GA-BP Neural Network-Based Prediction of Impact Resistance in Electric Vehicle Charging Piles

Impact resistance is crucial for assessing charging pile safety and reliability. This study proposes a prediction model, called GA-BP neural network, which achieved prediction errors below 5% and reduced computation time by over 95% in comparison to finite element analysis (FEA). Initially, the charging pile impact test platform is constructed, and a matching finite element simulation model is developed. The correctness of the simulation model is then verified by integrating the experimental findings. Furthermore, the Latin hypercube approach is used to create 200 sets of simulation schemes, and using the Python programming language, the impact resistance performance indicators of charging piles are automatically collected. Next, a genetic algorithm is used to optimize the initial weight and bias of the BP neural network, lastly, fine-tune the hyperparameters in the neural network to develop a prediction model for the impact resistance performance of the charging pile. The GA-BP model exhibits better accuracy and quicker training than the BP neural network.