Energy management optimization of fuel cell hybrid electric vehicle based on deep reinforcement learning
WANG Hao-cong
WANG Yue-yang
FU Zhu-mu
CHEN Qi-hong
TAO Fa-zhan
Abstract：For the fuel cell hybrid electric vehicle equipped with lithium battery and ultracapacitor,to reduce the overall operation cost and prolong the lifespan of energy sources,an energy management strategy based on deep reinforcement learning is proposed in this paper.Firstly,according to the high power density characteristics of ultracapacitor,a power hierarchical structure based on a fuzzy adaptive filter is established,and based on the empirical degradation model of fuel cell and lithium battery,the cost function of energy source degradation is established.The equivalent consumption minimum strategy is used to balance the hydrogen consumption cost and energy source degradation cost,and the power allocation of energy sources is optimized to minimize the overall operation cost.Then,prioritized experience replay and soft update are introduced to improve the off-line training efficiency of deep reinforcement learning.Finally,the simulation is carried out under various driving cycles.The results show that compared with the strategy without considering degradation,the proposed strategy reduces hydrogen consumption by 11.8％under the world light vehicle test cycle,and can effectively slow down the degradation rate of fuel cell and lithium battery and reduce the overall operating cost of fuel cell hybrid electric vehicle.
Keywords：fuel cell hybrid electric vehicledeep reinforcement learningenergy sources degradationequivalent consumption minimizationdate drivenenergy management strategy
Publication Date：2024-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 1831-1841 )