The focus of this work is on two energy storage technologies, namely pumped storage hydroelectricity (PHS) and secondary batteries. Under secondary battery technologies, two potential technologies for grid-scale storage, namely high-temperature sodium-sulfur (NaS) battery and vanadium redox flow battery (VRFB), are investigated. PHS is a largescale (\u3e100 MW) technology that stores and generates energy by transporting water between two reservoirs at different elevations. The goal is to develop a detailed dynamic model of PHS and then design the controllers to follow the desired load trajectory accurately with high efficiency. The NaS battery and VRFB are advanced secondary batteries which can be charged and discharged rapidly. Since temperature excursion of high temperature NaS batteries especially under fast cycling conditions is a safety hazard and the temperature excursion can take place at some location within the cell where measurement is not feasible, the focus is on a model-based approach for transient analysis and development of novel thermal management techniques. A detailed thermo-electrochemical dynamic model of a single NaS has been developed. As a detailed cell model is computationally intractable for simulating large number of cells in the battery, various strategies such as coordinate transformation, orthogonal collocation, and model reformulation have been developed to obtain a reduced order model that solves significantly faster than the full, high-dimensional model but provides an accurate estimate of the key variables such as transient voltage/current/temperature profile in the cell. Sodium sulfur batteries need to be maintained within a temperature range of 300-4000C. Therefore, the focus was on developing thermal management strategies that can not only maintain the cell temperature near the optimum, but can effectively utilize the heat, improving the overall efficiency of the battery system. VRFBs can provide large amount of storage as the electrolytes are stored in separate tanks. However, the self-discharge reactions (due to crossover) along with the undesired side reactions and the dissolved water in the membrane, can significantly reduce the capacity. A dynamic model-based approach is developed for detection, identification, and estimation of capacity fade and SOC as a function of time. A model-based prognostic capability has been developed for estimating the remaining useful cell life
