Research On Power Battery Detection And Analysis Method Based On Deep Learning

With the rapid development of new energy technologies,the market demand for power batteries has gradually increased.Due to the frequent safety problems of power batteries in recent years,and people’s attention to power batteries has gradually shifted from capacity requirements to safety requirements.The power battery will age during the cycle of charge and discharge,and its performance will gradually decline.Continuous use of power batteries that are nearing retirement is prone to failure,thus affecting the normal operation of the power supply system.Therefore,timely detection of the state of health of power batteries and estimation of their remaining life are important to maintain the safety of power batteries.The detection and analysis of the power battery based on the deep learning method in this topic are as follows:Firstly,the background significance of power battery detection and its research status at home and abroad are introduced,the characteristics of model construction method and data-driven method are analyzed,and the deep learning algorithm in data-driven method is selected to detect and analyze power battery.Next,it describes the structure and basic working principle of the power battery,sets the battery detection standards.Using the public data source of NASA power lithium battery,the change process of the parameter data of the NASA battery in the charging and discharging experiment is described,and the parameters with high correlation with the battery health state are extracted by the gray correlation analysis method as the health factor.Secondly,considering that the parameter data of the power battery is time series,the Recurrent Neural Network（RNN）in the deep learning algorithm is often used to analyze the time series data.By consulting the literature and formula derivation,it is found that the gradient disappears or explodes when the RNN calculates the gradient and adjusts the weight matrix..The Long Short-Term Memory（LSTM）network adds the gating operation of the memory unit on the basis of the RNN to handle the gradient problem in the RNN.Then,a power battery detection model based on LSTM deep learning is constructed,and the Dropout technology and Adam optimizer used in the model are introduced,and the detection experiments and analysis of NASA’s power lithium battery data are carried out.Thirdly,in order to establish the connection between the past and the future of power battery data,the LSTM neural network is improved into a Bi-directional Long Short Term Memory（Bi LSTM）network,that is,the forward LSTM hidden layer and the reverse It is composed of hidden layers to LSTM,which optimizes the relatively single timing analysis capability of LSTM.Then,a power battery detection model based on Bi LSTM deep learning is designed,and the detection experiment analysis of NASA’s power lithium battery data is carried out.Fourthly,according to the characteristics of One Dimensional Convolutional Neural Network（1D CNN）suitable for one-dimensional data feature extraction,1D CNN and Bi LSTM are integrated.The deep features in the power battery data are extracted through the convolution layer and the pooling layer,and the Bi LSTM layer is used to analyze the correlation between the data bidirectionally.CNN-Bi LSTM improves the generalization ability of the detection model on the basis of Bi LSTM.Then,a power battery detection model based on CNN-Bi LSTM deep learning is established,and the detection experimental analysis of NASA’s power lithium battery data is carried out.Finally,after analyzing multiple sets of experimental results and evaluation indicators,the power battery detection model based on CNN-Bi LSTM deep learning has a stronger ability to detect the battery state of health,and the fitting degree between the battery state of health detection curve and the actual battery state curve is R~2Compared with the experimental results of RNN,LSTM and Bi LSTM detection models,the CNN-Bi LSTM detection model has higher prediction accuracy and stability.