An evolutionary optimized device for energy harvesting from traffic

In recent years the increase of the computational capability and the development of innovative multi-physics techniques has determined a growing interest towards modeling and optimization in engineering system design and green energy applications. In this context, advanced soft computing techniques can be applied by engineers to several problems and used within optimization process, in order to find out the best design and to improve the system performance. These techniques promise also to give new impulse to research on renewable systems and, especially in the last five year, on the so called Energy Harvesting Devices (EHDs). In this paper the optimization of a Tubular Permanent Magnet-Linear Generator for energy harvesting from traffic applications is presented. The optimization process is developed by means of hybrid evolutionary algorithms to reach the best overall system efficiency and the impact on the environment. Finally, an experimental validation of the designed EHD prototype is presented.