1. Electro-Chemo-Mechanics of Lithium Dendrite Growth

Lithium dendrites are metallic needle-like structures that electrodeposit on the anode of a battery during charging, and on continued cycling, penetrate the intermediate polymeric separator layer, and internally short-circuit the battery. Dendritic growth in lithium-based batteries is known to cause battery failures, fires and other accidents. Dendrites in lithium-based batteries thus remain a critical challenge in present-day graphite and future lithium metal anodes for high current density applications.  The problem of dendrites needs to be mitigated in order to extend the range (miles per charge) of electric vehicles and to be able to charge them very fast.

Analytical and phase field models have been developed in order to understand the fundamental science of dendrite growth. It has been shown that lithium electrodeposits grow from the top due to electrodeposition and from the base due to plastic extrusion. Phase field models further capture the local heterogeneities that are difficult to observe through experiments. Mechanical stresses, plastic flow, electric field, local concentration gradients, and the electrodeposit microstructure are readily captured through the phase field framework. The project identifies the key factors for dendrite growth and enables design of dendrite-free batteries.

2. Separators for Liquid Electrolyte Batteries

Separators separate the anode and the cathode in liquid electrolyte batteries. They prevent short circuits and impart mechanical rigidity. Separator pore size and morphology need to be carefully controlled to impede dendrite growth. Computational models developed in-house help rational design of porous separators for any given battery application.


3. Battery Degradation and Lifetime Prediction

Capacity fade in batteries occurs due to side reactions at the electrode-electrolyte interface and due to mechanical degradation of electrode particles. Such degradation is more pronounced in newer high voltage cathodes under deep charge-discharge cycles. In this project, each mechanism responsible for capacity fade is identified and  individually quantified to predict battery life.