Most safety problems of the lithium-ion battery are attributed to internal short circuits in the battery. There are many factors leading to the internal short circuit of Li-ion battery, and this paper makes the experimental repeatability and controllability of the internal short circuit of the battery better by establishing the mechanism model simulation. In the process of internal short-circuit heat generation in the battery, the battery thermal effect affects the electrochemical reaction of the battery, generating a larger short-circuit current, which releases more short-circuit heat. Therefore, a ternary battery electrochemical-thermal-internal short-circuit coupling mechanism model is established based on the characteristic connection between the three factors. Finally, a lithium-ion battery internal short-circuit diagnosis model is established by combining deep learning algorithms. Six evaluation parameters, including model training time, convergence speed, accuracy, precision, recall and F-value, are also compared, and the effectiveness of convolutional neural network (CNN) and long short-term memory neural network (LSTM) for classification and diagnosis of the severity of internal short circuit in batteries is compared: the CNN model gets better results for classification and diagnosis of internal short circuit in batteries. And lays the foundation for online diagnosis of battery internal short circuit.

With the increasing number of distributed power sources such as photovoltaic power and wind power and electric vehicles connected to the grid, the structure and operation state of the traditional distribution network have undergone great changes. Therefore, through the establishment of a distributed power grid-connected evaluation system, it has become an important research topic to evaluate the access of distributed power and the carrying capacity of electric vehicles in the distribution network of new power systems. Firstly, the situation of distributed power supply and electric vehicles and the impact of grid connection are introduced. Secondly, the traditional distribution network carrying capacity evaluation method is studied and introduced. Then, the distribution network carrying capacity evaluation considering uncertainty is reviewed and investigated. Finally, in view of the lack of existing research, further research is needed, aiming to provide a reference for the realization of distributed power supply and grid-connected EV carrying capacity evaluation and a scientific basis for the future operation planning of distributed power supply and EV integration into the distribution network.