Xiaoyu Huang

Model predictive regenerative braking control for lightweight electric vehicles with in-wheel motors

This paper presents a nonlinear model predictive controller for regenerative braking control of lightweight electric vehicles equipped with in-wheel motors. In-wheel-motors-driven electric vehicles possess significant advantages such as actuation flexibilities, torque control precision, and energy recovery improvement by direct regenerative braking control. The proposed controller not only improves the regenerative braking energy recovery by determining the front and rear braking torques independently but also prevents wheel locks during deceleration when the tire–road friction coefficient is low. The energy-saving objective is accomplished by including in the cost function the additional penalty term on the motor-to-battery regenerative braking power, while the safety objective is formulated as hard constraints on the longitudinal slip ratios of the wheels. Since the problem is based on a nonlinear vehicle longitudinal model, the global minimum within each time step is searched for by gridding the initial torque plane. Simulation results, based on a vehicle model in CarSim®, show that the proposed nonlinear model predictive controller is capable of restoring considerably more regenerative braking energy than a conventional proportional–integral controller supplemented with a feedforward control effort and another nonlinear model predictive controller with no consideration of the energy recovery and of maintaining a good vehicle-speed-tracking performance.

A robust wheel slip ratio control design combining hydraulic and regenerative braking systems for in-wheel-motors-driven electric Vehicles

Abstract This paper develops a robust wheel slip controller for in-wheel-motors-driven electric vehicles equipped with both hydraulic anti-lock braking systems (ABS) and regenerative braking (RB) systems. Based on a combination of optimal predictive control design and Lyapunov theory, the issue of uncertain vehicle parameters is well addressed. A novel braking torque distribution strategy is also introduced to achieve smooth regulation of the hydraulic pressure, such that pedal pulsating effect of the traditional ABS system can be relieved. By utilizing the larger working range of the hydraulic braking (HB) system and the faster response of the RB system, a better wheel slip control performance can be obtained. Moreover, the torque distributer helps to reach a good compromise between braking distance and the magnitude of the RB torque, which is directly related to the amount of regenerated energy. The effectiveness of the proposed control system has been validated in various simulations.

Robust Weighted Gain-Scheduling \(H_{\infty }\) Vehicle Lateral Motion Control With Considerations of Steering System Backlash-Type Hysteresis.

A robust weighted gain-scheduling H∞ control design for ground vehicles possessing steer-by-wire and drive/brake-by-wire functions is presented in this paper. The main control objective is to track vehicle yaw rate reference as well as to regulate vehicle lateral velocity with the hand-wheel steering angle and the external yaw moment as two control efforts. Two major challenges are overcome: 1) the backlash-type hysteresis embedded in the steering system introduces an additional disturbance term to the system and 2) the vehicle longitudinal velocity and the tire cornering stiffnesses are considered varying. Since the external inputs (reference) and backlash-type hysteresis are both involved in the system model, we propose to design a weighted H∞ gain-scheduling state-feedback controller. The feedback gains can be derived by solving a sequence of linear matrix inequalities. The relative importance of the steering system hysteresis and the reference signals can be tuned by a weighting factor. In addition, the gain-scheduling skill is employed to deal with the time-varying system parameters. Furthermore, the physical limitations of the actuators are considered by an eigenvalue placement technique. Simulation studies conducted in CarSim show that the proposed controller is capable of attenuating the effects of both steering system hysteresis and the time-varying parameters. Experimental results are also presented to demonstrate the effectiveness of this control algorithm in dealing with steering system backlash-type hysteresis when tracking yaw rate references.

Lightweight Vehicle Control-Oriented Modeling and Payload Parameter Sensitivity Analysis

This paper presents a 6-degree-of-freedom (DOF) control-oriented dynamic model for lightweight vehicles (LWVs) with explicit considerations of payload parametric variations. LWVs possess significant advantages in terms of fuel consumption, emissions, and transportation. However, due to the substantially reduced vehicle mass, the effects of payload parametric variations become more pronounced and have to be systematically incorporated in LWV control system designs and analyses. A control-oriented model that can capture such effects is thus foundational. Validation of this LWV control-oriented model is realized by comparing the model predictions against experimental results obtained from vehicle road tests of a lightweight electric ground vehicle. The comparisons show that the developed model captures the LWV essential dynamic characteristics and payload effects well. Sensitivity analyses with regard to the effects of different payload parametric variations on the selected LWV states were carried out to reveal the relative importance of payload parameters.

Eco-Departure of Connected Vehicles With V2X Communication at Signalized Intersections

Eco-driving at signalized intersections has significant potential for energy saving. In this paper, we focus on eco-departure operations of connected vehicles equipped with an internal combustion engine and a step-gear automatic transmission. A Bolza-type optimal control problem (OCP) is formulated to minimize engine fuel consumption. Due to the discrete gear ratio, this OCP is a nonlinear mixed-integer problem, which is challenging to handle by most existing optimization methods. The Legendre pseudospectral method combining the knotting technique is employed to convert it into a multistage interconnected nonlinear programming problem, which then solves the optimal engine torque and transmission gear position. The fuel-saving benefit of the optimized eco-departing operation is validated by a passenger car with a five-speed transmission. For real-time implementation, a near-optimal departing strategy is proposed to quickly determine the behavior of the engine and transmission. When a string of vehicles are departing from an intersection, the acceleration of the leading vehicle(s) should be considered to control the following vehicles. This issue is also addressed in this paper.

Active Fault-Tolerant Control for Electric Vehicles With Independently Driven Rear In-Wheel Motors Against Certain Actuator Faults

An active fault-tolerant control (AFTC) system is proposed in this paper for electric vehicles with independently driven in-wheel motors (IWMs). It comprises a baseline controller, a set of reconfigurable controllers, a fault detection and diagnosis (FDD) mechanism, and a decision mechanism. The baseline controller, which is actually a passive fault-tolerant controller, is applied to accommodate actuator faults and stabilize the faulty vehicle when the actuator fault occurs. After the fault is detected and estimated by the FDD mechanism, a proper reconfigurable controller is switched ON to achieve optimal postfault performance. Taking advantage of the robust gain-scheduling algorithm, the loss-of-effectiveness and additive faults of the IWMs can be accommodated by the baseline controller, and the estimation error of the FDD mechanism can be tolerated by the reconfigurable controllers. The results of simulations in CarSim and vehicle experimental tests show the effectiveness of this AFTC system in dealing with certain IWM faults.

Real-Time Estimation of Center of Gravity Position for Lightweight Vehicles Using Combined AKF–EKF Method

In this paper, a real-time center of gravity (CG) position estimator, which is based on a combined adaptive Kalman filter-extended Kalman filter (AKF-EKF) approach, for lightweight vehicles (LWVs) is proposed. Accurate knowledge of the CG longitudinal location and the CG height in the vehicle frame is helpful to the control of vehicle motions, particularly for LWVs, whose CG positions can be substantially varied by the payloads on board. The proposed estimation method, taking advantage of the separate front/rear torque control capability available in numerous LWV prototypes, only requires that the vehicle be excited longitudinally and/or vertically, thus avoiding potentially dangerous excitation of the vehicle lateral/yaw/roll motions. Moreover, additional parameters, such as vehicle moments of inertia, suspension parameters, and the tire/road friction coefficient (TRFC), are not necessary. A three-degree-of-freedom (3-DOF) vehicle dynamics model, taking the vehicle longitudinal velocity, the front-wheel angular speed, and the rear-wheel angular speed as states, is employed in the filter formulation. The designed estimator consists of two parts: an AKF for filtering noisy states and an EKF for estimating parameters. To minimize the effects of undesirable oscillation and bias in the filtered states, the optimization-based AKF judiciously tunes the suboptimal process noise covariance matrix in real time. Meanwhile, the EKF utilizes the filtered states from the AKF and takes the parameters as random walks. Simulation results exhibit the advantages of the AKF over the standard KF with fixed covariance matrices. Experimental results obtained from vehicle road tests show that the proposed estimator is capable of estimating the CG position with acceptable accuracy. Moreover, an investigation of the two-layer persistent excitation (PE) condition reveals that, although the CG height estimation largely depends on the excitation level in the maneuver, the CG longitudinal location can be always estimated via the input torque injections.

Robust Sideslip Angle Estimation for Lightweight Vehicles Using Smooth Variable Structure Filter

In the design of vehicle stability control (VSC) systems for ground vehicles, sideslip angle plays a vital role and its estimation has long been an active research topic. Accurate estimation of sideslip angle is more difficult for lightweight vehicles (LWVs) because their parameters are prone to significant changes with loading conditions — the amount and position of the payload. In this paper, a robust sideslip angle estimator based on a recently emerging smooth variable structure filter (SVSF) is presented. This sideslip angle estimator is suitable for LWVs because it is almost non-sensitive to the changes of the system parameters. A four-state vehicle lateral dynamic model including a pseudo-Burckhardt tire model is employed in the filter design. Compared with the widely utilized extended Kalman filter (EKF), the SVSF shows much better robustness against modeling errors. It is also more favorable in terms of tuning effort and computational speed. Simulation studies were conducted based on a high-fidelity vehicle model in CarSim®, where the vehicle took the form of a lightweight electric ground vehicle with independent in-wheel motors. The performance of the SVSF was shown by comparisons against the EKF under different settings for model parameters.Copyright

Adaptive vehicle planar motion control with fast parameter estimation

This paper presents a new adaptive control scheme for vehicle planar motion control with fast parameter estimation in moderate maneuvers in real-time. The parameters to be estimated include vehicle mass and yaw moment of inertia, which are important to the control systems for lightweight vehicles (LWVs) whose mass and yaw moment of inertia values change substantially with payload variations. A traditional Lyapunov-type adaptive control design rarely provides the bonus of accurate estimation of the true vehicle parameter values due to insufficient excitation in normal/moderate driving maneuvers. In this paper, to circumvent the obstacle of low adaptation rate, the traditional update law is supplemented with a new adaptive compensator to achieve much faster parameter convergence. The new design preserves the tracking performance and poses no additional requirements on the driving maneuvers. Simulation results show that the proposed adaptive scheme is capable of constructing true values of vehicle mass and yaw moment of inertia in just a few seconds once the corresponding persistent excitation is satisfied.

Nonlinear model predictive control for improving energy recovery for electric vehicles during regenerative braking

This paper presents a nonlinear model predictive control (NMPC) scheme and a case study for improving the regenerative braking (RB) energy recovery for electric vehicles (EV) with in-wheel motors. The first part deals with a braking torque split problem, that is, given a desired vehicle longitudinal velocity profile, design braking torques for front and rear wheels independently to increase the RB energy recovery. The second part provides a case study to see the effects of different vehicle velocity profiles, with the same initial and terminal velocities and desired travelling distance, on RB energy recovery. The controller developed in the first part employs a three degrees-of-freedom longitudinal vehicle dynamic model with explicit considerations on the experimentally-measured, motor-to-battery RB efficiency map. Simulation results show that the proposed NMPC is capable of restoring more RB energy than a conventional PI controller does. The case study clearly shows the great potential in planning a priori velocity trajectory that is optimal in terms of energy recovery for RB control of EVs with in-wheel motors.