Lithium-Ion Battery State of Health Estimation

Abstract

Accurate state of health (SoH) estimation is essential for advancing lithium-ion battery technology and optimizing operating strategies. However, this remains challenging as internal ageing mechanisms are not fully understood, difficult to measure, and require time-intensive experiments. This work addresses these challenges by proposing a novel methodology that improves the accurate parametrisation of physics based models and presents an efficient design of experiments.

An in-depth experimental ageing study demonstrates that degradation is usage-history dependent, with prior operating history influencing future behaviour and the onset of nonlinear ageing phenomena such as the knee-point. Depending on conditions, the knee-point was delayed by up to 50\% (400 full-cycle equivalents) or did not occur at all. Reversible short-term capacity fade and recovery are also observed, diminishing with over the cell's lifetime. These findings highlight the complexity of ageing processes and confirm that capacity alone is an inadequate health metric.

Incremental capacity (IC) analysis is evaluated as a non-invasive diagnostic tool, with simulations and experiments showing that IC-derived features provide deeper insight into degradation and can be linked to specific mechanisms, offering reliable indicators of SoH ($R_{\text{ave}}^{2}$ = 0.96). These features are leveraged to improve the parametrisation of physics-based degradation models, addressing identifiability issues and enabling their integration into a hybrid machine learning framework for robust SoH estimation.

To identify the most informative operating points for model training, a novel design of experiments methodology was developed. This approach combines synthetic datasets, an IC-based surrogate model, and data pruning to reduce the number of test cells required by 86\% (to 35 cells), thereby lowering experimental cost and duration. Overall, this work advances SoH estimation by integrating diagnostic techniques, physics-based modelling, and efficient experimental design.

Date of Award	18 Feb 2026
Original language	English
Awarding Institution	University of Bath
Supervisor	Christopher Vagg (Supervisor), Frank Marken (Supervisor), Tom Fincham Haines (Supervisor) & Nico Didcock (Supervisor)