电力数据驱动的电池剩余寿命预测研究OA北大核心CSTPCD

The development of new energy systems increasingly emphasizes the state monitoring and performance prediction of electronic equipment.The battery is an important part of the new energy system,and the accurate monitoring and prediction of its service life and performance is of profound significance to improve the performance of electronic equipment,reduce maintenance costs and enhance energy efficiency.However,due to the influence of various complex factors on the battery performance,predicting its remaining life remains a major challenge.To solve these problems,a new battery remaining life prediction model was proposed.Firstly,the in-depth theoretical research was conducted on residual neural network(ResNet),bidirectional long short-term memory(BiLSTM)network and multi-head self-attention(MHSA)mechanism.Then,based on the above theories,the battery remaining life prediction model based on MHSA-Res-BiLSTM was constructed,and its hyperparameters were optimized.Finally,the battery remaining life prediction experiment was carried out to verify the performance of the proposed MHSA-Res-BiLSTM network.The experimental results showed that the proposed model performed excellently in the prediction of battery remaining life.Compared with other prediction models,the proposed prediction model had lower mean absolute error and root mean square error.The battery remaining life prediction model based on MHSA-Res-BiLSTM has good predictive performance and convergence performance,which can provide theoretical and technical support for the health management of batteries in new energy systems.