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Record W2263755423 · doi:10.4271/2006-01-1602

Development of a Model-Based Powertrain and Vehicle Simulator for ECU Test Benches

2006· article· en· W2263755423 on OpenAlexaff
W. James Allen, Pierre Grondin, Wensi Jin, Alan Soltis

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2006
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsPowertrainAutomotive engineeringSimulationComputer scienceTest (biology)TorqueEngineeringPhysics

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Traditionally, bench testing of electronic control unit (ECU) software relies heavily on the use of static simulators. They are simple to set up and low cost. However, static simulators lack the programmability, I/O scalability, and the support for new sensor and actuator interfaces. They do not have standard and extensive support for test automation, which is critical for achieving a high degree of test case coverage and regression testing. Lastly, they are unable to take advantage of plant models for closed-loop testing. In short, with the increasingly sophisticated ECU technologies, static simulators can no longer keep pace with the testing requirements that an ECU development team must meet.</div> <div class="htmlview paragraph">In this paper, Opal-RT and Delphi present the development of a new modular bench top simulator designed to replace the static simulators currently in use. The first part of the paper discusses the overall architecture of the system and the design decisions made to reduce system cost so the resulting simulator can be deployed in large numbers. The second part of the paper describes each of the following major functional areas of the system, including rationales behind the design and its benefit observed from the initial deployment.</div> <div class="htmlview paragraph"> <ul class="list disc"> <li class="list-item"><div class="htmlview paragraph">I/O configuration and management</div> </li> <li class="list-item"><div class="htmlview paragraph">Graphical user interface</div> </li> <li class="list-item"><div class="htmlview paragraph">Tactile interface</div> </li> <li class="list-item"><div class="htmlview paragraph">Test automation</div> </li> </ul> </div> <div class="htmlview paragraph">The third and the last part of the paper details the system's support for two advanced features, closed-loop simulation and dynamic software verification. The first, closed-loop simulation, was made possible by controlling the simulator using a model created using a commercially available modeling program. By running a model, the simulator is then able to support the full range of test activities from open-loop functional checkout to closed-loop system validation. This model-based test environment also makes it possible for test engineers to adapt simulators for specific test needs by editing the model using a commercially available modeling program. The second, dynamic software verification, allows a simulator to access ECU's internal variables through the CAN Calibration Protocol (CCP) and IEEE ISTO-5001NEXUS interface for “whitebox” verification.</div>

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 categoriesMeta-epidemiology (narrow)
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 score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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.

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

Citations3
Published2006
Admission routes1
Has abstractyes

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