Title :
Application of Genetic Algorithm for Braking Force Distribution of Electric Vehicles
Author :
Guo, Jingang ; Wang, Junping ; Cao, Binggang
Author_Institution :
Sch. of Mech. Eng., Xi´´an Jiaotong Univ., Xi´´an, China
Abstract :
Braking force distribution plays an important role in energy recovery of electric vehicles. A methodological approach for the braking force distribution using genetic algorithm is described. In view of vehicle stability, motor characteristic and battery safety, a constrained optimization problem is formulated. The objection is to maximize regenerated brake energy, and various limitations are considered as constraints. Genetic algorithm is used in optimizing distributing braking force between regenerative braking and friction brakes. The simulation results show that the approach is effective. On the basis of comprehensive consideration over braking torque required and the limitations, the approach makes the best of motor braking torque, and can enhance the battery regenerated brake energy remarkably for typical driving cycles.
Keywords :
electric motors; electric vehicles; friction; genetic algorithms; regenerative braking; torque; battery safety; braking force distribution; constrained optimization problem; electric vehicle; energy recovery; genetic algorithm; mechanical friction brake; motor braking torque; motor characteristics; regenerative braking; vehicle stability; Axles; Electric vehicles; Force control; Friction; Genetic algorithms; Hybrid electric vehicles; Stability; Torque; Vehicle safety; Wheels; braking force distribution; electric vehicles; genetic algorithm; optimization;
Conference_Titel :
Industrial Electronics and Applications, 2009. ICIEA 2009. 4th IEEE Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4244-2799-4
Electronic_ISBN :
978-1-4244-2800-7
DOI :
10.1109/ICIEA.2009.5138577