Efficiency improvement of vehicle active suspension based on multi-objective integrated optimization

Abstract

Active suspension can effectively resolve the contradictions between vehicle ride comfort and stability. However, a new contradiction between the active suspension performance and efficiency is aroused. Active suspension with excellent performance requires high actuation power and force in an aggressive condition, which is usually an excess capacity for normal conditions. To improve the efficiency and capacity utilization rate, this paper conducted an investigation on the efficiency and utilization rate of vehicle active suspension based on a seven degrees-of-freedom full vehicle mode with a linear quadratic Gaussian active suspension controller. The multiple objectives of active suspension performance and efficiency are integrally optimized via genetic algorithm with an elaborately designed penalty function. The proposed integration of multiple objectives is proved effective according to the comprehensive comparison analysis. The overall performance of the optimized suspension achieved the Pareto optimality. Not only a better balance between the ride comfort and stability is accomplished, but also the active suspension utilization rate is improved. By this method, the obtained Pareto optimality set can greatly improve the parameters matching and design of the active suspension.

Keywords

Active suspension efficiency improvement genetic algorithm integrated multi-objective optimization vehicle dynamics

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