State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application

By Maitane Berecibar, M. Garmendia, I. Gandiaga, Jonathan Crego, I. Villarreal. 2016

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Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “Based on the DV curve, the peak (DVP) [58]and valley (DVV) [35], the peak location (DVPL) [32], [59], and the valley location (DVVL) [34], [60] are extracted as HIs.”
Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries (2017, methodology): “These techniques have conventionally been used for detailed cell analyses, such as understanding degradation mechanisms [16], [17], however recent studies have considered the use of portions of the IC/DV curve for online capacity estimation [18], [19], [20], [21], [22].”
A Comparison between Electrochemical Impedance Spectroscopy and Incremental Capacity-Differential Voltage as Li-ion Diagnostic Techniques to Identify and Quantify the Effects of Degradation Modes within Battery Management Systems (2017, background): “However, charging and discharging the cell at such low currents is difficult to perform in a real application since the time required would be prohibitive [19].”
The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles (2018, methodology): “The existing estimation approaches include the degradation model-based approaches [13]–[15], the approaches based on incremental capacity analysis (ICA) and differential voltage analysis (DVA) [16], [17], and other voltage-based approaches.”