Novel Electric Bus Energy Consumption Model Based on Probabilistic Synthetic Speed Profile Integrated With HVAC
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a novel and generic model to calculate the Electric Bus Energy Consumption (EBEC) without the need for a high-resolution speed profile data. The proposed model generates a set of speed profiles using the basic information of the bus trip: trip time, trip length, and distances between successive bus stops. The generated speed profiles could accurately reflect the various traffic conditions and speed behaviors of real-world situations. Roadway Level of Service (LoS) is incorporated in the proposed model to simulate different traffic conditions. Further, a stochastic model for the bus speed profile is adopted to simulate the probability of the bus to stop at each on-route designated stop. The generated speed profiles are then inputted to an accurate EBEC model that considers the route topography, auxiliary loads (lighting, sound, and radio systems) and the impact of the weather conditions. The operation of the heat, ventilation and air conditioning system (HVAC) is also incorporated in the model using the thermal mass balance principle. Using the proposed model, the characteristics of EBEC on a given route can be evaluated through generating a set of speed profiles for the studied route. The proposed model provides transit network planners with a useful tool to appropriately design electric-based transit networks when there is a lack or unavailability of real-time and high resolution data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it