Real-Time Integrated Energy Management Strategy Applied to Fuel Cell Hybrid Systems

Matthieu Matignon, Toufik Azib (), Mehdi Mcharek, Ahmed Chaibet and Adriano Ceschia
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Matthieu Matignon: ESTACA’LAB, S2ET Department, ESTACA Engineering School–Paris Saclay, 12 Avenue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France
Toufik Azib: ESTACA’LAB, S2ET Department, ESTACA Engineering School–Paris Saclay, 12 Avenue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France
Mehdi Mcharek: ESTACA’LAB, S2ET Department, ESTACA Engineering School–Paris Saclay, 12 Avenue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France
Ahmed Chaibet: Laboratoire DRIVE Nevers, Université de Bourgogne, 58027 Nevers, France
Adriano Ceschia: ESTACA’LAB, S2ET Department, ESTACA Engineering School–Paris Saclay, 12 Avenue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France

Energies, 2023, vol. 16, issue 6, 1-21

Abstract: Integrating hydrogen fuel cell systems (FCS) remains challenging in the expanding electric vehicle market. One of the levers to meet this challenge is the relevance of energy supervisors. This paper proposes an innovative energy management strategy (EMS) based on the integrated EMS (iEMS) concept. It uses a nested approach combining the best of the three EMS categories (optimization-based (OBS), rules-based (RBS), and learning-based (LBS) strategies) to overcome the real-time operating condition limitations of the fuel cell hybrid electric vehicle (FCHEV). Through a fuel cell/battery hybrid architecture, the purpose is to improve hydrogen consumption and manage the battery state of charge (SOC) under real-time driving conditions. The proposed iEMS approach is based on an OBS with optimal control to make the energy-optimal decision. However, it requires the adaptations of real-time operating conditions and a dynamic SOC horizon management. These requirements are supported by combining an RBS based on expert and fuzzy rules to compute the SOC target on each sliding window and an LBS based on fuzzy C-mean clustering to enhance the cooperative environment data processing and adapt it to the FHCEV topology. Our approach obtained simple and realistic system behaviors while having an acceptable computing time suitable for real time constraint. It was then designed and validated using a 27-h real-time measured database. The results show the effectiveness of the proposed iEMS concept with an excellent performance close to the optimal offline strategy (an under 2% consumption gap).

Keywords: integrated EMS; FCS integration; real-time control; fuel cell/battery; driving pattern recognition; battery usage strategy; online optimization; stochastic operating conditions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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