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Technical Brief

ECMS Controller Robustness in Flex-Fuel Hybrid Vehicles

[+] Author and Article Information
Chris Manzie

Department of Mechanical Engineering,
The University of Melbourne,
Victoria 3010, Australia
e-mail: manziec@unimelb.edu.au

Olivier Grondin

Control, Signals, and Systems Department,
IFP Energies Nouvelles,
1 et 4, avenue de Bois-Preau,
Rueil-Malmaison 92500, France
e-mail: olivier.grondin@ifpen.fr

Antonio Sciarretta

Control, Signals, and Systems Department,
IFP Energies Nouvelles,
1 et 4, avenue de Bois-Preau,
Rueil-Malmaison 92500, France
e-mail: antonio.sciarretta@ifpen.fr

Gianluca Zito

Control, Signals, and Systems Department,
IFP Energies Nouvelles,
1 et 4, avenue de Bois-Preau,
Rueil-Malmaison 92500, France
e-mail: gianluca.zito@ifpen.fr

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 10, 2013; final manuscript received April 29, 2014; published online August 8, 2014. Assoc. Editor: Luis Alvarez.

J. Dyn. Sys., Meas., Control 136(6), 064504 (Aug 08, 2014) (7 pages) Paper No: DS-13-1018; doi: 10.1115/1.4027561 History: Received January 10, 2013; Revised April 29, 2014

Control algorithms for hybrid vehicles have undergone extensive research and development leading to near-optimal techniques being employed and demonstrated in prototype vehicles over the previous decade. The use of different implementations of optimal controllers is inevitably linked through the assumed knowledge of the system being controlled. With the growing interest in alternative fuels, such as ethanol, liquified petroleum gas (LPG), and compressed natural gas (CNG) due to enhanced emissions and fuel security considerations, a natural extension is to hybridize these engines to improve fuel economy and CO2 emissions. This step is complicated by the potential variation in fuel composition seen with many gasoline and diesel alternatives, leading to uncertainty in the models used by the hybrid powertrain controller. This work investigates the robustness of one hybrid powertrain optimal control approach, the equivalent consumption minimization strategy (ECMS). Two case studies are performed involving experimentally obtained engine maps from two significantly different prototypes flex-fuel vehicles to quantify the potential impact of map error caused by incorrect fuel assumptions.

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References

Figures

Grahic Jump Location
Fig. 1

Difference in flow rate (g/s) between E5 and E85 fuels as a function of torque for fixed engine speeds for (left) ET3 and (right) F4R engines

Grahic Jump Location
Fig. 2

Efficiency contours for the ET3 engine running on (left) E5 and (right) E85 fuel

Grahic Jump Location
Fig. 3

Efficiency contours for the F4R engine running on (left) E5 and (right) E85 fuel

Grahic Jump Location
Fig. 4

ET3 engine operating points superimposed on efficiency contours for the WLTP running on E5 fuel with the ECMS using (left) E5 and (right) E85 information only

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