A Review on Current Methods for Estimating the Condition of a Battery used in a Stationary Energy Storage System
Keywords:
Artificial intelligence (AI), Electrochemical, Energy storage systems (ESS), Machine learning (ML), Non-intrusive techniques, Renewable energy integrationAbstract
With the increasing deployment of stationary energy storage systems (ESS) for grid support and renewable energy integration, the reliable assessment of battery condition becomes paramount for ensuring system performance, longevity, and overall economic viability. This paper provides a comprehensive analysis of the current methods employed for estimating the condition of batteries in stationary energy storage systems. Electrochemical methods provide valuable insights into the internal state of batteries, but they often require specialized equipment and may not be suitable for continuous monitoring in operational settings. Furthermore, the paper discusses the importance of incorporating advanced diagnostic tools, such as thermal imaging and acoustics, to complement existing methods. These non-intrusive techniques can provide additional information about battery performance and ageing, offering a holistic approach to condition monitoring.