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“Our BMS is already providing us with data. We don’t need cloud-based battery analytics.” This common misconception is one we often encounter with new customers. Battery Management Systems (BMS) and predictive analytics are not interchangeable; they are pieces of the same puzzle, ensuring performance and safety. A BMS intervenes during acute issues, while predictive analytics foresees critical developments and ensures asset health. Learn more about the synergy between BMS and predictive analytics in this blog article.
Battery Energy Storage Systems (BESS) are playing a pivotal role for renewable energies. These BESS are composed of thousands of battery modules, each containing multiple cells connected in serial and parallel. This makes them extremely complex—requiring vigilant supervision and management. This is where the BMS comes into play—serving as the brains of the battery modules. Despite its vital role, the BMS has limitations. In this article, we'll delve into standard BMS functionalities, explore its limitations, and highlight the symbiotic role of cloud-based analytics.
Battery management systems (BMS) monitor and manage individual battery cells within a Battery Energy Storage System (BESS). A BESS is comprised of multiple racks, each comprised of several battery modules. Each module is equipped with at least one BMS responsible for overseeing the battery cells in real time.
The primary functions of a battery management system include:
1 Additional measures such as moisture, gas, acceleration, or electrochemical impedance spectroscopy (EIS) are possible.
The main role of a BMS is to react swiftly to immediate issues or anomalies within the battery system. This reactive approach is essential to prevent catastrophic failures, overheating, or other hazardous situations. The BMS relies on robust and fast-reacting algorithms based on rules or logic to detect predefined threshold violations and respond promptly. However, rules and pre-defined thresholds do not take into account the unique operating conditions of the particular storage asset.
While the BMS serves as a vital guardian of battery modules, it has certain limitations:
The BMS limitations mentioned can be addressed through cloud-computing analytics. With advanced algorithms, such as battery modeling and machine learning-based techniques, cloud computing can provide insights that go far beyond the onsite BMS. For instance, cloud-based analytics can pinpoint battery anomalies before they escalate into dangerous situations. Or it can identify underperforming cells that are resulting in lost revenue for asset owners.
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In the evolving landscape of energy storage, BMS and cloud-based battery data analytics have a symbiotic relationship that ensures the reliability, performance, and longevity of the system. While the BMS serves as the immediate guardian of battery health, cloud analytics offer an additional layer of value and safety.
Together, they collectively enhance safety, facilitate proactive maintenance, and improve the overall performance of energy storage systems. As renewable energy adoption continues to grow, the collaboration between BMS and cloud analytics will play a pivotal role in ensuring the sustainability and reliability of our energy infrastructure.
Dr. Georg Angenendt is a scientist and entrepreneur with expertise in mobility and utility-scale battery energy storage systems (BESS). His research on testing, modeling, commissioning, and optimization of battery storage systems has been published in international journals and at conferences. Since 2020, he is the Chief Technology Officer at ACCURE Battery Intelligence, developing advanced analytics software to help companies assess battery risk, ensure safety, and maximize asset value. His personal passion is Martial Arts: mixed martial arts, luta livre, grappling, boxing and Brazilian jiu-jitsu.