Comparative Estimation of Electric Vehicle Rolling Resistance Coefficient in Winter Conditions
Bibliographic record
Abstract
The electric/hybrid vehicle are promising technologies and practical for the transport and the environment. For energy management, the vehicle longitudinal dynamics used to estimate vehicle power demands depend on several parameters, including its mass and its rolling conditions. This study compared the methods of estimating the rolling resistance coefficient and mass to establish energy planning system of an electric vehicle in winter conditions. Different from reported approaches, which are limited to estimate only one parameter, the vehicle mass or the rolling resistance, this paper used two efficient methods which, simultaneously estimate vehicle mass and rolling resistance coefficient. The first method is based on RLS algorithm, while the second is based on neural network. The estimated values of rolling resistance coefficient retrieved from these methods are similar to those provided by a third method, which estimates only the rolling resistance by considering the mass as input and using RLS algorithm. Although results retrieved from the three methods show that the estimated values converge to real values with a margin error that does not exceed 10%, we suggest that the first and the third method, using the RLS algorithm and giving an online estimation, are more accurate and more suitable in snow covered road conditions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".