An Optimization Method For Power Distribution Of Battery/supercapacitor Hybrid Power Supply

Due to the transient demand for high power in pure electric vehicles such as startup and acceleration,using a single battery as a power source can cause certain damage to the battery and reduce its service life.To provide a better power output for pure electric vehicles under complex driving conditions,a hybrid power supply consisting of two power sources,namely,a battery and a supercapacitor,is used as the vehicle’s power source.Hybrid power supply has both the characteristics of stable battery discharge for a long time and the characteristics of supercapacitor providing large current instantaneously.To improve the economy of pure electric vehicles and give full play to the advantages of hybrid power supply,it is often necessary to develop appropriate energy management strategies.This work focuses on parameter matching and structural design of hybrid power supply,and studies the energy management strategies of hybrid power supply based on intelligent optimization algorithms.The main contributions of this work are as follows:(1)The advantages and disadvantages of four existing conventional hybrid energy storage system structures are analyzed,and a new fully active hybrid energy storage system topology structure combining series parallel technology which can achieve multiple working modes to meet complex driving conditions is proposed.(2)According to the required power and other data,the various parts of the hybrid energy storage system are selected and matched with parameters.Based on the design parameters,a simulation model of the hybrid energy storage system is established through MATLAB/Simulink.The real-time distribution of hybrid power supply power is realized by combining Proportion Integration Differentiation(PID)and MATLAB Function modules.(3)Taking the energy consumption rate of pure electric vehicles as the optimization goal,the relationship between energy consumption rate and battery discharge current is established,and the energy management strategies based on three intelligent optimization algorithms,namely,particle swarm optimization algorithm,seeker optimization algorithm,and whale optimization algorithm are established.(4)The simulation results of energy management strategies based on particle swarm optimization algorithm,crowd search algorithm,and whale optimization algorithm are compared,and the advantages and disadvantages of the three energy management strategies are summarized from the aspects of simulation time,optimization iterations,and so on.