Simulation of Lithium Battery Health State Operation and Maintenance Mining Model under Big Data
The operation and maintenance mining of lithium battery health status can control the current health status of lithium battery in real time and ensure the normal use of power-using equipment.We proposed an O&M mining model for lithium battery health status under big data,analyzed the overall architecture of the model,and used CNN-LSTM neural network model for lithium battery health status estimation.Firstly,ten alternative lithium battery health feature factors were selected,and then gray correlation analysis and principal component analysis were per-formed to extract five health feature factors,which are cycle number,average temperature in discharge phase,average temperature in charge phase,total discharge time,and constant current discharge time;Then convolutional neural net-work was used to extract local features of health feature factors,and long and short-term neural network was used to mine time series feature The CNN-LSTM combined neural network model was constructed by using convolutional neu-ral network to extract local features of health feature factors and long and short term neural network to mine time se-ries feature factors.The experimental results show that the proposed combined network model has high prediction ac-curacy,and the average absolute error is reduced by 37%and 62%,and the root mean square error is reduced by 17%and 39%compared with the LSTM and BP single network models.
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