Hybrid Model Predictive Control of Semiactive Suspension in Electric Vehicle with Hub-Motor

• Published in: Applied sciences Vol. 11; no. 1; p. 382
• Main Authors: Jiang, Hong, Wang, Chengchong, Li, Zhongxing, Liu, Chenlai
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2021
• Subjects: Algorithms; Constraint modelling; Control methods; Control systems design; Control theory; Controllers; Damping; electric vehicle; Electric vehicles; Electromagnetic forces; Excitation; hub motor; hybrid model predictive control; Kalman filters; Magnetic fields; Optimal control; Optimization; Passenger comfort; Roads; semiactive suspension; Semiactive suspensions; Simulation; Vibration
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11010382

In hub-motor electric vehicles (HM-EVs), the unbalanced electromagnetic force generated by the HM will further deteriorate the dynamic performance of the electric vehicle. In this paper, a semiactive suspension control method is proposed for HM-EVs. A quarter HM-EV model with an electromechanical coupling effect is established.The model consists of three parts: a motor model, road excitation model and vehicle model. A hybrid model predictive controller (HMPC) is designed based on the developed model, taking into account the nonlinear constraints of damping force. The focus is on improving the vertical performance of the HM-EV. Then, a Kalman filter is designed to provide the required state variables for the controller. The proposed control algorithm and constrained optimal control (COC) algorithm are simulation compared under random road excitation and bump road excitation, and the results show that the proposed control algorithm can improve ride comfort, reduce motor vibration, and improve handling stability more substantially.