State of charge and state of health diagnosis of batteries with voltage-controlled models

The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.

Automated summary

Accurately diagnosing the SOC and SOH of batteries is crucial for battery users and manufacturers. This study presents a new algorithm that uses battery voltage as input for a voltage-controlled model to accurately estimate SOC and SOH. The algorithm is self-calibrating, robust against cell aging, allows SOH estimation from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.