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Random Forest Based Li-Ion Battery Capacity Prediction Subject to Capacity Recovery Effect

In decades, a series of researches on capacity prediction have been conducted where capacity degradation processes have been assumed to be irreversible. This paper presents an approach considering the capacity recovery effect taken place during dynamic fading processes. In this approach, firstly, some features significantly affecting the level of battery capacity recovery are comprehensively analyzed and extracted. Then the random forest (RF) is applied to model and cognize the capacity degradation process with consideration of the capacity recovery effect. To verify the efficiency of the proposed approach, Experiments based on published datasets have been conducted by comparison. The results point out that the proposed method could cognize the real maximum capacity more accurately with the consideration of capacity recovery effect.