DRT-based modelling framework for Li-ion cells

TL;DR

This paper introduces EIS2MOD, a novel DRT-based modelling framework for Li-ion cells that accurately predicts battery behavior across various conditions with low computational cost, aiding battery management systems.

Contribution

The paper presents a new DRT-based framework for Li-ion battery modelling that combines electrochemical impedance spectroscopy with physical circuit models, improving accuracy and efficiency.

Findings

High accuracy in voltage prediction with RMSE below 1.50%

Effective modelling over full SoC and temperature ranges

Low computational load suitable for real-time BMS

Abstract

The correct assessment of battery states is essential to maximize battery pack performances while ensuring reliable and safe operation. This work introduces EIS2MOD, a novel modelling framework for Li-ion cells based on Distribution of Relaxation Time (DRT). A physically based Electric Circuit Model (ECM) is developed starting from Electrochemical Impedance Spectroscopy (EIS) and Open Circuit Voltage (OCV) measurements. DRT is applied to deconvolve the electrochemical phenomena from the EIS. The presented methodology is based on: i) DRT calculation from EIS, ii) DRT analysis for ECM configuration and iii) Model parameters extraction and fitting. The proposed framework is applied to large format Li-ion pouch cells, which are tested over the whole State of Charge (SoC) range and a wide temperature range (-10{\deg}C to 35{\deg}C). Different current profiles have been tested to validate the model, showing its high accuracy in reproducing the battery cell behavior (e.g. RMSE on the battery terminals voltage lower than 1.50% for driving cycle simulations at variable temperature and SoC). An additional advantage of EIS2MOD is its light computational load thus offering an attractive framework for battery management system implementation.