In the rapidly evolving energy sector, the efficient utilization of lithium-ion batteries (LiBs) is paramount, and a recent study published in the *World Electric Vehicle Journal* (or *Journal of Electric Vehicle* in English) offers a promising advancement in this arena. Researchers, led by Chunsheng Cui from the School of Electrical and Control Engineering at North University of China, have developed a novel method for predicting the remaining useful life (RUL) of LiBs, addressing critical challenges in data training and degradation smoothing.
The study introduces a construction method for periodic time series, which transforms the original degradation data of LiBs into a new dataset. This innovative approach tackles the issues of insufficient training data and smooth degradation, common hurdles in RUL interval prediction methods. By combining variational mode decomposition (VMD) and gated recurrent unit (GRU), the researchers have created a fusion model that significantly enhances the accuracy of RUL predictions.
“Our method not only improves the prediction accuracy but also provides a more reliable framework for the practical application of LiBs,” said Cui. This advancement is crucial for industries relying on LiBs, such as electric vehicles and renewable energy storage systems, where precise RUL predictions can lead to better resource management and cost savings.
The effectiveness of the proposed RUL interval prediction method was verified using the CALCE battery dataset and NCA battery dataset, demonstrating its potential for real-world applications. The study’s findings could revolutionize how industries approach battery management, ensuring more efficient and sustainable use of these critical energy storage devices.
As the energy sector continues to evolve, the ability to accurately predict the RUL of LiBs will play a pivotal role in optimizing performance and reducing waste. This research, published in the *World Electric Vehicle Journal*, marks a significant step forward in this direction, offering a robust tool for industries to leverage in their pursuit of energy efficiency and sustainability.