Dynamic heat and mass transfer model of an electric oven for energy analysis

In this paper, a new heat and mass transfer model for an electric oven and the load placed inside is presented. The developed model is based on a linear lumped parameter structure that differentiates the main components of the appliance and the load, therefore reproducing the thermal dynamics of several elements of the system including the heaters or the interior of the product. Besides, an expression to estimate the water evaporation rate of the thermal load has been developed and integrated in the model so that heat and mass transfer phenomena are made interdependent. Simulations and experiments have been carried out for different cooking methods, and the subsequent energy results, including energy and power time-dependent distributions, are presented. The very low computational needs of the model make it ideal for optimization processes involving a high number of simulations. This feature, together with the energy information also provided by the model, will permit the design of new ovens and control algorithms that may outperform the present ones in terms of energy efficiency

Simplified models for heating system optimisation using a thermal-electrical analogy

The well-known electrical analogy for thermal modelling is based on the observation that Fourier's equation for one dimensional heat transfer takes the same form as Ohm's law. This provides a system for creating and resolving complex heat transfer problems using an established set of physically-based laws. The present article illustrates the concept for adjacent rooms in a modern university building, and investigates some of the modelling issues involved. The electrical analogy is chosen so that the models can be extended and used for future research into demand-side control of multiple buildings on the university network, requiring a fast computation time. For illustrative purposes, the present article is limited to a relatively straightforward two-room system, for which the modelling equations are conveniently represented and solved using MATLAB-SIMULINK. The coefficients of this model are estimated from data using standard nonlinear optimisation tools. For comparison, the article also develops an equivalent multiple-input Transfer Function form of the model. Finally, suggestions are made for the inclusion of occupancy estimates in the model

A cost and time effective novel methodology to determine specific heat capacity of lithium-ion cells

A cost and time-effective novel method for determining the specific heat capacity of prismatic and pouch lithium-ion cells using a simple setup and easily available equipment is presented in this paper. Specific heat capacity is an important thermal parameter of lithium-ion cells which is not readily available or provided by the cell manufacturers. The results found in this work are compared with the calorimetrically determined values of the specific heat capacity and a maximum error of 5% and 1.4% have been found for the pouch and the prismatic cells, respectively. The minimum error in the specific heat capacity for the pouch and the prismatic cells are 0.7% and 0.1%, respectively. The thermal parameters obtained using this methodology have been used to model the surface temperature of a prismatic cell during the application of a dynamic pulsed power as well as New European Driving Cycle (NEDC) and Worldwide harmonized Light vehicles Test Cycle (WLTC) drive cycles-based power traces. The excellent matching of the measured and simulated cell\u27s surface temperature during both the NEDC and WLTC drive cycles demonstrates that the thermal parameters determined using this new method can be used to model the surface temperature of the cell

Thermal modeling, analysis and control using an electrical analogy

Modeling and identification of thermal systems is a problem frequently treated in theoretical and application domains. Most of these systems have been modeled using black-box structures whose parameters are identified using temperature measurements. Although black-box models have achieved good results in terms of temperature evolution, they cannot model variables which had not been measured in the identification test. In this article we present a new method to build grey-box thermal models based on electrical equivalent circuits which not only give information about temperatures evolution, but also about heat fluxes and thermal energy stored in the system. The partially unknown parameters of the models are identified using temperature measurements and applying nonlinear optimization techniques. The obtained state space representation can be used to develop a deterministic state space temperature controller that provides better accuracy than classical PID controllers. Our proposal is complemented with various examples of a real application in an electric oven