Light-Commercial Electric Vehicle Design: Total Cost of Ownership Assessment
Bibliographic record
Abstract
Governments in all continents are regulating and limiting the emissions generated by the transportation system. In this scenario, diesel engines will be out of all major markets between 2030 and 2040. In Brazil, the most prominent automotive market in South America, the introduction of the PL-8 regulations imposes the auto manufacturers to introduce new propulsion technologies starting in 2025. This paper studies the architecture selection and component sizing of an electric propulsion system for a light-commercial vehicle transformation from an internal diesel combustion (IC) engine to a full battery electric vehicle (BEV). The paper investigates four different driveline architectures and compares the results with the original IC vehicle regarding longitudinal performances (e.g., acceleration, maximum speed, and gradeability), energy consumption efficiency, CO2 emissions, and the total cost of ownership. In the end, the electric vehicle is evaluated as an investment by calculating its internal rate of return (IRR), payback (PB), and return on investment (ROI). The longitudinal performances and energy consumption efficiency are estimated using a one-dimensional (1D) model developed using Matlab/Simulink. The total cost of ownership and the projected vehicle retail price are determined based on the system sizing defined in this study and cost models from the literature review.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".