Concurrent Estimation of a Vehicle’s Mass and Auxiliary Power
Bibliographic record
Abstract
The goal of this paper is to estimate the mass and auxiliary power of a vehicle simultaneously. Auxiliary power is the portion of the load power that is consumed by any auxiliary devices such as A/C compressor which is connected to the engine directly. This estimation has many potential applications especially in power management control systems of hybrid and plug-in-hybrid vehicles to improve their efficiency. The parameter estimation algorithm is based on power balance of the vehicle. That is, total generated power by the engine should be equal to the power required for moving the vehicle plus the power consumed by the auxiliary devices. After developing the system model, Kalman filter is applied for the estimation of the auxiliary power and vehicle mass. The proposed estimation algorithm uses the signals available through the vehicle control area network (CAN), and no extra sensor is required. It is assumed that the road grade is provided by a Global Positioning System (GPS) installed in the car. Simulations are presented to show the performance of the estimation algorithm in both city and highway driving cycles. The estimated and actual results are in very good agreement.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".