This study addresses the control problem in Electric Vehicles (EVs), specifically focusing on designing a robust dynamic control system capable of estimating parametric u...
Abstract:
This study addresses the control problem in Electric Vehicles (EVs), specifically focusing on designing a robust dynamic control system capable of estimating parametric u...Show MoreMetadata
Abstract:
This study addresses the control problem in Electric Vehicles (EVs), specifically focusing on designing a robust dynamic control system capable of estimating parametric uncertainties and external disturbances. The EV under consideration is equipped with an Active Front Steering (AFS) system, which introduces an incremental steering angle to the front wheels. Additionally, the vehicle significantly enhances the powertrain, employing Permanent Magnet Synchronous Motors (PMSMs) mounted on the left and right wheel axle shafts. This configuration not only provides longitudinal traction but also facilitates appropriate yaw torque control, similar to the functionality of a Rear Torque Vectoring (RTV) system. This paper demonstrates how integrating the AFS system and RTV within the framework of integral control ensures that the vehicle can accurately track a predetermined trajectory, even in the presence of environmental disturbances, parametric uncertainties, or unmodeled dynamics. However, the proposed control strategy relies on precise information from the EV, particularly lateral velocity, which is challenging to measure directly in practical applications. To address this issue, a robust nonlinear observer is proposed to reconstruct the lateral velocity. Furthermore, High-Order Sliding Mode (HOSM) estimators are employed to approximate the parametric uncertainties and external perturbations in finite-time. The controller's performance is evaluated through simulations of a demanding double-step steer maneuver, conducted using Simulink software.
This study addresses the control problem in Electric Vehicles (EVs), specifically focusing on designing a robust dynamic control system capable of estimating parametric u...
Published in: IEEE Access ( Volume: 12)
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1.
Y. Hori, “Future vehicle driven by electricity and control—Research on four-wheel-motored `UOT electric March I,”' IEEE Trans. Ind. Electron., vol. 51, no. 5, pp. 954–962, Oct. 2004, doi: 10.1109/TIE.2004.834944.
2.
D. Kim, S. Hwang, and H. Kim, “Vehicle stability enhancement of four-wheel-drive hybrid electric vehicle using rear motor control,” IEEE Trans. Veh. Technol., vol. 57, no. 2, pp. 727–735, Mar. 2008, doi: 10.1109/TVT.2007.907016.
3.
R. Wang, Y. Chen, D. Feng, X. Huang, and J. Wang, “Development and performance characterization of an electric ground vehicle with independently actuated in-wheel motors,” J. Power Sources, vol. 196, no. 8, pp. 3962–3971, Apr. 2011.
4.
S. Cai, J. L. Kirtley, and C. H. T. Lee, “Critical review of direct-drive electrical machine systems for electric and hybrid electric vehicles,” IEEE Trans. Energy Convers., vol. 37, no. 4, pp. 2657–2668, Dec. 2022, doi: 10.1109/TEC.2022.3197351.
5.
K. Deepak, M. A. Frikha, Y. Benemar, M. El Baghdadi, and O. Hegazy, “In-wheel motor drive systems for electric vehicles: State of the art, challenges, and future trends,” Energies, vol. 16, no. 7, p. 3121, Mar. 2023.
6.
Z. Li, S. Pan, K. Mao, and Y. Xu, “Combined acceleration slip regulation for multi-wheel distributed electric drive vehicles considering torque loss factor,” Control Eng. Pract., vol. 146, May 2024, Art. no. 105893.
7.
G. Amato and R. Marino, “Adaptive slip vectoring for speed and yaw-rate control in electric vehicles with four in-wheel motors,” Control Eng. Pract., vol. 135, Jun. 2023, Art. no. 105511.
8.
C. Acosta–Lúa, B. Castillo–Toledo, S. Di Gennaro, and A. Toro, “Nonlinear robust regulation of ground vehicle motion,” presented at the Proc. IEEE Conf. Decis. Control., May 2007.
9.
L. Etienne, C. A. Lua, S. Di Gennaro, and J.-P. Barbot, “A super-twisting controller for active control of ground vehicles with lateral tire-road friction estimation and CarSim validation,” Int. J. Control, Autom. Syst., vol. 18, no. 5, pp. 1177–1189, May 2020.
10.
C. A. Lua and S. Di Gennaro, “Nonlinear adaptive tracking for ground vehicles in the presence of lateral wind disturbance and parameter variations,” J. Franklin Inst., vol. 354, no. 7, pp. 2742–2768, May 2017.
11.
C. A. Lua, D. Bianchi, and S. Di Gennaro, “Nonlinear observer-based adaptive control of ground vehicles with uncertainty estimation,” J. Franklin Inst., vol. 360, no. 18, pp. 14175–14189, Dec. 2023, doi: 10.1016/J.JFRANKLIN.2023.10.024.
12.
Z. Shuai, H. Zhang, J. Wang, J. Li, and M. Ouyang, “Combined AFS and DYC control of four-wheel-independent-drive electric vehicles over CAN network with time-varying delays,” IEEE Trans. Veh. Technol., vol. 63, no. 2, pp. 591–602, Feb. 2014, doi: 10.1109/TVT.2013.2279843.
13.
T. Ming, W. Deng, and Y. Wang, “Integrated chassis control with optimal tire force distribution for electric vehicles,” in Proc. IEEE Int. Conf. Syst. Man, Cybern., Oct. 2015, pp. 2504–2509, doi: 10.1109/SMC.2015.438.
14.
J. Ni, J. Hu, and C. Xiang, “Envelope control for four-wheel independently actuated autonomous ground vehicle through AFS/DYC integrated control,” IEEE Trans. Veh. Technol., vol. 66, no. 11, pp. 9712–9726, Nov. 2017, doi: 10.1109/TVT.2017.2723418.
15.
X. Ji, X. He, C. Lv, Y. Liu, and J. Wu, “A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation,” Vehicle Syst. Dyn., vol. 56, no. 6, pp. 923–946, Nov. 2017, doi: 10.1080/00423114.2017.1401100.
16.
L. Zhang, H. Ding, K. Guo, J. Zhang, W. Pan, and Z. Jiang, “Cooperative chassis control system of electric vehicles for agility and stability improvements,” IET Intell. Transp. Syst., vol. 13, no. 1, pp. 134–140, Nov. 2018.
17.
C. Lv, X. Hu, A. Sangiovanni-Vincentelli, Y. Li, C. M. Martinez, and D. Cao, “Driving-style-based codesign optimization of an automated electric vehicle: A cyber-physical system approach,” IEEE Trans. Ind. Electron., vol. 66, no. 4, pp. 2965–2975, Apr. 2019, doi: 10.1109/TIE.2018.2850031.
18.
C. A. Lua, B. Castillo-Toledo, and S. D. Gennaro, “Integrated active control of electric vehicles,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 13781–13788, Jul. 2020, doi: 10.1016/J.IFACOL.2020.12.886.
19.
X. Zhang, Y. Wang, X. Yuan, Y. Shen, Z. Lu, and Z. Wang, “Adaptive dynamic surface control with disturbance observers for battery/supercapacitor-based hybrid energy sources in electric vehicles,” IEEE Trans. Transport. Electrific., vol. 9, no. 4, p. 1, Dec. 2022, doi: 10.1109/TTE.2022.3194034.
20.
J. Dong, J. Li, Q. Gao, J. Hu, and Z. Liu, “Optimal coordinated control of active steering and direct yaw moment for distributed-driven electric vehicles,” Control Eng. Pract., vol. 134, May 2023, Art. no. 105486.
21.
L. Jin, H. Zhou, X. Xie, B. Guo, and X. Ma, “A direct yaw moment control frame through model predictive control considering vehicle trajectory tracking performance and handling stability for autonomous driving,” Control Eng. Pract., vol. 148, Jul. 2024, Art. no. 105947.
22.
Z. Fang, J. Liang, C. Tan, Q. Tian, D. Pi, and G. Yin, “Enhancing robust driver assistance control in distributed drive electric vehicles through integrated AFS and DYC technology,” IEEE Trans. Intell. Vehicles, vol. 1, no. 3, pp. 1–14, Feb. 2024, doi: 10.1109/TIV.2024.3368050.
23.
S. Zhang, A. Shen, X. Luo, Q. Tang, and Z. Li, “An adaptive strategy for PMSM-based disturbance estimation and online parameter identification,” IEEE/ASME Trans. Mechatronics, vol. 29, no. 4, pp. 2522–2533, Aug. 2024, doi: 10.1109/TMECH.2023.3333382.
24.
Z. Chen, X. Dai, and M. Faizan, “Speed stability and anti-disturbance performance improvement of an interior permanent magnet synchronous motor for electric vehicles,” World Electr. Vehicle J., vol. 14, no. 11, p. 311, Nov. 2023.
25.
C. Yang, T. Hua, Y. Dai, X. Huang, and D. Zhang, “Second-order nonlinear disturbance observer based adaptive disturbance rejection control for PMSM in electric vehicles,” J. Electr. Eng. Technol., vol. 18, no. 3, pp. 1919–1930, Sep. 2022.
26.
G. Kim, S. You, S. Lee, D. Shin, and W. Kim, “Robust nonlinear torque control using steering wheel torque model for electric power steering system,” IEEE Trans. Veh. Technol., vol. 72, no. 7, pp. 1–7, Jul. 2023, doi: 10.1109/TVT.2023.3248301.
27.
D. Zhang, H. Zhang, X. Li, H. Zhao, Y. Zhang, S. Wang, T. Ahmad, T. Liu, F. Shuang, and T. Wu, “A PMSM control system for electric vehicle using improved exponential reaching law and proportional resonance theory,” IEEE Trans. Veh. Technol., vol. 72, no. 7, pp. 1–11, Jul. 2023, doi: 10.1109/TVT.2023.3236628.
28.
A. Mousaei, N. Rostami, and M. B. Bannae Sharifian, “Design a robust and optimal fuzzy logic controller to stabilize the speed of an electric vehicle in the presence of uncertainties and external disturbances,” Trans. Inst. Meas. Control, vol. 46, no. 3, pp. 482–500, Jun. 2023, doi: 10.1177/01423312231178169.
29.
M. W. Hasan, A. S. Mohammed, and S. F. Noaman, “An adaptive neuro-fuzzy with nonlinear PID controller design for electric vehicles,” IFAC J. Syst. Control, vol. 27, Mar. 2024, Art. no. 100238, doi: 10.1016/J.IFACSC.2023.100238.
30.
H. B. Pacejka, “Semi-empirical tyre models,” in Tyre and Vehicle Dynamics. Amsterdam, The Netherlands : Elsevier, 2006, pp. 156–215.