Abstract：In order to solve the problem that it is difficult to accurately predict the remaining useful life (RUL) of lithium battery, a prediction model of improved long-term and short-term memory network based on particle filter (PF-LSTM) considering various life decay characteristics and data sequence is proposed and applied to the RUL prediction of lithium battery.The health factors closely related to the capacity decline are extracted from the battery historical charge and discharge aging data as the input of the LSTM network, and the global optimization ability of the PF algorithm is used to find the optimal parameters, including the number of neurons, learning rate, node abandonment rate, batch size, training steps and other six parameters to improve the prediction ability of the network; the introduction of Dropout layer to avoid network over-fitting and improve the generalization ability of the model.Based on the experimental verification of NASA PCoE battery data set, the capacity estimation and life of four batteries under different prediction starting points are predicted and compared with LSTM, SVR searched by grid and other algorithms.The experimental results show that the root mean square error RMSE and the average absolute error MAE of PF-LSTM capacity estimation are less than 2%, and the life prediction error is less than 3 cycles, which is the highest compared with other algorithms.
吴忠强,胡晓宇,马博岩,侯林成,曹碧莲. 基于PF-LSTM的锂电池剩余使用寿命预测[J]. 计量学报, 2023, 44(6): 939-947.
WU Zhong-qiang,HU Xiao-yu,MA Bo-yan,HOU Lin-cheng,CAO Bi-lian. Prediction of the Remaining Useful Life of Lithium-ion Batteries Based on PF-LSTM. Acta Metrologica Sinica, 2023, 44(6): 939-947.
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2023, Vol. 44 
