Capacity Estimation of Lithium-ion Batteries Using Invariance Property in Open Circuit Voltage Relationship

TL;DR

This paper introduces a novel method for estimating lithium-ion battery capacity using only one cycle of open-circuit voltage data, leveraging an invariance property to improve accuracy and reduce testing requirements.

Contribution

The method uniquely utilizes the invariance in OCV-SOC relationship across aging cycles to estimate capacity from partial data without extensive training or full cycle testing.

Findings

Achieves a mean absolute relative error of 0.85% in capacity estimation.

Works with partial charge/discharge data and dynamic discharge data.

Requires only one OCV cycle for modeling.

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, we propose a novel capacity estimation method 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, we can estimate the capacity from dynamic discharge data without requiring dedicated data collection tests. We observed an invariance property 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 0.85\% in capacity estimation across 12 samples from 344 aging cycles.