A Review of Three-Port Integrated On-Board Charger and Auxiliary Power Module in Electric Medium- and Heavy-Duty Vehicles
Bibliographic record
Abstract
Electric medium- and heavy-duty vehicles (eMHDVs), compared to their gasoline counterparts, are a promising solution in alleviating the global dependence of fossil fuel energy usage in the transportation sector. However, increased vehicle mass due to energy storage and powertrain upgrades require research and development efforts to improve eMHDV performance. Addressing power and voltage requirements of the powertrain, HV battery, and auxiliary loads can facilitate a bridge towards the metrics of conventional MHDV builds. DC-DC converters utilized in the on-board charger (OBC) and auxiliary power module (APM) of an eMHDV are therefore the focal point of this article, wherein a three-port converter (TPC) arises. To lay foundation, opportunities for OBCs across the wide spectrum of eMHDV models, vocations, and masses are identified through a study based on available specifications from 93 state-of-the-art (SOA) vehicle models, combined with 23,000 days of fleet data. OBCs are deemed feasible for eMHDVs due to long available off-shift charging times coupled with SOA battery capacities. Elevated power requirements of high-voltage and low-voltage auxiliaries in eMHDVs are also outlined. Then, TPCs are compared to conventional DC-DC converter solutions in terms of volume, efficiency, and cost. Despite elevated OBC and APM power requirements, TPCs are concluded to not be a limitation in eMHDV applications. Finally, a topological based review of SOA TPCs is performed outlining key technological gaps, design requirements and recommendations, and future research required for expansion to eMHDVs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".