A Review on Modular Multilevel Converters in Electric Vehicles
Bibliographic record
Abstract
This paper discusses the state of the art of different topologies of Modular Multilevel Converters (MMC) used in Electric Vehicle (EV) power-train. A comparative study of recently proposed MMC used as a propulsion application in EV is elaborated here for the first time in EV research field. First, this paper delivers a general overview on multilevel converters associated with their various types and advancements. Then, it discusses the change from Internal Combustion Engine Vehicle (ICEV) to EV. Finally, it conducts a comparative study on the existed MMC topologies by categorizing them into five sections according to their types and contribution. First section includes the topologies that follow the same MMC architecture of cascaded half bridges. Second section discuses topologies consisting of cascaded H-bridge (CHB) and points any recorded contribution in comparison with conventional topologies. Third section focuses on topologies that reduces switching elements significantly making the whole system more reliable, cost competitive, more efficient and more size compressed. Fourth section tackles topologies with hybridized energy storage system using Ultra-Capacitors (UC) in order to track its impact on power density limitation. Last section adopts hybridized multilevel converters to observe its effect on system's efficiency and switching losses. The contribution of this paper is pointing on the strength and weakness of each topology in terms of fault tolerance, balance control, size, reliability, efficiency, cost, power density, mobility range, switching elements and switching losses.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".