Parameter optimization design of vehicle E-HHPS system based on an improved MOPSO algorithm. (September 2018)

• Record Type: Journal Article
• Authors: Zhao, Wanzhong; Luan, Zhongkai; Wang, Chunyan
• Is Part Of: Advances in engineering software. Volume 123(2018)
• Page Start: 51
• Page End: 61
• Publication Date: 2018-09
• Subjects: Electric-hydraulic hybrid steering -- parameter optimization -- multi-objective particle swarm optimization -- decomposition method; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2018.05.011
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Dynamics model and evaluation indexes of novel E-HHPS system are proposed. The coupling of system parameters is analyzed aimed at the system characteristics. The multi-objective parameter optimization model of E-HHPS system is established. An improved MOPSO algorithm is proposed to solve the E-HHPS optimization model. Abstract: To improve the handling stability as well as reduce the steering energy consumption of heavy commercial vehicle, a novel electric-hydraulic hybrid power steering (E-HHPS) system with multiple steering modes is presented, which enables the vehicle to acquire the steering handiness at low speed and better steering road feeling at high speed by switching the actuator unit according to the current working condition. In this paper, to achieve the design goals of E-HHPS system, which are to reduce steering energy consumption and improve steering stability, three evaluation indexes of E-HHPS system are established, which convert the E-HHPS system parameter optimization problem into a multi-objective optimization model. Because it is difficult to approximate the Pareto front of the transformed optimization model by basic algorithms, a multi-objective particle swarm optimization algorithm based on adaptive decomposition (MOPSO/AD) is proposed. Test functions are used to verify the performance of the algorithm and test results show that the MOPSO/AD algorithm has better comprehensive performance and stability compared with the basic MOPSO algorithm and MOEA/D algorithm. The MOPSO/AD algorithm is applied to solve the E-HHPS system optimization model and simulation results show that the proposed MOPSO/AD algorithm has better convergence in solving the E-HHPS parameter optimization problem compared with MOPSO, which enables the optimized E-HHPS system has good handling stability and low steering energy consumption.