Performance evaluation and prediction for electric vehicle heat pump using machine learning method. (August 2019)

• Record Type: Journal Article
• Authors: Wang, Yufeng; Li, Wanyong; Zhang, Ziqi; Shi, Junye; Chen, Jiangping
• Is Part Of: Applied thermal engineering. Volume 159(2019)
• Publication Date: 2019-08
• Subjects: Electrical vehicle -- Heat pump -- Refrigerant injection -- Machine learning -- SVR -- Adaboost.R2; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2019.113901
• Languages: English
• ISSNs: 1359-4311

Abstract

Abstract: A method to predict the performance of R134a heat pump with EVI (Economized Vapor Injection), is proposed in the present study. Models using SVR (Support Vector Regression) as the base estimator and Adaboost.R2 as the ensemble method, are established to predict the heating capacity and COP of the heat pump. Different feature sets for the model input are formed, based on the working principle of the heat pump system and correlation analysis. Parameters of the models are optimized to improve prediction performance. The simulation results are compared with the experimental results, and the relative errors for heating capacity and COP prediction are within 8.5%. Moreover, the impacts of injection pressure on the EVI heat pump system are discussed and simulated using the model established. The optimum injection pressure of the heat pump system can be obtained from the model under different working conditions.