Research on Model Prediction Energy Management Strategy with Variable Horizon. (May 2017)

• Record Type: Journal Article
• Authors: Cao, Jianfei; Peng, Jiankun; He, Hongwen
• Is Part Of: Energy procedia. Volume 105(2017)
• Page Start: 3565
• Page End: 3570
• Publication Date: 2017-05
• Subjects: Model prediction energy management strategy -- variable horizon -- dynamic programming -- HIL experiments; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2017.03.819
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: In order to improve the accuracy of model prediction energy management strategy, reconstruction and filling methods for state-loss of driving cycle are mainly studied. In this paper, the way to control the forecast accuracy by changing prediction time-scale is proposed and a real-time prediction model with variable horizon is constructed. Combined with dynamic programming, an energy management strategy based on model prediction control(MPC) with variable horizon is finally established. The correctness of this strategy is verified by hardware-in-loop(HIL) experiment. And the result show that the prediction accuracy could reach 8.203 km/h and fuel consumption is 18.3485L/100 km and the electricity consumption is 13.1081Wh, which has been improved comparing with traditional MPC with fixed horizon.