期刊
JOURNAL OF POWER SOURCES
卷 462, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jpowsour.2020.228132
关键词
Adaptive H-infinity filter; Least square support vector machine; Model-based method; State of charge
资金
- National Natural Science Foundation of China [51775063, 61763021]
- National Key R&D Program of China [2018YFB0104000]
- Fundamental Research Funds for the Central Universities [2018CDQYQC0035]
- EU [845102-HOEMEVH2020-MSCA-IF-2018]
In this paper, an adaptive fusion algorithm is proposed to robustly estimate the state of charge of lithium-ion batteries. An improved recursive least square algorithm with a forgetting factor is employed to identify parameters of the built equivalent circuit model, and the least square support vector machine algorithm is synchronously leveraged to estimate the battery state of health. On this basis, an adaptive H-infinity filter algorithm is applied to predict the battery state of charge and to cope with uncertainty of model errors and prior noise evaluation. The proposed algorithm is comprehensively validated within a full operational temperature range of battery and with different aging status. Experimental results reveal that the maximum absolute error of the fusion estimation algorithm is less than 1.2%, manifesting its effectiveness and stability when subject to internal capacity degradation of battery and operating temperature variation.
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