Suspension parameter optimal design to enhance stability and wheel wear in high-speed trains

Chen, Xiangwang; Shen, Longjiang; Hu, Xiaoyi; Li, Guang; Yao, Yuan

doi:10.1080/00423114.2023.2224905

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…Yao et al [15] presented a method for the centralized optimization of key bogie suspension parameters based on vehicle lateral robust stability. Chen et al [16] investigated the stability/wear Pareto optimization of bogie suspension parameters for high-speed passenger trains. Chen et al [17][18][19] optimized the suspension parameters and selection of oscillator suspension and achieved Actuator optimal placement for high-speed trains to improve hunting stability.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Bogie Stability for Minimum Radius Curve of Battery Track Engineering Vehicle

Shen,

Zhao,

Wang

et al. 2024

Applied Sciences

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A battery track engineering vehicle faces challenges such as derailment and other safety concerns when navigating an R20m minimum radius curve, primarily owing to its low vertical and horizontal stabilities. To address these issues, a methodology integrating genetic optimization algorithms with a rigid and flexible coupled multi-body dynamics simulation is proposed to optimize the primary suspensions of the bogie of the vehicle. Initially, a multi-objective optimization model combining rigid and flexible coupled multi-body dynamics of battery track engineering vehicles with a genetic optimization algorithm was formulated. Subsequently, the optimal Latin hypercube design was applied to analyze the sensitivity of vertical and horizontal stability to various suspension parameters. Finally, a non-dominated sorting genetic algorithm (NSGA-II) and an archive-based micro genetic algorithm (AMGA) were applied to optimize the primary suspensions to enhance stability. Consequently, a set of optimal suspension parameter combinations was obtained. A notable enhancement was observed in the lateral stability of the optimized battery track engineering vehicles by 23.33% and in the vertical stability by 3.5% when traversing the R20m minimum radius curve, thereby establishing a theoretical foundation for further improving the running safety of railway vehicles and resolving the shortcomings of less research on the smallest radius curve.

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Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Bogie Stability for Minimum Radius Curve of Battery Track Engineering Vehicle

Shen,

Zhao,

Wang

et al. 2024

Applied Sciences

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“…In response to these challenges, dedicated researchers in the field of railway transportation have invested substantial effort to elevate the performance of rail vehicle suspension systems. In recent years, intelligent control strategies have emerged as a promising avenue [5,6]. The ANFIS technique has garnered attention due to its hybrid nature, combining the strengths of fuzzy logic and neural networks.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Dynamic Analysis of Adaptive Neuro-Fuzzy Inference System-Based Intelligent Control Suspension System for Passenger Rail Vehicles Using Magnetorheological Damper for Improving Ride Index

Sharma,

Choi

et al. 2023

Sustainability

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The ride comfort and safety of passenger rail vehicles depend on the performance of the suspension system in attenuating vibrations induced by track irregularities. This paper investigates the effectiveness of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based semi-active controlled suspension system using a magnetorheological fluid damper in reducing nonlinear lateral vibrations of a passenger rail vehicle. A complete rail vehicle model is developed, including the carbody, front and rear bogies, and the passive suspension system’s nonlinear stiffness and damping characteristics are considered from experimental data. The passive suspension model is validated through experiments, and an ANFIS-based controller is incorporated with the secondary vertical suspension system to improve ride behavior. Three semi-active suspension strategies are considered under varying speeds and track irregularities, and their effectiveness is compared to the nonlinear passive suspension system in terms of rms acceleration, rms displacement, ride quality, and comfort. The results shows that the ANFIS-based semi-active suspension system with a magnetorheological fluid damper outperforms the passive suspension system and semi-active strategies in all tested conditions. There is a reduction in rms acceleration by approximately 11.11% to 23.64% and rms displacement by about 5.36% to 32.06%. Moreover, it significantly improves ride quality (9.20% to 31.02%) and comfort (9.96% to 31.50%). The rms acceleration and displacement are reduced, and the Sperling ride index and Percentage Reduction Index values demonstrate that the ANFIS-based semi-active suspension effectively minimizes the impact of rail irregularities and vibrations, resulting in a significant gain in ride quality and passenger comfort.

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Advancements in Vibration Analysis for Rail Vehicle Dynamics

Duppala,

Palli,

Raju

et al. 2024

Energy, Environment, and Sustainability

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Suspension parameter optimal design to enhance stability and wheel wear in high-speed trains

Cited by 6 publications

References 36 publications

Multi-Objective Optimization of Bogie Stability for Minimum Radius Curve of Battery Track Engineering Vehicle

Multi-Objective Optimization of Bogie Stability for Minimum Radius Curve of Battery Track Engineering Vehicle

Modelling and Dynamic Analysis of Adaptive Neuro-Fuzzy Inference System-Based Intelligent Control Suspension System for Passenger Rail Vehicles Using Magnetorheological Damper for Improving Ride Index

Advancements in Vibration Analysis for Rail Vehicle Dynamics

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