A Methodology for Modelling of Driveline Dynamics in Electrified Vehicles
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The assessment and control of driveline dynamics is only possible if a representative model is available. A driveline model enables engineers to estimate the system’s reactions for different torque inputs and shows how those inputs impact drivability and comfort. Modelling methods in literature are frequently designed only for internal combustion engine vehicles, disregarding electrified powertrains. To remedy that, a modelling method for electrified drivelines is presented. It simplifies the inclusion of dynamic factors such as road resistances, flexibility, friction, and inertias. The method consists in drawing a vertical diagram of the drivetrain topology where each key component is represented as a block. Newton’s second law is used to balance torque in each block connection, from propelling systems to the wheels. State variables and inputs are defined accounting for the powertrain topology. The result is an observable and controllable state-space model that can be further used for driveline control or state estimation, depending on the application. This paper shows in detail the steps to achieve such driveline models. It also proves the controllability and observability of the models developed. Finally, the method is applied to model three commonly used electrified driveline topologies - the series hybrid, the parallel hybrid, and the split hybrid. The outcomes of this study are an intuitive method for driveline modeling as well as ready-to-use state-space models of three commonly used electrified driveline topologies that can be used for drivability analysis.</div></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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".