Research On Energy Management Strategy Of Compound Power Supply For Electric Loader Based On Multiple Population Genetic Algorithm

With the increasing emphasis on new energy technologies and environmental protection by government,energy-efficient and emission-reducing electric loaders have gradually become a research hotspot in the loader industry.Aiming at the problems of low power density of electric loaders driven by single power battery and the damage of battery service life by high current under violent fluctuating load,the integration of supercapacitors with high power density and fast charge-discharge rates is introduced.By combining the supercapacitors with the power battery,a compound power supply is formed,which can enhance the performance of the power system and prolong the lifespan of the power battery.Based on this,this paper takes a specific50-type electric loader as the research object and focuses on the optimization of compound power supply parameter matching and the design of energy management strategies.Through comprehensive analysis of the "V" cycle working conditions of wheeled loaders and four typical topologies of compound power systems,a semi-active compound power system topology with supercapacitors as the main control unit was selected.The component selection and simulation model construction of the power battery,supercapacitors,and DC/DC converters were completed.Based on the energy and power requirements of the electric loader,with the goal of minimizing the initial configuration cost of the compound power system,the optimization of compound power supply parameter matching was accomplished using a genetic algorithm.Based on the operating characteristics of the electric loader,a fuzzy control-based energy management strategy for compound power supply is proposed.Fuzzy controllers are designed for both driving and braking operating conditions.To address the issue of excessive reliance on expert experience and strong subjectivity in the knowledge base design of the fuzzy controller,a globally optimized energy management strategy for compound power supply based on multiple population genetic algorithm is proposed.The endpoint coordinate parameters of the membership functions in the fuzzy controller are optimized using multiple population genetic algorithm under the "V" cycle working conditions.The fuzzy control strategies before and after optimization are simulated and compared.The results indicate that both the pre-optimized and post-optimized fuzzy control strategies effectively leverage the performance advantages of the supercapacitor,enabling it to collaborate with the power battery in achieving energy management objectives.The fuzzy control strategy optimized by multiple population genetic algorithm better utilizes the potential of the supercapacitor’s "peak shaving and valley filling" working characteristics compared to the pre-optimized strategy.The capacity degradation of the power battery is delayed by 10.23%,and its current fluctuates more smoothly.The overall energy consumption of the system is reduced by 5.11%,and the braking energy recovery efficiency is improved by 5.21%.These results validate the effectiveness of the multiple population genetic algorithm in optimizing the fuzzy control-based energy management strategy for compound power supply.