Simulation of dynamic programming energy management strategy for composite energy storage loaders
This paper studies the optimization of the energy management strategy for a composite energy storage loading machine with multiple energy sources and a complex structure.The compound energy storage loader integrates three energy supply systems:the diesel engine system,battery drive system,and hydraulic system.The diesel engine system provides the primary power required for the loader and serves as the main energy source.The electric drive system,which is second only to the engine system in importance,uses the motor to supply energy for the vehicle's electronic equipment.The hydraulic drive system provides power for lifting operations and recovers energy.Due to the diverse energy sources and complex structure of the composite energy storage loading machine,efficiently allocating and managing these energy sources presents a critical challenge.While the traditional adaptive neuro-fuzzy inference system(ANFIS)can manage energy to some extent,it cannot achieve global optimal control.Therefore,this time,dynamic programming algorithm is used to optimize the loader's energy management strategy in four typical working conditions,so as to achieve the global optimization strategy of energy management under all working conditions.In the research process,a backward simulation model of the loading machine was established in Matlab/Simulink,and a dynamic programming-based algorithm was designed to optimize the energy management strategy of the vehicle controller.The dynamic programming algorithm solves the optimal torque allocation problem between the engine,motor,and hydraulic pump/motor in reverse order,based on the known driving and loading conditions.The goal of this strategy is to maximize the fuel economy of the engine while satisfy the vehicle's power requirements.In the optimization process of the dynamic programming algorithm,three parameters,namely battery SOC,regenerative brake accumulator SOC,and potential energy recovery accumulator SOC,which can reflect the driving and working state of the vehicle and have a great impact on the calculation result,are taken as state variables.The selection of state variables not only meets the requirements of the dynamic programming algorithm but also accurately represents the loader's actual operating state.Following the algorithm's steps,the global optimal energy allocation problem of the loader is solved.The optimal energy allocation strategy of the loader under four different working conditions is then obtained.After completing this work,simulations are conducted using the proposed energy management optimization strategy and the ANFIS-based strategy on the loader model.Performance indicators under four typical working conditions are compared and analyzed.The simulation results demonstrate that the dynamic programming strategy performs better in terms of fuel economy.Under typical working conditions,the fuel economy index improves by 5.41％.This further confirms that the dynamic programming algorithm achieves significant improvements in optimizing the energy management strategy of the composite energy storage loader.To verify the reliability of the simulation results,hardware-in-the-loop experiments are conducted using a dSPACE system and test bench.The experimental results closely match the simulation results,confirming the reliability of the dynamic programming control strategy.This verification process not only validates the effectiveness of the dynamic programming algorithm but also supports the practical application of the composite energy storage loader.In conclusion,this paper establishes a backward simulation model for the loader,designs an energy management optimization method based on dynamic programming,conducts simulation analyses under multiple working conditions,and verifies the approach through hardware-in-the-loop experiments.Our model effectively optimizes the energy distribution of the composite energy storage loader,enhances its fuel economy,and provides a valuable reference for the optimal design and practical application of hybrid power systems.
composite energy storagedynamic programminghardware in the loopfuel economy
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