A Gear Decision of Hybrid Electric Truck Based on Neural Network

Abstract

The basic shifting law of hybrid electric vehicles is formulated in a static environment and cannot adapt to the complex and changeable vehicle operating environment. Therefore, its gear decision must comprehensively consider the driver's driving intention, road conditions and vehicle operation parameters, etc. It can effectively solve the unnecessary shifting phenomenon and the cyclic shifting problem of the vehicle under complex road surface, and can improve the dynamic performance and economy of the vehicle. The purpose of this paper is to analyze the gear position decision of hybrid electric truck based on neural network. In the experiment, the main parameters of the hybrid truck are determined, and the gear prediction algorithm is used. Experiments are carried out in two aspects: the solution of the optimal dynamic shift curve of the hybrid truck and the example simulation and analysis of gear decision based on neural network.