A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Abstract

Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.

An Experimental Study on Prototype Lithium–Sulfur Cells for Aging Analysis and State-of-Health Estimation

“…This outcome demonstrate that the proposed estimation technique is relatively robust against the measurement noise error in UAH calculation and it can work under real working condition with RMSE around 5% (3.3% + 1.5% = 4.8%). Although, this level of accuracy is not perfect however, it is comparable with similar studies in the literature where an estimator is designed for Li-ion batteries [50], [51]. Figure 11: Sensitivity of estimation RMSE to UAH measurement error 5.…”

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

“…The problem of state measurement of LIBs has been widely studied, most notably for estimation of the battery state of charge (SoC), while literature pertaining to SoH estimation remains less prevalent. Focuses of recent works in the area of SoH estimation have included incremental capacity (IC)/differential voltage (DV) measurement, 19 Coulomb counting, 20 (dual) extended Kalman filters 21,22 or empirical health degradation models such as those developed by Perez et al 23 However, the bulk of literature pertaining to SoH estimation focuses on prognostics and health management of existing battery systems, 24 with less emphasis placed on end of life characterisation of batteries. Furthermore, a majority of SoH estimation works focus on single cells.…”

Revealing the Impact of High Current Overcharge/Overdischarge on the Thermal Safety of Degraded Li-Ion Batteries

“…As the primary power source of EVs, LIB safety and duration performance have a crucial impact on the popularization and application of EVs [6][7][8][9]. However, the degradation effect and safety issues limit the LIBs' further application [10]. LIB degradation is determined by many factors, such as cycling operation, ambient temperature, and large current rates [11][12][13].…”