Optimization of Spring-Damper orientation in Double-Wishbone type Suspension geometry using Genetic Algorithm in Python

The orientation of the spring-damper system in a suspension geometry is a critical but hidden factor in vehicle performance characteristics. Spring and damper mounting characteristics are the significant factors to ensure proper contact of the tire with the ground, maintain ride height, minimize forces on spring, smooth ride, and driver comfort, as varying stiffness for spring orientation will affect the acceleration gain due to road excitation. Determining the spring orientation is conventionally a long and iterative process that involves lots of computational simulations and analytical expressions that should align with the practical vehicle constraints. Due to numerous possible orientations, the designer would randomly pick the orientation and do the simulation, which reduces the solution's reliability and the better solutions remain unexplored. This paper proposes a new methodology to optimize spring damper orientation in a suspension geometry using a genetic algorithm in Python Programming Language. This model aims to use a genetic algorithm to optimize the spring-damper system's orientation in double-wishbone type suspension geometry by maintaining optimal ride frequency and static ride height, minimizing shear forces on spring based on the constraints and variations in its mounting position. Further, the fitness of each is calculated based on performance characteristics developed through specific orientation. The algorithm provides ten best solutions out of hundreds of possible orientations, allowing designers to narrow down from optimized results based on CAD simulations. Results obtained through analysis using genetic algorithm predicted results much closer to that obtained on ADAMS software.