Enhanced State-of-Health and State-of-Charge Estimation for Batteries via Internal Resistance Variations and Usable Capacity Correlations

Disciplines

Electrical and Computer Engineering

Abstract (300 words maximum)

Internal resistance of a battery reflects its distinct characteristics, including factors such as state of health (SOH), state of charge (SOC), reversibility, thermal runaway, etc. The paper develops a simple approach based on modified intermittent current interruption (ICI) methods for characterizing the internal resistance (𝑅𝑖𝑛𝑑) and variations of internal resistance (Δ𝑅𝑖𝑛𝑑) at different SOC and different C-rates. The decreasing trend of 𝑅𝑖𝑛𝑑 while increasing SOC, alongside the notable increase in 𝑅𝑖𝑛𝑑 coupled with capacity loss, highlights the potential of 𝑅𝑖𝑛𝑑 as a reliable indicator for assessing both SOC and SOH of the battery. More importantly, the paper presents a pioneering study on Δ𝑅𝑖𝑛𝑑 between discharged states and charged states and identifies a strong correlation between Δ𝑅𝑖𝑛𝑑 and the usable capacity of the battery, indicating that Δ𝑅𝑖𝑛𝑑 could be an enhanced parameter for predicting the SOC and SOH of the batteries in Battery Management System (BMS) applications. The Δ𝑅𝑖𝑛𝑑 model, which allows us to focus on the changes caused by charge transfer during the lithiation and delithation process, provides a more robust solution for predicting SOC and SOH of the battery while overcoming cell-to-cell variations in battery packs.

Academic department under which the project should be listed

SPCEET - Electrical and Computer Engineering

Primary Investigator (PI) Name

Beibei Jiang

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Enhanced State-of-Health and State-of-Charge Estimation for Batteries via Internal Resistance Variations and Usable Capacity Correlations

Internal resistance of a battery reflects its distinct characteristics, including factors such as state of health (SOH), state of charge (SOC), reversibility, thermal runaway, etc. The paper develops a simple approach based on modified intermittent current interruption (ICI) methods for characterizing the internal resistance (𝑅𝑖𝑛𝑑) and variations of internal resistance (Δ𝑅𝑖𝑛𝑑) at different SOC and different C-rates. The decreasing trend of 𝑅𝑖𝑛𝑑 while increasing SOC, alongside the notable increase in 𝑅𝑖𝑛𝑑 coupled with capacity loss, highlights the potential of 𝑅𝑖𝑛𝑑 as a reliable indicator for assessing both SOC and SOH of the battery. More importantly, the paper presents a pioneering study on Δ𝑅𝑖𝑛𝑑 between discharged states and charged states and identifies a strong correlation between Δ𝑅𝑖𝑛𝑑 and the usable capacity of the battery, indicating that Δ𝑅𝑖𝑛𝑑 could be an enhanced parameter for predicting the SOC and SOH of the batteries in Battery Management System (BMS) applications. The Δ𝑅𝑖𝑛𝑑 model, which allows us to focus on the changes caused by charge transfer during the lithiation and delithation process, provides a more robust solution for predicting SOC and SOH of the battery while overcoming cell-to-cell variations in battery packs.