A full drive-by-wire electric vehicle, named Urban Future Electric Vehicle (UFEV) is developed, where the four wheels' traction and braking torques, four wheels' steering angles, and four active suspensions (in the future) are controlled independently. It is an ideal platform to realize the optimal vehicle dynamics, the marginal-stability and the energy-efficient control, it is also a platform for studying the advanced chassis control methods and their applications.
A centralized control system of hierarchical structure for UFEV is proposed, which consist of Sensor Layer, Identification and Estimation Layer, Objective Control Layer, Forces and Motion Distribution Layer, Executive Layer.
In the Identification and Estimation Layer, identification model is established by utilizing neural network algorithms to identify the driver characteristics. Vehicle state estimation and road identification of UFEV based on EKF and Fuzzy Logic Control methods is also conducted in this layer. In the Objective Control Layer, a real-time ideal reference model of vehicle dynamics for drivers with different characteristics are built up with Radical Basis Function (RBF) neural network by using the driving simulator test data, which is used for the control objective of the UFEV. In the forces and motion distribution layer, the control objective (resultant forces and moment of the vehicle) is converted to a constrained optimization control problem, the optimized objective function can be chosen according to different goals, such as the maximum tire-force margin, the minimum energy consumption, then, solving the optimization problem and send the distributed forces and motions to each actuator (e.g. longitudinal tire forces and steering angles of four wheels).
Simulation and experimental verification show that the systematic solution proposed in this paper are effective for the UFEV.