The Fixed Route Electric Vehicle Charging Problem with nonlinear energy management and variable vehicle speed
Bibliographic record
Abstract
The problem of an individual who wants to plan a long route in an electric vehicle where charging decisions are needed can be modeled as an instance of the Fixed Route Electric Vehicle Charging Problem (FRVCP). We developed a mixed-integer programming model that optimally solves a new variant of the FRVCP, the FRVCP with nonlinear energy management (FRVCP-NLEM). It considers charging times as a nonlinear function and allows to decide at which speed to drive on each segment of the route while considering the non-linearity of energy consumption functions. The non-linearity of all functions has been solved using multiple linear approximations. The proposed model is tested using an electric vehicle trip planner called PlaniCharge that uses realistic energy consumption and charging functions that take into account external factors such as temperature and road topology. The model is tested under different road types such as urban or highway routes. The proposed model is able to optimally solve most test instances within seconds. Results show that varying the vehicle speed is an important factor to consider under low temperatures and for long-range routes as it can reduce total route duration. Some routes cannot be completed at maximum speed and require varying driving speed on segment to be able to reach the destination.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".