Multi-objective Online Energy Management Strategies for Fuel Cell Bus Considering Stack Efficiency

In order to improve the economy of fuel cell buses and the durability of fuel cells, this article proposes a multi-objective online energy management strategy (EMS) based on online learning Markov chain (MC) power prediction and improved dynamic programming (DP) solution under the framework of model predictive control (MPC). The penalty term of fuel cell efficiency is introduced into the objective function and combined with fuel cell degradation and hydrogen consumption to achieve multi-objective optimization control. According to the predicted power data, an optimization method for pre-calculation of SOC operating upper and lower limits is proposed to realize online calculation. The simulation test is carried out by MATLAB/Simulink. The simulation results show that compared to the rule-based EMS, the proposed strategy can greatly reduce the hydrogen consumption, improve the proportion of fuel cell output power in the high efficiency working range, control the fluctuation of fuel cell output power, prolong the fuel cell lifetime, and improve vehicle economy.