Co-Optimization of Adaptive Cruise Control and Hybrid Electric Vehicle Energy Management With Clutch Engagement Decision Control via Reinforcement Learning
Bibliographic record
Abstract
The co-optimization of adaptive cruise control (ACC) and energy management strategy (EMS) can improve the energy-saving capability of a hybrid electric vehicle (HEV). Co-optimization for a series-parallel HEV involves continuous control variables, such as the engine speed and torque, and discrete control variables, such as the clutch engagement decision. To solve the mixed-integer optimal control problem, we propose a hybrid-action reinforcement learning (HARL) algorithm called parameterized proximal policy optimization (PA-PPO) for series-parallel HEV co-optimization of ACC and EMS to minimize the clutch engagement/disengagement times, energy consumption, and guarantee the car-following performance. Firstly, the ACC model and the control-oriented HEV EMS model considering the clutch engagement decision control are constructed and combined to form the co-optimization model. Secondly, the PA-PPO algorithm is proposed and trained to solve the co-optimization problem. Compared to model predictive control (MPC), the proposed PA-PPO algorithm achieves similar fuel consumption while significantly reducing clutch engagement/disengagement times and jerk values. In addition, the computation time of PA-PPO is five-orders-of-magnitude smaller than MPC. Finally, the PA-PPO control performance is validated using a Simulink high-fidelity model (HFM).
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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 | 0.000 |
| 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".