Teardown Analysis and Fea Motor Model of Chevrolet Bolt Ev Drivetrain
Bibliographic record
Abstract
Electric vehicles (EVs) are increasingly seen as a key element in the future of transportation. Consequently, substantial research efforts are focused on improving EV powertrain efficiency, reducing noise, vibration, and harshness (NVH), lowering costs, and minimizing size. A primary area of focus within powertrain research is the electric traction machine, a critical component driving EV performance. Benchmarking data is essential for advancing research and technology in this field. However, publicly available data on traction machines remains limited, posing a challenge for researchers. To address this gap, a comprehensive teardown study was conducted on the drive unit of the 2017 Chevrolet Bolt, including the 150 kW traction machine and gearbox. Each component was meticulously measured for size and weight, with the findings presented in this paper. An electromagnetic Finite Element Analysis (FEA) model was developed using measured rotor, stator, and magnet geometries to validate motor performance compared to published data. Simulation results showed discrepancies of less than 1.6 % for torque and 3% for torque ripple. Additional detailed teardown data from this study is made available online to support ongoing EV powertrain research.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".