Braking energy management strategy for electric vehicles based on working condition prediction
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".