TY - GEN
T1 - Multi-objective Optimal Sizing and Real-time Control of Hybrid Energy Storage Systems for Electric Vehicles
AU - Yu, Huilong
AU - Cao, Dongpu
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/18
Y1 - 2018/10/18
N2 - Hybrid energy storage system (HESS) has been recognized as one of the most promising solutions to overcome the drawbacks of the expensive and short life lithium-ion battery with low power density, by introducing a proper number of supercapcitors. However, the hybridization introduces complicated sizing and energy management problems. This papers aims to investigate the sizing and real-time energy management of a devised HESS for electric vehicles with an electric race car as a case study. In particular, a proposed multi-objective Bi-level optimal sizing and control framework is implemented to find the optimal parameters of the energy management algorithm, the optimal number of the lithiumion battery cells and the supercapacitor banks. The simulation results have validated the effectiveness of the investigated methodology in minimizing the total mass of the HESS and maximizing the cycle life of the lithium-ion battery.
AB - Hybrid energy storage system (HESS) has been recognized as one of the most promising solutions to overcome the drawbacks of the expensive and short life lithium-ion battery with low power density, by introducing a proper number of supercapcitors. However, the hybridization introduces complicated sizing and energy management problems. This papers aims to investigate the sizing and real-time energy management of a devised HESS for electric vehicles with an electric race car as a case study. In particular, a proposed multi-objective Bi-level optimal sizing and control framework is implemented to find the optimal parameters of the energy management algorithm, the optimal number of the lithiumion battery cells and the supercapacitor banks. The simulation results have validated the effectiveness of the investigated methodology in minimizing the total mass of the HESS and maximizing the cycle life of the lithium-ion battery.
UR - http://www.scopus.com/inward/record.url?scp=85056795706&partnerID=8YFLogxK
U2 - 10.1109/IVS.2018.8500629
DO - 10.1109/IVS.2018.8500629
M3 - Conference contribution
AN - SCOPUS:85056795706
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 191
EP - 196
BT - 2018 IEEE Intelligent Vehicles Symposium, IV 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Intelligent Vehicles Symposium, IV 2018
Y2 - 26 September 2018 through 30 September 2018
ER -
