Title :
Battery-supercapacitor electric vehicles energy management using DP based predictive control algorithm
Author :
Lin Xiaofeng ; Hu Meipin ; Song Shaojian ; Yang Yimin
Author_Institution :
Coll. of Electr. Eng., Guangxi Univ., Nanning, China
Abstract :
To achieve a reasonable power split scheme of Li battery pack and supercapacitor hybrid electric vehicles, we propose dynamic programming (DP) based predictive control algorithm (PCA) in this paper. First, the model of the vehicle plant is established consisting of mathematical models of supercapacitor and Li battery pack. Then, the PCA based control system is designed in order to make full use of future road information. Thirdly, a DP-based-controller is proposed to minimize the cost function which consists of power loss and constrains of output. The simulation suggests that the proposed strategy can generate reasonable power split by taking the power loss, constraints of two sources and flatness of power output of Li battery pack into account.
Keywords :
dynamic programming; energy management systems; hybrid electric vehicles; predictive control; secondary cells; supercapacitors; DP based predictive control; Li; Li battery pack; battery supercapacitor; cost function; dynamic programming; electric vehicles energy management; future road information; hybrid electric vehicles; mathematical models; power loss; power split scheme; vehicle plant; Batteries; Cost function; Mathematical model; Predictive control; Roads; Supercapacitors; Vehicles; Li battery pack; dynamic programming; electric vehicle; energy management; predictive control algorithm; supercapacitor;
Conference_Titel :
Computational Intelligence in Vehicles and Transportation Systems (CIVTS), 2014 IEEE Symposium on
Conference_Location :
Orlando, FL
DOI :
10.1109/CIVTS.2014.7009474