The charging load forecasting is of significant importance to the economic operation of charging stations and the stable operation of power systems. The charging stations couple power systems with transportation systems. Their charging loads are not only affected by the driver’s driving and charging behavior simultaneously, but also by the above two networks. There is much literature reporting load forecasting combined with historical charging loads in temporal dimension. However, the spatial data of charging stations in the neighboring areas is helpful for load forecasting, as they share common conditions, including traffic and weather factors. This paper proposes load forecasting of electric vehicle charging stations based on the spatiotemporal multi-graph convolutional networks (STMGCN). STMGCN contains three components: gated dilated causal convolution, spatiotemporal attention mechanism, and multi-graph convolutional layer. Firstly, the load model based on graph structure is established according to the historical load and geographical information of charging stations. Then, a load forecasting method of multiple charging stations is proposed, which can effectively share spatiotemporal relationships among stations. Finally, experiments on real-world dataset illustrate that STMGCN is capable of improving the accuracy of charging stations load forecasting compared with the baselines, which shows the effectiveness of the model.

This study establishes for the first time a P2D-coupled non-ideal double-layer capacitor model (P2D-CNIC), which can be used for mechanism analysis under high-frequency periodic signal excitation. The novelty of this work is the consideration of the generally neglected electric double-layer capacitance and its dispersion effects, especially the capacitance of the solid electrolyte interface (SEI) film. The dispersion effect of the model is verified by a periodic current excitation signal and the corresponding phase change in the voltage response. Under sinusoidal alternating current (AC) excitation, a comparative analysis was conducted between the traditional P2D model, the traditional P2D model coupled with the ideal double-layer capacitor (P2D-CIC), and the proposed P2D-CNIC mechanism model. Furthermore, three models were evaluated under periodic short-circuit pulse discharge conditions to verify the accuracy and reliability of P2D-CNIC. The simulation results are used to analyze the dominant order of faradaic and non-Faraday processes under sinusoidal AC excitation, thereby providing insights into the internal mechanism analysis of lithium batteries under high-frequency cycling conditions.

This study proposes a charging efficiency calculation model based on an equivalent internal resistance framework. A data-driven neural network model is developed to predict the charging efficiency of lithium titanate (LTO) batteries for 5% state of charge (SOC) segments under various charging conditions. By considering the impact of entropy change on the open-circuit voltage (OCV) during the charging process, the accuracy of energy efficiency calculations is improved. Incorporating battery data under various charging conditions, and comparing the predictive accuracy and computational complexity of different hyperparameter configurations, we establish a backpropagation neural network model designed for implementation in embedded systems. The model predicts the energy efficiency of subsequent 5% SOC segments based on the current SOC and operating conditions. The results indicate that the model achieves a prediction error of only 0.29% under unknown charging conditions while also facilitating the deployment of the neural network model in embedded systems. In future applications, the relevant predictive data can be transmitted in real time to the cooling system for thermal generation forecasting and predictive control of battery systems, thereby enhancing temperature control precision and improving cooling system efficiency.