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SuperBox: A 4-in-1 Power Electronics Solution for Dual-Motor Electric Vehicles

2025· article· en· W4412987107 on OpenAlexaff
Wesam Taha, Yicheng Wang, Aniket Anand, Hossain Mohammadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsChrysler (Canada)
Fundersnot available
KeywordsDual (grammatical number)Electric motorPower electronicsElectronicsAutomotive engineeringElectrical engineeringTraction motorPower (physics)Brushed DC electric motorComputer scienceEngineeringAC motorVoltagePhysics

Abstract

fetched live from OpenAlex

The integration of power electronics systems in electric vehicles (EVs) is increasingly attractive for achieving compactness and cost-effectiveness. This paper introduces the SuperBox, a highly integrated 4-in-1 power electronics solution specifically designed for EV applications. The scope of SuperBox encompasses the functionalities of on-board charging, DC boost charging, motor traction, and auxiliary power modules (APMs), all within a single housing. While leveraging established topologies to facilitate rapid industry adoption, SuperBox is distinguished by its novel approach to functional integration. This integration is achieved through three key technical strategies, namely utilizing motor windings for charging, employing a threeport transformer to magnetically couple the high-voltage and low-voltage batteries while maintaining galvanic isolation, and repurposing the traction inverter as a totem-pole power factor correction (PFC) circuit in charging mode. Implementation of such features eliminates the need for the PFC stage of the on-board charger as well as the primary-side circuitry of the APM converter. Subsystem simulations demonstrate the efficacy of SuperBox to achieve significant cost savings while delivering performance on par with conventional systems. Preliminary benchmarking suggests a 15% saving in the power stage cost compared to conventional non-integrated solutions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.218
Teacher spread0.213 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2025
Admission routes1
Has abstractyes

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