Optimal design and applicability of electric power steering system for automotive platform

The ongoing need for better fuel economy and lower exhaust pollution of vehicles has increased the employment of electric power steering(EPS)in automotives.Optimal design of EPS for a product family reduces the development and fabrication costs significantly.In this paper,the TOPSIS method along with the NSGA-Ⅱis employed to find an optimum family of EPS for an automotive platform.A multi-objective optimization problem is defined considering road feel,steering portability,RMS of Ackerman error,and product family penalty function(PFPF)as the conflicting objective functions.The results for the single objective optimization problems and the ones for the multi-objective optimization problem,as well as two suggested trade-off design points are presented,compared and discussed.For the two suggested points,performance at one objective function is deteriorated by about 1%,while the commonality is increased by 20%–40%,which shows the effectiveness of the proposed method in first finding the non-dominated design points and then selecting the trade-off among the obtained points.The results indicate that the obtained trade-off points have superior performance within the product family with maximum number of common parts.

MODIFIED GENETIC ALGORITHM APPLIED TO SOLVE PRODUCT FAMILY OPTIMIZATION PROBLEM

The product family design problem solved by evolutionary algorithms is discussed. A successful product family design method should achieve an optimal tradeoff among a set of competing objectives, which involves maximizing commonality across the family of products and optimizing the performances of each product in the family. A 2-level chromosome structured genetic algorithm （2LCGA） is proposed to solve this class of problems and its performance is analyzed in comparing its results with those obtained with other methods. By interpreting the chromosome as a 2-level linear structure, the variable commonality genetic algorithm （GA） is constructed to vary the amount of platform commonality and automatically searches across varying levels of commonality for the platform while trying to resolve the tradeoff between commonality and individual product performance within the product family during optimization process. By incorporating a commonality assessing index to the problem formulation, the 2LCGA optimize the product platform and its corresponding family of products in a single stage, which can yield improvements in the overall performance of the product family compared with two-stage approaches （the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage）. The scope of the algorithm is also expanded by introducing a classification mechanism to allow mul- tiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. The effectiveness of 2LCGA is demonstrated through the design of a family of universal electric motors and comparison against previous results.