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

Zhi, Jinning; Wang, Xiaowei; Shi, Qinglu; Ye, Zhibin; Qi, Yapan

doi:10.1177/09544070221121838

Cited by 3 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. However, to our best knowledge, there is almost no research employing the MPCA for 4WD-EV stability control and improving energy consumption simultaneously.…”

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:

View full text Add to dashboard Cite

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.

show abstract

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:

View full text Add to dashboard Cite

show abstract

Drive force allocation control based on pump displacement optimization for hydraulic hybrid motor-assisted system

Zhang,

Yang,

Duan

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

To achieve rational coordination of the front and rear axle torque of commercial vehicles equipped with hydraulic hybrid motor-assisted systems (HHMAS), and further enhance the vehicle’s fuel efficiency and traction capability. A hydraulic pump displacement control strategy based on energy-efficiency-orientation (EEO) is proposed for driving force distribution. Firstly, the efficiency is analyzed from the perspective of system energy flow. Then, considering the time-varying efficiency characteristics of the hydraulic variable pump and hydraulic motor, the stochastic dynamic optimization problem is modeled as a markov decision process, and a value function estimation method is introduced to approximate the optimal energy efficiency of the entire HHMAS. Finally, the results indicate that the EEO strategy can enhance the fuel economy of vehicles, with a fuel-saving rate of 6.3% compared to the wheel-speed-following-control (WSFC) strategy. In addition, the proposed strategy exhibits superior traction capacity and climbing ability compared to the traditional WSFC strategy.

show abstract

Fault Compensation Control for Regenerative Braking of Distributed Drive Electric Vehicle Based on Hierarchical Control

Fang,

Zhao,

et al. 2023

SAE Technical Paper Series

View full text Add to dashboard Cite

<div class="section abstract"><div class="htmlview paragraph">For distributed drive electric vehicles (DDEV) equipped with an electronic hydraulic braking system (EHB) and four-wheel hub motors, when one or more hub motors have regenerative braking failure, because the braking torque of the four wheels is inconsistent, additional yaw moment will be formed on the vehicle, resulting in the loss of directional stability of the vehicle during braking. If it occurs at high speeds, it will further threaten driving safety. To solve the above problems, a new hierarchical control architecture is established in this paper. Firstly, taking DDEV as the research object, the vehicle dynamics model and EHB braking system model are built. Then, a state observer based on an adaptive Kalman filter is designed in the upper layer to estimate the vehicle’s sideslip angle and yaw rate in real time. In the judgment decision-making layer, the phase plane is used to divide the stability domain boundary of the vehicle, and the quasi-stability tolerance band judges the vehicle’s driving state. Secondly, the lower stability controller is constructed based on the sliding mode control theory. EHB can flexibly distribute hydraulic braking force to compensate for the vehicle’s braking force, offset the additional yaw moment, and maintain the straight line of the vehicle. Finally, experimental verification is carried out in Matlab/ CarSim and hardware-in-the-loop (HIL) platforms. The results show that the proposed method can effectively predict the state and closed-loop stability control of DDEV, and reduce the deviation distance caused by regenerative braking failure, effectively ensuring the vehicle in the event of regenerative braking failure driving safety.</div></div>

show abstract

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

Cited by 3 publications

References 24 publications

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

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

Drive force allocation control based on pump displacement optimization for hydraulic hybrid motor-assisted system

Fault Compensation Control for Regenerative Braking of Distributed Drive Electric Vehicle Based on Hierarchical Control

Contact Info

Product

Resources

About