METHOD FOR ESTIMATING THE CAPACITY OF LITHIUM BATTERY BASED ON CONVOLUTION LONG-SHORT-TERM MEMORY NEURAL NETWORK

摘要

The present invention relates to a method of estimating lithium battery capacity based on a convolution long-short-term memory neural network (CNN-LSTM). The present invention obtains a model that lithium battery capacity estimation through the four steps: processing a lithium battery's data, selecting parameters of an improved convolution long-short-term memory neural network using a genetic algorithm, training the improved CNN-LSTM, and testing model. Hyper-parameters of the improved CNN-LSTM are optimized using the genetic algorithm. Using the convolution neural network to extract the spatial features of lithium battery charge and discharge data, and then input these features into the improved long-short-term memory neural network to extract temporal features, estimated capacity is output through a fully connected layer finally. The present invention overcomes the limitation of the traditional model-based algorithm overly relying on the battery model and has the engineering application prospect.