Experimental investigation of the flame retardant and form-stable composite phase change materials for a power battery thermal management system

© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.An efficient battery thermal management system (BTMS) will undoubtedlypromote the performance and lifespan of battery packs. In this study, a novelflame-retarded composite PCMs composed by paraffin (PA), expanded graphite (EG), ammonium polyphosphate (APP), red phosphorus (RP) and epoxy resin (ER) has been proposed for battery module. The thermophysical and flame retardant properties are investigated at both macro and micro levels. The results show that the proposed composite PCMs with an APP/RP ratio of 23/10 exhibit the optimum flame retardant performance. Besides, the APP/RP-based composite PCMs for 18650 ternary battery module has also been researched comparing with air cooled and PCM with pure PA modes. The experimental results indicated that the fire retardant PCMs shown significant cooling and temperature balancing advantages for battery module, leading to a 44.7% and 30.1% reduction rate of the peak temperature and the maintenance of the maximum temperature difference within 1.36°C at a 3 C discharge rate for 25°C. Even at 45°C, the temperature uniformity can still be controlled within 5°C. Thus, this research indicates the composite PCM had good flame retardant and form stable properties, it would be utilized in BTMS, energy storage and other fields.Peer reviewe

Structural Optimization and Thermal Management with PCM-Honeycomb Combination for Photovoltaic-Battery Integrated System

© 2022 Xinxi Li et al. This is an open access article distributed under the Creative Commons Attribution License, https://creativecommons.org/licenses/by/4.0/Power lithium–ion batteries retired from the electric vehicles (EVs) are confronting many problems such as environment pollution and energy dissipation. Traditional photovoltaic (PV) battery systems are exhibiting many issues such as being bulky and expensive, high working temperature, and short service span. In order to address these problems, in this study, a novel PV–battery device integrating PV controllers and battery module into an independent device is proposed. Phase change material (PCM) as the energy storage material has been utilized in battery module, and the aluminum honeycomb is combined with PCM to improve the heat conductivity under natural convection conditions. Three types of PV battery systems including the general PV–battery integrated system (G–PBIS), honeycomb PV–battery integrated system (H–PBIS), and honeycomb–paraffin PV–battery integrated system (HP–PBIS) have been investigated in detail. The results reveal that the maximum temperature of the HP–PBIS coupling with the double–layer 10×165×75 mm3 PCM was reduced to 53.72°C, exhibiting an optimum cooling effect among various PV battery systems. Thus, it can be concluded that the aluminum honeycomb provides the structural reliability and good thermal conductivity, and the PCM surrounding battery module can control the temperature rising and balance the temperature uniformly. Besides, the optimum PV–battery integrated system performs a promising future in energy storage fields.Peer reviewedFinal Published versio

Attention guided global enhancement and local refinement network for semantic segmentation

The encoder-decoder architecture is widely used as a lightweight semantic segmentation network. However, it struggles with a limited performance compared to a well-designed Dilated-FCN model for two major problems. First, commonly used upsampling methods in the decoder such as interpolation and deconvolution suffer from a local receptive field, unable to encode global contexts. Second, low-level features may bring noises to the network decoder through skip connections for the inadequacy of semantic concepts in early encoder layers. To tackle these challenges, a Global Enhancement Method is proposed to aggregate global information from high-level feature maps and adaptively distribute them to different decoder layers, alleviating the shortage of global contexts in the upsampling process. Besides, a Local Refinement Module is developed by utilizing the decoder features as the semantic guidance to refine the noisy encoder features before the fusion of these two (the decoder features and the encoder features). Then, the two methods are integrated into a Context Fusion Block, and based on that, a novel Attention guided Global enhancement and Local refinement Network (AGLN) is elaborately designed. Extensive experiments on PASCAL Context, ADE20K, and PASCAL VOC 2012 datasets have demonstrated the effectiveness of the proposed approach. In particular, with a vanilla ResNet-101 backbone, AGLN achieves the state-of-the-art result (56.23% mean IoU) on the PASCAL Context dataset. The code is available at https://github.com/zhasen1996/AGLN.Comment: 12 pages, 6 figure

Med-Tuning: Exploring Parameter-Efficient Transfer Learning for Medical Volumetric Segmentation

Deep learning based medical volumetric segmentation methods either train the model from scratch or follow the standard "pre-training then finetuning" paradigm. Although finetuning a well pre-trained model on downstream tasks can harness its representation power, the standard full finetuning is costly in terms of computation and memory footprint. In this paper, we present the first study on parameter-efficient transfer learning for medical volumetric segmentation and propose a novel framework named Med-Tuning based on intra-stage feature enhancement and inter-stage feature interaction. Given a large-scale pre-trained model on 2D natural images, our method can exploit both the multi-scale spatial feature representations and temporal correlations along image slices, which are crucial for accurate medical volumetric segmentation. Extensive experiments on three benchmark datasets (including CT and MRI) show that our method can achieve better results than previous state-of-the-art parameter-efficient transfer learning methods and full finetuning for the segmentation task, with much less tuned parameter costs. Compared to full finetuning, our method reduces the finetuned model parameters by up to 4x, with even better segmentation performance

Experimental investigations on the correlations between the structure and thermal-electrochemical properties of over-discharged ternary/Si-C power batteries

© 2021 John Wiley & Sons Ltd. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1002/er.7274The thermal safety of power lithium-ion batteries（LIBs） has seriously affected the booming development of electric vehicles (EVs). Especially, owing to the requirement of high energy density, thermal runaway (TR) easily occurs in LIBs, resulting in a higher heat generation rate. Over-discharging is recognized as a common cause for TR. In the present research, the correlations between the structure and thermal-electrochemical properties of an over-discharged ternary/Si-C battery at room and high temperatures were investigated. The heat generation mechanisms of the batteries due to the maximum surface temperature and peak temperature difference variations during fast charging and discharging processes were investigated. Moreover, the electrochemical performances parameters of the batteries, such as voltage changing trend, discharge time, discharge capacity, internal resistance, electrochemical impedance spectroscopy (EIS) spectra, were analyzed. When the battery was discharged at 2.0C and 55°C, its maximum temperature and highest temperature difference reached 91.34°C and 13.24°C, respectively, finally resulting in a sharp decline in electrochemical performance. Furthermore, the root reasons for performance degradation and heat generation intensification of the over-discharged battery (ODB) were analyzed by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The cause of the aforementioned phenomenon is due to irreversible damage of the electrode materials. This research not only reveals the relevant relationship between the thermal behavior and the microscopic structure of the over-discharged ternary/Si-C battery under various temperature conditions but also provides valuable insights for improving the safety of LIBs modules even packs.Peer reviewe

Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise complexity. As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i.e. Med-DANet) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices. However, the issues of incomplete data analysis, high training costs, and the two-stage pipeline in Med-DANet require further improvement. To this end, this paper further explores a unified formulation of the dynamic inference framework from the perspective of both the data itself and the model structure. For each slice of the input volume, our proposed method dynamically selects an important foreground region for segmentation based on the policy generated by our Decision Network and Crop Position Network. Besides, we propose to insert a stage-wise quantization selector to the employed segmentation model (e.g. U-Net) for dynamic architecture adapting. Extensive experiments on BraTS 2019 and 2020 show that our method achieves comparable or better performance than previous state-of-the-art methods with much less model complexity. Compared with previous methods Med-DANet and TransBTS with dynamic and static architecture respectively, our framework improves the model efficiency by up to nearly 4.1 and 17.3 times with comparable segmentation results on BraTS 2019.Comment: Accepted by WACV 202

Experimental Investigation on Thermal Management of Electric Vehicle Battery Module with Paraffin/Expanded Graphite Composite Phase Change Material

The temperature has to be controlled adequately to maintain the electric vehicles (EVs) within a safety range. Using paraffin as the heat dissipation source to control the temperature rise is developed. And the expanded graphite (EG) is applied to improve the thermal conductivity. In this study, the paraffin and EG composite phase change material (PCM) was prepared and characterized. And then, the composite PCM have been applied in the 42110 LiFePO4 battery module (48 V/10 Ah) for experimental research. Different discharge rate and pulse experiments were carried out at various working conditions, including room temperature (25°C), high temperature (35°C), and low temperature (−20°C). Furthermore, in order to obtain the practical loading test data, a battery pack with the similar specifications by 2S∗2P with PCM-based modules were installed in the EVs for various practical road experiments including the flat ground, 5°, 10°, and 20° slope. Testing results indicated that the PCM cooling system can control the peak temperature under 42°C and balance the maximum temperature difference within 5°C. Even in extreme high-discharge pulse current process, peak temperature can be controlled within 50°C. The aforementioned results exhibit that PCM cooling in battery thermal management has promising advantages over traditional air cooling

Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier

Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and supervised classification methods. However, a high-performance classifier is desirable but challenging due to the existence of model hyperparameters. Conventional approaches generally rely on manual tuning, which is time-consuming and far from satisfying. Therefore, this work aims to propose a systematic method to automatically tune the hyperparameters by Bayesian parameter optimization for the random forest classifier. The recently launched Sentinel-2A/B satellites are drawn to provide the remote sensing imageries for land cover classification case study in Beijing, China, which have the best spectral/spatial resolutions among the freely available satellites. The improved random forest with Bayesian parameter optimization is compared against the support vector machine (SVM) and random forest (RF) with default hyperparameters by discriminating five land cover classes including building, tree, road, water and crop field. Comparative experimental results show that the optimized RF classifier outperforms the conventional SVM and the RF with default hyperparameters in terms of accuracy, precision and recall. The effects of band/feature number and the band usefulness are also assessed. It is envisaged that the improved classifier for Sentinel-2 satellite image processing can find a wide range of applications where high-resolution satellite imagery classification is applicable