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Research Papers

A Predictive Energy Management Strategy for Hybrid Electric Powertrain With a Turbocharged Diesel Engine

[+] Author and Article Information
Yi Huo

Department of Mechanical Engineering,
McMaster University,
Hamilton, ON L8S 4L8, Canada
e-mail: huoy@mcmaster.ca

Fengjun Yan

Department of Mechanical Engineering,
McMaster University,
Hamilton, ON L8S 4L8, Canada
e-mail: yanfeng@mcmaster.ca

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received September 26, 2017; final manuscript received January 23, 2018; published online March 20, 2018. Assoc. Editor: Beshah Ayalew.

J. Dyn. Sys., Meas., Control 140(6), 061017 (Mar 20, 2018) (11 pages) Paper No: DS-17-1487; doi: 10.1115/1.4039216 History: Received September 26, 2017; Revised January 23, 2018

This paper proposes an energy management strategy for a hybrid electric vehicle (HEV) with a turbocharged diesel engine. By introducing turbocharger to the HEV powertrain, air path dynamics of engine becomes extremely complex and critical to engine torque response during transient processes. Traditional strategy that adopts steady-state-map based engine model may not work properly in this situation as a result of its incapability of accurately capturing torque response. Thus, in this paper, a physical-law based air path model is utilized to simulate turbo “lag” phenomenon and predict air charge in cylinder. Meanwhile, engine torque boundaries are obtained on the basis of predicted air charge. A receding horizon structure is then implemented in optimal supervisory controller to generate torque split strategy for the HEV. Simulations are conducted for three cases: the first one is rule-based torque-split energy management strategy without optimization; the second one is online optimal control strategy using map-based engine model; and the third one is online optimal control strategy combining air path loop model. The comparison of the results shows that the proposed third method has the best fuel economy of all and demonstrates considerable improvements of fuel saving on the other two methods.

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References

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Figures

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Fig. 1

Torque and fuel consumption behavior of a turbocharged engine responding to full load input at constant engine speed

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Fig. 2

P2 hybrid powertrain architecture with a turbocharged diesel engine

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Fig. 3

A simple equivalent circuit of a battery

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Fig. 4

Compressor flow and efficiency map

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Fig. 5

Turbine flow and efficiency map

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Fig. 6

Brake specific fuel consumption map in steady-state with respect to engine speed and load

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Fig. 7

Control architecture of proposed HEV powertrain

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Fig. 8

The flowchart of MPCSC implementation in a prediction horizon

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Fig. 9

Vehicle speed tracking performance of three strategies

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Fig. 10

Comparison of engine speed and transmission input speed for three strategies

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Fig. 11

Engine demand torque and actual torque comparison in three strategies

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Fig. 12

Comparison of total requested power from driver demand for the three strategies

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Fig. 13

Comparison of fuel injection mass per cycle for three strategies

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Fig. 14

Comparison of electric motor torque for the three strategies

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Fig. 15

Comparison of SOC trajectory for three strategies

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Fig. 16

Comparison of fuel consumption for three strategies

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