Route Planning and Power Management for PHEVs With Reinforcement Learning
Bibliographic record
Abstract
In this article, we propose a novel control scheme for route planning with power management for plug-in hybrid electric vehicles (PHEVs). By considering the power management of PHEVs, we aim to find the route that leads to the minimum energy consumption. The scheme adopts a two-loop structure to achieve the control objective. Specifically, in the outer loop, the minimum energy consumption route is obtained by minimizing the difference between the value function of current round and the best value from all previous rounds. In the inner loop, the energy consumption index with respect to PHEV power management for each feasible route is trained with reinforcement learning (RL). Under the RL framework, a nonlinear approximator structure, which consists of an actor approximator and a critic approximator, is built to approximate control actions and energy consumption. In addition, the convergence of value function for PHEV power management in the inner loop and asymptotical stability of the closed-loop system are rigorously studied. Using Toyota Prius as the vehicle model and planning its route in a city with various navigation ranges, we demonstrate the effectiveness of the proposed control scheme with simulation studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".