Forecast and Uncertainty Analysis of Lithium-ion Batteries SOC Based on LSTM-NPKDE

doi:10.16628/j.cnki.2095-8188.2020.05.007

DIANQI YU NENGXIAO GUANLI JISHU ›› 2020, Vol. 590 ›› Issue (5): 44-50.doi: 10.16628/j.cnki.2095-8188.2020.05.007

• Research & Analysis • Previous Articles Next Articles

Forecast and Uncertainty Analysis of Lithium-ion Batteries SOC Based on LSTM-NPKDE

LI Wenqi¹, GAO Dongxue¹, LI Zhaohui², RAO Yufei², GU Bo³

1. State Grid Henan Electric Power Company, Zhengzhou 450000, China;
2. State Grid Henan Electric Power Research Institute, Zhengzhou 450052, China;
3. North China University of Water Resources and Electric Power, Zhengzhou 450045, China

Received:2020-01-15 Online:2020-05-30 Published:2020-05-29

Abstract

Abstract: A forecast method of the state of charge (SOC) of lithium-ion battery based on the long-short term memory (LSTM) network was proposed.The SOC of lithium-ion battery was forecasted by taking the charge-discharge current and voltage of lithium-ion battery as model inputs.The results show that the forecasting accuracy of LSTM network is higher than that of BP neural network,BP-PSO hybrid model and wavelet neural network (WNN).The nonparametric kernel density estimation (NPKDE) method was used to calculate the confidence interval of the SOC forecast of lithium-ion batteries.The calculation results show that the confidence interval based on NPKDE can accurately calculate the uncertainty of the SOC forecast of lithium-ion batteries at different confidence levels.

Key words: lithium-ion battery, state of charge (SOC), long-short term memory network, forecast uncertainty, confidence interval

CLC Number:

TM910

LI Wenqi, GAO Dongxue, LI Zhaohui, RAO Yufei, GU Bo. Forecast and Uncertainty Analysis of Lithium-ion Batteries SOC Based on LSTM-NPKDE[J]. DIANQI YU NENGXIAO GUANLI JISHU, 2020, 590(5): 44-50.

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Forecast and Uncertainty Analysis of Lithium-ion Batteries SOC Based on LSTM-NPKDE

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