During the charging process of electric vehicles

the conversion between AC and DC power

as well as the transformation between electrical and chemical energy

inevitably leads to electrical energy loss. To accurately calculate the magnitude of this loss with limited measurement equipment

this paper proposes a charging pile efficiency calculation theory based on Kmeans-ADE-LSTM. This method adopts a memory neural network to train the model with historical charging data

deriving a model relationship between the DC side electrical data

temperature data and charging efficiency

and then validates it with new charging data. Innovatively

the collected charging data is subjected to Kmeans clustering analysis before efficiency calculation

and the processed data is then used for subsequent neural network training. Additionally

to address the difficult problem of selecting the mutation coefficient in traditional differential evolution algorithms

this paper employs an efficient global optimization algorithm-the differential evolution algorithm-to adaptively process the mutation coefficient and optimally select the hyper parameters of the LSTM. Based on the correlation between charging efficiency and time

etc.

the charging conversion efficiency is calculated in conjunction with the LSTM neural network. The comparative experimental results demonstrate that the proposed Kmeans-ADE-LSTM charging pile charging efficiency prediction method has a high level of predictive accuracy.