Dynamic response multiobjective optimization of road vehicle ride quality—A computational multibody system approach

Mohajer, Navid; Asayesh, Hamid; Nahavandi, Saeid

doi:10.1177/1464419316664653

Cited by 12 publications

(5 citation statements)

References 40 publications

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“…Mohajer et al 9 adopted a genetic algorithm to optimise ride safety and comfort. The optimisation objectives included suspension travel, vibration isolation, force index, and road holding.…”

Section: Intelligent Optimisation Algorithmsmentioning

confidence: 99%

Optimisation of electric vehicle with the in-wheel motor as a dynamic vibration absorber considering ride comfort and motor vibration based on particle swarm algorithm

Zhao

Fan

2022

Proceedings of the Institution of Mechanical Engineers, Part K:

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Compared with other electric vehicles, the ride comfort of the electric vehicle with the in-wheel motor as a dynamic vibration absorber has significantly improved. However, the in-wheel motor is used as a dynamic vibration absorber, aggravating the motor vibration, thus affecting motor performance and life. In addition, the space inside the wheel is limited, so the vibration displacement of the motor relative to the hub needs to be constrained. In fact, the literature on ride comfort of the electric vehicle with the in-wheel motor as a dynamic vibration absorber has hardly considered motor vibration. So, this article comprehensively investigates the ride comfort and motor vibration of this electric vehicle. Firstly, the vibration model of the electric vehicle with the in-wheel motor as a dynamic vibration absorber is established. Then, the optimisation model of this electric vehicle is founded. Next, the multi-objective particle swarm optimisation algorithms based on adaptive grid and crowding distance are used to optimise the model, and these two algorithms are compared. The optimal solutions in three typical cases are obtained. Finally, the vibration responses of this electric vehicle model before and after optimisation, the traditional electric vehicle model, and the in-wheel motor drive electric vehicle model are compared in case 1. The results show that all vibration responses are improved to different degrees after optimisation; the algorithm based on crowding distance can seek out the optimal solutions faster, but it takes longer to complete the whole optimisation process.

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Section: Intelligent Optimisation Algorithmsmentioning

confidence: 99%

Optimisation of electric vehicle with the in-wheel motor as a dynamic vibration absorber considering ride comfort and motor vibration based on particle swarm algorithm

Zhao

Fan

2022

Proceedings of the Institution of Mechanical Engineers, Part K:

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“…Similar to the kinematic model, a dynamic bicycle model is generally used for designing PTCs [6,[11][12][13][14]. Figure 2 shows the geometry of a dynamic bicycle model.…”

Section: Dynamic Modelmentioning

confidence: 99%

Review and performance evaluation of path tracking controllers of autonomous vehicles

Rokonuzzaman

Mohajer

Nahavandi

et al. 2021

IET Intelligent Trans Sys

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Autonomous Vehicles (AVs) have shown indelible and revolutionary effects on accident reduction and more efficient use of travel time, with outstanding socio‐economic impact. Despite these benefits, to make AVs accepted by a wide demographic and produce them on an industrial scale with a reasonable price, there are still a number of technological and social challenges that need to be tackled. Path Tracking Controller (PTC) of AVs is one of the high potential subsystems that can be further improved in order to achieve more accurate, robust and comfortable tracking performance. This study provides a critical review and simulation study of several selected techniques used for the design of PTC of AVs. The AVs are assumed to have limited controllability due to non‐holonomic constraints, such as car‐like vehicles and differential drive mobile robots. A detailed discussion will be provided on the simulation outcomes as well as the pros and cons of each technique for the sake of implementation and improvement of state‐of‐the‐art PTC.

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“…are important approaches to improving vehicle ride comfort. Although the road test is the most commonly used method in the auto industry, the simulation analysis method has also been developed for its advantages of a shorter calculation time and lower development costs [8], as well as its ability to shorten the development time through various software with precise models [9]. Therefore, numerous efforts have been devoted to modeling, simulation analyses and optimization of suspension systems in order to control vibration and improve ride comfort.…”

Section: Parameters Matching and Suspension Systems Optimizationmentioning

confidence: 99%

“…Meanwhile, comprehensive optimization, including ride comfort, road holding, and other objectives that involve conflict performance in the automotive industry [16], is a popular research topic. Mohajer et al [8] established a versatile half-car two track model where the damping behavior of the suspension is assumed to be linear. The GA methodology is then applied to optimize the ride comfort, vibration isolation, suspension travel, force index, and road holding.…”

Section: Parameters Matching and Suspension Systems Optimizationmentioning

confidence: 99%

Ride Comfort Analysis and Multivariable Co-Optimization of the Commercial Vehicle Based on an Improved Nonlinear Model

Chen

Zheng

et al. 2020

IEEE Access

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The performance of a suspension system is affected by the behavior and posture of its components. However, published studies usually conduct this research using the based-equivalent model without considering the characteristic curves or postures. In this paper, an improved ride comfort model that considers three nonlinearities in suspensions is first developed, and this model is validated through experimental results and demonstrates good accuracy. Then, the dynamic response is presented to investigate the effects of multilevel suspension parameters and nonlinear factors on ride comfort, and it is concluded that the front chassis suspension is the most significant system for ride comfort. Next, a multivariable co-optimization method based on the improved model is proposed to obtain more accurate optimized results that are more suitable for automotive applications. Subsequently, a multiobjective genetic algorithm (MGA) is applied to obtain the Pareto solution set. Furthermore, comparing the RMS value before and after optimization shows an obvious reduction, with averages of 19.7%, 17.8%, and 12.0% for the weighted root mean square (RMS) of the driver seat acceleration and the RMS of the working spaces of the front chassis suspension and the rear chassis suspension, respectively. Finally, the results are also verified by experiments, indicating that the improved ride comfort model and the multivariable co-optimization method are feasible and practical.INDEX TERMS Ride comfort, nonlinearity of suspension components, multivariable co-optimization, bivariate analysis, commercial vehicle.

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Dynamic response multiobjective optimization of road vehicle ride quality—A computational multibody system approach

Cited by 12 publications

References 40 publications

Optimisation of electric vehicle with the in-wheel motor as a dynamic vibration absorber considering ride comfort and motor vibration based on particle swarm algorithm

Optimisation of electric vehicle with the in-wheel motor as a dynamic vibration absorber considering ride comfort and motor vibration based on particle swarm algorithm

Review and performance evaluation of path tracking controllers of autonomous vehicles

Ride Comfort Analysis and Multivariable Co-Optimization of the Commercial Vehicle Based on an Improved Nonlinear Model

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