Application of PID optimisation control srategy based on particle swarm optimisation(PSO)for battery charging system
The issue of protecting health of residents of urbanised areas from the effect of excessive particulate matter and toxic components of car exhaust gases imposes the need of introduction of clean electric vehicles to the market.In order to solve the problem of nonlinear and lagging in charging process of electric vehicle battery,an application of PID optimisation control strategy based on particle swarm optimisation(PSO)for battery charging system is studied in this paper.The main goal here is to design a PID controller with a flexible structure and adaptive tuning of its parameters,which leads to improvement of charging efficiency of the battery.The traditional tuning method for PID parameter is difficult to give the appropriate parameters,which affects the battery charging efficiency.PSO is a population-based evolutionary algorithm and is proved to be robust in solving problems featuring nonlinearity,non-differentiability and multi-dimensionality.The advantages of PSO are its relative simplicity and stable convergence characteristic with good computational efficiency.Aim at the defects of basic PSO such as slow convergence speed,low convergence precision and easy to be premature,a modified PSO is proposed by adjustments to the inertia weight,learning factor and local optimum value.And the modified PSO algorithm has stronger global convergence ability in the early stage and stronger local convergence ability in the later stage which can preferably avoid trapping into the local optimal solution.Five different classical functions are selected for optimisation test to verify the feasibility and effectiveness of the modified algorithm.Comparisons are made with the performance indexes of the standard PSO,such as the number of successful search and the average convergence time,and it is shown that the modified PSO algorithm can highly accelerate the PSO for the convergence rate and improve its convergence accuracy.The optimised PID parameters are applied to the battery charging control system.And the experimental results show that the battery charging process possesses better dynamic performance and the charging efficiency of the battery has increased from 86.44%to 91.47%.
battery PID parameters particle swarm optimisation
Linzhang WU Cuicui ZHOU Tiezhou WU Junjie ZHANG
Hubei Key Laboratory for High-efficiency Utilisation of Solar Energy and Operation Control of Energy Storage System,Hubei University of Technology
国际会议
武汉
英文
373-382
2018-08-21（万方平台首次上网日期，不代表论文的发表时间）