Research Papers

Vehicle Dynamics Control of eAWD Hybrid Electric Vehicle Using Slip Ratio Optimization and Allocation

[+] Author and Article Information
Jose Velazquez Alcantar

Advanced Research and Engineering,
Ford Motor Company,
Dearborn, MI 48124
e-mail: jvelaz42@ford.com

Francis Assadian

Department of Mechanical and Aerospace
University of California, Davis,
Davis, CA 95616
e-mail: fassadian@ucdavis.edu

Ming Kuang

Advanced Research and Engineering,
Ford Motor Company,
Dearborn, MI 48124
e-mail: mkuang@ford.com

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received November 6, 2017; final manuscript received February 21, 2018; published online April 9, 2018. Assoc. Editor: Mahdi Shahbakhti.

J. Dyn. Sys., Meas., Control 140(9), 091010 (Apr 09, 2018) (12 pages) Paper No: DS-17-1553; doi: 10.1115/1.4039486 History: Received November 06, 2017; Revised February 21, 2018

Hybrid electric vehicles (HEV) offer improved fuel efficiency compared to their conventional counterparts at the expense of adding complexity and at times, reduced total power. As a result, HEV generally lack the dynamic performance that customers enjoy. To address this issue, the paper presents a HEV with electric all-wheel drive (eAWD) capabilities via the use of a torque vectoring electric rear axle drive (TVeRAD) unit to power the rear axle. The addition of TVeRAD to a front wheel drive HEV improves the total power output. To further improve the handling characteristics of the vehicle, the TVeRAD unit allows for wheel torque vectoring (TV) at the rear axle. A bond graph model of the proposed drivetrain model is developed and used in cosimulation with carsim. The paper proposes a control system, which utilizes slip ratio optimization to allocate control to each tire. The optimization algorithm is used to obtain optimal slip ratio targets to at each tire such that the targets avoid tire saturation. The Youla parameterization technique is used to develop robust tracking controllers for each axle. The proposed control system is ultimately tested on the drivetrain model with a high fidelity carsim vehicle model for validation. Simulation results show that the control system is able to maximize vehicle longitudinal performance while avoiding tire saturation on a low μ surface. More importantly, the control system is able to track the desired yaw moment request on a high-speed double-lane change (DLC) maneuver through the use of the TVeRAD to improve the handling characteristic of the vehicle.

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Fig. 4

Electric all-wheel drive drivetrain model interaction with carsim vehicle dynamics model

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Fig. 2

Schematic TVeRAD architecture

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Fig. 1

Schematic of eAWD HEV drivetrain architecture

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Fig. 3

Bond graph models of eAWD drivetrain: (a) front axle and (b) TVeRAD

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Fig. 5

Frequency response of TVeRAD plant

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Fig. 6

Vehicle model for slip ratio control allocation

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Fig. 7

Tire force versus slip curves for constant μ and constant normal load: (a) constant μ and (b) constant normal load

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Fig. 8

Tire penalization map as a function of Cx and tire normal load

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Fig. 9

Schematic of slip ratio optimization strategy

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Fig. 10

Frequency response of closed-loop (T), sensitivity (S), Youla (Y) and controller (Gc) of the high-level yaw rate controller

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Fig. 14

Simulation results for low μ launch: (a) slip ratio allocation and tracking and (b) vehicle longitudinal performance

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Fig. 13

Simulation results for low μ launch: (a) motor and total wheel torque response and (b) front axle tire penalization

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Fig. 11

Frequency response of closed-loop (T), sensitivity (S), Youla (Y) and controller (Gc) of the low-level slip tracking controllers

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Fig. 12

High-level overview of complete control architecture

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Fig. 15

Comparison of slip allocation control system versus open-loop control during double-lane change maneuver at 140 KPH

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Fig. 16

Simulation results for double-lane change: (a) slip ratio targets and tracking performance and (b) motor torques and TVeRAD motor powers




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