We demonstrate that a simple acoustic time-of-flight experiment can measure the state of charge and state of health of almost any closed battery. An acoustic conservation law model describing the state of charge of a standard battery is proposed, and experimental acoustic results verify the simulated trends; furthermore, a framework relating changes in sound speed, via density and modulus changes, to state of charge and state of health within a battery is discussed. Regardless of the chemistry, the distribution of density within a battery must change as a function of state of charge and, along with density, the bulk moduli of the anode and cathode changes as well. The shifts in density and modulus also change the acoustic attenuation in a battery. Experimental results indicating both state-of-charge determination and irreversible physical changes are presented for two of the most ubiquitous batteries in the world, the lithium-ion 18650 and the alkaline LR6 (AA). Overall, a one-or two-point acoustic measurement can be related to the interaction of a pressure wave at multiple discrete interfaces within a battery, which in turn provides insights into state of charge, state of health, and mechanical evolution/degradation. Broader contextRecent advances in the mechanical understanding of electrochemical energy storage devices based on stress/strain investigations have provided significant improvements in battery systems and materials. To date, the field has lacked a non-invasive, field-deployable method for monitoring these complex mechanics in practical cells. Here, we show a simple model and experiment together as a potentially universal in operando, field-deployable tool for determining the mechanical evolution, state-of-charge and state-of-health of any closed battery using acoustic time of flight analysis. The technique is tested against off-theshelf lithium ion and alkaline batteries: the acoustics correlate strongly to state-of-charge and state-of-health on a second-to-second basis. This technique provides new physical measurements into two completely different batteries that were sold by the billion in 2014 alone. We show that property distributions of batteries can be determined in unmodified full cells in real time, in operando, without electrodes, and using only a single point of contact. In previous studies these measurements have been related, via complex lab equipment, to rapid battery fade as well as safety concerns. This work outlines simple methods to greatly increase these types of measurements in a manner that can be readily embedded into battery management systems.

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...

A minimal-architecture zinc–bromine battery that eliminates expensive balance-of-plant components is demonstrated with stable performance and low cost.

A Bi2O3 in β-MnO2 composite cathode material has been synthesized using a simple hydrothermal method and cycled in a mixed KOH-LiOH electrolyte with a range of concentrations. We show that, at a KOH:LiOH molar ratio of 1:3, both proton insertion and lithium insertion occur, allowing access to a higher fraction of the theoretical capacity of the MnO2 while preventing the formation of ZnMn2O4. This enables a capacity of 360 mAh/g for over 60 cycles, with cycling limited more by anode properties than traditional cathodic failure mechanisms. The structural changes occurring during cycling are characterized using electron microscopy and in situ synchrotron energy-dispersive X-ray diffraction (EDXRD) techniques. This mixed electrolyte shows exceptional cyclability and capacity and can be used as a drop-in replacement for current alkaline batteries, potentially drastically improving their cycle life and creating a wide range of new applications for this energy storage technology.

The colloidal stability of functionalized graphene sheets (FGSs) in aqueous sodium dodecyl sulfate (SDS) solutions of different concentrations was studied by optical microscopy and ultraviolet-visible light absorption after first dispersing the FGSs ultrasonically. In up to ∼10 μM SDS solutions, FGSs reaggregated within a few minutes, forming ramified structures in the absence of SDS and increasingly compact structures as the amount of SDS increased. Above ∼10 μM, the rate of reaggregation decreased with increasing SDS concentration; above ∼40 μM, the suspensions were colloidally stable for over a year. The concentration of ∼40 μM SDS lies 2 orders of magnitude below the critical surface aggregation concentration of ∼1.8 mM SDS on FGSs but above the concentration (∼18 μM) at which SDS begins to form a monolayer on FGSs. Neither surface micelle nor dense monolayer coverage is therefore required to obtain stable aqueous FGS dispersions. We support our experimental results by calculating the van der Waals and electrostatic interaction energies between FGSs as a function of SDS concentration and show that the experimentally observed transition from an unstable to a stable dispersion correlates with a transition from negative to positive interaction energies between FGSs in the aggregated state. Furthermore, our calculations support experimental evidence that aggregates tend to develop a compact structure over time.