Lithium-ion battery degradation: how to model it.

Area of Science:

• Materials Science
• Electrochemistry
• Computational Modeling

Background:

• Lithium-ion battery degradation prediction is vital for automotive, aviation, and energy storage sectors.
• Existing models often fail to account for interactions between multiple degradation mechanisms within a single electrode.

Purpose of the Study:

• To develop and present the first model coupling more than two degradation mechanisms in the negative electrode of lithium-ion batteries.
• To map complex, path-dependent degradation pathways and understand their impact on battery life.

Main Methods:

• Coupling of four distinct degradation mechanisms within the PyBaMM open-source modeling environment.
• Utilizing a novel approach to visualize and analyze internal degradation processes and their consequences.

Main Results:

• Identification of five distinct end-of-life pathways for the same battery cell, contingent on usage patterns.
• Demonstration of how specific degradation patterns, like loss of lithium inventory and active material, influence battery lifespan.

Conclusions:

• The developed model enables prediction of battery life based on usage, allowing for life extension strategies.
• Parameterization of degradation models remains a significant challenge requiring international collaborative efforts.