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Record W4391528200 · doi:10.1109/tte.2024.3362063

Design and Control of a Multiport Bidirectional Converter for Fuel Cell Range Extended Vehicles With Onboard Solar Generation

2024· article· en· W4391528200 on OpenAlexafffund
Pengfei Zheng, Cun Wang, Jennifer Bauman

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2024
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRange (aeronautics)Fuel cellsControl (management)Automotive engineeringEngineeringComputer scienceElectrical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

Though electric vehicles have the benefits of zero tailpipe emissions and convenient overnight charging, other challenges remain such as limited driving ranges, slow refueling while on-the-go, lithium supply issues, and emissions from some sources of electricity generation (e.g., coal). Fuel cell powered vehicles address the refueling time issue, which will also ease range concerns if hydrogen fueling stations are available. Furthermore, on-board solar generation can replace a portion of the vehicle’s grid charging needs and extend driving range. For both options, a smaller battery could be used, meaning less lithium is required. However, the power electronic architecture for such a solar fuel cell range extended vehicle (S-FCREV) would be complex and costly with conventional separate converters. Thus, this paper proposes the first practical multi-port converter that can perform all S-FCREV requirements with a low component count, including partial electrical isolation. This paper also proposes a simple Triple PWM and Triple Phase Shift (TPTPS) control scheme to enable flexible power flow between different power sources. A 3.3-kW prototype is designed, built, and tested to validate the topology and control. The experimental results show that the proposed topology achieves 97.8% peak efficiency with the proposed control.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.696

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 head0.014
GPT teacher head0.206
Teacher spread0.193 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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