Battery Performace Diagnostics

Data-Driven Battery Management System for Enhanced Reliability and Safety in Energy Storage Applications

• This research aims to develop an advanced, data-driven Battery Management System (BMS) to ensure the reliability and safety of batteries used in energy storage applications across various sectors. With the increasing expansion of application areas, this system also integrates online learning technologies to enable optimized energy management, adapting to individual consumption patterns. 

• The study focuses on designing a comprehensive framework that includes the development of cutting-edge battery degradation characteristic extraction methods and an early warning system to detect anomalies, ensuring continued battery reliability and safety. The overarching goal is to establish an optimal management model to improve the longevity and efficiency of energy storage systems.

Development of a Smart Battery Management System for Enhanced Performance, Reliability, and Safety

• This research focuses on the development of a data-driven Battery Management System (BMS) designed to enhance the performance, reliability, and safety of batteries used in energy storage applications. As battery usage expands across various industries, there is an increasing need for intelligent systems capable of managing and optimizing battery health and energy efficiency. 

• The proposed BMS leverages advanced data analytics and machine learning techniques to monitor battery behavior, predict degradation patterns, and provide real-time management solutions. In addition, the system incorporates a highly efficient management algorithm that dynamically adjusts operational parameters, such as charging/discharging cycles and energy distribution, to optimize battery life and performance. This algorithm uses historical and real-time data to make predictive adjustments, ensuring that the battery operates within optimal conditions while preventing overuse or underuse.