Energy efficiency and stability of electric vehicles utilising direct yaw moment control

Sun, Peikun; Trigell, Annika Stensson; Drugge, Lars; Jerrelind, Jenny

doi:10.1080/00423114.2020.1841903

Cited by 21 publications

(9 citation statements)

References 29 publications

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“…Assuming no tyre differences, the mechanical characteristics and cornering stiffness of each tyre are consistent. A vehicle model with three DOFs was established without considering the torsional vibration, shimmy and other vibration effects, 25,26 as shown in Figure 2.…”

Section: Vehicle Dynamic Modelmentioning

confidence: 99%

Torque allocation strategy based on economy and stability for electric vehicle considering controllability after motors failure

Zhi

Wang

Shi

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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To reduce the energy consumption and improve the stability of distributed drive electric vehicles, a torque allocation strategy based on an economy and stability optimisation function (ESOF) and a fuzzy proportional-integral-derivative rule control (FPRC) strategy are proposed while considering motor efficiency, braking energy recovery and motor failure. First, the vehicle dynamics and motor equivalent models are established. Subsequently, a torque prediction model and fuzzy controller for the vehicle are designed to calculate the total desired torque and yaw moment, respectively. A torque optimisation function is established to minimise power losses in the electric motor and maximise braking energy recovery, and it is solved using an improved genetic algorithm. While satisfying vehicle driving constraints, the ESOF-based controller can effectively coordinate the operation of each motor in the high-efficiency range under driving and braking conditions. After one motor fault is detected, the ESOF-based controller is replaced with an FPRC-based controller to distribute the vehicle demand torque. A co-simulation platform integrating MATLAB/Simulink and CarSim is developed to verify the effectiveness of the proposed ESOF-based controller in the New European Driving Cycle (NEDC) and Federal Test Procedure 75 (FTP75) driving cycles. The effectiveness of the FPRC-based controller in step steering condition is verified using the co-simulation platform. The simulation results indicate that the vehicle economy and driving range of the ESOF-based controller improved compared with the results afforded by the typical torque distribution strategy based on the front–rear axle dynamic load ratio. The average efficiencies of the motors in the NEDC and FTP75 driving cycles increased by 2.94% and 2.4%, respectively. More importantly, the FPRC-based controller can more significantly improve the steering stability of a vehicle with motor failure compared with the ESOF-based controller.

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Section: Vehicle Dynamic Modelmentioning

confidence: 99%

Torque allocation strategy based on economy and stability for electric vehicle considering controllability after motors failure

Zhi

Wang

Shi

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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show abstract

“…Zhao et al 30 directly coordinated the stability & economy upon a torque allocation technique and tracked the reference speed based on a fuzzy algorithm. 31 studied a switching control principle among safety and energy efficiency based on a phase-plane decomposition method. 32 further dealt with the economy and stability problem for EVs with in-wheel-motor failures.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control allocation based on adaptive sliding mode control strategy for enhancing the lateral stability of four-wheel-drive electric vehicles

Ao¹,

Wong²,

Huang

2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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A novel hierarchical direct yaw moment controller is designed to enhance the lateral stability of the four-wheel-drive electric vehicle. The adaptive sliding mode control (ASMC) technique in the upper-layer controller is employed to compute an additional yaw moment. The lower-layer controller distributes this yaw moment into each independent wheel by utilizing model predictive control allocation (MPCA). The proposed MPCA aims to mitigate the performance deterioration induced by in-wheel motor dynamics and optimize the power consumption stemming from the additional yaw moment. Co-simulation and hardware-in-the-loop (HIL) test is conducted to verify the performance of the proposed controller. Validation results show that the proposed hierarchical ASMC-MPCA controller outperforms the sliding mode control MPCA (SMC-MPCA) and the integrated nonlinear model predictive control (NMPC) with the lowest root-mean-square errors [Formula: see text] of yaw rate, sideslip angle, lateral deviation, and lowest power consumption. Additionally, the chattering phenomenon in SMC-MPCA can be suppressed effectively by adaptively estimating the parameter uncertainties. The proposed ASMC-MPCA controller also consumes less computational resources than the NMPC and SMC-MPCA, which indicates that the ASMC-MPCA is more suitable for an automotive onboard controller. The comparison between hierarchical and integrated controller frameworks also shows that the hierarchical framework is more suitable for production vehicles under non-powerful vehicle control units.

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“…In order to reduce the uncertainty and disturbance in the yaw process of passenger cars, Zhang et al proposed an adaptive fuzzy control to perform direct yaw moment control and adjust the weight coefficients of the side slip angle and yaw rate in real time through fuzzy control [20]. Sun, P. [21] proposed a direct yaw moment control which can improve the stability and energy efficiency of electric vehicles. In this scheme, a stability judgment strategy is designed based on the yaw rate and the side slip angle, and the switching principle of alternating control between energy saving and stability is formulated.…”

Section: Introductionmentioning

confidence: 99%

Research on Stability Control Algorithm of Distributed Drive Bus under High-Speed Conditions

Zhu,

Wei,

Ping

et al. 2023

WEVJ

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Aiming at the instability problem of a four-wheel independent drive electric bus under high-speed conditions, this paper first designs a vehicle yaw stability controller based on a linear two-degree-of-freedom model and a linear quadratic programming (LQR) algorithm. A vehicle roll stability controller is then designed based on a linear three-degree-of-freedom model and a model predictive control algorithm (MPC). Moreover, a coordinated control rule based on the lateral load transfer rate (LTR) is designed for the coupled problem of yaw and roll dynamics. Finally, the effectiveness of the proposed control algorithm is verified by simulation. The obtained results show that when the vehicle is running at a high speed of 90 km/h, the stability control algorithm can control the yaw rate tracking error within 0.05 rad/s. In addition, the control algorithm can reduce the maximum amplitude of the side slip angle, the maximum value of the roll angle, the maximum value of the roll angular velocity, and the amplitude of the lateral acceleration by more than 96%, 81.1%, 65.0%, and 11.1%, respectively.

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Energy efficiency and stability of electric vehicles utilising direct yaw moment control

Cited by 21 publications

References 29 publications

Torque allocation strategy based on economy and stability for electric vehicle considering controllability after motors failure

Torque allocation strategy based on economy and stability for electric vehicle considering controllability after motors failure

Model predictive control allocation based on adaptive sliding mode control strategy for enhancing the lateral stability of four-wheel-drive electric vehicles

Research on Stability Control Algorithm of Distributed Drive Bus under High-Speed Conditions

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