Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium Ion Batteries, Part-1: Performance Measures

TL;DR

This paper investigates how errors and uncertainties in the open circuit voltage to state of charge (OCV-SOC) curve affect battery management systems, highlighting sources of error and proposing models to enhance safety and reliability.

Contribution

It is the first to analyze the impact of OCV-SOC modeling errors and uncertainties on SOC and capacity estimation in lithium-ion batteries.

Findings

Identifies key sources of uncertainty in OCV-SOC curves.

Proposes models to incorporate uncertainties into battery management.

Highlights the importance of accounting for errors to improve system reliability.

Abstract

The open circuit voltage to the state of charge (OCVSOC) characteristic is crucial for battery management systems. Using the OCV-SOC curve, the SOC and the battery capacity can be estimated in real-time. Accurate SOC and capacity information are important to carry out the majority of battery management functionalities that ensure a safe, efficient, and reliable battery pack power system. Numerous approaches have been reported in the literature for improved SOC estimation and battery capacity estimation. These approaches focus on various estimation and filtering techniques to reduce the effect of measurement noise and uncertainties due to hysteresis and relaxation effects. Even though all the existing approaches to SOC estimation rely on the OCV-SOC characterization, little attention was paid to investigating the possibility of errors in the OCV-SOC characterization and the effect of uncertainty in the OCV-SOC curve on SOC and capacity estimates. In this paper, which is the first part of a series of three papers, the effect of OCV-SOC modeling error in the overall battery management system is discussed. The different sources of uncertainties in the OCV-SOC curve include cell-to-cell variation, temperature variation, aging drift, cycle rate effect, curve-fitting error, and measurement/estimation error. The proposed uncertainty models can be incorporated into battery management systems to improve their safety, performance, and reliability.