Monitoring and energy management approach for a fuel cell hybrid electric vehicle
Benali Tifour 1  
,   Boukhnifer Moussa 2  
,   Hafaifa Ahmed 3  
,   Tanougast Camel 2  
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University of Djelfa (ALgeria)
University of Lorraine, LCOMS, F-57000 Metz, France
University of Djelfa
Benali Tifour   

University of Djelfa (ALgeria)
Submission date: 2020-03-22
Final revision date: 2020-06-02
Acceptance date: 2020-06-15
Online publication date: 2020-06-17
Publication date: 2020-06-17
Diagnostyka 2020;21(3):15–29
Recently, the reduction of fuels consumption is a global challenge, in particular for significant investments in the automotive sector, in order to optimize and control the parameters involved for the partial or total electrification of vehicles. Thereby, the energy management system remains the axis of progress for the development of fuel cell hybrid electric vehicles. The fuzzy controller has been widely adopted for energy monitoring, where the determination of its parameters is still challenging. In this work, this problem is investigated through a secondary development of a fuzzy energy monitoring system based on the Advisor platform and particle swarm optimization. The latter is used to determine, for different driving conditions, the best parameters that increase the fuel economy and reduce the battery energy use. As a result, five tuned fuzzy energy monitoring system models with five sets of parameters are obtained. Evaluation results confirm the effectiveness of this strategy, they also show slight differences between them in terms of fuel economy, battery state of charge variations, and overall system efficiency. However, the fuzzy energy monitoring system tuned under multiple conditions is the only one that can guarantee the minimum of the state of charge variations, no matter the driving conditions.
Bhatti A.R, Salam Z. A rule-based energy management scheme for uninterrupted electric vehicles charging at constant price using photovoltaic-grid system. Renewable Energy 2018; 125:384-400.
Cao Y, Yao H, Wang Z, Jermsittiparsert K, Yousefi N. optimal designing and synthesis of a hybrid pv/fuel cell/wind system using meta-heuristics. Energy Reports 2020;6:1353-1362.
Caux S, Hankache W, Fadel M, Hissel D. On-line fuzzy energy management for hybrid fuel cell systems, International Journal of Hydrogen Energy 2010;35(5):2134-2143.
Chun-Yan L, Guo-Ping L. Optimal fuzzy power control and management of fuel cell/battery hybrid vehicles. Journal of power sources 2009; 192(2): 525-533.
Guo J, He H, Peng J. Real-time energy management for plug-in hybrid electric vehicle based on economy driving pro system. Energy Procedia 2019;158:2689-2694.
Hemi H, Ghouili J, Cheriti A. A real time fuzzy logic power management strategy for a fuel cell vehicle. Energy Conversion and Management 2014; 80:63-70.
Kandidayeni M, Soleymani M, Ghadimi AA. Designing an optimal fuzzy controller for a fuel cell vehicle considering driving patterns. Scientia Iranica 2016;23(1):218-227.
Kennedy J, Eberhart R. Particle swarm optimization. In Proceedings of ICNN'95-International Conference on Neural Networks 1995; 4: 1942-1948.
Li Q, Chen W, Li Y, Liu S, Huang J. Energy management strategy for fuel cell/battery/ ultracapacitor hybrid vehicle based on fuzzy logic. International Journal of Electrical Power & Energy Systems. 2012;43(1):514-525.
Mehta R, Verma P, Srinivasan D, Yang J. Double-layered intelligent energy management for optimal integration of plug-in electric vehicles into distribution systems. Applied Energy 2019; 233/2341:146-155.
Michalczuk M, Ufnalski B, Grzesiak L.M. Particle swarm optimization of the fuzzy logic controller for a hybrid energy storage system in an electric car. 2016 18th European Conference on Power Electronics and Applications. EPE'16 ECCE Europe:1-10.
Odeim F, Roes J, Wülbeck L, Heinzel A. Power management optimization of fuel cell/battery hybrid vehicles with experimental validation. Journal of Power Sources. 2014;252:333-343.
Ouddah N, Boukhnifer M, Raisemche A, Two control energy management schemes for electrical hybrid vehicle, 10th IEEE International Multi-Conferences on Systems, Signals & Devices 2013 (SSD13)
Radaideh MI, Radaideh MI, Kozlowski T. Design optimization under uncertainty of hybrid fuel cell energy systems for power generation and cooling purposes. International Journal of Hydrogen Energy 2020;45(3):2224-2243.
Ravey A, BlunierB, Miraoui A.Control Strategies for Fuel-Cell-Based Hybrid Electric Vehicles: From Offline to Online and Experimental Results. IEEE Transactions on Vehicular Technology 2012; 61(6): 2452-245.
Satyapal Sunita. Hydrogen and Fuel Cells Overview, 12-Apr-2017 [online]. Available: [Accessed- 24 - Mar-2019].
Sedighizadeh M, Esmaili M, Mohammadkhani N. Stochastic multi-objective energy management in residential microgrids with combined cooling, heating, and power units considering battery energy storage systems and plug-in hybrid electric vehicles. Journal of Cleaner Production 2018; 195: 301-317
Sulaiman N. Hannan MA, Mohamed A, Majlan EH, Wan Daud WR. A review on energy management system for fuel cell hybrid electric vehicle: Issues and challenges. Renewable and Sustainable Energy Reviews 2015;52:802-814.
Tanc B, Arat HT, Baltacıoğlu E, Aydın K. Overview of the next quarter century vision of hydrogen fuel cell electric vehicles. International Journal of Hydrogen Energy 2019; 44: 10120-10128.
The National Renewable Energy Laboratory. USA.2003.ADVISOR Advanced Vehicle Simulator, advisor-2003-00-r0116. Available: [Accessed- 01-27/ Mar-2019].
Tremblay O, Dessaint LA. Experimental validation of a battery dynamic model. World Electric Vehicle Journal 2009;3:1-10.
Wang Y, Moura SJ, Advani SG, Prasad AK. Power management system for a fuel cell/battery hybrid vehicle incorporating fuel cell and battery degradation. International Journal of Hydrogen Energy 2019;44(16):8479-8492.
Wipke KB, Cuddy MR, Burch SD. ADVISOR 2.1: A user-friendly advanced powertrain simulation using a combined backward/forward approach. IEEE transactions on vehicular technology 1999; 48(6): 1751-1761.
Wu X, Zhang K, Cheng M. Optimal control of constrained switched systems and application to electrical vehicle energy management. Nonlinear Analysis: Hybrid Systems 2018; 30: 171-188.
Zhang G, Chen W, Li Q. Modeling, optimization and control of a FC/battery hybrid locomotive based on ADVISOR. International Journal of Hydrogen Energy 2017;42(29):18568-18583.
Zhang R, Tao J. Ga-Based fuzzy energy management system for Fc/Sc-Powered HEV considering H2 consumption and load variation. EEE Transactions on Fuzzy Systems 2018; 26(4): 1833-1843.