The Feasibility of Electric Vehicles as Taxis in a Canadian Context
Bibliographic record
Abstract
This study combined real-world driving data and battery simulations to evaluate electric taxi (eTaxi) feasibility in Canada. On-road data from two gasoline fueled taxis, (1-shift, 55,280 km/year; 2-shift, 108,700 km/year) provided performance targets for simulations of electric vehicles (EVs). A project objective was to assess to what degree an EV could attain these targets over a 6-year period, an expected taxi service life. The on-road taxi data were sparse; measured as average speeds for one minute periods. They were integrated with a second set of on-road driving data from a study with a Tesla Model S 70D. These Tesla data had one second temporal resolution, featuring speeds, temperatures and battery power draws. The on-road taxi data were partitioned into segments, and scaled to data from a corresponding regime from the Tesla tests, producing power draw and driving speed at one second resolution, for the battery simulation. The simulation implemented an equivalent circuit representation of an EV battery, derived from discharge curves of its constituent cells. A capacity fade model developed and validated from literature data allowed investigation of long term vehicle performance. Simulations showed that with overnight recharging only, the 1-shift taxi met 89% of the driving schedule. With mid-shift recharging and as-needed depleted state charging, both types of taxis achieved over 98 % of driving targets during the 6-year vehicle life. Per scenario, after six years, the 1-shift taxi had 67-75% of the original battery capacity, while the 2-shift had 44-49%. Battery degradation mainly depended on total driving distance and was less impacted by charging rate. These initial forecasts are part of a broader project that also indicates favorable economics, leading to a forthcoming second phase of research involving on-road tests with EVs partnered with Canadian taxi operators.
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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".