Enhancing Energy Management Strategy for Battery Electric Vehicles: Incorporating Cell Balancing and Multi-Agent Twin Delayed Deep Deterministic Policy Gradient Architecture
Bibliographic record
Abstract
This paper introduces a real-time multi-objective adaptive Energy Management Strategy (EMS) based on a Multi-Agent Reinforcement Learning (MARL) architecture. Leveraging Twin Delayed Deep Deterministic Policy Gradient (TD3) methods, this EMS continuously monitors the system, striking a balance between front and rear electric drive operations, cell balancing in batteries, and other crucial parameters affecting battery aging. It not only meets driver requirements but also determines the optimal power levels for Electric Motors (EMs), reducing battery depletion and aging. Validation employs a 2021 Motor Vehicle Challenge model with two electric motors. Results indicate the advantages of the proposed EMS, meeting driver power needs across diverse environmental conditions. Furthermore, it achieves a final state of charge (SOC) within a mere 0.3% deviation from the Dynamic Programming (DP) approach. The EMS excels by effectively balancing battery cells and optimizing temperature, mitigating long-term battery aging. Importantly, it outperforms the highest reported SOC value in the 2021 Motor Vehicle Challenge while satisfying all specified criteria.
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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 | 0.002 | 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 | 0.001 |
| 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 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".