Deep Learning Based Distributed Meta-Learning for Fast and Accurate Online Adaptive Powertrain Fuel Consumption Modeling
Bibliographic record
Abstract
Connected vehicle-based distributed meta-regression (CV-DMR) algorithms from our previous work facilitate online adaptive microscopic fuel consumption modeling, which can support model-based vehicle and powertrain control (MB-VPC) for fuel-efficient vehicle operations. By taking advantage of accessible online computational resources from connected vehicles (CVs) and CV remote data center, CV-DMR can adapt the microscopic fuel consumption model to an unseen driving condition with limited training data. However, the computational complexity of obtaining the normalized feature map in CV-DMR during training is high. It can take long time to adapt the model to a new driving condition, especially when the new driving condition is considerably different from the pre-learnt driving conditions. To reduce the computational complexity of learning/training, we propose to apply deep neural networks as the model representation method and newly design efficient deep learning based CV-supported distributed meta-learning (CV-DML) algorithms for adaptive fuel consumption modeling. To further generalize the model and reduce model adaptation time, the concept of meta-suggestion is newly proposed in CV-DML. Substantial proof-of-concept experiments are conducted with steady-state and transient vehicle engine data to evaluate the model performance in terms of model prediction accuracy, model adaptation speed, and fuel saving. Compared to the physical model and non-CV-supported model adaptation, the prediction accuracy of CV-DML is improved by 34%–87% and 17%–30%, respectively. Due to the improved accuracy, up to 9.4% of fuel is saved. To achieve the same the prediction accuracy and fuel saving, the model adaptation of CV-DML is 18 to 1,300 times faster than that of CV-DMR.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".