A Joint Energy Consumption Optimization Algorithm for Hybrid Air-Cushion Vehicles

Abstract

Hybrid air-cushion vehicles (ACVs) provide a solution to transportation on soft terrain, whereas they also bring a new problem of excessive energy consumption. In order to minimize energy consumption in a computational manner, a Genetic Algorithm (GA) - Neural Network (NN) joint optimization algorithm is proposed to online calculate the corresponding optimal vehicle running parameters for given soil conditions. This is realized in three steps. (1) The energy consumption index is firstly simplified as a constrained function with respect to only two independent vehicle running parameters. (2) The optimal solutions are figured out offline with respect to some specific soil conditions by the designed GA optimizer. (3) The optimal solutions are figured out online with respect to general soil conditions by the designed NN optimizer, which is trained using the above offline-obtained data. The feasibility of the joint algorithm is supported by experiments, whose results show an effective integration of the GA’s advantage in complex function optimization and the NN’s advantage in generalization ability and computing speed.