Fast charging is a key differentiator for OEMs, but avoiding lithium plating remains a fundamental challenge in Li-ion battery system design. It can lead to capacity loss and severe safety risks. Our latest whitepaper explains how anode potential modeling can prevent lithium plating risks.
Fast charging is critical for EV adoption, but it increases the risk of lithium plating. Lithium plating occurs when lithium deposits as metal on the anode, rather than properly intercalating into the electrode material. This can lead to capacity loss, performance degradation, and serious safety risks such as internal short circuits and thermal runaway.
One of the biggest challenges in preventing lithium plating is that it cannot be directly measured in a full-cell setup. Instead, battery engineers rely on indirect indicators and extensive experimental testing, making it difficult to accurately define safe charging protocols.
At TWAICE, we’ve tackled this challenge with our latest Anode Potential Simulation Model. It enables fast and reliable lithium plating prediction to optimize charging strategies.
We’ve explored this topic in depth in our latest whitepaper, where we break down the mechanics of lithium plating and how anode potential modeling can optimize fast charging protocols.
The whitepaper describes:
.jpeg)
In the first session of the TWAICE & Camelot BESS Lifecycle Webinar Series, experts from Camelot Energy Group and TWAICE share hard-won lessons from real-world energy storage projects—helping you get BESS development right from the start.
View Webinar
(1).webp)
%20(1).webp)
.webp)
Marie Sayegh is Technical Solutions Engineer at TWAICE since 2023. She has a Degree in Electrical and Electronic Engineering from the Lebanese University and a PhD degree from the University of Technology of Compiègne, France in partnership with Safran Electrical & Power (previously Zodiac Aerospace). She has more than 10 years experience in battery's modeling and testing, and in developing Battery Management System algorithms especially State indicators such as State of Charge and State of Health.
Alexander Karger is a battery and machine learning engineer at TWAICE, where he works on developing cutting-edge battery models for the electric, thermal, and aging behavior. At the same time, he pursues a Ph.D. at the Chair of Electrical Energy Storage Technology led by Prof. Dr.-Ing. Andreas Jossen at the Technical University of Munich (TUM), and regularly publishes his latest findings. He holds an M.Sc. in Aerospace Engineering, having studied at TUM and the University of Stuttgart. His career includes experience with Porsche and GE Aviation. He joined TWAICE in 2019, initially to complete his master’s thesis.