Lifetime Performance Analysis of Imbalanced EV Battery Packs and Small-Signal Cell Modeling for Improved Active Balancing Control
Bibliographic record
Abstract
Conventional battery balancing techniques target fast voltage or state-of-charge convergence while improving circuit-level metrics such as power density, efficiency, and component count. System-level metrics, including prolonged pack cycle life, reduced degradation rate, and increased energy capacity, are often overlooked. In this work, the lifetime discharge energy of imbalanced battery packs is quantified and compared using Monte-Carlo-style battery lifetime transient simulations with experimentally captured drive cycles. Computations were carried out across$>$20 000 core-hours on the Niagara supercomputer at the SciNet HPC Consortium. Imbalance was introduced by sampling the cell capacity and impedance values from normal distributions with up to$\sigma$=5% capacity/impedance variation at the beginning-of-life. Conventional balancing techniques are found to have little lifetime energy benefit over no balancing. A linearized equivalent circuit model (L-ECM) technique is introduced for small-signal analysis of battery packs with arbitrary capacity and impedance imbalance. An L-ECM-based balancing control is found to have a lifetime discharge energy improvement of up to 53.2% in the worst-case pack lifetime energy over no balancing. The L-ECM balancing control is demonstrated experimentally to provide 9.2% increased single-cycle discharge energy compared to no balancing in a Tesla Model S battery module.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".