Designing a set of efficient regenerative braking strategies with a performance index tool
Bibliographic record
Abstract
The goal of this study is to design efficient regenerative braking strategies for a recreational three-wheel rear-wheel-drive hybrid electric vehicle. Current studies provide several optimal regenerative braking strategies, but no tool to obtain a set of acceptable strategies. A performance index tool is thus proposed and used to evaluate the efficiency of a given strategy. With this tool it is then possible to define a set of efficient regenerative strategies. The performance index is based on knowledge of a global efficiency map defined as the ratio of the incoming battery power to the extracted kinetic power. Two simulators of the vehicle are implemented in MATLAB/Simulink: one with a rear-wheel slip model and the other without slip considerations. They also include the longitudinal dynamics of the vehicle and the efficiency of the electrical drive from the electric motor to the battery. They were validated with experimental measurements of several accelerations and decelerations on a dry asphalt road from 0 km/h to 60 km/h. The simulated global efficiency map was also experimentally validated by regenerative braking measurements on a dry asphalt road from 50 km/h to 0 km/h. The simulated global efficiency map is used to design the optimal strategy (with and without wheel slip considerations). The performance map deduced from the global efficiency map was used to define the boundaries for the optimal strategy deviations and hence to limit the regenerated energy drop. Simulations show that there is a wide range of acceptable strategies from 0 km/h to 50 km/h on a dry asphalt road and hence give the driver the possibility of modulating the regenerative braking within a good energy recapture level. Finally, the design methodology presented with a simulated global efficiency map is also applicable with an experimental efficiency map which can be updated online.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".