Latest revision as of 11:21, 16 February 2021

Abstract

Hybrid electric vehicles (HEVs) are equipped with multiple power sources for improving the efficiency and performance of their power supply system. An energy management (EM) strategy is needed to optimize the internal power flows and satisfy the driver's power demand. To achieve maximum fuel profits from EM, many solution methods have been presented. Optimal solution methods are typically not feasible in an online application due to their computational demand and their need to have a priori knowledge about future vehicle power demand. In this paper, an online EM strategy is presented with the ability to mimic the optimal solution but without using a priori road information. Rather than solving a mathematical optimization problem, the methodology concentrates on a physical explanation about when to produce, consume, and store electric power. This immediately reveals the vehicle characteristics that are important for EM. It is shown that this concept applies to many existing HEVs as well as possible future vehicle configurations. Since the method only focuses on typical vehicle characteristics, the underlying algorithm requires minor computational effort and can be executed in real time. Clear directions for online implementation are given in this paper. A parallel HEV with a 5-kW integrated starter/generator (ISG) is selected to demonstrate the performance of the EM strategy. Simulation results indicate that the proposed EM strategy exhibits similar behavior as an optimal solution obtained from dynamic programming. Profits in fuel economy primarily arise from engine stop/start and energy obtained during regenerative braking. This latter energy is preferably used for pure electric propulsion where the internal combustion engine is switched off. © 2008 IEEE.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1109/tvt.2008.919988

• http://resolver.tudelft.nl/uuid:2513e70e-9a7c-4130-8578-794738edbff5

• https://research.tue.nl/nl/publications/online-energy-management-for-hybrid-electric-vehicles(613da293-c173-4e01-ab9c-52995bd2ece2).html

• http://xplorestaging.ieee.org/ielx5/25/4665392/04460906.pdf?arnumber=4460906,

http://dx.doi.org/10.1109/tvt.2008.919988

• https://ieeexplore.ieee.org/document/4460906,

https://dblp.uni-trier.de/db/journals/tvt/tvt57.html#KesselsKBK08,

https://trid.trb.org/view/1324851,

https://repository.tudelft.nl/view/tno/uuid:2513e70e-9a7c-4130-8578-794738edbff5,

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4460906,

https://www.narcis.nl/publication/RecordID/oai%3Apure.tue.nl%3Apublications%2F613da293-c173-4e01-ab9c-52995bd2ece2,

http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000004460906,

https://www.infona.pl/resource/bwmeta1.element.ieee-art-000004460906,

https://doi.org/10.1109/TVT.2008.919988,

https://research.tue.nl/en/publications/online-energy-management-for-hybrid-electric-vehicles,

https://academic.microsoft.com/#/detail/2099360030