Lei, Yuyang (Tianjin University), Song, Kang (TianJin University), Xie, Hui (Tianjin University), Lv, Dongxuan (Tianjin University)

Heavy-Duty Truck Predictive Energy-Efficient Cruise Control with Dynamic Multi-Objective Weights

Scheduled for presentation during the Regular session "Powertrain Control II" (FrB3), Friday, November 1, 2024, 10:30−10:50, 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

In the logistics and transportation industry, energy-efficient control of heavy-duty trucks is crucial for reducing fuel consumption and lowering operational costs. In traditional energy efficiency optimization algorithms for heavy-duty trucks, the lack of consideration for gradient information ahead significantly limits the potential for energy efficiency optimization in this large inertia system. In this study, a control algorithm combining the iterative updating of the road gradient information and multi-objective optimization of dynamic variable trade-offs is introduced. Firstly, a real-time estimation algorithm based on sliding window parameters online learning is designed for real-time iterative updating of road slope data. Secondly, the multiobjective trade-off model predictive control algorithm of fuel consumption dynamic variable weights for economy, power performance and smoothness is designed. Validated on Highprecision simulation platform, the results show that fuel consumption is reduced by 2.84% compared to the conventional nonlinear predictive cruise control(PCC) algorithm by dynamic variable weights multi-objective optimization on the basis of foreseeing the slope information of 150 meters ahead.