TY - GEN
T1 - Genetic algorithm study on control strategy parameter optimization of hybrid powertrain system
AU - Qiang, Song
AU - Ping, Zhao
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/29
Y1 - 2017/9/29
N2 - Based on the optimization design, the mathematical model of the control strategy parameter optimization taking the minimum fuel consumption as the objective function is established. Then, taking hybrid bulldozer as an example, the genetic algorithm is used to solve the optimization problem. Through optimization, the fuel consumption reduces 4.1% further more compared with conventional bulldozer under the same working condition. Using this method, it is easier to find a set of optimal parameters to shorten the calibrated time of the controller in a real hybrid electric vehicle.
AB - Based on the optimization design, the mathematical model of the control strategy parameter optimization taking the minimum fuel consumption as the objective function is established. Then, taking hybrid bulldozer as an example, the genetic algorithm is used to solve the optimization problem. Through optimization, the fuel consumption reduces 4.1% further more compared with conventional bulldozer under the same working condition. Using this method, it is easier to find a set of optimal parameters to shorten the calibrated time of the controller in a real hybrid electric vehicle.
KW - Control strategy
KW - Genetic algorithm
KW - Hybrid powertrain system
KW - Hybrid vehicle
KW - Parameter optimization
UR - http://www.scopus.com/inward/record.url?scp=85034607837&partnerID=8YFLogxK
U2 - 10.1109/IAEAC.2017.8054421
DO - 10.1109/IAEAC.2017.8054421
M3 - Conference contribution
AN - SCOPUS:85034607837
T3 - Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017
SP - 2256
EP - 2260
BT - Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017
A2 - Xu, Bing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017
Y2 - 25 March 2017 through 26 March 2017
ER -