Predictive Energy Management for Fuel Cell Hybrid Electric Vehicles

Yang Zhou (), Alexandre Ravey () and Marie-Cécile Péra ()
Additional contact information
Yang Zhou: School of Automation, Northwestern Polytechnical University
Alexandre Ravey: FEMTO-ST (UMR CNRS 6174), FCLAB (USR CNRS 2007), Univ. Bourgogne Franche-Comté, UTBM
Marie-Cécile Péra: FEMTO-ST (UMR CNRS 6174), FCLAB (USR CNRS 2007), Univ. Bourgogne Franche-Comté, UTBM

A chapter in Intelligent Control and Smart Energy Management, 2022, pp 1-44 from Springer

Abstract: Abstract Fuel cells are gradually becoming the competitive alternative to conventional internal combustion engines due to their high system efficiency and zero-local emission property. Nevertheless, the high manufacturing cost and the limited lifetime of fuel cell systems still remain the major barrier toward the massive promotion of fuel cell electric vehicles. To reduce the vehicle’s operating cost, reliable energy management strategies should be devised to coordinate the outputs of multiple energy sources in hybrid powertrain. This chapter intends to present the development of predictive energy management strategy for fuel cell hybrid electric vehicles, especially focusing on the possibility of combining the driving predictive information with the real-time optimization framework. To this end, two driving prediction techniques are proposed, namely, a vehicle speed forecasting approach and a driving pattern recognition method. Thereafter, model predictive control is adopted for real-time decision-making with the assistance of the predicted information. Validation results indicate that the proposed control strategy outperforms the benchmark control strategies in terms of fuel economy and fuel cell durability, thereby verifying the control performance improvement imposed by driving prediction integration.

Keywords: Fuel cell hybrid electric vehicle; Energy management strategy; Driving prediction techniques; Model predictive control; Real-time optimization (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-84474-5_1

Ordering information: This item can be ordered from
http://www.springer.com/9783030844745

DOI: 10.1007/978-3-030-84474-5_1

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().