An efficient and robust method for lithium-ion battery capacity estimation using constant-voltage charging time. (15th January 2023)

• Record Type: Journal Article
• Authors: Yang, Jufeng; Li, Xin; Sun, Xiaodong; Cai, Yingfeng; Mi, Chris
• Is Part Of: Energy. Volume 263:Part B(2023)
• Publication Date: 2023-01-15
• Subjects: Lithium-ion battery -- State-of-health (SoH) -- Capacity estimation -- Constant-voltage charging time -- Moving average filter (MAF); Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.125743
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: 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 ( T CV )-based model. First, the evolution of T CV -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 ( T s s) utilized in the online application, a T s -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. Highlights: Investigated the evolution of T CV -based SoH model versus charging cut-off currents. Constructed a two-layer SoH model considering different charging cut-off currents. Proposed a sampling period-adaptive moving average filter. Proved the robustness with different cut-off conditions and sampling periods.