Torque Vectoring for High-Performance Electric Vehicles: An Efficient MPC Calibration

Lucchini, Andrea; Formentin, Simone; Corno, Matteo; Piga, Dario; Savaresi, Sergio M.

doi:10.1109/lcsys.2020.2981895

Cited by 49 publications

(19 citation statements)

References 17 publications

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“…Another direction of future research will be dedicated to the development of hybrid control techniques with focus on TP‐MT fuzzy control. Several intelligent control features [38–44] will be integrated including fuzzy model features as, for example, those given in other studies [45–56]. The systematic analysis of developed CSSs is also needed as both research directions are focused on the performance improvement, which should be guaranteed accordingly.…”

Section: Discussionmentioning

confidence: 99%

Tensor product‐based model transformation approach to cart position modeling and control in pendulum‐cart systems

Hedrea

Precup

Petriu

et al. 2021

Asian Journal of Control

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The paper presents the application of the tensor product (TP)‐based model transformation technique to model and control the cart position of single‐input multi‐output pendulum‐cart systems (PCSs). The modeling is first carried out. The derived TP model, the nonlinear model of PCS, and the laboratory equipment are tested in the same open‐loop scenario, and their corresponding outputs are collected, measured, and compared. The parallel distributed compensation (PDC) technique is next applied to TP‐based cart position controller design. A linear state feedback control design is also designed in order to conduct a comparative analysis. The two proposed control system structures are experimentally validated and tested in the same scenarios on the laboratory equipment, and their performance indices are analyzed and compared.

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Section: Discussionmentioning

confidence: 99%

Tensor product‐based model transformation approach to cart position modeling and control in pendulum‐cart systems

Hedrea

Precup

Petriu

et al. 2021

Asian Journal of Control

View full text Add to dashboard Cite

show abstract

“…Despite the unfavorable increase of the unsprung mass of the vehicle, the ability to steer each wheel in various road situations [8,9], as well as the use of electric regenerative braking [10], provide an opportunity to improve the traction properties of the vehicle [11][12][13]. At the same time, the use of a direct drive in the wheel requires the implementation of new technological solutions, such as active suspension [14,15] or a spring system for the electric drive module [16][17][18], the development of various braking algorithms and systems such as anti-lock braking system (ABS), brake assist (BAS), electronic brakeforce distribution (EBD), electronic stability control (ESP) or acceleration slip regulation (ASR), and the use of an electronic differential with the possibility of driving the wheels in the 4 × 2, 4 × 4 system [19].…”

Section: Introductionmentioning

confidence: 99%

Studies on Energy Consumption of Electric Light Commercial Vehicle Powered by In-Wheel Drive Modules

Szewczyk

Łebkowski

2021

Energies

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This article presents the results of energy consumption research for an electric light commercial vehicle (eLCV) powered by a centrally located motor (4 × 2 drive system) or motors placed in the vehicle’s wheels (4 × 4 drive system). For the considered constructions of electric drive systems, mathematical models of 4 × 2 and 4 × 4 drive systems were developed in the Modelica simulation environment, based on real data. Additionally, the influence of changes in the vehicle loading condition on the operation of the motor mounted in the wheel and the energy consumption of the drive module was investigated. On the basis of the conducted research, a comparative analysis of energy consumption by electric drive systems in 4 × 2 and 4 × 4 configurations was carried out for selected test cycles. The tests carried out with the Worldwide harmonized Light vehicles Test Cycles (WLTC) test cycle showed a roughly 6% lower energy consumption by the 4 × 4 drive system compared to the 4 × 2 configuration.

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“…For example, the possibility to generate a differential torque between left and right wheels permits to influence the vehicle lateral dynamics: this is called torque vectoring (TV). Mainly, TV systems regulate the vehicle yaw rate to improve safety and drivability (Lucchini et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Regarding multi-layer strategies, the single control problems have been investigated in depth. Concerning path tracking, the literature presents many interesting approaches ranging from: Model Predictive Control (MPC) (Guo et al, 2020;Katriniok et al, 2013;Beal and Gerdes, 2013;Falcone et al, 2007;Liniger et al, 2017), sliding mode (Tagne et al, 2013), nonlinear control (Peters et al, 2011), to potential fields (Rossetter et al, 2004). MPC is considered the state of the art.…”

Section: Introductionmentioning

confidence: 99%

Linear Parameter Varying Path Tracking Control for Over-Actuated Electric Vehicles

Gimondi

Corno

Savaresi

2021

Front. Control Eng.

Self Cite

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This paper discusses a multi-layer linear parameter varying path tracking controller for autonomous full electric vehicles with 4 wheel drive. Many approaches for path tracking are present in the literature, but the majority of them utilize only the steering wheel. Nowadays, the massive introduction of electric vehicles opens new opportunities for vehicle dynamics control; multiple electric motors, via differential torque, offer an alternative way to influence vehicle motion. The multi-layer structure, managing separately steering wheel and electric motors, permits the exploitation of pre-existing control systems and maintains a straightforward tuning process. Furthermore, the robustness of the path tracking controller is enhanced by introducing tire nonlinearities. Linear parameter varying models offer a direct way to incorporate tire characteristics in the design process. Exploiting H∞ technique the scheduled controller can be seamlessly synthezised. The proposed strategy has been compared in simulation to a benchmark integrated, i.e., a unique controller that manages all the actuators, approach. During double lane changes beyond the vehicle limits, the multi-layer controller guarantees stability and excellent tracking with an average error of 12 and 24 cm on high and low grip respectively.

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Torque Vectoring for High-Performance Electric Vehicles: An Efficient MPC Calibration

Cited by 49 publications

References 17 publications

Tensor product‐based model transformation approach to cart position modeling and control in pendulum‐cart systems

Tensor product‐based model transformation approach to cart position modeling and control in pendulum‐cart systems

Studies on Energy Consumption of Electric Light Commercial Vehicle Powered by In-Wheel Drive Modules

Linear Parameter Varying Path Tracking Control for Over-Actuated Electric Vehicles

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