TY - JOUR
T1 - A comparative study of extremum seeking methods applied to online energy management strategy of fuel cell hybrid electric vehicles
AU - Zhou, Daming
AU - Ravey, Alexandre
AU - Al-Durra, Ahmed
AU - Gao, Fei
N1 - Funding Information:
This work is supported by European Commission H2020 grant ESPESA (H2020-TWINN-2015) EU Grant agreement No: 692224 .
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017/11/1
Y1 - 2017/11/1
N2 - As an online adaptive optimization algorithm, the extremum seeking method (ESM) can be effectively employed to find an optimal operating point of a static nonlinear system in real-time. This paper presents a comparative study of different ESM schemes for online energy management strategy of fuel cell hybrid electric vehicles (FCHEVs). By applying the extremum seeking controller, the fuel cell system operating points can be maintained in a high efficiency region and thus saving the hydrogen consumption. In addition, battery state of charge (SOC) is considered as the input of penalty function for extremum seeking controller, in order to prevent over-discharging/over-charging of the lithium-ion battery during the FCHEVs operation. Different schemes of ESM presented in this comparative study consist of first-order ESM, high-pass filter based ESM, and band-pass filter based ESC. The main evaluation criteria in this comparative study include the utilization of lithium-ion battery, the fluctuation of fuel cell system output power, the fuel cell system efficiency and the hydrogen consumption. A Hardware-In-the-Loop (HIL) platform is used to experimentally validate the presented comparative study. Experimental comparison results show that, the performance of all the presented ES controllers is close to that of offline benchmark dynamic programming. The band-pass filter based ES controller is preferred to improve both the performance and durability of energy storage system (ESS) in FCHEVs, since this controller is found to have a good ability to limit the fuel cell power dynamics.
AB - As an online adaptive optimization algorithm, the extremum seeking method (ESM) can be effectively employed to find an optimal operating point of a static nonlinear system in real-time. This paper presents a comparative study of different ESM schemes for online energy management strategy of fuel cell hybrid electric vehicles (FCHEVs). By applying the extremum seeking controller, the fuel cell system operating points can be maintained in a high efficiency region and thus saving the hydrogen consumption. In addition, battery state of charge (SOC) is considered as the input of penalty function for extremum seeking controller, in order to prevent over-discharging/over-charging of the lithium-ion battery during the FCHEVs operation. Different schemes of ESM presented in this comparative study consist of first-order ESM, high-pass filter based ESM, and band-pass filter based ESC. The main evaluation criteria in this comparative study include the utilization of lithium-ion battery, the fluctuation of fuel cell system output power, the fuel cell system efficiency and the hydrogen consumption. A Hardware-In-the-Loop (HIL) platform is used to experimentally validate the presented comparative study. Experimental comparison results show that, the performance of all the presented ES controllers is close to that of offline benchmark dynamic programming. The band-pass filter based ES controller is preferred to improve both the performance and durability of energy storage system (ESS) in FCHEVs, since this controller is found to have a good ability to limit the fuel cell power dynamics.
KW - Extremum-seeking method (ESM)
KW - Fuel cell hybrid electric vehicles (FCHEVs)
KW - Hardware-In-the-Loop (HIL) platform
KW - Online energy management strategy
UR - http://www.scopus.com/inward/record.url?scp=85042199496&partnerID=8YFLogxK
U2 - 10.1016/j.enconman.2017.08.079
DO - 10.1016/j.enconman.2017.08.079
M3 - Article
AN - SCOPUS:85042199496
SN - 0196-8904
VL - 151
SP - 778
EP - 790
JO - Energy Conversion and Management
JF - Energy Conversion and Management
ER -