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 (π πππ‘) and variations of internal resistance (Ξπ πππ‘) at different SOC and different C-rates. The decreasing trend of π πππ‘ while increasing SOC, alongside the notable increase in π πππ‘ coupled with capacity loss, highlights the potential of π πππ‘ as a reliable indicator for assessing both SOC and SOH of the battery. More importantly, the paper presents a pioneering study on Ξπ πππ‘ between discharged states and charged states and identifies a strong correlation between Ξπ πππ‘ and the usable capacity of the battery, indicating that Ξπ πππ‘ could be an enhanced parameter for predicting the SOC and SOH of the batteries in Battery Management System (BMS) applications. The Ξπ πππ‘ 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
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 (π πππ‘) and variations of internal resistance (Ξπ πππ‘) at different SOC and different C-rates. The decreasing trend of π πππ‘ while increasing SOC, alongside the notable increase in π πππ‘ coupled with capacity loss, highlights the potential of π πππ‘ as a reliable indicator for assessing both SOC and SOH of the battery. More importantly, the paper presents a pioneering study on Ξπ πππ‘ between discharged states and charged states and identifies a strong correlation between Ξπ πππ‘ and the usable capacity of the battery, indicating that Ξπ πππ‘ could be an enhanced parameter for predicting the SOC and SOH of the batteries in Battery Management System (BMS) applications. The Ξπ πππ‘ 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.