As a crucial factor in the improvement of energy efficiency for power-using electromechanical products, the flow, conversion and distribution of energy are closely related to design variables of products. Simultaneously, performance is the constraint of energy efficiency and is strongly affected by design variables. In order to improve a product’s energy efficiency without compromising performance, an energy flow model with a basic energy flow element (EFE) was built on a functional basis and its modelling procedure is presented in this paper. Containing function, design variable and characteristic energy in EFEs, as well as the interface parameters between EFEs and environment, the model contributes to logically clarifying the relationship between design variables and performance. With the refrigerator as an example, the effectiveness of the energy flow model is verified by a comparison between simulation, based on an energy flow model, and experimentation. Furthermore, five critical design variables of a 265 L refrigerator were screened with the model. Test results of the improved prototype meet the requirements of operating rate and temperature uniformity, and the daily electricity consumption was reduced by about 9%. Comparison between the design results of the energy flow model and the testing results of the prototype demonstrates that the energy efficiency improvement method based on energy flow model is effective.

The design parameters are the most momentous factors in carrying out performance matching. For complex electromechanical products with a large number of design parameters, determining a set of critical design parameters which have a great influence on the performance is the premise of performance matching. In this paper, from a systematic perspective, a screening method of critical design parameters based on energy and a causal model is proposed. Since energy is the driving force of the product operation to achieve performance, the design parameters affect the performance through energy flow in the product. Therefore, the causal model among design parameters, characteristic energy, and performance is established, where its path coefficients are determined based on the quantitative calculation of the energy flow simulation model. Then, the performance pertinence is defined and calculated to describe the comprehensive influence of the design parameters on performance and to screen the critical parameters. Finally, the performance matching process is presented to support the performance matching. With a refrigerator as an example, 5 parameters were screened from 11 variable design parameters, and day power consumption decreased by 6.85%, which verifies the effectiveness of the method.