基于BP神经网络的自动挡汽车换挡规律优化研究  

Research on Optimization of Automatic Vehicle Shifting Maps Based on BP Neural Network

作　　者：钱天宇 马朝永[1] Qian Tianyu;Ma Chaoyong(Beijing Key Laboratory of Advanced Manufacturing Technology,Faculty of Materials and Manufacturing,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学材料与制造学部先进制造技术北京市重点实验室,北京市100124

出　　处：《农业装备与车辆工程》2021年第6期60-62,74,共4页Agricultural Equipment & Vehicle Engineering

摘　　要：换挡规律对于自动挡汽车的燃油经济性至关重要。通过训练BP神经网络来预测目标车型的实时挡位,利用MATLAB/Simulink将训练好的神经网络封装成模块嵌入到AVL Cruise整车模型中,对目标车型在WTCL工况下进行模拟。结果表明,BP神经网络可以优化换挡规律,使发动机运行在更加经济性的工作区间,有效降低了整车燃油消耗,提高了整车燃油经济性,在整车性能开发与换挡策略编制上具有一定的工程应用性。The shifting maps are very important for the fuel economy of auto transmission vehicles.Train the BP neural network to predict the real-time gear of the target vehicle.Use MATLAB/Simulink to encapsulate the trained neural network into a module and embed it in the AVL Cruise vehicle model to simulate under WTCL conditions.The results show that the BP neural network can optimize the shifting maps,make the engine run in a more economical working range.It can effectively reduce the fuel consumption of the entire vehicle,and improve the fuel economy of the entire vehicle.It has certain engineering applicability in vehicle performance development and shift strategy compilation.

关 键 词：换挡规律 燃油经济性 BP神经网络 联合仿真 

分 类 号：U463.2[机械工程—车辆工程]