Our company's large-scale industrial lead-acid battery "LL1500-W series" was developed for the purpose of mitigating fluctuations in wind power plants. It has been installed in several locations since 2010, and operation data has been accumulated. Life prediction based on the analysis of operation data is a core technology of our "energy storage solutions business", and in recent years, big data analysis using AI has become indispensable, as it has rapidly developed. JMP, which has functions suitable for exploratory data analysis, was effective in analyzing a wide range of data, such as time-series data during operation and regular inspection data, and in interpreting the relationships between individual analysis results from a bird's-eye view. In this study, we analyzed the operation data of the LL1500-W large-scale energy storage system and obtained knowledge about factors that affect life prediction and life, such as long-term changes in the relationship between current and voltage, and the impact of differences in the installation environment between batteries on life. By utilizing this knowledge in the analysis of each installation location, we can obtain the deterioration state of the storage batteries in the system in real time, and we expect to develop an energy storage service that simultaneously meets the user's energy storage needs and the conservation of battery life.
