MétaCan
Menu
Back to cohort

Digital Closed Loop Control of a Three Port Series Resonant Converter for Electric Vehicles

2024· article· en· W4396575025 on OpenAlexaff
Kyle Kozielski, Guvanthi Abeysinghe Mudiyanselage, Rachit Pradhan, Giorgio Pietrini, Ashish K. Solanki, Parthasarathy Nayak, Mehdi Narimani, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSeries (stratigraphy)Port (circuit theory)Loop (graph theory)ConvertersControl (management)Closed loopElectrical engineeringDigital controlComputer scienceVoltageEngineeringControl theory (sociology)Control engineeringMathematics

Abstract

fetched live from OpenAlex

With the trend towards vehicle electrification, three port DC-DC converters allow an interface between the high-voltage battery pack, a 12 V battery pack and the 48 V auxiliary loads within the electric vehicle compared to conventional two-port converters. Due to the dependence of auxiliary loads on driving behaviour and passenger input, a robust controller is essential for a three-port converter to handle dynamically changing load profiles on the loading ports. This paper presents a design procedure of a digital closed loop controller (DCLC) utilizing decoupling control for a three port series resonant converter (TPSRC). The DCLC is developed on a hardware demonstrator for the voltage mode control of the loading ports (ports 2 and 3). Delays introduced from the practical implementation of the DCLC are defined to formulate inner and outer loop frequency responses and assist in compensator design. Load stability analysis is considered to determine the admissible operating region of the designed compensators. A soft start strategy to limit inrush currents in the TPSRC is utilized. Experimental results demonstrating the DCLC under voltage reference and step load changes on the hardware demonstrator of a 6 kW TPSRC are presented.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.544

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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 designSimulation or modeling
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 routes1
Has abstractyes

Explore more

Same topicAdvanced DC-DC ConvertersFrench-language works237,207