Research Papers

Analysis and Control of Torque Split in Hybrid Electric Vehicles by Incorporating Powertrain Dynamics

[+] Author and Article Information
Mehran Bidarvatan

Mechanical Eng.-Eng. Mechanics Department,
Michigan Technological University,
Houghton, MI 49931-1295
e-mail: mbidarva@mtu.edu

Mahdi Shahbakhti

Mechanical Eng.-Eng. Mechanics Department,
Michigan Technological University,
Houghton, MI 49931-1295
e-mail: mahdish@mtu.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received July 30, 2017; final manuscript received May 3, 2018; published online June 18, 2018. Assoc. Editor: Beshah Ayalew.

J. Dyn. Sys., Meas., Control 140(11), 111009 (Jun 18, 2018) (11 pages) Paper No: DS-17-1392; doi: 10.1115/1.4040219 History: Received July 30, 2017; Revised May 03, 2018

Hybrid electric vehicle (HEV) energy management strategies usually ignore the effects from dynamics of internal combustion engines (ICEs). They usually rely on steady-state maps to determine the required ICE torque and energy conversion efficiency. It is important to investigate how ignoring these dynamics influences energy consumption in HEVs. This shortcoming is addressed in this paper by studying effects of engine and clutch dynamics on a parallel HEV control strategy for torque split. To this end, a detailed HEV model including clutch and ICE dynamic models is utilized in this study. Transient and steady-state experiments are used to verify the fidelity of the dynamic ICE model. The HEV model is used as a testbed to implement the torque split control strategy. Based on the simulation results, the ICE and clutch dynamics in the HEV can degrade the control strategy performance during the vehicle transient periods of operation by around 8% in urban dynamometer driving schedule (UDDS) drive cycle. Conventional torque split control strategies in HEVs often overlook this fuel penalty. A new model predictive torque split control strategy is designed that incorporates effects of the studied powertrain dynamics. Results show that the new energy management control strategy can improve the HEV total energy consumption by more than 4% for UDDS drive cycle.

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Fig. 1

Prior studies for developing control strategies for torque split in parallel HEVs

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Fig. 2

Verification of the ICE dynamic model against experimental data for predicting fuel consumption at three engine loads: Te,l = 35 N·m, N = 3296–3670 rpm, θi = 18.9 deg, Te,l = 25 N·m, N = 2297–2754 rpm, θi = 12.2 deg, Te,l = 15 N·m, N = 1238–1788 rpm, θi = 9.6 deg, Δ(θ) = θt − θi is throttle angle change

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Fig. 3

Verification of the ICE dynamic model against experimental engine speed and brake torque at Te,l = 30 N·m. eave and σe are average and standard deviation for the model's prediction error, respectively

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Fig. 4

Efficiency map of E-machine at motoring and generating modes

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Fig. 5

The impact of intake air flow dynamics on required injected fuel

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Fig. 6

Impact of fuel transport dynamics on required injected fuel: (a) Cold start (Tcool = −15 °C) and (b) fully warm-up (Tcool = 80 °C)

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Fig. 7

Impact of engine dynamics (air flow, fuel transport, and rotational dynamics) on required injected fuel

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Fig. 8

Impact of clutch dynamics and engine dynamics (air flow, fuel transport, and rotational dynamics) on required injected fuel

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Fig. 9

Assessment of the steady-state supervisory controller on two HEV testbeds using (i) steady-state ICE maps and (ii) dynamic models for the ICE and clutch

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Fig. 10

Zoom-in view of Fig. 9 from 15 s to 40 s

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Fig. 11

Effect of simulation testbed on the calculated vehicle fuel consumption and the battery SOC for the UDDS drive cycle when the steady-state supervisory controller is utilized

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Fig. 12

Performance of the steady-state and newly designed dynamic HEV supervisory controllers (Sec. 3) during UDDS drive cycle. The simulation testbed is the dynamic HEV plant model.

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Fig. 13

Zoom-in views of Fig. 12 for (a) 1258–1268 s and (b) 1270–1275 s

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Fig. 14

Cumulative (ICE and battery) energy consumptions and cumulative kinetic energies at the wheels



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