基于人工鱼群算法的车辆平顺性优化分析

平顺性是汽车重要特性之一,平顺性优化分析属于组合优化问题,同时其非线性特性导致优化实质上是一个非线性多峰的优化问题,为了有效解决此类复杂优化的求解问题,近年来基于随机搜索优化算法建立了一种新型的人工鱼群算法。该文将人工鱼群算法应用到汽车平顺性优化分析研究中,以某8×4载货车为研究对象,建立9自由度汽车平顺性模型,对影响汽车平顺性的重要参数进行优化分析。优化结果表明,加速度均方根平均下降16.82%,在60 km/h时下降最大,加速度均方根下降21.24%,有效提高了重型车的平顺性能。因此,利用该模型可对汽车平顺性进行预测或评估。

Vehicle ride comfort analysis and optimization based on artificial fish swarm algorithm

Ride comfort is of great importance feature for the heavy truck, its optimization can improve the driver's drivingconditions to reduce fatigue, and make the goods safer.Primary factors that can influence ride comfort are form andparameters of suspension, which are suspension stiffness, suspension damp and their combination.When the form ofsuspension is confirmed, more reasonable parameters can be selected by optimization method to improve ride comfort.Ridecomfort optimization analysis belongs to the combinatorial optimization problem, at the same time, the nonlinearcharacteristics in optimization is essentially a nonlinear multimodal optimization problem.In this paper, at first, a nine degree of freedom vehicle vibration model was established; Vehicle driver seat acceleration simulation tests wereconducted with different vehicle speed.Also, both time and frequency domain analysis was implemented with MATLABsoftware development platform.On the whole, with the increase of the speed of the vehicle, the acceleration root mean square of vehicle driver seat became larger, so the vehicle ride comfort performance reduced.Especially at low speed andhigh acceleration change is more obvious.But in 40~80 km/h, the acceleration change quite gentle.That means to achievethe better economy and the vehicle ride comfort performance, the vehicle speed keeping in a medium speed is better.Based on C level road and the speed of 70 km/h, with an eight by four dump truck as experimental object, the ride comforttests were conducted, moreover the test results compared with the results of simulation.The compared results showed thatthe simulation and the test were very close.And then, today technology was coming to a stage of intersection, infiltration,and interaction with multi subjects.More and more issues on complexity, non linearity, and system have come to us.Todeal with such complexity of system, conventional techniques have become incapable, and to seek an optimizationalgorithm, which adapt to large scale parallel with intelligent characteristics, has been a primary research target of relatedsubjects.The artificial fish algorithm was proposed to optimize ride comfort.The artificial fish swarm algorithm (AFSA), anew method based on animal behaviors and the typical application of behaviorism artificial intelligence, was proposed byan internal scholar in recent years.It used the operators such as prey, swarm, follow and random behavior.The algorithmparameters, such as population, step size, sense of distance, the largest try number, crowded degree coefficient and thelargest number of iterations, has a great impact on the performance of the convergence.At the end, the artificial fishalgorithm was used to optimize ride comfort by reasonable selection of the suspension parameters.The objective functionwas the acceleration root mean square of vehicle driver seat to be minimized.The decision variables were front suspensionstiffness and damp.Moreover AFSA need to set up the appropriate algorithm parameters.For example, population scale,step size, sense of distance, the largest try number, crowded degree coefficient and the largest number of iterations was100, 100, 20 000, 100, 9 and 50.Where the population scale N was called the number of possible values of suspensionparameters within the value range, step size was suspension parameters increasing or decreasing the amount of eachiteration, and sense of distance visual was variables scope of each iteration.Optimization results show that the accelerationroot mean square average fell by 16.82%, the biggest fell by 21.24% in 60 km/h, so it effectively improves the ride comfort h performance of heavy vehicles.