成果報告書詳細
管理番号20130000000350
タイトル*平成24年度中間年報 安全・低コスト大規模蓄電システム技術開発/共通基盤研究/系統安定化用蓄電システムの劣化診断基盤技術の開発
公開日2013/5/29
報告書年度2012 - 2012
委託先名学校法人早稲田大学
プロジェクト番号P11007
部署名スマートコミュニティ部
和文要約
英文要約 This project aims to develop diagnosis technology of energy storage system for grid stabilization, and to contribute to introductions of natural energy equipments of the photovoltaic generation and wind power generation with energy storage system by the development of the diagnosis technology in the near future. In this project, "diagnosis technology development of large scale energy storage system" and "development of generation system for electric power change profile" were investigated for development of energy storage system for grid stabilization.
The capacity and the internal resistance of large-scale lithium ion batteries (LIBs) become higher and lower, respectively, for the application to electric vehicles and large-scale power storage systems. Electrochemical impedance spectroscopy (EIS) using conventional frequency response analyzer (FRA) - potentiostat systems is not easy to measure the impedance of the LIB because of its low internal resistance. In this situation, we focused on the power controller of LIB system to create a square wave for analyzing the battery health. We successfully obtained frequency response using several square potential and current inputs generated by power controller. New techniques called “Square-Potential / Current Electrochemical Impedance Spectroscopy (SP-EIS, SC-EIS)” demonstrated to be suitable method for EIS without using FRA systems. Additionally, we applied SC-EIS to evaluate a degradation of commercial LIB with the charge-discharge cycling. From results of non-degraded and degraded LIB, impedance spectra were roughly same as conventional FRA measurements.
To provide a general framework of generating plausible residential electric load and photovoltaic energy patterns for diagnosis of energy storage system, we improved our data imputation procedures for missing observations and generated yearly complete data sets of residential electric demand and photovoltaic energy for a preliminary test of diagnosis. We also proposed a selection method of some typical charge-discharge wave patterns used in energy storage diagnosis. In addition, we obtained real-world electricity data sets of wind turbines and preprocessed them towards a further generalization of the situations in which the diagnosis system is applicable.
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