自适应粒子群算法汽车传动系统参数优化匹配  

Adaptive Particle Swarm Optimization Algorithm for Optimal Matching of Automobile Transmission System Parameters

作　　者：吴素珍 郑群雄 毕建平 WU Suzhen;ZHENG Qunxiong;BI Jianping(He’nan Institute of Engineering,School of Mechanical Engineering,He’nan Zhengzhou 451191,China)

机构地区：[1]河南工程学院机械工程学院,河南郑州451191

出　　处：《机械设计与制造》2024年第12期51-55,共5页Machinery Design & Manufacture

基　　金：国家自然科学基金项目(U1804162);河南省重点研发与专项(212102210059);河南工程学院培育基金(PYXM202019)。

摘　　要：为提高汽车的动力性,降低汽车的燃油消耗,提出一种传动系统参数多目标优化匹配方法。基于机械式传动系统,分别以百公里燃油消耗量和(0~100)km/h加速时间为优化分目标,构建整车动力性模型和经济性模型;通过设定不同的动力性约束指标,引入加权系数法和罚函数,建立了多工况下整车传动系统的参数优化模型。为提高传动系参数的匹配程度,提出一种基于动态学习因子和自适应调节惯性权重策略下的改进自适应粒子群优化算法,获得整车传动系统参数的最优集。仿真结果表明,改进后的算法收敛速度快,更具“活”性,很好地避免了算法的“早熟收敛”,较传统的自适应算法而言,在六循环工况下的百公里油耗减少了1.5%,(0~100)km/h加速时间缩短了2.3%,最高车速也提高了0.53%,这些结果都充分验证了改进的自适应粒子群算法的可靠性和有效性。In order to improve the power of the car and reduce the fuel consumption of the car,a multi-objective optimization matching method of transmission system parameters is proposed.Based on the mechanical transmission system,the fuel consumption of 100 kilometers and the acceleration time of(0~100)km/h are respectively optimized sub-objectives to construct the vehicle dynamics model and the economic model by setting different dynamic constraint indicators,the weighting coefficient is introduced Method and penalty function,the parameter optimization model of the vehicle transmission system under multiple working conditions is established.In order to improve the matching degree of drive train parameters,an improved adaptive particle swarm optimization algorithm based on dynamic learning factor and adaptive adjustment of inertia weight strategy is proposed to obtain the optimal set of vehicle drive train parameters.The simulation results show that the improved algorithm converges faster and is more“active”,and it avoids the“premature convergence”of the algorithm.Compared with the traditional adaptive algorithm,the fuel consumption per 100 kilometers under the six-cycle working condition It is reduced by 1.5%,(0~100)km/h acceleration time is shortened by 2.3%,and the top speed is also increased by 0.53%.These results fully verify the reliability and effectiveness of the improved adaptive particle swarm algorithm.

关 键 词：传动系参数 自适应粒子群算法 仿真 参数优化匹配 

分 类 号：TH16[机械工程—机械制造及自动化] TK05[动力工程及工程热物理]