Driving-Aware Battery Thermal Management System in Electric Vehicles: Incorporating Cell Discharge Rate, Temperature, and Aging
Bibliographic record
Abstract
This article presents a novel approach to battery thermal management control in electric vehicles (EVs), focusing on the establishment of a power loss model that incorporates temperature and aging effects on internal resistance, thereby enabling accurate estimation of battery power loss for optimized battery thermal management systems (BTMS). In addition, this article introduces a BTMS design capable of both heating and cooling, aiming to maintain optimal battery temperature and enhance battery efficiency and longevity. The proposed methodology includes an offline optimization layer to improve battery longevity and BTMS energy efficiency and an online control layer to maintain a safe battery temperature operation. The adaptability of this BTMS design for real-time applications in various climates is achieved by integrating discharge rate (c-rate) information from the drive cycle. This results in a two-level, driving-aware BTMS control system tailored to varying driving patterns specific to commuter applications. Consequently, this research significantly advances EV battery thermal management by addressing key challenges such as reducing power loss estimation error by up to 28%, optimizing temperature regulation, improving power efficiency by up to 7 kJ for different drive cycles, and enhancing battery aging by more than 3% per life cycle, while ensuring adaptability to various driving patterns for commuters.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".