Abstract: The invention discloses a charging state analysis method of a electric vehicle based on electrical characteristic sequence analysis. The method includes following steps: step S1, obtaining voltage sampling data and current sampling data of the electric vehicle during charging; step S2, setting a time interval, so as to divide the voltage sample data and the current sampling data obtained in step S1 into multiple data sets; step S3, calculating an electrical characteristic vector of each time interval; step S4. adding the calculation results of Step S3 to a temperature sensing value T, and generate an electrical characteristic sequence of whole charging cycle; step S5, inputting the electrical characteristic sequence of the electric vehicle into a trained Time Recurrent Neural Network (TRNN) in sequence to obtain corresponding results; if the result is 1, it means normal; if the result is 0, it means abnormal.

Abstract: The invention discloses a non-invasive load decomposition method, which includes: step 1, obtaining the power fingerprint information of each load; step 2, clustering the operating state of loads through the clustering algorithm, calculate statistical values of each cluster, and encoding the operating state of electrical appliances; step 3, establishing a hidden Markov model with multiple-parameters and calculating the model parameters; step 4, performing state recognition based on Viterbi algorithm and obtaining predicted state sequence; step 5, according to the predicted state sequence and the statistical values of each cluster, decomposing the load power based on the maximum likelihood estimation principle; step 6, outputting the state sequence and power decomposition results. The invention solves the conventional load identification algorithm problems, such as complex model, insufficient use of electrical features and low accuracy of unknown information.