Safety probability based multi-objective optimization of energy-harvesting suspension system. (15th October 2020) - Li, Shiying; Xu, Jun; Gao, Haonan; Tao, Tao; Mei, Xuesong
Abstract: An energy-harvesting suspension system of a vehile can improve both the ride comfort and fuel efficiency. However, such a suspension design is a multi-parameter and multi-objective optimization problem. To solve this problem, the sensitivities of performance criteria to the parameters of an energy-harvesting suspension are studied firstly, with analytical solutions and numerical solutions of the root mean squares of the design criteria. To get a trade-off between the conflicting performance indexes of the suspension, a non-dimensional mixed index based on safety probability is proposed to evaluate the performance, where the ride comfort probability and handling stability probability are adopted to unify the dimensions. On this basis, the genetic algorithm is adopted to solve the multi-parameter optimization problem. Numerical analysis and experiments are conducted to verify the performance of the optimized energy-harvesting suspension system. Results show that ride comfort and handling stability are two conflicting performance indexes, and only a Pareto optimal result can be obtained by multi-objective optimization algorithm. With the proposed mixed index of suspension as the optimal object, the ride comfort can be improved significantly, while the handling stability is ensured. Additionally, for a given energy-harvesting suspension, by merely optimizing the damping coefficient, the suspension performance can be improved with variations of road conditions, which illustrates the potential good performance of the energy-harvesting suspension. Highlights: A non-dimensional mixed index based on safety probability is proposed. The inerter is fully considered in the suspension modeling and operation. The sensitivity of performance criteria to the parameters is studied. The damping coefficient is optimized at different road levels. The safety probability based optimization is analyzed verified.