The Remaining useful life (RUL) prediction is of great concern for the reliability and safety of lithium-ion batteries in electric vehicles (EVs), but the prediction precision is still unsatisfactory due to the unreliable measurement and fluctuation of data. Aiming to solve these issues, an adaptive sliding window-based gated recurrent unit neural network (GRU NN) is constructed in this paper to achieve the precise RUL prediction of LIBs with the soft sensing method. To evaluate the battery degradation performance, an indirect health indicator (HI), i.e., the constant current duration (CCD), is firstly extracted from charge voltage data, providing a reliable soft measurement of battery capacity. Then, a GRU NN with an adaptive sliding window is designed to learn the long-term dependencies and simultaneously fit the local regenerations and fluctuations. Employing the inherent memory units and gate mechanism of a GRU, the designed model can learn the long-term dependencies of HIs to the utmost with low computation cost. Furthermore, since the length of the sliding window updates timely according to the variation of HIs, the model can also capture the local tendency of HIs and address the influence of local regeneration. The effectiveness and advantages of the integrated prediction methodology are validated via experiments and comparison, and a more precise RUL prediction result is provided as well.

The emergence of visible light communication technology has alleviated the pressure of wireless network communication and provided new ways for vehicle networking technology. Therefore, it is necessary to analyze the performance changes in visible light communication in vehicle networking communication. This article mainly considers the impact of vehicle speed on the quality of visible light communication technology services during the driving duration. Firstly, the impact of vehicle speed on signal-to-noise ratio changes was analyzed. Secondly, the channel states in different V2I/V2V scenarios were analyzed. Finally, the transformation in signal-to-noise ratio in V2I/V2V scenarios was analyzed through simulation.

The diagnosis of intermittent faults is crucial in the field of maintenance support. However, most existing studies focus on the analysis of intermittent faults of single components, ignoring the more complex intermittent failures of equipment functions due to the coupling of multivariate anomalous states in the fault propagation process. Existing diagnostic methods based on fault propagation models, which mainly focus on one-dimensional temporal or logical relationships, fall short in representing and reasoning about intermittent faults caused by temporal and state coupling. In this paper, we propose a Temporal Constrained Dynamic Uncertain Causality Graph (TC-DUCG) model to fill this gap and effectively model intermittent faults. The model not only considers the probability of fault propagation among variables, but also integrates temporal constraints. It also presents a diagnostic reasoning process to investigate potential causes of intermittent faults. We provide an illustrative example to demonstrate the effectiveness of the proposed method in diagnosing intermittent faults.