Data-driven battery capacity estimation based on partial discharging capacity curve for lithium-ion batteries

Abstract

An accurate maximum capacity estimation is critical to ensure the safety and reliability of lithium-ion batteries (LIBs). In this paper, we first investigate the relationship between discharging capacity corresponding to non-lower cutoff voltage and the maximum capacity based on which, a novel health indicator (HI) derived from the partial constant current discharging capacity curves is proposed. Unlike the previous work on incremental capacity (IC) curves, the capacity at equispaced voltage is extracted as the HI without the subjective differentiating or filtering process. It can be demonstrated that not only the HI and the area enclosed under the IC curve are homologous but also the HI is highly correlated with the maximum capacity by the Pearson correlation coefficient method. The Gaussian process regression (GPR) is employed to capture the mapping relationship between the HI and the maximum capacity. The proposed method is verified on a dataset generated from aging tests of eight 105 Ah batteries, performed under three different aging conditions. The results show that the maximum remaining capacity can be accurately estimated no matter the extrapolation estimation or the online estimation. Further, the influence of the training sample size and the voltage window length on the capacity estimation is also analyzed to evaluate the effectiveness and flexibility of the method. The maximum root mean square percentage error (RMSPE) is below 3.5 % in online capacity estimation only using the data within 10 mV. This method has been demonstrated well adapted to different voltage ranges, charging/discharging rates, and temperatures.