Internet of Things (IoT) data management and web interface for battery management system (BMS)

This thesis investigates the connection of a Battery Management System (BMS) to an Internet of Things (IoT) cloud server and using a web interface to monitor and control the batteries in the stack. Using the IoT connectivity for communication with a web interface and battery-data acquisition can benefit the BMS in a few aspects. Implementing this also ensures the safety and reliability of the battery systems when used in an industrial environment. The main premise of the thesis is extracting the key data measured by the BMS and translating it into meaningful information, such that it will be useful for algorithms built for large scale data. With the Artificial Intelligence (AI) and Machine Learning (ML) services provided by the cloud servers, new algorithms can be added for processing the data and improving our knowledge about the best methods for battery management. Improving battery management algorithms can result in several benefits to battery systems. These may include safer battery plants, extended battery life, improved accuracy for prediction of the performance of batteries used for second battery life, and improvement of battery chemistry.