Techno-economic comparison on charging modes of battery heavy-duty vehicles in short-haul delivery: A case study of China
Bibliographic record
Abstract
It is critical to arrange appropriate charging infrastructure in advance to decarbonise heavy freight through electrification. Based on the same service level, this study conducted a techno-economic comparison of charging modes for battery heavy-duty vehicles in short-haul delivery, covering the broadest range of charging modes, including slow, fast (150kW), fast (350kW), swap, and overhead catenary. The techno-economic performance is obtained by a model composed of five evaluation indicators, in which the ratio of service capacity to cost and the average financial net present value are specific. The factors influencing the techno-economic performance of charging modes are further explored based on case analysis. Results show that the charging modes of slow, fast (150kW), fast (350kW), swap, and overhead catenary are not profitable under the corresponding facility utilisation rates of 40%, 20%, 20%, 30%, and 70%, or under operating years of 5, 3, 2, 4, and 12 years. Fast charging, at both 150 and 350 kW, has a better advantage in profitability based on the highest average financial net present value. Swap charging is best regarding energy supplement efficiency, but it is not profitable when the battery swapping price is less than 0.8 CNY/kWh. Overhead catenary charging was the most effective system per unit cost due to the highest ratio of service capacity to cost. The insights, the precise prediction of the charging demand, the focus on the charging price, and the comprehensive improvement in the facility utilisation rate are crucial for the success of charging service providers.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".