Full Battery Pack Modelling: An Electrical Sub-Model Using an EECM for HEV Applications
Bibliographic record
Abstract
With a transition towards electric vehicles for the transport sector, there will be greater reliance put upon battery packs; therefore, battery pack modelling becomes crucial during the design of the vehicle. Accurate battery pack modelling allows for: the simulation of the pack and vehicle, more informed decisions made during the design process, reduced testing costs, and implementation of superior control systems. To create the battery cell model using MATLAB/Simulink, an electrical equivalent circuit model was selected due to its balance between accuracy and complexity. The model can predict the state of charge and terminal voltage from a current input. A battery string model was then developed that considered the cell-to-cell variability due to manufacturing defects. Finally, a full battery pack model was created, capable of modelling the different currents that each string experiences due to the varied internal resistance. The model was then validated with real-life data from the “Hill Route” section of the First Group Millbrook Fuel Economy Test Version 5.0 drive cycle of a mild hybrid electric bus. Results showed a strong correlation with the measured data and both the state of charge and terminal voltage simulations of the model. For the string model, results showed that there was a slight variance in the state of charge between cells in a string with varied capacities. However, terminal voltages between cells did not vary significantly with variances in internal resistance. Future work includes the creation of a thermal sub-model and an ageing sub-model, which considers whether the location of a cell within a pack has a correlation with its degradation. These sub-models will then be integrated and used as a full battery pack model.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".