Energy Management Strategy Based on a Novel Speed Prediction Method

Sensors

Wen Sun¹  Yuanjian Zhang²  Jiaming Xing³  Liang Chu³  Zhuoran Hou³ 

[1] College of Automotive Engineering, Changzhou Institute of Technology, Changzhou 213032, China;School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast BT9 5AG, UK;State Key Laboratory of Automotive Dynamic Simulation and Control, Jilin University, Changchun 130021, China;

关键词: speed prediction;    deep learning;    energy management strategy;    model predictive control;   

DOI  :  10.3390/s21248273

来源: DOAJ

【 摘 要 】

Vehicle speed prediction can obtain the future driving status of a vehicle in advance, which helps to make better decisions for energy management strategies. We propose a novel deep learning neural network architecture for vehicle speed prediction, called VSNet, by combining convolutional neural network (CNN) and long-short term memory network (LSTM). VSNet adopts a fake image composed of 15 vehicle signals in the past 15 s as model input to predict the vehicle speed in the next 5 s. Different from the traditional series or parallel structure, VSNet is structured with CNN and LSTM in series and then in parallel with two other CNNs of different convolutional kernel sizes. The unique architecture allows for better fitting of highly nonlinear relationships. The prediction performance of VSNet is first examined. The prediction results show a RMSE range of 0.519–2.681 and a R2 range of 0.997–0.929 for the future 5 s. Finally, an energy management strategy combined with VSNet and model predictive control (MPC) is simulated. The equivalent fuel consumption of the simulation increases by only 4.74% compared with DP-based energy management strategy and decreased by 2.82% compared with the speed prediction method with low accuracy.

【 授权许可】

Unknown