Research On Energy Management Strategy Of Hybrid Power Bulldozer Based On Improved Grey Wolf Algorithm

Bulldozers,as engineering machinery applied in industries such as construction,water conservancy,and agriculture,can significantly improve the efficiency of engineering operations.However,engineering machinery represented by bulldozers has the characteristic of serious pollution.The advent of pure electric bulldozers provides a good solution to this problem.However,the bulldozer has a large load and a large current impact,which is easy to cause battery aging.In order to solve the problem of battery aging of pure electric bulldozer,this paper studies the following aspects of lithium battery super capacitor hybrid power bulldozer:(1)The improved grey wolf algorithm is used to optimize the energy management strategy of the hybrid power bulldozer.Based on the grey wolf algorithm,the Improved Whale-Grey Wolf Algorithm(IWOGWO)is obtained by introducing nonlinear convergence factor,adaptively adjusting the number of leading wolves,adaptively adjusting the weight of leading wolves,and integrating whale algorithm and grey wolf algorithm.Six test functions are used to compare the IWOGWO algorithm with three classical algorithms and one improved grey wolf algorithm,and the effectiveness of the IWOGWO algorithm is verified.(2)According to the pure electric bulldozer provided by the cooperative enterprise,the AMESIM and SIMULINK co-simulation model of the pure electric bulldozer is built.The simulation model is calibrated and verified according to the traction test of the pure electric bulldozer,and the accuracy of the simulation model is proved.According to the actual working characteristics of the bulldozer,a simulation cycle including various working conditions is defined,and the simulation cycle is used to simulate the pure electric bulldozer,which verifies the accuracy of the simulation model and provides a basis for the research of the hybrid power bulldozer.(3)The configuration and supercapacitor parameters of the hybrid power bulldozer are selected,and the simulation model of the hybrid power bulldozer is built according to the calibrated pure electric bulldozer simulation model.The energy management strategy based on certain rules and the energy management strategy based on fuzzy control are built for the hybrid power bulldozer.In order to improve the performance of energy management strategy based on fuzzy control,the generalization ability of SVM algorithm is used to optimize the fuzzy controller.In order to improve the performance of SVM algorithm,IWOGWO algorithm is used to optimize SVM algorithm,and IWOGWO_SVM algorithm is obtained.The performance of IWOGWO_SVM algorithm is verified according to UCI standard database.The IWOGWO_SVM algorithm is used to generalize and optimize the fuzzy controller,and the energy management strategy based on SVM_FUZZY is obtained.The pure electric bulldozer,the hybrid power bulldozer based on the certain rules,and the hybrid power bulldozer based on SVM_FUZZY are simulated.The cumulative ampere-hour throughput and battery energy loss of each model are compared to verify the advantages of the hybrid power bulldozer based on SVM_FUZZY.(4)The hybrid power of the hybrid power bulldozer is further optimized,and the average total cost of a single operation and the number of runs in the life cycle of the hybrid power bulldozer are optimized as optimization indicators.In order to improve the optimization ability,the nonlinear convergence factor,the fusion whale algorithm and the grey wolf algorithm are introduced to improve the Multi-objective Optimization Grey Wolf Algorithm,and the MIWOGWO algorithm is obtained.The MIWOGWO algorithm is used to optimize two objectives.Compared with the pure electric bulldozer,the hybrid power bulldozer can reduce the cost by up to 6.65%and increase the service life by up to 16.55%.The compromise scheme of the hybrid power bulldozer in saving cost and improving durability is given.