An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation

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