Electrochemical Properties of Pristine and Vanadium Doped LiFePO4 Nanocrystallized Glasses

In our recent papers, it was shown that the thermal nanocrystallization of glassy analogs of selected cathode materials led to a substantial increase in electrical conductivity. The advantage of this technique is the lack of carbon additive during synthesis. In this paper, the electrochemical performance of nanocrystalline LiFePO4 (LFP) and LiFe0.88V0.08PO4 (LFVP) cathode materials was studied and compared with commercially purchased high-performance LiFePO4 (C-LFP). The structure of the nanocrystalline materials was confirmed using X-ray diffractometry. The laboratory cells were tested at a wide variety of loads ranging from 0.1 to 3 C-rate. Their performance is discussed with reference to their microstructure and electrical conductivity. LFP exhibited a modest electrochemical performance, while the gravimetric capacity of LFVP reached ca. 100 mAh/g. This value is lower than the theoretical capacity, probably due to the residual glassy matrix in which the nanocrystallites are embedded, and thus does not play a significant role in the electrochemistry of the material. The relative capacity fade at high loads was, however, comparable to that of the commercially purchased high-performance LFP. Further optimization of the crystallites-to-matrix ratio could possibly result in further improvement of the electrochemical performance of nanocrystallized LFVP glasses.

Highly Stable Low Redox Potential Quinone for Aqueous Flow Batteries

Aqueous organic redox flow batteries are promising candidates for large-scale energy storage. However, the design of stable and inexpensive electrolytes is challenging. Here, we report a highly stable, low redox potential, and potentially inexpensive negolyte species, sodium 3,3',3'',3'''-((9,10-anthraquinone-2,6-diyl)bis(azanetriyl))tetrakis(propane-1-sulfonate) (2,6-N-TSAQ), which is synthesized in a single step from inexpensive precursors. Pairing 2,6-N-TSAQ with potassium ferrocyanide at pH 14 yielded a battery with the highest open-circuit voltage, 1.14 V, of any anthraquinone-based cell with a capacity fade rate <10%/yr. When 2,6-N-TSAQ was cycled at neutral pH, it exhibited two orders of magnitude higher capacity fade rate. The great difference in anthraquinone cycling stability at different pH is interpreted in terms of the thermodynamics of the anthrone formation reaction. This work shows the great potential of organic synthetic chemistry for the development of viable flow battery electrolytes and demonstrates the remarkable performance improvements achievable with an understanding of decomposition mechanisms.

An End-to-End Learning Framework for Battery Capacity-Fade Trajectory Prediction Using Early Life Data

Predicting the capacity-fade trajectory of a lithium-ion (Li-ion) battery cell is a critical task given its broad utility throughout the battery product life cycle. Even more useful is estimating a battery cell’s capacity-fade trajectory when this cell has not exhibited any noticeable capacity degradation. Accurately predicting the entire capacity-fade trajectory using early life data enables more efficient cell design, operation, maintenance, and evaluation for second-life use. To accomplish this challenging task, we propose an end-to-end learning framework combining empirical capacity fade models and data-driven machine learning models, in which the two types of models are closely coupled. First, we evaluate the accuracy of a library of relevant empirical models which have been shown to model the observed capacity fade of Li-ion cells with reasonable accuracy. After selecting a model, we formulate an end-to-end learning problem that simultaneously fits the chosen empirical model to estimate the capacity fade curve and trains a machine learning model to estimate the best-fit parameters of the empirical model. By solving this end-to-end learning problem, rather than sequentially executing the separate tasks of fitting the capacity fade model and training the machine learning model, we achieve a more optimal solution which is shown to better balance these two objectives. Our proposed end-to-end learning framework is evaluated using a publicly available battery dataset consisting of 124 lithium-iron-phosphate/graphite cells charged with various fast-charging protocols. This dataset was split into training, primary test, and secondary test datasets. Our method performs on par with existing early prediction methods in terms of cycle life prediction, attaining root-mean-square errors of 84 cycles and 169 cycles for primary and secondary test datasets, respectively. In addition to the cycle life prediction, our method possesses a unique ability to predict the entire capacity-fade trajectory.