Reinforcement Learning Based Integrated Energy and Thermal Management System for Electric Vehicles Considering Battery Aging and Cabin Comfort
Bibliographic record
Abstract
Electric vehicles (EVs) are paving the way toward a sustainable future by reducing carbon footprints and gaining widespread global acceptance; effectively utilizing the EV battery's power is critical. This paper explores an integrated approach toward simultaneous thermal and energy management (IETM) in battery electric vehicles. A Deep Reinforcement Learning agent is used for the proposed EMS, considering battery health, passenger comfort and thermal requirements. Driving pattern recognition is integrated into the EMS using Fuzzy C Means Clustering and velocity predictor based on the 'Inverted Transformer'. The iTransformer achieves RMSE improvements of 32.1 % and 36.4 % over LSTM for the UDDS and WLTP cycles, respectively. The IETM methodology is presented along with a discussion of the HVAC, cabin, battery, and vehicle models. In hot ambient conditions, the IETM controller registers improvements in HVAC energy consumption of$\mathbf{2. 7 2 \%}$and$\mathbf{8 \%}$, and in battery degradation of 2.5 % and 3.7 %, compared to MPC and PID controllers, respectively.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".