolerance against horizontal/vertical misalignment are considered as objective functions during the optimization process. The design variables of the proposed algorithm are the shield plate length and width, ferrite bar length and width, the overlapping length of the coils, and the coil width and inner length of the coil. Power electronic limitations by defining the IPT's quality factor ( 4sMax), efficiency of all possible solutions greater than 80% ( η > 80%), and upper/lower limits of design parameters are considered as the constraints of this optimization problem. The time-harmonic electromagnetic physics model of the BPP is analyzed using an finite element method magnetics (FEMM) software coupled with MATLAB. A nondominated genetic algorithm (NSGA-II) is employed as the optimization solver, in which the electromagnetic measurements from the FEMM software are used to evaluate the fitness values of the proposed objectives. The proposed BPP design optimization is applied on a 10-kW IPT system as a case study. The optimization results produced 15 Pareto optimal solutions. A validation study of two Pareto front solutions (PFS) is also presented. The Pareto optimal solutions allow the designer to select the best design parameters based on the objectives of highest priority.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".