Simulating Electric Refuse Collecting Vehicle Performance: Permanent Magnet Versus Switched Reluctance Traction Motor
Bibliographic record
Abstract
Refuse collecting vehicles (RCVs) that use internal combustion engines (ICEs) have very poor mileage, mainly due to their tendency to idle for long periods and frequent number of stops. This type of drive cycle is better suited for electric RCVs (ERCVs) which consume much less energy during idle periods and are generally more efficient. ERCVs traditionally use traction motors that incorporate rare earth metals in their design. These permanent magnet motors (PMMs) have as draw-backs: high cost of magnet material, sensitivity to demagnetization and increased concern towards properly securing magnets in the rotor. Switched reluctance motors (SRMs) do not use magnets and thus do not suffer from these drawbacks. Their low-cost design and high durability offer an enticing possibility for an ERCV, which typically sees heavy use and requires significant maintenance. The ERCV is also potentially less affected by the SRM torque ripple. This paper examines substitution of a conventional PMM with an SRM in an ERCV. A model is created in MATLAB/Simulink to represent the ERCV and performance of the vehicle using both motors is analyzed. SRM thermal analysis during the drive cycle is also conducted using a Motor-CAD model previously designed to assess maximum operating temperatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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 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".