Ultrathin Heat Spreader Thermal Management of Lithium-ion Batteries for EV and Energy Storage
Bibliographic record
Abstract
Lithium ion (Li-ion) batteries are low cost and have a high energy density and a small volume. Thus, battery packs comprised of multiple Li-ion batteries have become the dominant energy source for electric vehicles (EV) and hybrid electric vehicles (HEV), and have contributed to their current popularity, as has fast-charge technology, which is also used in EVs and HEVs. However, due to the high power requirement of EVs and HEVs under high-speed operating conditions or fast charging conditions, Li-ion battery packs suffer from high temperatures if no appropriate thermal management system is installed, leading to battery performance degradation and even thermal runaway [1]. The energy efficiency, safety, and life of power batteries, such as lithium-ion batteries, are very sensitive to temperature, and the performance and stability of Liion batteries are reduced in the abnormal temperature range [2]. The temperature change of batteries is usually inevitable because they are affected by environmental conditions and release heat by a series of chemical reactions during charging and discharging. Therefore, it is essential to develop a smart thermal management system that maintains the proper temperature range for power batteries. In this talk, key technologies for a smart thermal management system of power batteries for electric vehicles and energy storage system will be presented. The key technologies are based on our recent findings that utilize multiscale micro/nanostructured surfaces for integrated wicks [3]. These surfaces manipulate the nucleation site density that controls the heat transfer coefficient and critical heat flux for the evaporator of a heat spreader [4, 5]. The multiscale micro/nanostructured wick design and micro/nano multiscale structure fabrication techniques are crucial for controlling the capillary flow and evaporation that improve the effective thermal conductivity of the thermal management system for power batteries. We presented a novel technique for the thermal management of power batteries utilizing ultrathin heat spreaders. Temperature significantly affects the energy efficiency, safety, life, and performance of a lithium-ion battery pack in electric vehicles (EVs). Therefore, controlling the temperature of the battery pack within a certain range has become a challenge in the development of EVs, especially in fast charging with high charge rates (C-rates). An ultrathin thermal ground planebased battery thermal management system was developed, which utilized 0.4 mm thick ultrathin thermal ground planes and cooling fans as a heat sink. The thermal performance of the novel battery thermal management system was experimentally investigated at 2.2 C to 4 C FC regimes under environmental temperatures from 10 ℃ to 50 ℃. The battery thermal management system was able to maintain a mean surface temperature of 55Ah lithium iron phosphate (LiFeO_4, LFP) batteries below 42.7 ℃ even at a 4 C charge rate and achieve good surface temperature uniformity in all cases. At an ambient temperature as high as 50 ℃, the battery thermal management system can still maintain the mean battery surface temperature under 57.3 ℃. The temperature rise, temperature uniformity, and thermal resistance gained improvements of up to 23.3%, 28.4%, and 62.6%, respectively, compared to a battery thermal management system with the same dimensions as copper heat spreaders. The effects of different pores densities of the mesh in the ultrathin thermal ground plane were also studied. The battery thermal management system showed brilliant performance in controlling the temperature of the battery pack, which was capable of being a viable solution for high-power battery thermal management in EVs.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".