Data-Driven Modeling and Simulation Approach for Energy Demand Prediction of Off-Road Electric Truck
Bibliographic record
Abstract
As the adoption of electrical transportation continues to rise, understanding and addressing performance degradation becomes increasingly critical to ensuring reliability and efficiency. This paper proposes a data-driven modeling and simulation approach for off-road electric trucks (e-truck). It employs a comprehensive simulation framework incorporating vehicle dynamics, terrain variability, and operational conditions to predict energy consumption with high precision. The model includes representations of the control system, electrical motor, regenerative energy mechanisms, and vehicle dynamics. This approach links the e-truck physical modeling to the realistic input data such as road conditions, truck loading, and driving behavior to provide valuable insights into the realistic operational performance of the e-truck. The developed approach addresses two main simulation models. The first one is the Terra-mechanics model that is developed in the Vortex environment to estimate the e-truck rolling resistance for different soil types. The second is the energy model, which is designed in Maplesim to estimate the instantaneous power demand and the energy required to perform a mining cycle. In the specific mining case study, rolling resistance and e-truck loading are the top sensitive variables in the energy prediction model. The fully loaded e-truck consumes 52.5% energy more than the empty truck, on average with 5% rolling resistance. The estimated energy demand increases by 55.9% for each percentage increase of rolling resistance, on average with the maximum e-truck loading. Detailed route simulation and sensitivity analysis were reported.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".