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- All Subjects: energy
- Creators: Arizona State University
Description
Buildings consume nearly 50% of the total energy in the United States, which drives the need to develop high-fidelity models for building energy systems. Extensive methods and techniques have been developed, studied, and applied to building energy simulation and forecasting, while most of work have focused on developing dedicated modeling approach for generic buildings. In this study, an integrated computationally efficient and high-fidelity building energy modeling framework is proposed, with the concentration on developing a generalized modeling approach for various types of buildings. First, a number of data-driven simulation models are reviewed and assessed on various types of computationally expensive simulation problems. Motivated by the conclusion that no model outperforms others if amortized over diverse problems, a meta-learning based recommendation system for data-driven simulation modeling is proposed. To test the feasibility of the proposed framework on the building energy system, an extended application of the recommendation system for short-term building energy forecasting is deployed on various buildings. Finally, Kalman filter-based data fusion technique is incorporated into the building recommendation system for on-line energy forecasting. Data fusion enables model calibration to update the state estimation in real-time, which filters out the noise and renders more accurate energy forecast. The framework is composed of two modules: off-line model recommendation module and on-line model calibration module. Specifically, the off-line model recommendation module includes 6 widely used data-driven simulation models, which are ranked by meta-learning recommendation system for off-line energy modeling on a given building scenario. Only a selective set of building physical and operational characteristic features is needed to complete the recommendation task. The on-line calibration module effectively addresses system uncertainties, where data fusion on off-line model is applied based on system identification and Kalman filtering methods. The developed data-driven modeling framework is validated on various genres of buildings, and the experimental results demonstrate desired performance on building energy forecasting in terms of accuracy and computational efficiency. The framework could be easily implemented into building energy model predictive control (MPC), demand response (DR) analysis and real-time operation decision support systems.
ContributorsCui, Can (Author) / Wu, Teresa (Thesis advisor) / Weir, Jeffery D. (Thesis advisor) / Li, Jing (Committee member) / Fowler, John (Committee member) / Hu, Mengqi (Committee member) / Arizona State University (Publisher)
Created2016
Description
Lithium ion batteries have emerged as the most popular energy storage system, but they pose safety issues under extreme temperatures or in the event of a thermal runaway. Lithium ion batteries with inorganic separators offer the advantage of safer operation. An inorganic separator for lithium ion battery was prepared by an improved method of blade coating α-Al2O3 slurry directly on the electrode followed by drying. The improved separator preparation involves a twice-coating process instead of coating the slurry all at once in order to obtain a thin (~40 µm) and uniform coat. It was also found that α-Al2O3 powder with particle size greater than the pore size in the electrode is preferable for obtaining a separator with 40 µm thickness and consistent cell performance. Unlike state-of-the-art polyolefin separators such as polypropylene (PP) which are selectively wettable with only certain electrolytes, the excellent electrolyte solvent wettability of α-Al2O3 allows the coated alumina separator to function with different electrolytes. The coated α-Al2O3 separator has a much higher resistance to temperature effects than its polyolefin counterparts, retaining its dimensional integrity at temperatures as high as 200ºC. This eliminates the possibility of a short circuit during thermal runaway. Lithium ion batteries assembled as half-cells and full cells with coated α-Al2O3 separator exhibit electrochemical performance comparable with that of polyolefin separators at room temperature. However, the cells with coated alumina separator shows better cycling performance under extreme temperatures in the temperature range of -30°C to 60°C. Therefore, the coated α-Al2O3 separator is very promising for application in safe lithium-ion batteries.
ContributorsSharma, Gaurav (Author) / Lin, Jerry Y.S. (Thesis advisor) / Chan, Candace (Committee member) / Kannan, Arunachala (Committee member) / Arizona State University (Publisher)
Created2016