The Energy Storage Report 2024

Now available to download, covering deployments, technology, policy and finance in the energy storage market

How to use AI & machine learning to predict battery lifecycles


Batteries for stationary storage applications usually have design lifetimes of between 10 and 15 years. But even batteries produced by the same machine and on the same day show substantial differences in their aging behavior. With BESS playing an integral role in the transition to renewables and the long-term sustainability of our energy grid, strategic battery asset management and monitoring deployed assets are key to reducing operational risks and maximising profitability.

This webinar is for project leaders of BESS systems, asset managers, owners and operators who want to accurately track and predict battery safety, performance, and aging. We will present how to build a data-driven foundation for BESS asset management using cloud-based battery analytics, how to leverage existing data from the battery management systems (BMS) and differentiate the role of the BMS from analytics.

Learn more about best practices for:

  • Safety first: Predicting and preventing fires and downtimes
  • Data-driven battery analytics in the cloud
  • Identifying underperforming systems and strategies to get assets back on track
  • Using battery fitness tracking to improve procurement
  • Optimizing operation strategies and conditions to extend asset lifetimes
  • Battery data acquisition and cloud storage

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