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The stability and drivability of distributed drive electric vehicles over split-friction regions and with actuator faults are studied in this paper by using control allocation. Under the integrated control architecture of high-, medium-, and low-level controllers, a slip-ratio-based synchronization approach is presented to redistribute the driving force for all the driving wheels. The synchronization of slip ratios on wheels is accomplished by autonomously regulating the distribution parameters in the design of a low-level controller. Therefore, the driving force and moment can be adjusted from the design of the regulator on distribution parameters so that the stability and driving performance are ensured when electric vehicles drive over split-friction regions. Subsequently, the concept of distribution parameters is extended to handle actuator faults in electric vehicles by regulating the driving force via the adjustment of distribution parameters. The proportional fault, additive fault, and motor saturation are considered in this paper by compensating for the loss of driving force from the force allocation approach. Numerical examples using CarSim with MATLAB/Simulink and human/hardware-in-the-loop validations are illustrated to demonstrate the efficacy and performance of the proposed control schemes. • A slip-ratio-based force allocation control is presented for DDEVs over split-friction regions. • Auto-regulation of distribution parameters ensures stability of DDEVs with actuation faults. • Integrated controller with force allocation and fault-tolerant control are validated using HIL. [ABSTRACT FROM AUTHOR]

Exploring the drift maneuver could extend the dynamic control envelope and application range of autonomous vehicles. This paper presents a novel autonomous drift controller for a distributed drive electric vehicle, whose configuration provides more possibilities for drift. In the upper-level controller, a control channel recombination method transforms the over-actuated system into a standard square system, which is compatible with the proposed fuzzy-integral sliding-mode controller considering the input coupling and uncertain disturbance of the system to bring the vehicle into a marginally stable condition. The operation of the lower-level controller considers the dynamic characteristics of actuators and tires, and distributes the "virtual control input" among each physical actuator. This controller's performance with fast response and strong robustness was proved through the bench test. • A novel autonomous drift controller is proposed. • Redundancy and coupling of control inputs and uncertain disturbance are considered. • Controller effectiveness verified via bench tests with a distributed drive electric vehicle prototype. • Results prove the superiority of the proposed autonomous drift controller. [ABSTRACT FROM AUTHOR]

This paper researched an estimation method based on Extended Kalman Filter (EKF) for distributed drive electric vehicle states. A 7 DOF closed-loop vehicle simulation platform including driver model of preview follower method and ‘Magic formula’ tire model was established. A general 2-input–1-output and 3 states estimation system was established, considering the white Gauss measurement noise. The estimation algorithm was applied to a four-motor-driven vehicle during a double-lane-change process. The results showed that EKF estimator could effectively estimate the states of distributed drive electric vehicle with varying speed under simulative experimental condition. [ABSTRACT FROM AUTHOR]

This paper proposes a fast model predictive control allocation (MPCA) approach to minimize the tire slip power loss on contact patches for distributed drive electric vehicles (DDEV). In this strategy, two assumptions are set up from a practical focus: (1) the vehicle acceleration and yaw rate are measurable by global position system (GPS)/ inertial navigation system (INS) and inertial measurement unit (IMU), respectively; (2) the longitudinal velocity, road adhesion factor, and vehicle yaw rate are arranged to be "already known" by advanced estimators. For the strategy design, a CarSim-embedded driver model and a linear quadratic regulator (LQR) based direct yaw moment controller, are respectively applied to calculate the desired longitudinal traction and yaw moment as a virtual input first. Then, a MPCA method is proposed to reasonably distribute the virtual input among four in-wheel motors in order to optimize the tire slip power loss and vehicle stability performance. To accurately characterize tire slip power loss in MPCA, a tire slip estimator is established for tire slip information acquirement. Moreover, addressing on the heavily computational challenge in MPCA, a modified continuation/generalized minimal residual (C/GMRES) algorithm is employed. Since the traditional C/GMRES algorithm cannot directly solve the inequality constraint problem, the barrier functions are applied for transforming the inequality constraints to equivalent cost. According to Pontryagin's minimum principle (PMP) conditions, the existence and uniqueness for solution of the modified C/GMRES algorithm are strictly proved. Subsequently, a Karush–Kuhn–Tucker​ (KKT) condition based approach is developed to fast gain the optimally initial solution in C/GMRES algorithm for extending application. Finally, numerical simulation validations are implemented and demonstrate that the proposed MPCA can ensure the compatibility between the tire slip power loss reduction and vehicle stability in a computationally efficient way. • A MPCA method is proposed for DDEVs to save tire slip energy and guarantee vehicle stability. • C/GMRES algorithm is adopted in MPCA for fast solving. • Barrier function method is used to handle inequality constraints in C/GMRES algorithm. • Sufficiency that solution is existent and exclusive in C/GMRES algorithm is proved. • A KKT based approach is applied for fast initialization in C/GMRES algorithm. [ABSTRACT FROM AUTHOR]