Multienergy Management Strategy of Fuel Cell Hybrid Vehicle Based on Distributed Parameter Model

Abstract

To increase the fuel efficiency of fuel cells, lengthen their useful lives, and fulfil the demands for high energy and high power during the operation of hybrid electric vehicles. This paper's goal is to thoroughly examine the distributed parameter model-based multienergy management technique of fuel cell hybrid electric vehicles. Firstly, the simplified model of a hybrid system is constructed according to the distributed parameter model, and the fuel cell model, unidirectional DC/DC converter, and battery are described in detail. The multienergy management of hybrid electric vehicles based on improved deep Q-learning is adopted, the multienergy management strategy based on deep Q-learning is designed, so as to reduce fuel consumption and improve the working efficiency of fuel cells, optimize the energy distribution of lithium batteries and fuel cells, and adopt the experience playback mechanism of summation tree structure in the process of strategy training to complete the multienergy management of hybrid electric vehicles. The strategy described in this study can successfully increase the overall power performance of fuel cell hybrid vehicles, extend the battery's service life, and increase fuel economy, according to simulation results, which has a certain practical value.