Safe Deep Reinforcement Learning for Energy Management of Electrified Vehicles: Optimal Action Filtering and Battery-in-the-Loop Validation
Bibliographic record
Abstract
Despite the promising performance of deep reinforcement learning (DRL)-based energy management systems (EMS) for electrified vehicles, persistent safety concerns regarding control actions hinder their real-world deployment. This paper proposes a safe DRL-based EMS for hybrid electric vehicles (HEVs) that guarantees zero safety violations during both training and deployment. A model-based safety layer is developed with prior knowledge to filter unsafe actions with minimal disruption to agent exploration. This safety metric evaluation is embedded into the reward function via a Lagrangian relaxation method, enabling adaptive penalization of constraint violations and prompting efficient policy learning. A safe soft actor-critic (SSAC) EMS is then trained under stochastic conditions, including random initial battery state-of-charge (SOC) and multi-modal driving cycles. The approach is validated through simulation and battery-in-the-loop (BIL) experiments. The proposed reward formulation accelerates safety-aware policy learning by 38.5% compared to conventional methods. In BIL tests, SSAC-EMS achieves over 95% fuel efficiency relative to the offline dynamic programming (DP) benchmark under unseen driving cycles and initial SOCs, with actions consistently respecting safety constraints. Compared to the adaptive equivalent consumption minimization strategy (AECMS), SSAC-EMS improves fuel economy by 6–10% while delivering more stable SOC regulation and smoother engine operation.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| 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".