Influence of half-cell profiles on voltage-based lithium-ion battery degradation diagnostics
Creators: name: Bin-Mat-Arishad, Izzuan
Publisher: name: Loughborough University
Year: 2024
Subjects: classificationCode: 400199
subject: Aerospace engineering not elsewhere classified
classificationCode: 400299
subject: Automotive engineering not elsewhere classified

description: Lithium-ion batteries are crucial for modern energy storage, powering devices ranging from portable electronics to electric vehicles. However, their performance and lifespan are significantly affected by degradation, which poses challenges in ensuring their reliability and safety. The research addresses key research gaps by understanding how half-cell potential profiles affect model estimation accuracy, emphasising the need to account for ageing effects in degraded cells. It also identifies current limitations in voltage-based diagnostic models, particularly the challenges of using half-cell potential profiles from other sources to represent the composite electrodes in the model estimation accuracy. To assess the model’s limitations, the study examines the impact of Incremental Capacity and Differential Voltage analysis on curve fitting and estimation accuracy. The findings show that the fitting of the cell’s voltage profile alone is able to estimate all electrode parameters with 95% accuracy. However, incorporating specific weightings from Incremental Capacity and Differential Voltage analysis further improves the estimation accuracy. This highlights the importance of including these analyses in model fittings, as they capture critical features essential for accurate estimation. Further advancement in model validation was done by introducing a new method involving the creation of pre-aged coin cells using a three-electrode setup. This approach enables the construction of coin cells with known level of degradation, providing a practical way to validate voltage-based diagnostics model under conditions that is more accurately reflect real-world battery use. The findings highlight that using fresh half-cell profiles for degradation assessment in voltage-based degradation diagnostics model can lead to significant inaccuracies, particularly in estimating key factors such as the Loss of Lithium Inventory and Loss of Active Material.Through a complete teardown of coin cells at the end of life and subsequent measurements on aged half-cell potential profiles, the results indicate that using fresh half-cell potential profiles in the model leads to less accurate degradation estimates compared to those using aged profiles, especially as the cell ages. However, aged half-cell potential profiles are less effective in estimating cell parameters at the beginning of life compared to models using fresh half-cell profiles. These findings demonstrate that the accuracy of model estimation decreases as the cell ages when fresh half-cell potential profiles are used. To effectively deploy a voltage-based degradation diagnostics model, the model should consider the ageing in the half-cell potential profiles as well. The research also reveals the complex degradation behaviours within silicon-graphite electrodes, showing that silicon degrades more rapidly than graphite as a result of its substantial volume changes during cycling. This posed a challenge to the voltage-based degradation diagnostics to identify and separate the degradation contributions of each materials. The challenge was addressed by introducing a new approach that separates the degradation contributions of silicon and graphite within composite anodes by identifying distinct peaks in Differential Voltage plots. Furthermore, since the half-cell potential profiles for each materials in composite electrodes are unavailable for the commercial cell case. The accuracy of the estimation must be verified using half-cell potential profiles from other sources. The study identified the correlation between the peak distances for Differential Voltage analysis in for full-cell and half-cell potential profiles with the model estimation accuracy. The knowledge contributions in this thesis advance the field of battery diagnostics by understanding the limitation of applying voltage-based degradation diagnostics models. These findings have significant implications for the design and management of future lithium-ion battery systems with the potential to improve their durability and safety.