Estimating the graphite electrode phase evolution from electrode potential measurements
Journal article, 2026

Knowledge of the internal state of lithium-ion batteries is crucial for the development of safe charging control algorithms. Most commonly, graphite is used as the negative electrode, possessing a high specific capacity and cycling stability. When such lithium-ion batteries are charged, lithium intercalates into the layered carbon structure, and the electrode material transitions through a series of phases differentiated by the number of carbon layers between each lithium layer. Each phase has different electrochemical properties, making it interesting to accurately track the phase content during lithiation. However, this quantity can only be measured through spectroscopic experiments, which cannot be included in any battery application. In this work, a method based on incremental capacity analysis and kernel smoothing is introduced to estimate the phase content of graphite electrodes from the electrode potential during constant-current charging. This kernel density function (KDF) method is validated at low currents using physics-based simulations of multiphase electrode dynamics, achieving an average phase estimation error below one percentage point per phase. Furthermore, we apply the KDF-method to experimentally measured coin cell data, for which the estimated phase content closely agrees with simulations in the mid to high range state of charge.

Author

Isac Borghed

Chalmers, Electrical Engineering, Systems and control

Xiaolei Bian

Chalmers, Electrical Engineering, Systems and control

Yicun Huang

Chalmers, Electrical Engineering, Systems and control

Torsten Wik

Chalmers, Electrical Engineering, Systems and control

Materials and Design

0264-1275 (ISSN) 1873-4197 (eISSN)

Vol. 266 116218

Faster fast-charging and extended lifetime of lithium-ion batteries by new onboard monitoring of degradation using feedback from new cheap sensors

Swedish Energy Agency (2023-200447, P2023-00144), 2023-09-01 -- 2027-08-31.

Subject Categories (SSIF 2025)

Materials Chemistry

Energy Engineering

Computational Mathematics

Signal Processing

Driving Forces

Sustainable development

Areas of Advance

Energy

DOI

10.1016/j.matdes.2026.116218

More information

Created

5/18/2026