Energy Management Strategy And Battery Optimization Research Of A Fuel Cell Electric Vehicle

With increasingly serious environmental pollution and energy concerns,the development of new energy vehicles is an important measure to solve this problem.Fuel cell electric vehicles have the advantages of zero emissions,high energy conversion efficiency,and diverse fuel sources,and they are considered to be one of the important directions for the sustainable development of automobiles in the future.Since FCHEVs are powered by multiple power sources,energy management strategies play a key role in FCHEVs.It can directly affect the operating point of power sources,extend the life of components,and improve vehicle economics.Meanwhile,the design of the energy management strategies and optimization of battery are highly coupled,and both of them can affect the fuel economy of the vehicles,which brings challenges to the optimal control of fuel cell electric vehicles.This study takes the X37 fuel cell electric vehicle of the Dongfeng Motor Company as the research object,minimizes the hydrogen consumption of the system as the optimization goal,and researches the energy management strategies and battery optimization of fuel cell electric vehicles.First of all,the advantages and disadvantages of the existing fuel cell electric vehicle structure are analyzed,and this paper selects a fuel cell and a battery as the energy sources of the hybrid power system.Based on this,the power system of the fuel cell electric vehicle is modeled.Considering the vehicle auxiliary power requirements,a mathematical model of each component is established in MATLAB.Complex nonlinear models such as hydrogen consumption of the fuel cell and efficiency of drive motor are described in the form of MAP.The battery model is a first-order dynamic system of state of charge and output power.Secondly,with the goal of minimizing system hydrogen consumption,a bi-loop dynamic programming algorithm and a convex optimization algorithm were used to optimize energy management strategies and battery capacities of the fuel cell electric vehicle,respectively,to compare the differences between the two optimization algorithms.In the dynamic programming algorithm,the influence of different quantized values of state variables on the optimization results is analyzed,and an appropriate quantized value is determined to ensure the accuracy and reduce the computational time of the algorithm.In the convex optimization algorithm,in order to meet the application conditions of the algorithm,system model is approximated to ensure the convexity of the model.Two standard driving cycles are selected to verify and compare the proposed optimization methods.From the simulation results,the differences in hydrogen consumption between the two algorithms are similar.The convex optimization algorithm is more efficient,but the optimized battery capacity has certain errors.The bi-loop dynamic programming algorithm has a long operation time,but the results obtained are more accurate.Finally,the current fuel cell systems generally have a disadvantage of slow transient response.This factor will directly affect the power distribution balance and battery optimization results.Therefore,this study compares the optimization results without considering the transient response of the fuel cell,6k W/s transient response and 3k W/s transient response,to explore the impacts of fuel cell transient response on energy management strategies and battery optimization.The simulation results show that the limitation of the transient response of fuel cells has no effect on the optimization results of the energy management strategies and the battery capacities.