The world’s first battery electric timber truck: analysis of the first two years of operation
Bibliographic record
Abstract
Electromobility plays a key role on the path toward a sustainable society, where electrification of freight transports can mitigate climate change by decreasing the use of fossil fuels, reducing noise, and improving air quality. For heavy trucks there are several challenges and aspects to consider. Among these are estimating total cost, estimating energy consumption, deciding on charging locations and capacity, fleet mix, and how to make route planning. Many companies are making investment decisions to introduce electric trucks without accurate information or any practical experience on these aspects. One reason is the lack of electric heavy trucks in actual operation and information on their use available. We present and analyze the performance from the first two years of operation of the world’s first fully battery electric timber truck at the forest company SCA operating in Sweden. The analysis is based on quantitative data from the Scania battery electric timber truck with more than 65,000 kms of operation, as well as qualitative data from unstructured interviews with persons involved in developing and operating the truck, both inside and outside SCA. The analysis provides important information and experiences of the transport, energy consumption based on multiple measurement systems and estimations, total cost, and a sensitivity analysis comparing diesel and electric heavy trucks using the most important input including electric and diesel price, purchasing price, government subsidies, C02 emission reduction, and charging downtime. From this, it is clear how electrical trucks can be competitive.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".