Browsing by Subject "renewable energy sources"

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    Achieving Energy-Saving, Continuous Redox Flow Desalination with Iron Chelate Redoxmers
    (AAAS, 2023-01-10) Xie, Rongxuan; Yue, Diqing; Peng, Zhenmeng; Wie, Xiaoliang; Mechanical Engineering, School of Engineering and Technology
    Desalination of saline water is becoming an increasingly critical strategy to overcome the global challenge of drinkable water shortage, but current desalination methods are often plagued with major drawbacks of high energy consumption, high capital cost, or low desalination capacity. To address these drawbacks, we have developed a unique continuous-mode redox flow desalination approach capitalizing on the characteristics of redox flow batteries. The operation is based on shuttled redox cycles of very dilute Fe2+/Fe3+ chelate redoxmers with ultralow cell overpotentials. The air instability of Fe2+ chelate is naturally compensated for by its in situ electrochemical generation, making the desalination system capable of operations with electrolytes at any specified state of charge. Under unoptimized conditions, fast desalination rates up to 404.4 mmol·m−2·h−1 and specific energy consumptions as low as 7.9 Wh·molNaCl−1 have been successfully achieved. Interestingly, this desalination method has offered an opportunity of sustainable, distributed drinkable water supplies through direct integration with renewable energy sources such as solar power. Therefore, our redox flow desalination design has demonstrated competitive desalination performance, promising to provide an energy-saving, high-capacity, robust, cost-effective desalination solution.
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    Application of Adaptive Estimation Techniques on Battery Fault Diagnosis
    (Office of the Vice Chancellor for Research, 2013-04-05) Singh, Amardeep; Izadian, Afshin; Anwar, Sohel
    High energy storage systems like Li-ion Batteries are one of the most widely used renewable energy sources today. They find applications in everyday electronic gadgetry, critical medical devices, hybrid & electric vehicles to name a few. Our study aims to observe continuously the state of the Li-ion battery and detect Over Charge (OC) and Over Discharge (OD) failures occurring in real time. Both conditions are detrimental to the health of the battery, while over charge can lead to overheating and thus vaporization of active material and hence explosion, over discharge can short the battery cell. However, these types of failures can be detected before they occur and by raising a flag before the system reaches the failure condition such failure modes can be avoided. Different battery models based on equivalent circuit approach are constructed using the impedance spectroscopy data from Li-ion battery cells. Kalman filters are used to estimate the state of each system and subsequent residuals are generated for each model. Multiple model adaptive estimation is then used, where the generated residuals are evaluated and the fault probabilities are generated. Based on these probabilities, the system is classified as normal operation, OC fault or OD fault. Simulation results show that the battery faults can be detected and diagnosed in real time, thereby proving to an effective way of Li-ion battery fault diagnosis.