An Eco-Cruise Control for Electric Vehicles Moving on Slope Road with Constant Speed
Bibliographic record
Abstract
The unreasonable actuation of electric vehicle’s motor drive system usually results in a lot of unwanted energy consumption on a slope road. This paper proposes an eco-cruise control (ECC) scheme based on the driving condition estimation to decrease electric vehicle’s energy consumption in the constant-speed cruise control mode. The eco-cruise control scheme is realized by reducing the unreasonable actuation of the motor drive system. The vehicle’s total mass and pitch angle are estimated in real time by using an improved base-vector-based cross iteration estimator (BVCIE). Based on the estimated results, the required torque is predicted. Combining the speed deviation between the desired speed and the real speed, and the torque deviation between the required torque and the real torque, a three-power nonlinear controller of the ECC scheme is designed. The ECC scheme is validated on a slope road with different cruise speeds on a cosimulation platform, and the results indicate that the proposed strategy enjoys a better speed maintenance ability and energy efficiency compared with the benchmarked cruise control.
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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 | 0.000 |
| 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) | 0.001 | 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".