Comparative Evaluation of Battery Electric and Internal Combustion Vehicles in On-Demand Shared-Ride Services: Energy and Operational Efficiency
Bibliographic record
Abstract
As battery electric vehicles (BEVs) become increasingly prevalent on roads, shared-ride services are expected to operate with mixed fleets comprising both BEVs and internal combustion engine vehicles (ICEVs). This shift necessitates a timely evaluation of the operational efficiency and energy performance of these vehicle types within an on-demand, shared autonomous mobility system. The paper develops an Autonomous Vehicle Hybrid Sharing (AVHS) platform, which integrates dynamic stochastic control to simultaneously minimize operational and traveler costs. An optimization-based realtime vehicle assignment algorithm manages diverse demand scenarios and varying fleet compositions. Simulation results across multiple scenarios highlight that ICEVs offer superior service performance under high-demand conditions, primarily due to BEVs' limited driving range and extended recharging durations. Specifically, ICEVs can accommodate up to 8% more ride requests compared to fully electrified fleets. Additionally, mixed-fleet configurations show a dependency on ICEVs to compensate for BEVs' downtime during charging periods. Future research directions include incorporating real-world networks, strategic placement of fast chargers, and leveraging machine learning for demand prediction to further enhance system resilience and operational efficiency.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".