Open AccessModel predictive control allocation for stability improvement of four‐wheel drive electric vehicles in critical driving condition
Author(s) -
Zhao Haiyan,
Ren Bingtao,
Chen Hong,
Deng Weiwen
Publication year - 2015
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2015.0437
Subject(s) - model predictive control , control theory (sociology) , engineering , torque , electric vehicle , controller (irrigation) , electronic stability control , stability (learning theory) , control (management) , control engineering , automotive engineering , slip (aerodynamics) , computer science , power (physics) , artificial intelligence , physics , quantum mechanics , machine learning , aerospace engineering , biology , agronomy , thermodynamics
To improve the vehicle stability of an electric vehicle (EV) with four in‐wheel motors, the authors investigate the use of a non‐linear control allocation scheme based on model predictive control (MPC) for EVs. Such a strategy is useful in yaw stabilisation of the vehicle. The proposed allocation strategy allows a modularisation of the control task, such that an upper level control system specifies a desired yaw moment to work on the EVs, while the control allocation is used to determine control inputs for four driving motors by commanding appropriate wheel slips. To avoid unintended side effects, skidding or discomforting the driver in critical driving condition, the MPC method, which permits us to consider constraints of actuating motors and slip ratio, is proposed to deal with this challenging problem. An analytical approach for the proposed controller is given and applied to evaluate the handing and stability of EVs. The experimental results show that the designed MPC allocation algorithm for motor torque has better performance in real time, and the control performance can be guaranteed in the real‐time environment.