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Remaining useful life (RUL) is a key indicator for assessing the health status of lithium (Li)-ion batteries, and realizing accurate and reliable RUL prediction is crucial for the proper operation of battery systems. As high-capacity Li batteries have more complex chemical properties, most of the current RUL prediction methods rely mainly on a priori knowledge to make judgments. As a result, prediction accuracy is not high. In this study, we developed a health indicator-capacity (HI-C) dual Gaussian process regression (GPR) model based on incremental capacity analysis (ICA) and optimized its kernel function to achieve accurate RUL prediction for 280 Ah high-capacity Li batteries. Validation against the United States National Aeronautics and Space Administration's four battery test datasets showed that the use of the HI-C dual GPR model resulted in a mean absolute percentage error and root mean square error of less than 0.02 and 0.04, respectively, for the four battery-rated capacity predictions. Additionally, this model achieved an absolute error of less than five battery failure turns. Compared with a single model, the HI-C dual GPR model not only had high accuracy but also solved the problem that the HI was not measurable in the actual battery operation, which made it more suitable for RUL prediction of Li batteries. [ABSTRACT FROM AUTHOR]

Abstract Remaining useful life (RUL) is a key indicator for assessing the health status of lithium (Li)-ion batteries, and realizing accurate and reliable RUL prediction is crucial for the proper operation of battery systems. As high-capacity Li batteries have more complex chemical properties, most of the current RUL prediction methods rely mainly on a priori knowledge to make judgments. As a result, prediction accuracy is not high. In this study, we developed a health indicator-capacity (HI-C) dual Gaussian process regression (GPR) model based on incremental capacity analysis (ICA) and optimized its kernel function to achieve accurate RUL prediction for 280 Ah high-capacity Li batteries. Validation against the United States National Aeronautics and Space Administration’s four battery test datasets showed that the use of the HI-C dual GPR model resulted in a mean absolute percentage error and root mean square error of less than 0.02 and 0.04, respectively, for the four battery-rated capacity predictions. Additionally, this model achieved an absolute error of less than five battery failure turns. Compared with a single model, the HI-C dual GPR model not only had high accuracy but also solved the problem that the HI was not measurable in the actual battery operation, which made it more suitable for RUL prediction of Li batteries.

Lithium (Li) metal is a promising anode material for high-energy-density Li batteries due to its high specific capacity. However, the uneven deposition of Li metal causes significant volume expansion and safety concerns. Here, we investigate the impact of a gradient-infused Li-metal anode using silver (Ag)-decorated carbonized cellulose fibers (Ag@CC) as a three-dimensional (3D) current collector. The loading level of the gradient-infused Li-metal anode is controlled by the thermal infusion time of molten Li. In particular, a 5 s infusion time in the Ag@CC current collector creates an appropriate space with a lithiophilic surface, resulting in improved cycling stability and a reduced volume expansion rate. Moreover, integrating a 5 s Ag@CC anode with a high-capacity cathode demonstrates superior electrochemical performance with minimal volume expansion. This suggests that a gradient-infused Li-metal anode using Ag@CC as a 3D current collector represents a novel design strategy for Li-metal-based high-capacity Li-ion batteries.