Control-oriented low-order models for the transient analysis of a domestic electric oven in natural convective mode. (25th January 2019) - Lucchi, M.
Highlights: Lumped-parameter models of a household oven in natural convective mode were created. Radiative contribution to heat transfer was accounted for through non-linear terms. The gray-box models were calibrated and validated experimentally. Non-linear terms improve the reliability of the model during transients. Abstract: Almost 25 % of the overall environmental impact of buildings can be attributed to appliances, among which electric domestic ovens represent a low-efficiency category when considering apartments. In order to improve their low efficiency, usually lying between 10 % and 12 %, the oven industry is often stimulated to develop more efficient technologies, among which there is the design of suitable strategies for the control of the oven center temperature, as required by the EN 60350-1 European standard, which regulates the energy consumption test. This work describes the simulation of the transient thermal behaviour of a domestic electric oven in natural convection heating mode by means of dynamic models suitable for control design purposes. A lumped-parameter approach based on the thermoelectric analogy was chosen, in order to obtain a low-order dynamic model with a proper level of discretization which allowed to reproduce the main temperatures within the cavity, in particular at the oven's centre and the temperature sensed by the probe used for oven control during normal operating conditions. Eight lumped parameters were introduced to represent walls, glass door, cavity air and temperature probe, and three parameters to simulate the thermal behaviour of the heaters were considered, resulting in an 11-th order model. Moreover, the influence of non-linearized radiative terms on model accuracy was investigated, leading to two different lumped-parameter, semi-physical models. Two sets of capacities and conductances were identified for both the models through an optimization procedure based on experimental data, giving a grey-box nature to the models; subsequently the prediction potential of the models was verified at different temperature set-points.