A hybrid Particle Swarm Optimization – Variable Neighborhood Search algorithm for Constrained Shortest Path problems

Marinakis, Yannis; Migdalas, Athanasios; Sifaleras, Angelo

doi:10.1016/j.ejor.2017.03.031

Cited by 126 publications

(44 citation statements)

References 53 publications

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“…Based on transfer function, the Fourier transform method is always employed to achieve parameters matching [25]. While, it is difficult to calculate the transfer function of a system with multi-degree-of-freedom.…”

Section: Initial Parameters Matching Of the In-wheel Damping Systemmentioning

confidence: 99%

Ride Comfort Optimization of In-Wheel-Motor Electric Vehicle with In-Wheel Vibration Absorber

Liu¹,

Gu²,

Zhang³

2017

Preprint

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Abstract:In this paper, an in-wheel vibration absorber for In-wheel-motor electric vehicle (IWM EV) is designed, and a comprehensive control strategy of in-wheel absorber and vehicle suspension is proposed to improve vehicle ride comfort. The proposed in-wheel vibration absorber, designed for suppressing the motor vibration, is composed of a spring and a controllable damper. The values of in-wheel spring stiffness and damper initial coefficient are determined by using the improved particle swarm optimization (IPSO) algorithm, which is carried on the typical driving condition. To deal with the negative interaction effects between vehicle suspension and in-wheel absorber, the linear quadratic regulator (LQR) algorithm is utilized to control suspension damper, and the fuzzy PID method is utilized to control in-wheel damper. Based on the four evaluation indexes including vehicle body vertical acceleration, suspension dynamic deflection, wheel dynamic load and motor wallop, the simulation results show that, the proposed LQR control of suspension effectively improves vehicle ride comfort, and the fuzzy PID control of in-wheel damper exhibits superior performance of motor vibration suppressing in comparison to conventional electric wheel.

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Section: Initial Parameters Matching Of the In-wheel Damping Systemmentioning

confidence: 99%

Ride Comfort Optimization of In-Wheel-Motor Electric Vehicle with In-Wheel Vibration Absorber

Liu¹,

Gu²,

Zhang³

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on the transfer function, the Fourier transform method is usually adopted to achieve parameter matching [32]. Since it is difficult to derive the transfer function for a system with multiple degrees-of-freedom, some scholars have suggested using the genetic algorithm to match the vehicle suspension parameters [22].…”

Section: Parameters Matching Of the In-wheel Spring And Rubber Bushingmentioning

confidence: 99%

Ride Comfort Optimization of In-Wheel-Motor Electric Vehicles with In-Wheel Vibration Absorbers

Liu

Zhang

2017

Energies

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This paper presents an in-wheel vibration absorber for in-wheel-motor electric vehicles (IWM EVs), and a corresponding control strategy to improve vehicle ride comfort. The proposed in-wheel vibration absorber, designed for suppressing the motor vibrations, is composed of a spring, an annular rubber bushing, and a controllable damper. The parameters of the in-wheel spring and rubber bushing are determined by an improved particle swarm optimization (IPSO) algorithm, which is executed under the typical driving conditions and can absorb vibration passively. To deal with negative interaction effects between vehicle suspension and in-wheel absorber, a linear quadratic regulator (LQR) algorithm is developed to control suspension damper, and meanwhile a fuzzy proportional-integral-derivative (PID) method is developed to control in-wheel damper as well. Through four evaluation indexes, i.e., vehicle body vertical acceleration, suspension dynamic deflection, wheel dynamic load, and motor wallop, simulation results show that, compared to the conventional electric wheel, the proposed suspension LQR control effectively improves vehicle ride comfort, and the in-wheel absorber exhibits excellent performance in terms of wheel and motor vibration suppression.

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“…Whereas, most of the existing methods are not very suitable for the in-wheel DVA due to the special structure and the complicated constraints. The particle swarm optimization (PSO) algorithm is a nonlinear global optimization method, which features a fast convergence speed, a good robustness, and the least sensitivity to the number of variables, and is very effective to solve the multi-objective optimization problems with constraint conditions [44]. Thus, the PSO algorithm is adopted to optimize the DVA stiffness K d and damping C d .…”

Section: Parameters Optimization Of the Dvamentioning

confidence: 99%

Integration Design and Optimization Control of a Dynamic Vibration Absorber for Electric Wheels with In-Wheel Motor

Liu

Huang

et al. 2017

Energies

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This paper presents an integration design scheme and an optimization control strategy for electric wheels to suppress the in-wheel vibration and improve vehicle ride comfort. The in-wheel motor is considered as a dynamic vibration absorber (DVA), which is isolated from the unsprung mass by using a spring and a damper. The proposed DVA system is applicable for both the inner-rotor motor and outer-rotor motor. Parameters of the DVA system are optimized for the typical conditions, by using the particle swarm optimization (PSO) algorithm, to achieve an acceptable vibration performance. Further, the DVA actuator force is controlled by using the alterable-domain-based fuzzy control method, to adaptively suppress the wheel vibration and reduce the wallop acting on the in-wheel motor (IWM) as well. In addition, a suspension actuator force is also controlled, by using the linear quadratic regulator (LQR) method, to enhance the suspension performance and meanwhile improve vehicle ride comfort. Simulation results demonstrate that the proposed DVA system effectively suppresses the wheel vibration and simultaneously reduces the wallop acting on the IWM. Also, the alterable-domain-based fuzzy control method performs better than the conventional ones, and the LQR-based suspension exhibits excellent performance in vehicle ride comfort.

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A hybrid Particle Swarm Optimization – Variable Neighborhood Search algorithm for Constrained Shortest Path problems

Cited by 126 publications

References 53 publications

Ride Comfort Optimization of In-Wheel-Motor Electric Vehicle with In-Wheel Vibration Absorber

Ride Comfort Optimization of In-Wheel-Motor Electric Vehicle with In-Wheel Vibration Absorber

Ride Comfort Optimization of In-Wheel-Motor Electric Vehicles with In-Wheel Vibration Absorbers

Integration Design and Optimization Control of a Dynamic Vibration Absorber for Electric Wheels with In-Wheel Motor

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