Application of Real-Life On-Road Driving Data for Simulating the Electrification of Long-Haul Transport Trucks
Bibliographic record
Abstract
The worldwide commitment to the electrification of road transport will require a broad overhaul of equipment and infrastructure. Heavy-duty trucks account for over one-third of on-road energy use. Electrified roadways (e-Hwys) are an emerging technology where electric vehicles receive electricity while driving via dynamic wireless power transfer (DWPT), which is becoming highly efficient, and can bypass the battery to directly serve the motor. A modeling study was undertaken to compare long-haul trucks on e-Hwys with conventional battery technology requiring off-road recharging to assess the most favorable pathway to electrification. Detailed data taken from on-road driving trips from five diesel transport trucks were obtained for this study. This on-road data provided the simulations with both real-life duty cycles as well as performance targets for electric trucks, enabling an assessment and comparison of their performance on e-Hwys or with fast recharging. Battery-only trucks were found to have lifetimes down to 60% original battery capacity (60% SOH) of up to 9 years with 1600 kWh packs, and were similar to conventional diesel truck performance. On e-Hwys smaller pack sizes in the 500 to 900 kWh capacity range were sufficient for the driving duty, and showed lifetimes upwards of 20 years, comparing favorably to the battery calendar life limit of about 26 years. For a 535 kWh battery pack, an e-Hwy DWPT level of 250 kW was sufficient for a 36 tonne truck to complete all the daily driving as defined by the diesel reference trucks, and reach a battery pack end of life point of 60% SOH.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".