Abstract
This paper proposes a predictive control method for rail vehicle air-conditioning systems. Due to heat transfer and diffusion, the air-conditioning system is a long-time-delay system. However, most air-conditioning systems use feedback control, which has problems such as long transition time, system shock, and mismatch between air cooling capacity and load, resulting in the waste of energy. Combined with feedforward and feedback control, a predictive control method with dynamic correction is proposed to solve this problem. Based on the load prediction, the real-time indoor temperature feedback link is added to send the cold air into the room in advance, which makes the room temperature stable, and the energy-saving effect significant. In the study, variance analysis of environmental factors is performed to improve the accuracy of the load prediction system, and the mean relative error (MRE) of the prediction reached 0.0112. By comparing the simulation results of predictive control and feedback control, it is proved that the predictive control with correction has a smoother room temperature curve. The energy-saving rate is about 25.2%.
Highlights
The air conditioning system is one of the main energyconsuming systems on rail transit vehicles
There are almost no room temperature sudden changes caused by load changes
This paper proposed a predictive control method for rail transit vehicle air-conditioning systems with dynamic correction
Summary
The air conditioning system is one of the main energyconsuming systems on rail transit vehicles. Rail transit vehicles have short operating lines, frequent passenger boarding, and rapid environmental changes [6], so the system lag will seriously affect the energy-saving control of air-conditioning systems. For this problem, predictive control can be a suitable solution. Literature [16] proposed a method for indoor temperature predictive control based on the multistep predictive model of Elman neural network for the predictive control of indoor temperature time-delay in VAV air-conditioning systems. The structure of this paper is as follows: Section 2 discusses the research methods, including theoretical analysis and simulation model building of load prediction system and air-conditioning predictive control system.
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