An efficient and robust method for lithium-ion battery capacity estimation using constant-voltage charging time

The state-of-health (SoH) estimation based on the constant-voltage (CV) charging data has been an interesting research topic in recent years. However, most of the existing estimation methods based on CV charging data are sensitive to the cut-off condition and/or require a relatively high storage resource as well as computing power, preventing the feasibility in real world applications. To extend the scope of the estimation method based on CV charging data, this paper proposes a quick and robust battery capacity estimation method using a two-layer CV charging time (TCV)-based model. First, the evolution of TCV-based SoH model with respect to different cut-off currents is investigated, and the detailed mathematical expressions of the model coefficients are derived based on the decoupled dynamic characteristics of the CV charging current. Second, considering the actual sampling periods (Tss) utilized in the online application, a Ts-adaptive moving average filter is proposed to filter the high -frequency measurement noise. Third, experimental results demonstrate that the proposed method can determine SoH with a root-mean-square error of less than 2.05% for two types of tested batteries under different charging protocols. In addition, the comparison study further highlights the superiority of the proposed method in terms of robustness, accuracy, computational cost, and storage consumption.

Automated summary

This paper proposes a quick and robust battery capacity estimation method using a two-layer constant-voltage charging time-based model. Through studying the evolution of the model under different cut-off currents and utilizing a Ts-adaptive moving average filter, the proposed method can accurately determine the battery's state of health in real-world applications.