Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle. (8th September 2020)

• Record Type: Journal Article
• Authors: Lin, Xinyou; Wang, Zhaorui; Wu, Jiayun
• Is Part Of: International journal of energy research. Volume 45:Number 2(2021)
• Page Start: 2629
• Page End: 2643
• Publication Date: 2020-09-08
• Subjects: back propagation neural network -- plug‐in fuel cell electric vehicle -- predictive control strategy -- velocity prediction; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.5956
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: The stochastic driving cycles pose a challenge to the actual implementation of the control strategy for fuel cell electric vehicles (FCEVs). Considering the problem, this research proposes a predictive control strategy based on velocity prediction. Firstly, a novel velocity prediction method is developed, which considers the prediction error of back propagation neural network (BPNN)‐based method. Then, it is incorporated into the predictive control strategy for a plug‐in FCEV. Finally, simulation studies are conducted to verify the effectiveness and superiority of the proposed predictive control strategy. Simulation results show that the proposed velocity prediction method can adaptive to different driving cycles with high accuracy. In another case, with the velocity prediction used in the predictive control strategy, hydrogen consumption reduces by 17.07% when compared with the traditional rule‐based strategy. All the results indicate that the designed velocity prediction approach made it possible to forecast vehicle velocity with relatively high precision and promising to promote the energy management strategy (EMS) to reduce the hydrogen consumption of the plug‐in FCEV.