Research On Parameter Matching And Energy Management Strategy Of Compound Power Supply For Electric Forklift

With the intensification of environmental pollution and the rapid development of the logistics industry,electric forklifts have been widely used as a non-polluting storage and transportation equipment.In the past,batteries were often used as power sources,and the power density was low,which could not meet the requirements of rapid load changes.As a high-power density energy storage component,the supercapacitor combines with the battery to buffer battery current and extend battery life.This paper takes the composite power electric forklift as the research object,mainly studies the parameter matching and energy management technology of the electric forklift composite power supply.The main work is as follows:Firstly,the topological structure of the composite power supply is analyzed and compared,considering its cost and controllability,the semi-active structure is selected as the topology structure of the composite power supply,and the power demand and energy demand are determined by using the analyzed forklift cycle conditions.Based on the parameters of the supercapacitor and the battery,the number of cells of the battery and the supercapacitor is determined by a genetic algorithm.Secondly,after analyzing the current research status,the state machine adaptive energy management strategy is proposed.All states are divided into 11 types,which correspond to the charge and discharge of different batteries and supercapacitors respectively.An energy management strategy based on instantaneous optimization is proposed.Establishing battery current fluctuation and composite power efficiency as an objective function of optimization can achieve local optimization of the objective function at any time.Then,in order to achieve the global optimization of the model and the sum of the absolute values of battery power as the target optimization function,an energy management strategy based on convex optimization is proposed.Based on this strategy,data training based on convex optimization energy management strategy is proposed.Learning and proposing an energy management strategy based on BP neural network.Finally,the MATLAB/Simulink software is used to build the forklift powertrain model.The results show that the state machine adaptive energy management strategy has the smallest battery charge and discharge current.Compared with the state machine adaptive energy management strategy,the current based on the instantaneous optimization energy management strategy The amount is reduced by 24.20%,and the SoH of the battery is increased by 10.77%,which embodies the optimization effect of the instantaneous optimization on the objective function;the energy management strategy based on the convex optimization can realize the case that the initial value of the supercapacitor SOC is basically the same as the cutoff value.The global optimization of the objective function;compared with the energy management strategy based on convex optimization,the energy management strategy based on BP neural network reduces the battery current penetration by 16.77%,the SoH of the battery increases by 6.68%,and the battery performance parameters are improved.And run online while ensuring global optimization.