Predictive Cruise Control of Full Electric Vehicles: A Comparison of Different Solution Methods⁎This work was supported in part by the China Automobile Industry Innovation and Development Joint Fund under Grant U1864206, and in part by the National Nature Science Foundation of China under Grant 61903153. Issue 10 (2021) - Chu, Hongqing; Dong, Shiying; Hong, Jinlong; Chen, Hong; Gao, Bingzhao
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.