Electric vehicles (EVs) are spreading rapidly and many counties are promoting hybrid and fully EVs through legislation. Therefore, an increasing amount of lithium ion batteries will reach the end of their usable life and will require effective and sustainable end-of-life management plan which include landfill disposal or incineration. The current research focuses on more sustainable methods such as remanufacturing, reuse and recycling in order to prepare for future battery compositions and provide insights to the need recycling methods to be developed to handle large amounts of batteries sustainably in the near future. The two most prominent material recovery techniques are hydrometallurgy and pyrometallurgy which are explored and assessed on their relative effectiveness, sustainability, and feasibility. Hydrometallurgy is a superior recycling method due to high material recovery and purity, very low emissions, high prevalence of chemical reuse and implementation of environmentally sustainable compounds. Expanding recycling technologies globally should take the research and technologies pioneered by Umicore to establish a sustainable recycling program for end-of-life EVs batteries. Emerging battery technology of Telsa show the most effective designs for high performance batteries includes the use of silicon which is expected to increase capacity of batteries in the future.

No abstract

No abstract

<p>Producing accurate hourly streamflow forecasts in large basins is difficult without a distributed model to represent both streamflow routing through the river network and the spatial heterogeneity of land and weather conditions. HydroForecast is a theory-guided deep learning flow forecasting product that consists of short-term (hourly predictions out to 10 days), seasonal (10 day predictions out to a year), and daily reanalysis models. This work focuses primarily on the short-term model which has award winning accuracy across a wide range of basins.</p> <p>In this work, we discuss the implementation of a novel distributed flow forecasting capability of HydroForecast, which splits basins into smaller sub-basins and routes flows from each subbasin to the downstream forecast points of interest. The entire model is implemented as a deep neural network allowing end-to-end training of both sub-basin runoff prediction and flow routing. The model's routing component predicts a unit hydrograph of flow travel time at each river reach and timestep allowing us to inspect and interpret the learned river routing and to seamlessly incorporate any upstream gauge data. </p> <p>We compare the accuracy of this distributed model to our original flow forecasting model at selected sites and discuss future improvements that will be made to this model.</p>

No abstract