Energy storage exhibits are pictured during the 135th session of the China Import and Export Fair in Guangzhou, south China's Guangdong Province, April 15, 2024 (Xinhua/Deng Hua)
Chinese researchers have proposed a new type of deep learning model to predict the lifetime of lithium-ion batteries (LIBs), according to a recent article published in the journal IEEE Transactions on Transportation Electrification.
The deep learning model effectively eliminated the dependence on a large amount of charging test data and provided a new idea for predicting battery life in real-time.
The article noted that the accurate lifetime prediction of LIBs is essential to the normal and effective operation of electric devices. However, such estimation faces huge challenges due to the nonlinear capacity degradation process and uncertain operating conditions of LIBs.
The researchers from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences and the Xi'an Jiaotong University proposed a deep learning model based on a small amount of charge cycle data to predict the target battery's current cycle life and remaining useful life.
The learning model can accurately predict the battery's current cycle life and remaining service life using only 15 charge cycle data. According to the experiment results, this data can make an accurate prediction.
The proposed model is expected to provide a solution for intelligent battery management, said CHEN Zhongwei, the director of the State Key Laboratory of Catalysis, DICP. (Xinhua)
Electric cars seem to be an ideal option among next-generation automobiles: They are eco-friendly and accelerate fast with less noise, among other benefits. The trouble is that every 300 kilometers you may have to wait hours to refill your batteries at a charging station.
Because of sodium's high abundance, low cost, and very suitable redox potential, effort has been made to develop applicable sodium-ion batteries. The research group led by Prof. YU Yan from University of Science and Technology of China of Chinese Academy of Sciences has realized remarkable electrochemical performance of phosphorus-based anode materials for sodium-ion batteries in long cycle life and rate capability by developing one rational strategy. This breakthrough enables sodium-ion battery to replace lithium-ion battery.
86-10-68597521 (day)
86-10-68597289 (night)
86-10-68511095 (day)
86-10-68512458 (night)
cas_en@cas.cn
52 Sanlihe Rd., Xicheng District,
Beijing, China (100864)
Copyright © 2002 - Chinese Academy of Sciences
