Electric vehicles have become a trend in recent years, and the lithium-ion battery pack provides them with high power and energy. The battery thermal system with air cooling was always used to prevent the high temperature of the battery pack to avoid cycle life reduction and safety issues of lithium-ion batteries. This work employed an easily applied optimization method to design a more efficient battery pack with lower temperature and more uniform temperature distribution. The proposed method consisted of four steps: the air-cooling system design, computational fluid dynamics code setups, selection of surrogate models, and optimization of the battery pack. The investigated battery pack contained eight prismatic cells, and the cells were discharged under normal driving conditions. It was shown that the optimized design performs a lower maximum temperature of 2.7 K reduction and a smaller temperature standard deviation of 0.3 K reduction than the original design. This methodology can also be implemented in industries where the battery pack contains more battery cells.

The appropriate temperature distribution is indispensable to lithium-ion battery module, especially during the fast charging of the sudden braking process. Thermal properties of each battery cell are obtained from numerical heat generation model and experimental data, and the deviation of thermophysical performance is analyzed by K-means clustering and hierarchical clustering to select battery cells with similar performance. Thermal performance of lithium-ion cells under different charging rates is investigated in experiments and the effects of different mini-channel designs discussed using numerical simulation, maximum temperature, maximum pressure, and temperature standard deviation are compared by both numerical calculation and experimental validation. Two kinds of cooling plates are selected, considering the uniformity of temperature distribution and energy consumption, respectively. All of these cooling plate designs have the ability to constrain the maximum temperature and temperature standard deviation within 306 K and 1.2 K, respectively. Additionally, this thermal management system does not need too much energy consumption. In experimental validation, deviation of maximum temperature is measured to be within 2.2 K and difference of temperature standard deviation is also within tolerance.

Industrial pipework maintenance inspection can be automated through machine vision-based effusion monitoring. However, colorless effusions such as water can be difficult to detect in a complex industrial environment due to weak illumination and poor visibility of the background. This paper deploys the reflective characteristics of effusion and its lower temperature compared to the environment in order to develop an automatic inspection system for power plant pipeworks’ maintenance. Such a system is aimed at detecting the colorless fluid effusion based on dual source images and a contour features algorithm. In this respect, a visible light source unit highlights the reflective features of the effusion edge. Meanwhile, high-definition images of the potential effusion are acquired under both visible and infrared lights. A customized image processing procedure extracts the potential effusion features from the infrared image to retrieve the region of interest for segmentation purposes and transfer such information to the visible light image to determine the effusion contour. Finally, a decision-making support tool based on the image contour closure is enabled for classification purposes. The implementation of the proposed system is tested on a real industrial environment. Experimental results show a classification accuracy up to 99%, demonstrating excellent suitability in meeting industrial requirements.

This paper reviews and addresses the principles of linear controller design of the fixed speed wind turbine system in above rated wind speed, using pitch angle control of the blades and applying modern control theory. First, the non-linear equations of the system are built in under some reasonable suppositions. Then, the non-linear equations are linearised at set operating point and digital simulation results are shown in this paper. Finally, a linear quadratic optimal feedback controller is designed and the dynamics of the closed circle system are simulated with digital calculation. The advantages and disadvantages of the assumptions and design method are also discussed. Because of the inherent characteristics of the linear system control theory, the performance of the linear controller is not sufficient for operating wind turbines, as is discussed.

The new energy electric vehicle, which takes clean electric energy as the main driving force, has no pollutants and exhaust emissions during its operation and has a higher energy utilization ratio than the fuel locomotive. Therefore, electric vehicles have been widely developed in recent years. The maximum temperature and temperature consistency of the battery pack in the electric vehicle have a great influence on the life and safety of the battery. In this paper, the thermal management system of the lithium battery pack was taken as the research object. The temperature distribution and uniformity of the battery pack under different heat dissipation conditions were analyzed based on computational fluid dynamics (CFD). The multi-objective optimization method of the battery pack thermal management system was carried out by combining the surrogate model with fast non-dominated sorting genetic algorithm (NSGA-II). The maximum temperature of the battery pack obtained from candidate point 1 is 310.72 K, which is 4.99 K lower than the initial model temperature, and the temperature standard deviation is 0.76 K, with a reduction rate of 51.9%. Experiment results showed that maximum difference between the optimized and experimental value of the maximum temperature is 0.8 K, and the error was within 1 K. Therefore, the multi-objective optimization method proposed in this paper has high accuracy.

In the smartphone manufacturing industry, detecting cover glass defects is crucial to product quality. To address this, this paper proposes DY-YOLO, an enhanced YOLOv8-based model for defect detection on smartphone cover glass. The model improves the accuracy and efficiency of detecting defects on cover glass surfaces in complex production environments. Specifically, the proposed Dynamic-Large Separable Kernel Attention (Dynamic-LSKA) module effectively suppresses interference from complex backgrounds, such as glass reflections, thereby reducing false detections. DY-YOLO integrates several innovations: the Dynamic-LSKA module for enhanced multi-scale perception, the Dynamic-C2f module for enhanced feature extraction, and the Advanced Screening Feature Bidirectional Path Aggregation Network (HSF-BPAN) for efficient fusion of advanced screening features. Additionally, DySample is used as a lightweight dynamic up-sampler to reduce computational cost. Extensive evaluations were conducted using two public benchmarks, Mobile Phone Screen Surface Defect Dataset (MSD) and Smartphone Screen Glass Dataset (SSGD). Results demonstrate that, compared to the baseline model, the proposed method achieves improvements of 1% and 0.6% in mAP@0.5 and mAP@0.5:0.95, respectively, on MSD, reaching 99.3% and 70.9%. On SSGD, the improvements are 4.8% and 2.6%, reaching 46% and 20.2%, respectively, surpassing the state-of-the-art methods in detection accuracy. Moreover, DY-YOLO achieves an excellent balance between performance and efficiency. With a parameter count comparable to the baseline but 33.3% lower computational cost, the model achieves an inference speed of 121.8 FPS, demonstrating its strong potential for real-time edge deployment on production lines. These results confirm the model's effectiveness and potential for industrial applications.