广西大学学报(自然科学版)
廣西大學學報(自然科學版)
엄서대학학보(자연과학판)
Journal of Guangxi University (Natural Science Edition)
2015年
5期
1092-1101
,共10页
何志刚%周成%盘朝奉%徐兴%郑亚峰
何誌剛%週成%盤朝奉%徐興%鄭亞峰
하지강%주성%반조봉%서흥%정아봉
电动汽车%传动系统%粒子群算法%动力性%经济性
電動汽車%傳動繫統%粒子群算法%動力性%經濟性
전동기차%전동계통%입자군산법%동력성%경제성
electric vehicle%powertrain system%particle swarm optimization%dynamic performance%economy
为提高纯电动车电机工作效率,以一款固定减速比电动汽车为原型,改为两档传动比方案,对传动比参数进行优化以提高电机工作效率。分别以汽车原地起步加速时间和ECE(欧洲经济委员会)十五循环工况下续驶里程作为纯电动汽车的动力性和经济性两个分目标函数,经过加权因子建立新的评价函数。采用线性递减惯性权重的粒子群算法( LDWPSO)对传动系统参数进行优化,利用Logistic混沌映射来初始化种群以避免粒子群算法早熟收敛和局部最优的问题,并将优化结果带入动力学模型中验证。结果显示,优化后的两档传动比方案较固定速比方案加速时间减小0.3 s以上,续驶里程提高8.51 km。表明通过采用粒子群优化算法优化汽车传动系统参数,汽车的动力性和经济性得到有效的提高。
為提高純電動車電機工作效率,以一款固定減速比電動汽車為原型,改為兩檔傳動比方案,對傳動比參數進行優化以提高電機工作效率。分彆以汽車原地起步加速時間和ECE(歐洲經濟委員會)十五循環工況下續駛裏程作為純電動汽車的動力性和經濟性兩箇分目標函數,經過加權因子建立新的評價函數。採用線性遞減慣性權重的粒子群算法( LDWPSO)對傳動繫統參數進行優化,利用Logistic混沌映射來初始化種群以避免粒子群算法早熟收斂和跼部最優的問題,併將優化結果帶入動力學模型中驗證。結果顯示,優化後的兩檔傳動比方案較固定速比方案加速時間減小0.3 s以上,續駛裏程提高8.51 km。錶明通過採用粒子群優化算法優化汽車傳動繫統參數,汽車的動力性和經濟性得到有效的提高。
위제고순전동차전궤공작효솔,이일관고정감속비전동기차위원형,개위량당전동비방안,대전동비삼수진행우화이제고전궤공작효솔。분별이기차원지기보가속시간화ECE(구주경제위원회)십오순배공황하속사리정작위순전동기차적동력성화경제성량개분목표함수,경과가권인자건립신적평개함수。채용선성체감관성권중적입자군산법( LDWPSO)대전동계통삼수진행우화,이용Logistic혼돈영사래초시화충군이피면입자군산법조숙수렴화국부최우적문제,병장우화결과대입동역학모형중험증。결과현시,우화후적량당전동비방안교고정속비방안가속시간감소0.3 s이상,속사리정제고8.51 km。표명통과채용입자군우화산법우화기차전동계통삼수,기차적동력성화경제성득도유효적제고。
In order to make the motor of a pure electric vehicle work more efficiency, a two-speed gearbox was used instead of a settled gear in an electric vehicle, and the ratio of the two-speed gear-box was optimized aiming at maximizing operation efficiency of the motor. Standing start acceleration time and driving range of the ECE ( Economic Commission of Europe ) drive cycle are respectively taken as two sub-objective functions for the dynamic performance and energy economy of the EV( E-lectric Vehicle), a new evaluation function is built by weighting factors. An optimization is per-formed by using Linear Decrease Weight Particle Swarm Optimization ( LDWPSO) and chaotic map-ping is adopted in initial population to solve the premature convergence and easily trapped in the lo-cal optimum problem. The simulation results indicate that the time of acceleration can be shorted at least 0. 3 s and the mileage can be lengthened 8. 51 km. The results show that the dynamic perform-ance and energy economy of the vehicle can be improved effectively by using the particle swarm opti-mization algorithm to optimize the parameters of the vehicle driving system.