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Record W4381786077 · doi:10.1109/tpel.2023.3288393

A Review of Electric Vehicle Auxiliary Power Modules: Challenges, Topologies, and Future Trends

2023· review· en· W4381786077 on OpenAlexaff
Cun Wang, Pengfei Zheng, Jennifer Bauman

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2023
Typereview
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNetwork topologyElectrical engineeringVoltageReliability (semiconductor)Component (thermodynamics)Power (physics)EngineeringTraction (geology)Battery (electricity)Computer scienceAutomotive engineeringTopology (electrical circuits)Reliability engineeringElectronic engineeringMechanical engineeringComputer network

Abstract

fetched live from OpenAlex

The auxiliary power module (APM) is a vital component in electric vehicles (EVs) that enables efficient power transfer from the traction battery to low-voltage electrical loads and the 12 V battery. As the EV industry continues to evolve, APM design is facing increasingly stringent challenges, including the need for higher power ratings, higher voltage ranges, higher reliability, and higher power density, with a continued need for efficient operation. However, there is a lack of literature that comprehensively reviews recent APM topologies based on these evolving requirements. This article fills this research gap by comprehensively reviewing current APM topologies from both academia and industry, including unidirectional and bidirectional topologies, single-stage and two-stage topologies, and multifunctional topologies. Furthermore, this article explores several potential future trends for APM design and development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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.

Opus teacher head0.035
GPT teacher head0.316
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations80
Published2023
Admission routes1
Has abstractyes

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