The low cost, significant reduction potential and relative safety of the zinc electrode is a common hope for a reductant in secondary batteries, but it is limited mainly to primary implementation due to shape change. In this work, we exploit such shape change for the benefit of static electrodes through the electrodeposition of hyper-dendritic nanoporous zinc foam. Electrodeposition of zinc foam resulted in nanoparticles formed on secondary dendrites in a three-dimensional network with a particle size distribution of 54.1-96.0 nm. The nanoporous zinc foam contributed to highly oriented crystals, high surface area and more rapid kinetics in contrast to conventional zinc in alkaline mediums. The anode material presented had a utilization of 88% at full depth-of-discharge (DOD) at various rates indicating a superb rate capability. The rechargeability of Zn 0 /Zn 2+ showed significant capacity retention over 100 cycles at a 40% DOD to ensure that the dendritic core structure was imperforated. The dendritic architecture was densified upon charge-discharge cycling and presented superior performance compared with bulk zinc electrodes.

Ultrasonic analysis was used to predict the state of charge and state of health of lithium-ion pouch cells that have been cycled for several hundred cycles. The repeatable ultrasonic trends are reduced to two key metrics: time of flight shift and total signal amplitude, which are then used with voltage data in a supervised machine learning technique to build a model for state of charge (SOC) prediction. Using this model, cell SOC is predicted to ∼1% accuracy for both lithium cobalt oxide and lithium iron phosphate cells. Elastic wave propagation theory is used to explain that the changes in ultrasonic signal are related to changes in the material properties of the active materials (i.e., elastic modulus and density) during cycling. Finally, we show the machine learning model can accurately predict cell state of health with an error ∼1%. This is accomplished by extending the data inputs into the model to include full ultrasonic waveforms at top of charge. A key component of an electric vehicle is the battery management system (BMS), which is responsible for controlling the operating conditions on a given battery cell (or stack of cells) in order to optimize the performance and lifetime of the full battery system. The most effective battery management systems must be able to track battery state of charge (SOC), state of health (SOH) and cell failure, including early prediction of catastrophic failure. Despite the importance of this task, being able to reliably determine SOC, SOH and failure at low cost still presents a significant challenge. A range of methods exist at present, however the simplest methods can prove inaccurate, and more complex methods are not suitable for low-cost, in-operando SOC determination. 1-3For instance, in its most common implementation, SOC prediction consists of voltage monitoring (direct measurement) combined with coulomb-counting (book-keeping).1 This can present challenges for a variety of reasons. First, for voltage measurements, the flatness of voltage readings over the majority of battery capacity, especially for lithium iron phosphate (LFP) cells, presents difficulties. 4 Furthermore, voltage fade, changing cell impedances, and varying discharge rates impact measured voltage, obscuring true SOC. Second, coulomb counting is also an inexact science, as discharge rate, environmental factors such as temperature, and cell degradation can all impact the actual capacity for any given discharge. This can lead to a cycle of abuse, whereby discharge conditions lead to an incorrect estimate of SOC, and therefore the cell becomes inadvertently over-discharged. This causes damage to the cell, which leads to further inaccuracy in the SOC prediction, resulting in continued over-discharging and cell damage. Effectively, a battery "death-spiral" ensues.One method to further increase the accuracy of battery management systems is to introduce a technique that can directly measure the physical state of the battery to enhance the determination of SOC, SOH, and cell failure, especially when applied i...

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Understanding the evolution of the electrochemical constituents of a battery during discharge can offer detailed information about state of charge as well as failure mechanisms. However, typical methods of characterizing the internal components of batteries are often only applicable post mortem. Previous work [1] has used energy-dispersive x-ray diffraction (EDXRD) spectroscopy to image discrete volumes within Zn-MnO2 “alkaline” batteries, and has shown the evolution of the internal components during discharge. Most notably, the oxidation of the anode from Zn to ZnO has been quantified as a function of state of charge. Recently, there has been popular interest [2] in the tendency of an alkaline AA battery to bounce after being dropped on its end when discharged to full capacity, compared to a flat landing with minimal bounce when the battery is as-received. This bounce test presents a non-destructive method of assessing the material properties of the battery, and thus the state of the electrochemical constituents. In this work, we present an explanation for this bouncing, and quantify it by measuring the coefficient of restitution (COR) of alkaline AA batteries as a function of depth of discharge (DOD). The COR is shown to be constant at low DOD, but then begins to rise rapidly at 20% DOD, finally saturating at a value of 0.63 +/- 0.05 at 50% DOD, as shown in Fig. 1. We have found this rise and saturation to correlate strongly to EDXRD spectra, showing that increase in COR corresponds to the formation within the anode of a contiguous pathway of ZnO particles from the separator to the current collector. The saturation is best explained due to densification of the anode core to a porous ZnO solid. The process is outlined in Fig. 2, with Fig. 3 confirming this process through SEM micrographs of as-received and fully discharged batteries. Of note is the sensitivity of the COR to the amount of ZnO formation, which rivals the sensitivity of in situ energy-dispersive x-ray diffraction spectroscopy. Based on these results, we suggest future methods that can incorporate a transducer/detector system in which the state of charge of a cell can be measured in situ without interruption of the battery system operation. References: [1] J. W. Gallaway, C. K. Erdonmez, Z. Zhong, M. Croft, L. A. Sviridov, T. Z. Sholklapper, D. E. Turney, S. Banerjee, and D. A. Steingart, J. Mater. Chem. A 2, 2757 (2014). [2] "How to test a AA battery, easiest way for any battery fast, easy!" http://www.youtube.com/watch?v=Y_m6p99l6ME (2013). Acknowledgements: This work was performed with financial support from the National Science Foundation CMMI 1402872, Department of Energy ARPA-E RANGE DE-AR0000400, and the Laboratory Directed Research and Development Program of Brookhaven National Laboratory (LDRD-BNL) under Contract No. DE-AC02-98CH 10866 with the U.S. Department of Energy. Use of the National Synchrotron Light Source, Brookhaven National Laboratory, was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-98CH10886. S. B. also acknowledges the Rutgers-Princeton IGERT in Nanotechnology for Clean Energy. Figure captions: Fig. 1. Coefficient of restitution as a function of capacity passed. COR increases at 80% state of charge, and saturates at 50% state of charge. Fig. 2. The progression of ZnO formation in the anode. a) The initial anode gel comprised of Zn particles in an electrolyte/cellulose matrix. b) Formation of Type I ZnO shells on Zn particles. Oxidation occurs preferentially at the separator. c) Formation of a percolation pathway. As all particles become clad in ZnO shells, a contiguous network of ZnO-clad particles forms from separator to current collector (highlighted in green). d) Densification of the anode. Type I ZnO shells grow and Zn particles oxidize to Type II ZnO. Fig. 3. a) SEM image of as-received" cell, where the coarse zinc/electrolyte gel can be seen surrounding the current collector. b) SEM image of the same cell after full discharge (2850 mAh passed), the anode now largely converted to ZnO. A density gradient can be seen, with the region of compact growth closest to the separator. Scale bar = 1 mm