New Model Could Support Better Battery Design
New Model Could Support Better Battery Design
Lithium plating on the graphite anode degrades performance and can lead to fires. A mesoscale model offers a tool for predicting when plating starts and how quickly it progresses.
Fast-charging lithium-ion batteries power the vast majority of the devices we use every day, including cellphones, computers, and electric vehicles. Unfortunately, a major limitation is that they are susceptible to overheating and catching fire. As important as fast charging is for making electric vehicles (EV) more competitive and convenient, the underlying electrochemical phenomena that occur during fast charging are still poorly understood.
Weiyu Li, an assistant professor of mechanical engineering at University of Wisconsin-Madison, has created a groundbreaking computational model that explains the plating phenomena that cause lithium-ion batteries to fail—which could revolutionize the future production of lithium-ion batteries.
“I was drawn to this area because I saw the need for predictive modeling to reduce costly trial-and-error in battery design,” Li said. “The ‘aha’ moment came when I realized existing theories only accounted for the extremes—either ion depletion in the electrolyte or saturation in the solid—while ignoring a large intermediate regime where lithium plating still occurs. That missing piece inspired me to develop a more comprehensive and predictive framework.”
Li’s model focused on lithium plating on a graphite anode in a lithium-ion battery. The model reveals how the complex interplay between ion transport and electrochemical reactions drives lithium plating, which degrades battery performance and lifespan.
A major challenge Li faced in developing her model was integrating multiple physical phenomena—diffusion, electromigration, intercalation, and plating kinetics—into a tractable and insightful model.
Weiyu Li’s model shows the conditions where lithium plating can occur. Image: Weiyu Li/University of Wisconsin
“Deriving an analytical expression that links lithium plating onset to both material properties and operating conditions, while maintaining clarity and computational efficiency, required careful simplification without sacrificing accuracy,” Li said.
Another challenge was bridging scales—connecting micro-scale processes in SEI (solid-electrolyte interphase) to macro-scale battery performance in ways that can directly inform design and operation.
Li was surprised that her results countered the common assumption that a negative potential at the anode directly signals lithium plating.
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“Our analysis showed that a negative potential may indicate conditions that favor plating, but it’s not equivalent to plating itself,” she said. “The actual onset depends on a more nuanced interplay of ionic transport, electrochemical kinetics, and material characteristics. This insight has major implications for battery diagnostics and safety strategies.”
Mechanical engineers will be interested in Li’s mesoscale modeling, which offers a physics-informed tool to predict lithium plating onset and progression—not just from an electrochemical angle, but with future extensions incorporating mechanical and thermal effects. This work directly informs thermal management strategies, structural integrity, system-level battery design, and predictive maintenance.
“Moreover, our development of real-time, adaptive charging protocols sits at the intersection of control theory, intelligent systems, and energy engineering—making it highly relevant to mechanical engineers working on fast-charging EV platforms, aerospace applications, and beyond,” Li said.
Another key innovation was deriving a predictive, analytical expression for the onset of lithium plating, which maps out regimes of safe and unsafe operation across different materials and charging conditions. This analytical clarity enables engineers to quickly assess and optimize fast-charging protocols without resorting to brute-force simulations.
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“It helps mechanical engineers bridge the gap between material-level electrochemistry and system-level design and control,” she said.
Li plans to further develop her model by designing real-time adaptive charging protocols using the insights from our model.
“We’re integrating machine learning and control theory to create algorithms that can adjust charging rates on the fly, minimizing plating risk while maximizing speed,” she said. “This includes expanding the framework to study alternative anodes, like silicon and sodium, and their unique mechanical and chemical challenges.”
Li’s methodology for identifying critical conditions leading to failure or inefficiency can be applied beyond batteries. For instance, any system involving reactive transport in constrained environments—such as catalysis, fuel cells, or even biological systems—could benefit from the analytical insights and control frameworks she has developed.
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“What excites me most,” Li said, “is how modeling can serve as a bridge between disciplines. By blending theory, simulation, and data-driven techniques, we can make battery science more predictive and translatable. I hope this work encourages greater integration of mechanical engineering perspectives in electrochemical system design, and vice versa.”
Mark Crawford is a technology writer in Corrales, N.M.
Weiyu Li, an assistant professor of mechanical engineering at University of Wisconsin-Madison, has created a groundbreaking computational model that explains the plating phenomena that cause lithium-ion batteries to fail—which could revolutionize the future production of lithium-ion batteries.
“I was drawn to this area because I saw the need for predictive modeling to reduce costly trial-and-error in battery design,” Li said. “The ‘aha’ moment came when I realized existing theories only accounted for the extremes—either ion depletion in the electrolyte or saturation in the solid—while ignoring a large intermediate regime where lithium plating still occurs. That missing piece inspired me to develop a more comprehensive and predictive framework.”
Li’s model focused on lithium plating on a graphite anode in a lithium-ion battery. The model reveals how the complex interplay between ion transport and electrochemical reactions drives lithium plating, which degrades battery performance and lifespan.
A major challenge Li faced in developing her model was integrating multiple physical phenomena—diffusion, electromigration, intercalation, and plating kinetics—into a tractable and insightful model.
Weiyu Li’s model shows the conditions where lithium plating can occur. Image: Weiyu Li/University of Wisconsin
Another challenge was bridging scales—connecting micro-scale processes in SEI (solid-electrolyte interphase) to macro-scale battery performance in ways that can directly inform design and operation.
Li was surprised that her results countered the common assumption that a negative potential at the anode directly signals lithium plating.
More on Advanced Energy Storage: Breakthrough Could Make for Long-Range EVs
“Our analysis showed that a negative potential may indicate conditions that favor plating, but it’s not equivalent to plating itself,” she said. “The actual onset depends on a more nuanced interplay of ionic transport, electrochemical kinetics, and material characteristics. This insight has major implications for battery diagnostics and safety strategies.”
Mechanical engineers will be interested in Li’s mesoscale modeling, which offers a physics-informed tool to predict lithium plating onset and progression—not just from an electrochemical angle, but with future extensions incorporating mechanical and thermal effects. This work directly informs thermal management strategies, structural integrity, system-level battery design, and predictive maintenance.
“Moreover, our development of real-time, adaptive charging protocols sits at the intersection of control theory, intelligent systems, and energy engineering—making it highly relevant to mechanical engineers working on fast-charging EV platforms, aerospace applications, and beyond,” Li said.
Another key innovation was deriving a predictive, analytical expression for the onset of lithium plating, which maps out regimes of safe and unsafe operation across different materials and charging conditions. This analytical clarity enables engineers to quickly assess and optimize fast-charging protocols without resorting to brute-force simulations.
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“It helps mechanical engineers bridge the gap between material-level electrochemistry and system-level design and control,” she said.
Real-time adaptive charging
Using her results, Li created a detailed diagram that provides guidance to help engineers design strategies to mitigate plating in lithium-ion batteries. These strategies allow engineers to determine the best way to adjust current densities during charging, based on the state of charge and the material properties, to avoid lithium plating.Li plans to further develop her model by designing real-time adaptive charging protocols using the insights from our model.
“We’re integrating machine learning and control theory to create algorithms that can adjust charging rates on the fly, minimizing plating risk while maximizing speed,” she said. “This includes expanding the framework to study alternative anodes, like silicon and sodium, and their unique mechanical and chemical challenges.”
Li’s methodology for identifying critical conditions leading to failure or inefficiency can be applied beyond batteries. For instance, any system involving reactive transport in constrained environments—such as catalysis, fuel cells, or even biological systems—could benefit from the analytical insights and control frameworks she has developed.
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“What excites me most,” Li said, “is how modeling can serve as a bridge between disciplines. By blending theory, simulation, and data-driven techniques, we can make battery science more predictive and translatable. I hope this work encourages greater integration of mechanical engineering perspectives in electrochemical system design, and vice versa.”
Mark Crawford is a technology writer in Corrales, N.M.
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