Liu, Junhu (Tianjin university), Qin, Tang (Tianjin university), Chen, Daxin (Tianjin University), Wang, GaoXiang (TianJin University), Chen, Tao (Tianjin University)

Driving Power Prediction of Heavy Commercial Vehicles Based on Multi-Task Learning

Scheduled for presentation during the Regular session "Powertrain Control II" (FrB3), Friday, November 1, 2024, 10:50−11:10, Room T3

7th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, Oct 30 - Nov 1, 2024, Dalian, China

This information is tentative and subject to change. Compiled on January 2, 2025

Abstract

With the continuous development of vehicle intelligence technology, developing predictive con trol strategy has become a research hotspot, and the prediction of vehicle driving power is crucial for de veloping such strategies. In the field of short-term power prediction, most approaches indirectly predict vehicle driving power by predicting speed, road grade, and other information based on deep learning single task methods. To reduce the calculation scale of the prediction model and solve the multivariable coupling prediction problem required for power prediction, this paper proposes a three-parameter prediction model for road grade, speed, and acceleration based on multi-task learning (MTL) network. The development and validation of the prediction model were based on actual vehicle data. Compared with traditional single-task prediction methods, the proposed model improves the accuracy of power predictions by more than 10%.