Electric vehicles survey and a multifunctional artificial neural network for predicting energy consumption in all-electric vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study contains a survey on the architecture of electric vehicles and an artificial neural network application for prediction of energy consumption in all-electric vehicles. In this study, the term “electric vehicles” (EVs) refers to various types of electrified vehicles. The technologies behind these electric vehicles were also discussed. The survey focuses on hybrid electric vehicles (HEVs), pure electric vehicles (PEVs), and plug-in hybrid electric vehicles (PHEVs). The study also presents the design simulation of a typical HEV. A hybrid electric vehicle was designed using ADVISOR, and it was compared with another car known as the targeted car. The fuel consumption of the designed car was found to be lower than that of the targeted car. The study also introduced a multifunctional artificial neural network model for predicting electrical energy consumption in all-electric vehicles. The proposed model has nine input variables, which are virtual functions calculated from the nine selected parameters using a virtual function formula. The number of input variables was made to be equal to the number of output variables so that the artificial neural network could simulate a unique solution. The proposed model was compared with a multi-output inverse function model of an artificial neural network. The accuracy of the proposed model was 1.24–6.85 times higher than that of the inverse function model for the nine case studies considered in terms of mean square error.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it