State of charge estimation for latent-heat thermal energy storage using data-driven hysteresis modeling and Koopman state observer

Key Points

• Accurate state of charge estimation is crucial for managing latent-heat thermal energy storage systems, enhancing efficiency in energy use.
• Using a data-driven hysteresis model, the approach significantly improves charge estimation accuracy up to 90%, as shown in various tests.
• This analysis employs a Koopman state observer, which helps interpret complex dynamical systems for better predictive modeling.
• Improved methods may enable more effective and efficient energy management strategies; further validation needed in diverse conditions.