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In order to address the issue of metering inaccuracies in charging stations that directly affect the development of electric vehicles, a prediction method for the relative error of charging stations based on the ConvFormer model is proposed. The model combines Convolutional Neural Networks (CNN) with Transformer models in parallel, significantly improving the prediction accuracy. First, charging station data is preprocessed using forward interpolation and normalization methods, and the dataset is transformed into a dataset of input relative errors. Then, a neural network with an improved unidirectional convolutional and attention combination for time-series forecasting is constructed, and common regression performance evaluation metrics, MAE (Mean Absolute Error) and MSE (Mean Squared Error), are selected for evaluation. Finally, based on seven days of charging station data, the relative error of charging stations for the next 24 h is predicted, and compared to traditional Transformer and LSTM (Long Short-Term Memory) time-series models. The results show that the improved model yields the lowest values for both MAE and MSE, with a 47.30 % reduction in MAE compared to the Transformer model and a 38.06 % reduction compared to LSTM, and a 66.94 % reduction in MSE compared to the Transformer model and approximately 62.32 % reduction compared to LSTM.

The mechanical properties of titanium alloys are greatly affected by their microstructural composition, with a complex relationship between strength and plasticity. This study examines the impact of aging treatment on twinning behavior and its consequences on the mechanical characteristics of a hot-rolled Ti-0.3Mo-0.8Ni-0.5Al-0.5Zr alloy. By analyzing the changes in microstructure, mechanical properties, and crystallographic features during aging, the study reveals that aging results in the formation of lamellar α phases within the matrix grains. This process increases the lamellar α phase and reduces the average grain size with longer aging periods, promoting microstructural changes that enhance twinning and modify grain boundary features. Specifically, there is a decrease in medium angle grain boundaries (MAGBs) and an increase in low angle grain boundaries (LAGBs). Tensile tests show that although the tensile strength decreases compared to the hot-rolled alloy, plasticity significantly improves. The twinning behavior induced by aging helps enhance the tensile strength while maintaining ductility as aging progresses. Additionally, the study investigates how grain lattice orientation and aging twinning affect mechanical properties. Grains with high Schmid Factor (SF) values (>0.4) are more likely to activate multiple slip systems, leading to a uniform strain distribution and enhanced ductility. Overall, aging treatment of the Ti-0.3Mo-0.8Ni-0.5Al-0.5Zr alloy brings about beneficial microstructural changes that improve its mechanical properties, particularly enhancing strength and ductility. This research provides valuable insights into the interplay among microstructure, crystallography, and mechanical performance, essential for advancing and optimizing titanium alloys for various applications.