This paper provides an update of annual economic costs imposed by fall injuries. Such costs include medical, rehabilitation, hospital costs, and the costs of morbidity and mortality. These costs are projected to the year 2020, based on changing demographic trends. The market for slip and fall injury prevention is analyzed for the elderly and for those in the workplace—two high risk groups. Questions as to whether this market operates in a socially desirable manner, or whether government intervention is justified on efficiency grounds, are considered. Essential aspects of cost-benefit analysis are reviewed in the context of a prospective evaluation of interventions to prevent slip and fall injuries. The cost-benefit analysis framework is applied to part of the FICSIT experiment (a major intervention to reduce falls among the elderly) and to recent revisions in Occupational Safety and Health Administration regulations directed at reducing workplace falls.
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.
This study investigates the evolution of material and electrochemical properties in commercial lithium-ion batteries during cycling. Results indicate that as-received batteries undergo a post-formation break-in period, which is signified by an initial, rapid evolution of the battery's properties before stabilizing. Break-in corresponds to non-chemical crosstalk, whereby physical changes in the negative electrode affect the electrochemical performance of the positive electrode. These findings demonstrate how interplay between components during early cycles can affect the future battery performance.
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