Thermal Energy Storage (TES) devices that use Phase Change Materials (PCMs) are becoming increasingly important in thermal management systems. TES devices leverage the constant-temperature thermal capacity of PCMs to decouple the absorption and rejection of thermal energy, similar to how batteries function in electrical systems. To maximize its efficiency and capabilities, control-oriented models of TES are necessary to predict its behavior and make informed control decisions. This paper proposes a switched Moving Boundary (MB) model that captures the key dynamics of the TES with significantly fewer dynamic states. The MB model uses a graph-based modeling approach to represent heat flow through the TES and a MB approach to model the time-varying liquid and solid regions of the PCM. Additionally, a Finite State Machine (FSM) is used to switch between four different modes of operation based on the State-of-Charge (SOC) of the TES. Numerical simulations comparing the proposed MB approach with traditional Fixed Grid (FG) approaches show that it accurately models the behavior of FG models while using far fewer states, leading to five times faster simulations. This makes it an attractive option for real-time control applications where speed is critical. Their research highlights how MB models can be used as effective tools for predicting TES behavior and making informed control decisions in various thermal management systems.
Thermal Energy Storage (TES) devices are important in systems that control temperature. They use special materials called Phase Change Materials (PCMs) to store and release heat at a constant temperature. To make sure they work well, we need models that can predict how they behave and help us make good decisions about how to use them. One kind of model is called a Switched Moving Boundary (MB) model, which uses graphs to show how heat moves through the TES device and how the PCM changes from liquid to solid. We can use a Finite State Machine (FSM) to switch between different modes of operation based on how much energy is stored in the TES device. These models can help us understand TES devices better and make them work more efficiently in different kinds of systems.
Definitions
- Thermal Energy Storage (TES): A system that stores thermal energy for later use.
- Phase Change Materials (PCMs): Special materials that can absorb or release heat at a constant temperature when they change from liquid to solid or vice versa.
- Control-oriented models: Mathematical models used to predict behavior and make informed control decisions.
- Moving Boundary (MB) model: A type of mathematical model used to describe the behavior of PCMs in TES devices.
- Finite State Machine (FSM): A mathematical tool used to switch between different modes of operation based on certain conditions or inputs.
Thermal Energy Storage (TES) Devices and Phase Change Materials (PCMs)
The use of Thermal Energy Storage (TES) devices that leverage the constant-temperature thermal capacity of Phase Change Materials (PCMs) is becoming increasingly important in thermal management systems. TES devices are similar to batteries in electrical systems, as they decouple the absorption and rejection of thermal energy. To maximize its efficiency and capabilities, control-oriented models are necessary to predict its behavior and make informed control decisions.
Switched Moving Boundary Model
This paper proposes a switched Moving Boundary (MB) model that captures the key dynamics of the TES with significantly fewer dynamic states than traditional Fixed Grid (FG) approaches. The MB model uses a graph-based modeling approach to represent heat flow through the TES and a MB approach to model the time-varying liquid and solid regions of the PCM. Additionally, a Finite State Machine (FSM) is used to switch between four different modes of operation based on the State-of-Charge (SOC) of the TES.
Numerical Simulations
Numerical simulations comparing this proposed MB approach with FG approaches show that it accurately models the behavior while using far fewer states, leading to five times faster simulations. This makes it an attractive option for real-time control applications where speed is critical. Their research highlights how MB models can be used as effective tools for predicting TES behavior and making informed control decisions in various thermal management systems.
Conclusion
In conclusion, Thermal Energy Storage devices have become increasingly important in thermal management systems due to their ability to decouple absorption and rejection of thermal energy similarly to how batteries function in electrical systems. This paper proposes a switched Moving Boundary model which captures key dynamics with significantly fewer dynamic states than traditional Fixed Grid approaches while still accurately modeling its behavior at five times faster speeds - making it an attractive option for real-time control applications where speed is critical.
Thermal Energy Storage (TES) devices, which leverage the constant-temperature thermal capacity of the latent heat of a Phase Change Material (PCM), provide benefits to a variety of thermal management systems by decoupling the absorption and rejection of thermal energy. While performing a role similar to a battery in an electrical system, it is critical to know when to charge (freeze) and discharge (melt) the TES to maximize the capabilities and efficiency of the overall system. Therefore, control-oriented models of TES are needed to predict the behavior of the TES and make informed control decisions. While existing modeling approaches divide the TES in to multiple sections using a Fixed Grid (FG) approach, this paper proposes a switched Moving Boundary (MB) model that captures the key dynamics of the TES with significantly fewer dynamic states. Specifically, a graph-based modeling approach is used to model the heat flow through the TES and a MB approach is used to model the time-varying liquid and solid regions of the TES. Additionally, a Finite State Machine (FSM) is used to switch between four different modes of operation based on the State-of-Charge (SOC) of the TES. Numerical simulations comparing the proposed approach with a more traditional FG approach show that the MB model is capable of accurately modeling the behavior of the FG model while using far fewer states, leading to five times faster simulations.
