TY - GEN
T1 - The Multi-objective Optimization of Cost, Energy Consumption and Battery Degradation for Fuel Cell-Battery Hybrid Electric Vehicle
AU - Ruan, Jiageng
AU - Zhang, Bin
AU - Liu, Bendong
AU - Wang, Shuo
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/2/26
Y1 - 2021/2/26
N2 - As one of the promising solutions to air pollution and energy crisis caused by the transportation sector, fuel cell hybrid electric vehicles (FC HEVs) attract great attention around the world. Given the under power of the fuel cell to meet the requirements of daily driving, a power supplement system, generally battery, is essential to make up a multi-power hybrid powertrain. In this paper, the power matching strategies are optimized, considering the system cost, energy efficiency, and battery degradation, by particle swarm optimization (PSO) algorithm. Based on the change of the degree of hybridization (DOH), two hybrid systems are proposed, and the corresponding optimal hybridization degrees of the hybrid powertrain are found under four groups of weighting factors. Based on multi-objective optimization, the optimal degrees powertrain hybridization of the hybrid are proposed to extend battery life, improve energy consumption, and reduce powertrain cost according to individual requirements.
AB - As one of the promising solutions to air pollution and energy crisis caused by the transportation sector, fuel cell hybrid electric vehicles (FC HEVs) attract great attention around the world. Given the under power of the fuel cell to meet the requirements of daily driving, a power supplement system, generally battery, is essential to make up a multi-power hybrid powertrain. In this paper, the power matching strategies are optimized, considering the system cost, energy efficiency, and battery degradation, by particle swarm optimization (PSO) algorithm. Based on the change of the degree of hybridization (DOH), two hybrid systems are proposed, and the corresponding optimal hybridization degrees of the hybrid powertrain are found under four groups of weighting factors. Based on multi-objective optimization, the optimal degrees powertrain hybridization of the hybrid are proposed to extend battery life, improve energy consumption, and reduce powertrain cost according to individual requirements.
KW - Battery
KW - electric vehicle
KW - fuel cell
KW - optimization
KW - particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=85103981398&partnerID=8YFLogxK
U2 - 10.1109/CPEEE51686.2021.9383396
DO - 10.1109/CPEEE51686.2021.9383396
M3 - Conference contribution
AN - SCOPUS:85103981398
T3 - 2021 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021
SP - 50
EP - 55
BT - 2021 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021
Y2 - 26 February 2021 through 28 February 2021
ER -
