Forecasting Battery Performance: A (Big) Data Approach at Fluence

We aim to address the benefits and potential challenges of using data from energy storage sites and Fluence labs to predict battery life. To achieve this, we first discuss the substantial amount of data we collect from each site, which can reach Terabytes of battery data annually, in addition to our in-house lab testing data. Our research employs a combination of empirical analysis, Artificial Intelligence, and machine learning-based techniques to leverage this data effectively for predicting battery life. Our presentation highlights how Fluence harnesses both empirical observations and advanced Artificial Intelligence algorithms, along with machine learning, to develop robust methods for predicting battery life. This research holds significant implications for optimizing battery management systems, providing a promising solution to the long standing challenge of accurately predicting battery life in large-scale energy storage settings. By combining empirical insights with the power of Artificial Intelligence and machine learning capabilities, we aim to enhance the efficiency and performance of energy storage systems, ultimately benefiting both industry and consumers.