Optimization of Thermal Comfort on Electric Buses : A Comprehensive Study on Passenger Satisfaction in Stockholm, Sweden
Bibliographic record
Abstract
The transition towards electrification in the bus sector is necessary to achieve the global climate goals and has gained significant traction in recent years. However, there are critical challenges associated with this transition, one of them being the absence of excess heat that traditional combustion engines provided to warm the bus cabin. Consequently, a large portion of the battery’s energy is consumed by the heating system. This thesis aims to address this issue by investigating the optimal indoor bus temperature in relation to thermal comfort and energy efficiency for different outdoor climate conditions. Measurements were conducted in Stockholm city during winter conditions and surveys were administered to passengers in order to assess their thermal comfort for different temperatures. The two methods Predicted Mean Vote (PMV-PPD) and Equivalent temperature (Teq) were used to evaluate thermal comfort and provide a basis for a generalized adapted theoretical model. Previous measurements conducted in Ottawa and Dubai were integrated into the analysis to incorporate different outdoor climate conditions. The results showed that the optimal bus temperature for Stockholm was 17.5 and 19.1°C for outside temperatures of 4 and 8 °C respectively. This indicates that the bus temperature can be lowered in relation to the current standard of 21 degrees. The analysis of Ottawa and Dubai, corresponding to outside temperatures of -14 and 39°C, showed that the optimal temperatures were 16.6 and 23.5 degrees respectively. The potential energy saving from reducing the bus temperature by one degree is 0.36 kWh per kilometer. Moreover, the analysis of time dependency in relation to thermal comfort showed that time has no significant impact on bus trips shorter than 15 minutes. The adapted theoretical model for the PMV-PPD method showed the best results when correlating to actual passenger responses. A sensitivity analysis of the measured parameters showed that fixed values and theoretical correlations could be employed for relative humidity, air velocity, and mean radiant temperature without affecting the output, thus reducing the number of sensors needed for future measurements. The clothing insulation values are highly dependent on geographic location and culture, thus it is not possible to develop an all-encompassing theoretical correlation for the clothing insulation. Further measurements are required in different climatic conditions for a more detailed and accurate analysis.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".