Lithium-Ion Batteries: Latest Advances and Prospects

Li3V2(PO4)3 Cathode Material: Synthesis Method, High Lithium Diffusion Coefficient and Magnetic Inhomogeneity

Li3V2(PO4)3 cathodes for Li-ion batteries (LIBs) were synthesized using a hydrothermal method with the subsequent annealing in an argon atmosphere to achieve optimal properties. The X-ray diffraction analysis confirmed the material's single-phase nature, while the scanning electron microscopy revealed a granular structure, indicating a uniform particle size distribution, beneficial for electrochemical performance. Magnetometry and electron spin resonance studies were conducted to investigate the magnetic properties, confirming the presence of the relatively low concentration and highly uniform distribution of tetravalent vanadium ions (V4+), which indicated low lithium deficiency values in the original structure and a high degree of magnetic homogeneity in the sample, an essential factor for consistent electrochemical behavior. For this pure phase Li3V2(PO4)3 sample, devoid of any impurities such as carbon or salts, extensive electrochemical property testing was performed. These tests resulted in the experimental discovery of a remarkably high lithium diffusion coefficient D = 1.07 × 10-10 cm2/s, indicating excellent ionic conductivity, and demonstrated impressive stability of the material with sustained performance over 1000 charge-discharge cycles. Additionally, relithiated Li3V2(PO4)3 (after multiple electrochemical cycling) samples were investigated using scanning electron microscopy, magnetometry and electron spin resonance methods to determine the extent of degradation. The combination of high lithium diffusion coefficients, a low degradation rate and remarkable cycling stability positions this Li3V2(PO4)3 material as a promising candidate for advanced energy storage applications.

Computational Investigation of Carbon Based Anode Materials for Li- and Post-Li- Ion Batteries

Due to its negligible capacity with respect to sodium intercalation, graphite is not suited as anode material for sodium ion batteries. Hard carbon materials, on the other hand, provide reasonably high capacities at low insertion potential, making them a promising anode materials for sodium (and potassium) ion batteries. The particular nanostructure of these functionalized carbon-based materials has been found to be crucially linked to the material performance. However, there is still a lack of understanding with respect to the functional role of structural units, such as defects, for intercalation and storage. To overcome these problems, the intercalation of Li, Na, and K in graphitic model structures with distinct defect configurations has been investigated by density functional theory. The calculations confirm that defects are able to stabilize intercalation of larger alkali metal contents. At the same time, it is shown that a combination of phonon and band structure calculations are able to explain characteristic Raman features typically observed for alkali metal intercalation in hard carbon, furthermore allowing for the quantification of the alkali metal intercalation inbetween the layers of hard carbon anodes.

A Method for Predicting the Remaining Useful Life of Lithium Batteries Considering Capacity Regeneration and Random Fluctuations

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is important for electronic equipment. A new algorithm is proposed to aim at the nonlinear degradation caused by capacity regeneration and random fluctuations. Firstly, the health state degradation curve of LIBs is divided into the normal degradation trend part, capacity regeneration part, and random fluctuation part. Secondly, the capacity degradation curve of LIBs is decomposed by the empirical mode decomposition (EMD) to obtain the known long-term degradation trend part of LIBs. Then, the long short-term memory (LSTM) neural network is used to predict the future normal degradation trend part based on the known long-term degradation trend part of LIBs. In addition, the LIBs’ state of health (SOH), the initial state of charge (SOC), and the rest time are taken as the inputs of Gaussian process regression (GPR) to predict the LIBs’ capacity regeneration part. After that, random numbers obeying the Stable distribution are generated as the random fluctuation part of LIBs. Finally, the Monte Carlo simulation is used to predict the probability density distribution of the RUL of LIBs. The paper is verified by the LIBs’ public dataset provided by the University of Maryland. The experimental results show that the predicted RMSE of the proposed method is lower than 0.6%.