Critical Literature Review on Optimization of Vehicle Suspension System using Bio-Mechanical Models

Abstract

The optimization of vehicle suspension systems using bio-mechanical models has become a critical area of research in automotive engineering, aiming to enhance both vehicle performance and passenger comfort. This interdisciplinary approach combines principles from biology, mechanics, and artificial intelligence to address the complexities of suspension dynamics. Since 2020, bio-inspired suspension designs have shown significant advancements, particularly in vibration isolation and energy efficiency.

Bio-mechanical models are inspired by the human musculoskeletal system's adaptability, enabling the development of advanced suspension systems that dynamically adjust to varying road conditions. Neural network-based control systems, mimicking human reflexes, have proven effective in real-time suspension adjustment. These systems employ deep learning techniques, such as convolutional neural networks (CNNs), to process sensor data and optimize suspension parameters, resulting in improved ride comfort and handling stability.

The integration of artificial intelligence and machine learning with bio-mechanical models has led to sophisticated control strategies, including hybrid approaches that combine fuzzy logic controllers with bio-inspired algorithms. These hybrid systems have demonstrated significant improvements in managing trade-offs between ride comfort, road holding, and handling stability.

Multi-objective optimization techniques, such as NSGA-II and Bayesian optimization, have been crucial in addressing the complex trade-offs in suspension design. Surrogate-assisted optimization has further accelerated the optimization process by using surrogate models to approximate complex objective functions, enabling more comprehensive exploration of design alternatives.

Despite these advancements, challenges remain in integrating bio-mechanical models into vehicle suspension systems, including the complexity of combining biological systems with mechanical engineering principles and the computational intensity of optimization methods. Future research should focus on developing real-time adaptive suspension systems, advanced sensor integration, and more sophisticated validation methods to ensure the reliability of optimized solutions in real-world applications..