Capacity estimation of lithium-ion batteries using invariance property in open circuit voltage relationship

Abstract

Lithium-ion (Li-ion) batteries are ubiquitous in electric vehicles (EVs) as efficient energy storage devices. The reliable operation of Li-ion batteries depends critically on the accurate estimation of battery capacity. However, conventional estimation methods require extensive training datasets from costly battery tests for modeling, and a full cycle of charge and discharge is often needed to estimate the capacity. To overcome these limitations, a capacity estimation method is proposed that leverages only one cycle of the open-circuit voltage (OCV) test in modeling and allows for estimating the capacity from partial charge or discharge data. Moreover, by applying it with OCV identification algorithms, the capacity can be estimated from dynamic discharge data without requiring dedicated data collection tests. The proposed method exploits an invariance property observed in the OCV versus state of charge relationship across aging cycles. Leveraging this invariance, the proposed method estimates the capacity by solving an OCV alignment problem using only the OCV and the discharge capacity data from the battery. Simulation results demonstrate the method's efficacy, achieving a mean absolute relative error of less than 0.4% with OCV test data and less than 2.6% with dynamic data in capacities across 10 aging batteries.