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Research Papers

Front/Rear Axle Torque Vectoring Control for Electric Vehicles

[+] Author and Article Information
David Ruiz Diez

Advanced Vehicle Engineering Centre,
Cranfield University,
Cranfield MK43 0AL, UK
e-mail: drd1807@gmail.com

Efstathios Velenis

Advanced Vehicle Engineering Centre,
Cranfield University,
Cranfield MK43 0AL, UK
e-mail: e.velenis@cranfield.ac.uk

Davide Tavernini

Centre for Automotive Engineering,
University of Surrey,
Surrey GU2 7XH, UK
e-mail: d.tavernini@surrey.ac.uk

Edward N. Smith

Advanced Vehicle Engineering Centre,
Cranfield University,
Cranfield MK43 0AL, UK
e-mail: e.smith@cranfield.ac.uk

Efstathios Siampis

Advanced Vehicle Engineering Centre,
Cranfield University,
Cranfield MK43 0AL, UK
e-mail: stathis@delta-motorsport.com

Amir Soltani

Advanced Vehicle Engineering Centre,
Cranfield University,
Cranfield MK43 0AL, UK
e-mail: m.m.soltani@cranfield.ac.uk

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received June 14, 2017; final manuscript received November 18, 2018; published online February 18, 2019. Assoc. Editor: Ardalan Vahidi.

J. Dyn. Sys., Meas., Control 141(6), 061002 (Feb 18, 2019) (12 pages) Paper No: DS-17-1303; doi: 10.1115/1.4042062 History: Received June 14, 2017; Revised November 18, 2018

Vehicles equipped with multiple electric machines allow variable distribution of propulsive and regenerative braking torques between axles or even individual wheels of the car. Left/right torque vectoring (i.e., a torque shift between wheels of the same axle) has been treated extensively in the literature; however, fewer studies focus on the torque shift between the front and rear axles, namely, front/rear torque vectoring, a drivetrain topology more suitable for mass production since it reduces complexity and cost. In this paper, we propose an online control strategy that can enhance vehicle agility and “fun-to-drive” for such a topology or, if necessary, mitigate oversteer during sublimit handling conditions. It includes a front/rear torque control allocation (CA) strategy that is formulated in terms of physical quantities that are directly connected to the vehicle dynamic behavior such as torques and forces, instead of nonphysical control signals. Hence, it is possible to easily incorporate the limitations of the electric machines and tires into the computation of the control action. Aside from the online implementation, this publication includes an offline study to assess the effectiveness of the proposed CA strategy, which illustrates the theoretical capability of affecting yaw moment that the front/rear torque vectoring strategy has for a given set of vehicle and road conditions and considering physical limitations of the tires and actuators. The development of the complete strategy is presented together with the results from hardware-in-the-loop (HiL) simulations, using a high fidelity vehicle model and covering various use cases.

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References

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He, Z. , Ou, Y. , and Yuan, J. , 2013, “ Research on the Torque Dynamic Distribution Algorithm of In-Wheel-Motor Electric Vehicle,” FISITA 2012 World Automotive Congress, Beijing, China, pp. 257–266.
Wheals, J. C. , Baker, H. , Ramsey, K. , and Turner, W. , 2004, “ Torque Vectoring AWD Driveline: Design, Simulation, Capabilities and Control,” SAE Paper No. 2004-01-0863.
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Siampis, E. , Velenis, E. , and Longo, S. , 2016, Front-to-Rear Torque Vectoring Model Predictive Control for Terminal Understeer Mitigation, CRC Press, Boca Raton, FL, pp. 153–160.
Soltani, A. , 2014, “ Low Cost Integration of Electric Power-Assisted Steering (EPAS) With Enhanced Stability Program (ESP),” Ph.D. thesis, Cranfield University, Cranfield, UK. http://dspace.lib.cranfield.ac.uk/handle/1826/8829
Polesel, M. , Shyrokau, B. , Tanelli, M. , Savitski, D. , Ivanov, V. , and Ferrara, A. , 2014, “ Hierarchical Control of Overactuated Vehicles Via Sliding Mode Techniques,” IEEE 53rd Annual Conference on Decision and Control (CDC), Los Angeles, CA, Dec. 15–17, pp. 4095–4100.
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Kaiser, G. , Liu, Q. , Hoffmann, C. , Korte, M. , and Werner, H. , 2012, “ Torque Vectoring for an Electric Vehicle Using an LPV Drive Controller and a Torque and Slip Limiter,” IEEE 51st Annual Conference on Decision and Control (CDC), Maui, HI, Dec. 10–13, pp. 5016–5021.
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Figures

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Fig. 2

Supervisory control level of IVCS

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Fig. 1

Structure of the integrated vehicle control system

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Fig. 3

Wheel steer angle plotted against steady-state lateral acceleration, for a generic uncontrolled car, and for the proposed front/rear TV controller. The gradient of the curves is the understeer gradient of the vehicle as defined in 2. The magnitude of the y-axis (steer angle) depends on the vehicle speed.

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Fig. 5

High level control

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Fig. 6

Schematic of the allocation algorithm

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Fig. 7

Single track vehicle model employed for available yaw moment calculation

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Fig. 9

HiL setup: driver setup with visualization

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Fig. 10

HiL setup: EPAS with the by-wire braking system

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Fig. 11

Constant acceleration step steer maneuver, with understeer gradient target KU = 0.0 deg/g: (a) steering wheel angle and vehicle speed, (b) calculated yaw moments in the allocation algorithm, (c) axle torque requests, (d) measured yaw rates versus yaw rate reference, and (e) TV transitioning (activation) factor and front/rear torque distribution kf

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Fig. 12

Constant acceleration step steer maneuver, with understeer gradient target KU = −0.5 deg/g: (a) steering wheel angle and vehicle speed, (b) calculated yaw moments in the allocation algorithm, (c) axle torque requests, (d) measured yaw rates versus yaw rate reference, and (e) TV transitioning (activation) factor and front/rear torque distribution kf

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Fig. 13

Constant acceleration step steer maneuver, with understeer gradient target KU = 1.0 deg/g: (a) steering wheel angle and vehicle speed, (b) calculated yaw moments in the allocation algorithm, (c) axle torque requests, (d) measured yaw rates versus yaw rate reference, and (e) TV transitioning (activation) factor and front/rear torque distribution kf

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Fig. 8

Front to rear TV map, high adhesion (μ = 1) and high vehicle sideslip (β = 3 deg), v =10 km/h, ψ = 20 deg/s, and δ = 1.7 deg

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Fig. 16

Constant acceleration and constant steer maneuver, with an understeer target gradient KU = 1.0 deg/g: (a) steering wheel angle and vehicle speed, (b) calculated yaw moments in the allocation algorithm, (c) axle torque requests, (d) measured yaw rates versus yaw rate reference, and (e) TV transitioning (activation) factor and front/rear torque distribution kf

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Fig. 17

Constant acceleration and constant steer maneuver XY coordinates, comparing the trajectory of the uncontrolled car and the car with TV on, for two different understeer gradient targets

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Fig. 14

Constant acceleration and step steer maneuver XY coordinates, comparing the trajectory of the uncontrolled car and the car with TV on, for three different understeer gradient targets

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Fig. 15

Constant acceleration and constant steer maneuver, with an understeer target gradient KU = 0.0 deg/g: (a) steering wheel angle and vehicle speed, (b) calculated yaw moments in the allocation algorithm, (c) axle torque requests, (d) measured yaw rates versus yaw rate reference, and (e) TV transitioning (activation) factor and front/rear torque distribution kf

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