Research on Deep Reinforcement Learning-based Intelligent Car-following Control and Energy Management Strategy for Hybrid Electric Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
摘要： 以研究智能混合动力汽车控制技术与深度强化学习算法为目标，首先，在两辆混合动力汽车的跟驰环境中，针对领航车提出一种基于深度值网络算法的能量管理策略，实现深度强化学习对发动机与机械式无级变速器的多目标协同控制；其次，针对跟随车建立基于深度强化学习的分层控制模型，实现面向智能混合动力汽车的上层跟车控制与下层能量管理；最后，仿真验证分层控制模型的有效性。结果表明，基于深度强化学习的跟车控制策略具有理想的跟踪性能；同时，基于深度强化学习的能量管理策略在领航车与跟随车中均实现了较好的燃油经济性；此外，基于深度强化学习的能量管理策略输出每组控制动作的平均时间为1.66 ms，保证了实时应用的潜力。
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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 | 0.001 | 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 | 0.001 |
| 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 it