Braking energy management strategy for electric vehicles based on working condition prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To improve the mileage capacity of electric vehicles (EVs), a dual-motor front-wheel-drive EV is considered as the research object. Through experiments with actual vehicles, data from four typical working conditions are collected; a C4.5 decision tree algorithm is developed to train a working condition recognition model. The long short termmemory neural network is used to train four deep-learning working condition prediction models, and the particleswarm algorithm is used to optimize their structural parameters. The braking strength, demand torque, and demand speed are determined based on the predicted working conditions. Based on four common braking energy recovery control strategies, front- and rear-wheel braking force distribution strategies are formulated according to the changes in braking strength. The maximum regenerative braking torque and remaining mechanical braking torque provided by the front wheels are optimized. The Seagull Optimization Algorithm is used to optimize the torque distribution of the dual motors on the front wheels and improve the working efficiency of the motors. In the experimental conditions, the recovered energy at 100 km is 2.6 kWh; the energy recovery rate is 19.1%, and the power consumption ratio is reduced by 15.8%, improving the EV cruising range.
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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 it