Integrated Motor Optimization and Route Planning for Electric Vehicle using Embedded GPU System
Bibliographic record
Abstract
For the route planning of electric vehicles (EV) a greater emphasize is placed on minimizing the energy consumption. The model used to calculate the energy along the path is typically based on the mechanical model of the vehicle. However, to be more accurate, one should also consider the motor losses. In this paper, we propose an integrated motor optimization and route planning for EV based on the Particle Swarm Optimization (PSO) and the Bellman-Ford (BF) routing algorithm. The PSO is used to calculate optimized magnetic flux settings for an induction motor for various operating points. The calculated settings maintain the high efficiency of the motor throughout the trip, but are also used to accurately calculate the motor losses prior to planning the route. The BF algorithm is used to calculate optimized routes. A Pareto front of optimized routes that minimized energy and distance is produced and allows the user to select the preferred route. Both the PSO and the BF are implemented in CUDA on an embedded NVIDIA Jetson TX2 graphics processing unit (GPU) for maximum performance. The system is tested on road maps with up to 4.6 million edges and provides a speedup of 19.3x for the PSO and 10.1x for the BF.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".