期刊
IEEE ACCESS
卷 8, 期 -, 页码 28533-28547出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2972344
关键词
Healthy features (HFs); grey relational analysis (GRA); entropy weight method (EWM); long short-term memory (LSTM); state of health (SOH)
资金
- National Natural Science Foundation of China [61763021, 51775063]
- National Key Research and Development Program of China [2018YFB0104000]
- Science and Technology Research Program of Chongqing Education Commission [KJQN201901539]
- EU-Funded Marie Sk<feminine ordinal indicator>odowska-Curie Individual Fellowships Project [845102-HOEMEV-H2020-MSCA-IF-2018]
Precise estimation of state of health (SOH) are of great importance for proper operation of lithium-ion batteries equipped in electric vehicles. For real applications, it is however difficult to estimate battery SOH due to stochastic operation, which in turn speeds up aging process of the battery. To attain the precise SOH estimation, an efficient estimation manner based on machine learning is proposed in this study. Firstly, the voltage profile during charging and discharging process and incremental capacity variation are acquired through the cycle life test, and the healthy features correlating to battery degradation are extracted. Secondly, the grey relation analysis and entropy weight method are employed to analyze the healthy features. Finally, the long short-term memory is established to achieve the SOH estimation of battery. The experimental results highlight that the proposed method can effectively predict the battery SOH with preferable accuracy, stability and robustness.
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