Electric vehicles (EVs) have emerged as a cornerstone of sustainable transportation, but their widespread adoption faces a critical safety challenge: lithium plating in lithium-ion batteries (LIBs). Lithium plating occurs when lithium ions accumulate on the surface of a battery’s negative electrode rather than intercalating properly into the graphite structure. This phenomenon typically happens during fast charging, at low temperatures, or at high states of charge, which can lead to rapid capacity degradation and even catastrophic safety incidents. Traditional detection methods either require specialized equipment or lack sufficient accuracy for real-world applications. Researchers from University of Shanghai for Science and Technology have now developed a feasible solution that could optimize EV battery safety monitoring.
This new research introduces an intelligent lithium plating detection and early warning system based on the Random Forest machine learning algorithm. This innovative approach analyzes data collected during pulse charging to identify the subtle electrical signatures that indicate lithium plating is occurring. What makes this approach novel is its ability to detect lithium plating with over 97.2% accuracy using only external electrical measurements. This means the technology can be implemented without modifying existing battery systems.
One of the key innovations in this research is the feature extraction method that transforms limited battery data into rich, multi-dimensional features. The researchers demonstrated that using single features alone achieved only 68.5% detection accuracy, while their multi-dimensional approach pushed accuracy beyond 97%. The system analyzes normalized internal resistance patterns and relaxation voltage characteristics during pulse charging, extracting subtle patterns that indicate lithium plating long before it becomes dangerous. This early detection capability could prevent battery degradation and potentially catastrophic safety incidents. The technology has immediate applications for electric vehicle manufacturers and battery management system developers. Because it requires only standard electrical measurements, it can be deployed as a software update to existing battery management systems or implemented in cloud-based monitoring platforms.
Looking ahead, the researchers plan to expand their dataset to include different types of lithium-ion batteries, enhancing the model’s versatility across various battery chemistries and form factors. This would make the technology applicable not just to EVs but also to energy storage systems, consumer electronics, and other applications relying on lithium-ion technology. The team is also exploring how this technology could be integrated with fast-charging protocols to dynamically adjust charging parameters based on real-time lithium plating risk assessment, potentially enabling faster charging without compromising battery longevity or safety.
As electric vehicles continue to replace internal combustion engines worldwide, technologies like this intelligent lithium plating detection system will play a crucial role in ensuring that the transition to electric mobility is not just environmentally beneficial but also safe and reliable for all users.
