A Comprehensive Review of the Pseudo‐Two‐Dimensional (P2D) Model: Model Development, Solutions Methods, and Applications
Bibliographic record
Abstract
Abstract Recently, a paradigm shift toward the tremendous use of portable and mobile consumer products is observed and it consequently increases the battery demand. Currently, lithium‐ion batteries (LIBs) are used in almost all kinds of devices, however, the increased usage of LIBs raises some safety concerns. For safe operation, it is necessary to investigate the all important chemical and physical features of LIBs. Several mathematical models are proposed such as equivalent circuit models (ECMs), physics‐based ECM and electrochemical models (EMs). Among them pseudo‐two‐dimensional (P2D) model is most famous and comprehensive physics‐based EM and used frequently to study the battery electrochemical phenomena. Hence, the focus of this review work is to summarize the reported literature on the P2D model governing laws, its simplified models, coupled models, solution techniques, parameter analysis, and its applications. The literature survey shows that the P2D and its simplified models are very helpful to analyze the important aspects of LIBs such as state of charge, state of health, recycling, battery manufacturing and design etc. Also, it can be seen that no single model can be fit to study every aspect of LIBs and design a comprehensive battery management system (BMS) but different models can be used to address the specific applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".