In order to avoid high-temperature and large rate discharge impact on the performance of battery modules, a microchannel liquid cooling battery thermal management system (BTMS) and BTMS virtual model of the microchannel structure based on digital twin (DT) is proposed. On the basis of accurate virtual simulation model, the computational fluid dynamics (CFD) model and the Gaussian process regression algorithm were combined to drive the optimization process in order to improve the cooling capacity of the system. The results show that the microchannel plates can greatly enhance the cooling capacity of the direct cooling system and effectively improve the uniformity of the coolant. The width of the microchannel plates and the side spacing actually represent the amount of coolant flowing through the inside and outside of the battery module, which significantly impacts the maximum temperature and maximum temperature difference. Increasing the coolant flow can only effectively improve the cooling capacity of the module to a limited extent. Gaussian process regression based on the DT virtual model is more suitable for analyzing the interaction between multiple factors and obtaining global optimization results. After optimization, the maximum temperature and the maximum temperature difference of the system are reduced by 4.02 °C and 5.05 °C, respectively. The proposed structure and method are expected to provide insights into the design and development of battery thermal management systems.

Condition monitoring (CM) is the escort of smart manufacturing, which guarantees safety, precision, and efficiency of production process. However, traditional CM capacities with limited storage and computing resources cannot follow the explosive growth of manufacturing data. Empowered by the emerging Internet of Things, cyber-physical system, and cloud computing, cloud manufacturing (CMfg) characterizes a new service-oriented technology, which is reshaping the CM paradigm into an agile, scalable, and interoperable mode. In this article, a CMfg-based CM platform for smart manufacturing is proposed. A 5G wireless communication network-based edge computing system is presented to achieve data acquisition, feature extraction, and emergency response with ultrareliability and low-latency communication. In order to deal with the issue of data management and analysis, a generic open platform communications unified architecture information model oriented to the workshop is proposed in the fog layer. The cloud monitoring platform is constructed encompassing message delivery, data storage, status visualization, and training of artificial intelligence models. Finally, a CMfg-based CM system and a CMfg-based tool wear estimation system are outlined as two industrial cases. The experiments verify the feasibility and effectiveness of the proposed platform.

Due to poor predictability of resources and difficulty in perception of task execution status, traditional Automatic Guide Vehicle (AGV) scheduling systems need a lot of extra time in the charging process. To solve this problem, a digital twin-based dynamic AGV scheduling (DTDAS) method is proposed, including four functions, namely the knowledge support system, the scheduling model, the scheduling optimization, and the scheduling simulation. With the features of virtual reality data interaction, symbiosis, and fusion from the digital twin technology, the proposed DTDAS method can solve the AGV charging problem in the AGV scheduling system, effectively improving the operating efficiency of the workshop. An AGV scheduling process in a discrete manufacturing workshop is taken as a case study to verify the effectiveness of the proposed method. The results show that, compared with the traditional AGV scheduling method, the DTDAS method proposed in this article can reduce makespan 10.7% and reduce energy consumption by 1.32%.

Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network.