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Record W4311729142 · doi:10.1177/09544070221139901

Hierarchical coordinated control strategy for regenerative braking energy recuperation with an electrobooster

2022· article· en· W4311729142 on OpenAlexaff
Hongyan Guo, Xu Zhao, Jun Liu, Feibai Zhu, Hong Chen, Dongpu Cao

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRegenerative brakeController (irrigation)Engine brakingRetarderAutomotive engineeringBrakeControl theory (sociology)Dynamic brakingComputer scienceControl (management)Energy recoveryTorqueControl engineeringEngineeringEnergy (signal processing)

Abstract

fetched live from OpenAlex

To guarantee the braking performance and safety of electric vehicles (EVs) and to improve their regenerative energy performance, a regenerative braking recuperation control strategy based on hierarchical coordinated control of an electrohydraulic brake system (eBooster) is proposed in this paper. First, considering the EV braking system characteristics, the eBooster model, vehicle dynamics model and energy recuperation model are established. Then, a hierarchical coordinated control strategy (HCCS) is proposed, and the top-level controller based on model predictive control (MPC) reasonably coordinates the braking torque according to the control objectives and constraints. Additionally, as per the eBooster characteristics, the bottom-level controller adopts a double closed-loop controller to improve the control accuracy and response speed. Finally, verification in the Simulink/AMESim joint simulation environment shows that the eBooster and the HCCS enable combined regenerative and hydraulic braking, which improves brake safety and performance.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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