Optimal Design of an Autotensioner in an Automotive Belt Drive System Via a Dynamic Adaptive PSO-GA

Abstract

Noise, vibration, and harshness performances are always concerns in design of an automotive belt drive system. The design problem of the automotive belt drive system requires the minimum transverse vibration of each belt span and minimum rotational vibrations of each pulley and the tensioner arm at the same time, with constraints on tension fluctuations in each belt span. The autotensioner is a key component to maintain belt tensions, avoid belt slip, and absorb vibrations in the automotive belt drive system. In this work, a dynamic adaptive particle swarm optimization and genetic algorithm (DAPSO-GA) is proposed to find an optimum design of an autotensioner to solve this design problem and achieve design targets. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization (PSO) algorithm to balance the convergence rate and global optimum search ability by adaptively adjusting the search velocity during the search process. genetic algorithm (GA)-related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the PSO algorithm to further exploit optimal solutions generated by the PSO algorithm. These operators are used to diversify the swarm and prevent premature convergence. The objective function is established using a weighted-sum method, and the penalty function method is used to deal with constraints. Optimization on an example automotive belt drive system shows that the system vibration is greatly improved after optimization compared with that of its original design.