Scholarlink

Purpose <p>This paper presents a Model Predictive Controller (MPC) to optimize the In-Wheel Motor -Active Suspension System (IWM-ASS) in electric vehicles, reducing vibration and enhancing ride comfort. The quarter-vehicle active suspension model incorporates dynamic damping within the in-wheel motor-driven system, utilizing the in-wheel motor as a Dynamic Vibration Absorber (DVA) to effectively mitigate forces transmitted to the motor bearing.</p> Method <p>A conventional Proportional Integral and Derivative (PID) controller is implemented initially and fine-tuned using a Genetic Algorithm (GA). MPC is introduced, which employs predictive modeling that dynamically adjusts suspension parameters in real-time under varying road conditions.</p> Results <p>Simulation results show that the MPC reduces RMS and frequency-weighted RMS of body acceleration by 24.82% and 58.75%, respectively, compared to passive suspension. Experimental results on an IWM-EV test bed indicate a 74% improvement in ride quality over the passive system and a 53% improvement over the conventional PID controller. The PID comparison is included as it represents a widely used benchmark in ASS.</p> Conclusion <p>This highlights the efficacy of MPC both on a simulation and experimental platforms, as an active suspension control strategy for electric vehicles equipped with IWM-ASS.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Efficient Vibration Control: Model Predictive Controller Design for In-Wheel Motor-Driven Electric Vehicle Suspension Systems

  • 期刊论文

  • 发表日期：

    2025年9月9日

查看全文
  • Fahira Haseen S,
  • Lakshmi P,
  • Madhupreetha S

摘要

Purpose

This paper presents a Model Predictive Controller (MPC) to optimize the In-Wheel Motor -Active Suspension System (IWM-ASS) in electric vehicles, reducing vibration and enhancing ride comfort. The quarter-vehicle active suspension model incorporates dynamic damping within the in-wheel motor-driven system, utilizing the in-wheel motor as a Dynamic Vibration Absorber (DVA) to effectively mitigate forces transmitted to the motor bearing.

Method

A conventional Proportional Integral and Derivative (PID) controller is implemented initially and fine-tuned using a Genetic Algorithm (GA). MPC is introduced, which employs predictive modeling that dynamically adjusts suspension parameters in real-time under varying road conditions.

Results

Simulation results show that the MPC reduces RMS and frequency-weighted RMS of body acceleration by 24.82% and 58.75%, respectively, compared to passive suspension. Experimental results on an IWM-EV test bed indicate a 74% improvement in ride quality over the passive system and a 53% improvement over the conventional PID controller. The PID comparison is included as it represents a widely used benchmark in ASS.

Conclusion

This highlights the efficacy of MPC both on a simulation and experimental platforms, as an active suspension control strategy for electric vehicles equipped with IWM-ASS.