Parameter optimization design of vehicle E-HHPS system based on an improved MOPSO algorithm

Zhao, Wanzhong; Luan, Zhongkai; Wang, Chunyan

doi:10.1016/j.advengsoft.2018.05.011

Cited by 29 publications

(15 citation statements)

References 38 publications

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“…Moreover, the MOPSO algorithm requires a storage set for storing the noninferior solutions found by the population to be established. Numerous researchers have conducted in-depth studies on the updating and selection of individual and global guides, the establishment of a storage set, and the parameter settings in the MOPSO algorithm and have obtained some results [24][25][26]. On large-scale problems, the MOPSO algorithm is still prone to premature convergence and faces difficulties in efficiently searching for the global optimal solution.…”

Section: Theoretical Basismentioning

confidence: 99%

A Multiobjective Particle Swarm Optimization Algorithm Based on Multipopulation Coevolution for Weapon‐Target Assignment

Wang

Zhang

et al. 2019

Mathematical Problems in Engineering

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Weapon-target assignment (WTA) is critical to command and decision making in modern battlefields and is a typical nondeterministic polynomial complete problem. To solve WTA problems with multiple optimization objectives, a multipopulation coevolution-based multiobjective particle swarm optimization (MOPSO) algorithm is proposed to realize the rapid search for the globally optimal solution. The algorithm constructs a master-slave population coevolution model. Each slave population corresponds to an objective function and is used to search for noninferior solutions. The master population receives all the noninferior solutions from the slave populations, repairs the gaps between the noninferior solutions, and generates a relatively optimal Pareto optimal solution set. In addition, to accelerate the slave populations searching for noninferior solutions and master population repairing the gaps between noninferior solutions, the particle velocity update method is improved. The simulation results show that the proposed algorithm has higher computational efficiency and achieves better solutions than existing algorithms capable of providing a good solution. The method is suitable for rapidly solving multiobjective WTA (MOWTA) problems.

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Section: Theoretical Basismentioning

confidence: 99%

A Multiobjective Particle Swarm Optimization Algorithm Based on Multipopulation Coevolution for Weapon‐Target Assignment

Wang

Zhang

et al. 2019

Mathematical Problems in Engineering

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“…8 In order to solve the above mentioned problems, a new electro-hydraulic compound steering (EHCS) system is proposed and its structure is shown in Figure 1. 9,10 (1: steering wheel; 2: steering column; 3: torque sensor; 4: reducing mechanism; 5: motor A; 6: rack and pinion steering gear; 7: power cylinder; 8: storage tanks; 9: the rotary valve; 10: the hydraulic pump; 11: motor B).…”

Section: Introductionmentioning

confidence: 99%

Coordinated control strategy for vehicle electro-hydraulic compound steering system

Guo

Zhao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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In order to reduce steering energy consumption and improve steering feeling of heavy commercial vehicle, a novel electro-hydraulic compound steering system is proposed, which combines the function of electro-hydraulic power steering and electric power steering. The electro-hydraulic compound steering system dynamic model is established and the coordinated control strategy of dual actuators is proposed by analyzing the structure and dynamic characteristics of electro-hydraulic compound steering system. The genetic algorithm is used to optimize the steering assist torque distribution, and an Elman neural network predictive controller with fuzzy compensation is designed to solve the nonlinear problem of the electro-hydraulic compound system. Finally, the simulation is carried out by using Amesim/Simulink. The simulation results show that the coordinated control strategy designed in this paper enables the electro-hydraulic compound steering system significantly better than the traditional electro-hydraulic power steering system in steering feeling and energy consumption.

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“…Recently, electric power steering (EPS) has been widely used in passenger cars due to its safety, energy saving, and environmental protection [1][2][3]. However, the traditional power supply in vehicles can only provide limited electric power which is not enough to let the EPS work normally on heavy-duty vehicles such as agricultural transport vehicles, trucks, and medium and large coaches [4,5]. The steering resistance torque is especially large when the vehicle is turning at low speeds, so the corresponding required steering power is far beyond the rated output power of the vehicle power supply (VPS).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Energy Management Strategy for the EPS with Hybrid Power Supply Based on PSO Algorithm

et al. 2020

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The traditional vehicle power supply is unable to meet the power requirement of electric power steering system (EPS) in heavy-duty vehicles at low speeds. A novel EPS with hybrid power supply (HP-EPS) is constructed in this paper, and a new optimized rule-based energy management strategy of hybrid power supply system is designed. The strategy determines the power distribution of the vehicle power supply (VPS) and super capacitor (SC), as well as the charging or discharging of SC. Furthermore, to minimize the output current fluctuation of the VPS, the optimization model of parameters in the strategy is established and the particle swarm optimization algorithm (PSO) algorithm is applied to optimize the rules in the energy management strategy. The verification for the designed energy management strategy is carried out in MATLAB/Simulink and results show that the output current peak of VPS decreases by 33% and its fluctuation depresses significantly. In addition, the SC is charged timely and fast, which is beneficial to guarantee enough state of charge (SOC) of SC. In conclusion, the optimized rule-based energy management strategy used for the HP-EPS system can meet the current requirement of EPS and effectively reduce the peak and fluctuation of the VPS output current.

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Parameter optimization design of vehicle E-HHPS system based on an improved MOPSO algorithm

Cited by 29 publications

References 38 publications

A Multiobjective Particle Swarm Optimization Algorithm Based on Multipopulation Coevolution for Weapon‐Target Assignment

A Multiobjective Particle Swarm Optimization Algorithm Based on Multipopulation Coevolution for Weapon‐Target Assignment

Coordinated control strategy for vehicle electro-hydraulic compound steering system

Optimization of Energy Management Strategy for the EPS with Hybrid Power Supply Based on PSO Algorithm

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