Editorial: Thermal management of electrochemical energy devices or systems
Bibliographic record
Abstract
Thermal Management of Electrochemical Energy Devices or SystemsIn the past year, we launched a Research Topic entitled Thermal Management of Electrochemical Energy Devices or Systems, and it is our pleasure to summarize the main findings in these accepted articles.Thermal management is of primary importance in affecting the capacity fade (performance loss) of Li-ion batteries in electric vehicles (EVs).Carnovale and Li investigated several thermal management methods/strategies on the capacity fade of Li-ion batteries using a validated integrated electrochemical-transport-thermal model, which includes three sub-models: battery performance model, degradation model and thermal model.The solid-electrolyte interface (SEI) film formation and growth and active material loss in the electrodes were considered in investigating the degradation Research Topic.They found that the temperature has a determinative influence on the battery capacity fade and it can be effectively controlled by adopting proper thermal management methods/strategies for heat dissipation, which is much more effective when the battery temperature is close to 20 ° C (also the optimal battery operation temperature) and can increase the battery lifetime by as much as 25%.Moreover, lower charge voltage induces less capacity fade over cycling.Flow fields play a significant role in affecting the performance of all-vanadium redox flow battery by promoting the uniform distribution of the electrolyte into the electrode and reducing the pumping power.Currently, the most common flow field configurations are serpentine flow field (SFF) and interdigitated flow field (IFF), but the superiority of SFF and IFF is unclear, which is affected by a variety of parameters.Duan et al. investigated the performance characteristics of flow batteries with IFF and SFF under various electrode parameters and operating conditions.It was found that the battery with IFF exhibits lower pressure drop and pumping power because of the shunt effect of IFF.However, the SFF outperforms IFF in promoting the uniform distribution of the electrolyte when the electrode porosity is higher than 810, but it is reversed when the electrode porosity decreases to below 714, indicating that there may be a performance reversal between SFF and IFF at different electrode porosities.Moreover, the current density, electrolyte input, and electrode thickness also lead to performance reversal
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.032 | 0.023 |
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".