The co-estimation of states for lithium-ion batteries based on segment data

The accurate estimation of the state of charge (SOC), state of health (SOH) and remaining useful life (RUL) in whole working cycles is an important prerequisite for ensuring the safe and stable operation of lithium-ion batteries. In order to estimate the three states simultaneously, this paper proposes a SOC-SOH-RUL co-estimation method by using the segment data of constant current charge. First, the method uses constant current charging segment data to extract the fused health feature (FHF). Gaussian process regression (GPR) is used to establish the capacity degradation model to reflect the relationship between the FHF and SOH to achieve the SOH estimation. Second, the same segment data and the current SOH of the battery are used to identify the parameters of the equivalent circuit model (ECM). The ECM-based SOC estimation is fulfilled by unscented Kalman filter (UKF). Finally, the FHF prediction model is established by least square support vector machine (LS-SVM). The prediction results of the FHF are input into the capacity degradation model to achieve the RUL estimation. The experimental results show that the proposed method can achieve the co-estimation of SOC-SOH-RUL by using segment data with high accuracy, stability and applicability.