Adaptive Power Allocation with Real-Time Monitoring and Optimization for Fuel Cell/Supercapacitor Hybrid Energy Storage Systems
Paper in proceeding, 2022

Electric vehicles powered by hybrid energy storage systems composed of fuel cells and supercapacitors are of great interest. To further improve the efficiency of such hybrid systems, better energy management strategies need to be developed. This paper proposes an adaptive power allocation method with real-time monitoring and optimization for fuel cell/supercapacitor hybrid energy storage systems used in electric vehicles. This method utilizes a low-pass filter to distribute power between fuel cells and supercapacitors. The cut-off frequency of the filter is obtained by splitting the load current spectrum according to the supercapacitor state of charge (SOC). The DC-link voltage fluctuation and the supercapacitor SOC are monitored in a real-time fashion. Consequently, a real-time optimization scheme is developed to reduce the dependence of the proposed algorithm on its initial parameters and enhance the adaptivity of the proposed algorithm. To validate the effectiveness of the proposed method, a Simulink model is developed and two standard drive cycles (i.e., NYCC and US06) are selected. Simulation results show that the DC-link voltage fluctuation drops significantly and the supercapacitor SOC can be effectively controlled.

adaptive power allocation

fuel cell

supercapacitor

real-time monitoring and optimization

Electric vehicles

Author

Qiuyu Li

ShanghaiTech University

Hengzhao Yang

Shanghai University

Qian Xun

Chalmers, Electrical Engineering, Electric Power Engineering

IECON Proceedings (Industrial Electronics Conference)

21624704 (ISSN) 25771647 (eISSN)

Vol. 2022-October
978-166548025-3 (ISBN)

IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society
Brussels, Belgium,

Areas of Advance

Transport

Energy

Subject Categories

Communication Systems

Energy Systems

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/IECON49645.2022.9968352

Related datasets

DOI: 10.1109/IECON49645.2022.9968352

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