Model Predictive Control Approach for CC-CV Operation of an Off-Board Battery Charger
Bibliographic record
Abstract
With the increase in adoption of electric vehicles (EVs), there is a significant demand for battery chargers with high power capacity. Typically, the off-board chargers have high power capability and are designed with a three-phase AC-DC converter and a DC-DC converter stage to charge the EV battery from the AC grid. Therefore, these chargers have to meet the grid codes, such as unity power factor operation and low current harmonic distortion (less than 5%) on the AC grid side. On the other hand, it is also expected to have a ripple-free current and voltage to charge the EV battery during constant current (CC) and constant voltage (CV) modes of operation, respectively. To handle these objectives, a model predictive control (MPC) framework is developed for an off-board charger under the CC-CV mode of operation. To implement the MPC, the discrete-time models of the charger are derived from the continuous-time models. The steady-state and transient performance of the proposed MPC for a 50 kW off-board charger is validated through MATLAB simulations. The MPC performance is further compared with the proportional-integral (PI)-based linear control method by considering performance indices such as AC grid current total harmonic distortion, current tracking error, DC-link voltage ripple, battery current ripple, and battery voltage ripple.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".