Thermal Analysis of High-Power Prismatic LiFePO<sub>4 </sub>batteries: Modeling and Experiment
Bibliographic record
Abstract
A 90Ah prismatic LiFePO4/graphite battery (Sinopoly Battery Ltd., China) is used for the experiments and thermal modeling [cf. Fig. 1]. In the experimental study, a power processing system is employed to cycle the experimental battery. Constant-current discharge tests at 1C-rate (90A), 2C-rate (180A), and 3C (270A) are performed to measure the battery voltage and surface temperatures. In the modelling study, a three-dimensional transient model has been developed to predict the thermal behaviour of the battery during galvanostatic processes. A transient heat conduction equation is sufficient to describe the thermal phenomenon of domain (1); while the heat transfer mechanism in domain (2) is modelled by the natural convection. A convective-radiation boundary condition is considered at the boundaries of the domain. The above-mentioned models are performed in a finite element solver (COMSOL Multiphysics, Version 4.4), and the accuracy of the model is validated through comparison with the experimental data. Some Results The rate of heat generation which is the main parameter calculation in the thermal model, is approximated from [1], g=I/ν[(V-V oc )+T dV oc/dT] (1) where I and V denote the battery operational voltage and current, respectively, ν represents the battery volume, V oc is the open circuit voltage of the battery and T is temperature. The heat generation terms can be determined by using experimental tests or electrochemical models, which the first approach is applied in this study. A constant value of -0.4337 mV/K adapted from Ref. [2], is considered for term dV oc/dT. The rate of heat generation caused by the polarization losses, V-Voc , is defined from the measured electrical performance of the battery. The heat generation rates inside the battery (domain (1)) during three different galvanostatic discharge processes (90 A, 180 A, and 270 A) are calculated based on Eq. (1) and the battery electrical performance, and then the obtained expressions are implemented into the thermal model. In Fig. 2, the modeling results for average surface temperature of the battery (shown by solid lines) are compared to the corresponding experimental data (symbols), during constant-current discharge processes with 90 A, 180 A, and 270 A. The comparison shows an excellent agreement between the predicted results and the data from the experiments. In future, an electrical model will be coupled to the presented model to describe the ohmic heating in the electrodes as a result of the electrical constriction resistance. [1] D. Bernardi, J. Electrochem. Soc., Vol. 132, pp. 5-12, (1985). [2] A. Samba et al. , J. Electrochimica Acta, Vol. 117, pp. 246–254, (2014). [3] P. Taheri et al., J. Electrochem. Soc., Vol. 160, pp. A1731–A1740, (2013).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".