Sizing of a Battery Pack Based on Series/Parallel Configurations for a High-Power Electric Vehicle as a Constrained Optimization Problem
Bibliographic record
Abstract
The design of electric vehicle batteries is a challenge requiring significant computational, human and material resources. This paper proposes a new framework to design a battery pack that allows quick iterations between designs and computes optimized configurations to give more information about the best available solutions. The battery pack is represented as a series/parallel configuration in a Constraint Satisfaction Problem (CSP) that is solved to exploit every possible configuration for specific vehicle requirements. The result is then used in a multi-objective Constrained Optimization Problem (COP) to determine the non-dominated (Pareto optimal) solutions according to selected objectives functions. Non-dominated solutions are computed by optimizing weighted-sum scalar functions as well as minimizing different types of distances to a design reference. A case study is explored to design a battery pack for an electric motorcycle. A 24s/6p battery module designed with this methodology has been validated by experiments and the battery pack has successfully been used at MotoStudent in 2018.
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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 | 0.001 | 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".