Welding quality directly affects the welding structure’s service performance and life. Hence, the effective monitoring welding defects is essential to ensure the quality of the weld structure. Owing to the non-uniformity of the shape, position and size of welding defects, it is a complicated task to analyze and evaluate the acquired welding defects images manually. Fortunately, deep learning has been successfully applied to image analysis and target recognition. However, the use of deep learning to identify welding defects is time-consuming and less accurate due to the lack of adequate training data samples, which easily cause redundancy into the classifier. In this situation, we proposed a new transfer learning model based on MobileNet as a welding defect feature extractor. By using the ImageNet dataset (non-welding defect data) to pre-train a MobileNet model, migrate the MobileNet model to the welding defects classification field. This article suggested a new TL-MobileNet structure by adding a new Full Connection layer (FC-128) and a Softmax classifier into a traditional model called MobileNet. The entire training process of TL-MobileNet model has been successfully optimized by the DropBlock technology and Global average pooling (GAP) method. They can effectively accelerate the convergence rate and improve the classification network generalization. By testing the proposed TL-MobileNet on the welding defects dataset, it turned out our model prediction accuracy has arrived at 97.69%. The experimental results show that in several aspects, TL-MobileNet have better performance than other transfer learning models and traditional neural network methods.

The lithium-ion battery plays a crucial role in the power supply of the electric vehicles (EVs). Battery remaining useful life (RUL) is critically vital to ensure the vehicles' safety and reliability. Due to the complicated aging mechanism, predicting RUL for the battery management systems is challenging. In this paper, a novel degradation indicator was constructed using the information extracted from the discharge voltage. The indicator reflected the complete and effective energy information from the voltage signals to reveal battery degradation characteristics. Additionally, an innovative FRGM-UPF framework was developed for RUL prediction in this study. To improve the accuracy and traceability of prediction, the framework adopted a novel fractional grey model (FRGM) to update the state transition equation in UPF. Meanwhile, the UPF was employed to extrapolate trends of the indicator and achieve the RUL prediction. The performances of FRGM-UPF with the degradation indicator were synthetically verified by the data from various types of batteries under different aging tests. The experimental results indicated that the proposed method could achieve precise prediction results and had a wide range of practicability and universality. The developed technologies could be incorporated with the other control algorithms for application in BMS of EVs.

Battery state-of-health (SoH) monitoring is of great importance to ensure the safety and reliability of battery systems. This study proposed an innovative SoH estimation method using hierarchical extreme learning machine (HELM) to improve the estimation robustness and accuracy without the complex parameter model was directly applied to establish the HELM-oriented online SoH estimation framework. First, the increase in mean ohmic resistance was constructed as a novel health indicator (HI) to characterize battery aging. Then, the HI was adopted for offline training to build an HELM model, which captures the underlying correlation between the extracted HI and capacity degradation. Finally, the datasets of four batteries at three different temperatures with dynamic loading profiles were used for validation. The results show that the SoH estimation errors are no more than 1.5%, while the training and estimation datasets are from the same temperature; when the SoH estimation is conducted at different temperatures, the maximum error is only 3.36%. The results indicate that the proposed method had good generalization and reliability for SoH estimation, which is applicable for dynamic scenarios with different temperatures.

Binocular vision measurement benefits from high prediction robustness and low structural complexity. However, there are still significant flaws in its accuracy. In this paper, binocular vision measurement of a rectangular workpiece is investigated. A new precise measurement method based on binocular vision is designed to achieve precise measurement of rectangular workpiece dimensions. Firstly, an algorithm for workpiece location based on Zernike moments and corner matching is proposed and employed to precisely locate the workpiece and extract the sub-pixel coordinates of discrete points on an image’s edge. Then, a novel stereoscopic matching algorithm combined with epipolar-geometry and cross-ratio invariance (CMEC) is proposed to improve the accuracy of binocular vision stereoscopic matching. Finally, a projection plane is introduced after the 3D reconstruction of discrete points in the workpiece contours by fitting the plane with least squares. The projection plane limits the coordinate fluctuations of discrete points. Furthermore, the data screening is used to further improve the accuracy of size calculation. The experimental results of the standard checkerboard and actual workpiece show that CMEC’s matching accuracy reached 99%, and the proposed method’s measurement accuracy reached 0.018 mm. This work presents a novel algorithm for stereoscopic matching in binocular vision and machine vision measurement.

The capacity/state‐of‐charge (SoC) and voltage of lithium–ion batteries are of prime importance in electric vehicles (EVs), so their condition‐monitoring techniques are extensively studied. This study focuses on the application of the grey system theory to the parameters analysing and predicting behaviour during the discharge/charge cycles of the battery. First, Grey relation analysis is applied to study and analyse the relationship between capacity/SoC and various influencing factors. Second, the segment Grey prediction model is proposed in order to test and improve the accuracy of the capacity/SoC prediction. Finally, based on the ageing data from the National Aeronautics and Space Administration Prognostics Data Repository, the effects of different Grey theory models, such as the GM(1,1), the Verhulst model and the segment Grey prediction model, are investigated. The results show that: (i) the GRA is efficient in figuring out the relationship between the capacity/SoC and various influencing factors; (ii) the segment Grey prediction model is an effective mode of prediction for EV batteries, because its accuracy is more reliable than other two Grey models; and (iii) the segment Grey prediction model is suitable for predicting the capacity/SoC of batteries under various loading conditions.