Research on energy-saving driving recognition and optimization model development based on driving road conditions and driving styles

Abstract

The development of intelligent transportation technology and the dual carbon policy have brought new opportunities for energy-saving driving. Energy-saving driving involves two problems, namely, real-time recognition of driving road conditions and driving styles for energy-saving driving evaluation, energy-saving vehicle speed calculation and energysaving driving guidance for non-energy-saving driving. Therefore, this paper designs a real-time recognition model of driving road conditions and driving styles and a global optimization energy-saving vehicle speed calculation model. Firstly, the urban roads in Tianjin and Hefei are selected to carry out the experimental analysis of the influencing factors of energy-saving driving. Secondly, through the selection of characteristic parameters, K-means clustering based on genetic algorithm optimization, and neural network training model based on genetic algorithm, the real-time recognition of driving road conditions and driving styles is realized in road test. Finally, based on the dynamic programming algorithm, a global optimization energy-saving driving strategy model is established to achieve energy-saving driving speed guidance for intense driving. The results show that using energy-saving speed guidance, the maximum reduction of vehicle driving energy consumption is 16.90 %, and the average reduction is 8.34 %.