Chu, Hongqing (Tongji university), Dong, Shiying (Jilin University), Hong, Jinlong (Jilin University), Chen, Hong (Tongji University), Gao, Bingzhao (Jilin University)

Predictive Cruise Control of Full Electric Vehicles: A Comparison of Different Solution Methods

Scheduled for presentation during the Regular session "Optimization and control for electrified vehicles" (MoBT2), Monday, August 23, 2021, 16:30−16:50, Room T2

6th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, August 23-25, 2021, Tokyo, Japan

This information is tentative and subject to change. Compiled on April 26, 2024

Abstract

The integration of electrification and intelligence is of great significance to alleviating range anxiety of electric vehicles. Predictive cruise control (PCC), which optimizes the longitudinal driving strategies by using the upcoming road traffic information, can further improve the vehicle economy. The paper gives the comparison of different solution methods regarding PCC problem of electric vehicles. The car-following optimization problem is formulated as a constrained nonlinear optimization problem. For ease of presentation, the car-following optimization problem is reformulated as a standard form of the optimization problem in continuous time domain. Then, the standard form of the optimization problem is transformed from a continuous form to a discrete form by using Euler method and Gauss pseudospectral method. Two common solution methods, that is dynamic programming and sequential quadratic programming, are used to solve the optimization problem in discrete form. Simulations are performed to demonstrate the comparison of different solution schemes.