Impact of Temperature on the A123 Li-Ion Battery Performance and Hybrid Electric Vehicle Range
Bibliographic record
Abstract
Within the last decade, the automotive industry has made major progress toward the electrification of drive trains and application of electrochemical power sources. Among available storage solutions, Li-ion batteries are considered as the most attractive and are set to be used in the next generation of hybrid and electric vehicles. This is due to their superiority in energy density, power density, and low self-discharge and high cycle life compared to other chemistries. However, there are some limitations associated with Li-ion battery; among them is the operating temperature range. Any deviation from a narrow temperature range may result in low overall performance and potential degradation of the cells. In this paper, impact of ambient temperature on the A123 Li-ion batteries performance is investigated. A123 cells have been tested under constant charge-discharge cycles, hybrid pulse power characterization (HPPC) tests and also standard drive cycle tests. Impact of temperature on the cell capacity, open circuit voltage (OCV) and internal resistance is discussed. Afterwards, a model of series hybrid Malibu developed in Autonomie software is presented. The battery model in Autonomie software has been calibrated and validated based on the conducted experiments. Finally, variations of the vehicle range and efficiency with the battery pack experiencing different temperatures is discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".