934 research outputs found

    A Real-time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles

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    This paper proposes a real-time nonlinear model predictive control (NMPC) strategy for direct yaw moment control (DYC) of distributed drive electric vehicles (DDEVs). The NMPC strategy is based on a control-oriented model built by integrating a single track vehicle model with the Magic Formula (MF) tire model. To mitigate the NMPC computational cost, the continuation/generalized minimal residual (C/GMRES) algorithm is employed and modified for real-time optimization. Since the traditional C/GMRES algorithm cannot directly solve the inequality constraint problem, the external penalty method is introduced to transform inequality constraints into an equivalently unconstrained optimization problem. Based on the Pontryagin’s minimum principle (PMP), the existence and uniqueness for solution of the proposed C/GMRES algorithm are proven. Additionally, to achieve fast initialization in C/GMRES algorithm, the varying predictive duration is adopted so that the analytic expressions of optimally initial solutions in C/GMRES algorithm can be derived and gained. A Karush-Kuhn-Tucker (KKT) condition based control allocation method distributes the desired traction and yaw moment among four independent motors. Numerical simulations are carried out by combining CarSim and Matlab/Simulink to evaluate the effectiveness of the proposed strategy. Results demonstrate that the real-time NMPC strategy can achieve superior vehicle stability performance, guarantee the given safety constraints, and significantly reduce the computational efforts

    Vehicle Dynamic Control of 4 In-Wheel-Motor Drived Electric Vehicle

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    A Yaw Stability Control Algorithm for Four-Wheel Independently Actuated Electric Ground Vehicles considering Control Boundaries

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    A hierarchical control algorithm of direct yaw moment control for four-wheel independently actuated (FWIA) electric ground vehicles is presented. Sliding mode control is adopted to yield the desired yaw moment in the higher layer of the algorithm due to the possible modeling inaccuracies and parametric uncertainties. The conditional integrator approach is employed to overcome the chattering issue, which enables a smooth transition to a proportional + integral-like controller, with antiwindup, when the system is entering the boundary layer. The lower level of the algorithm is given to allocate the desired yaw moment to four wheels by means of slip ratio distribution and control for a better grasp of control boundaries. Simulation results, obtained with a vehicle dynamics simulator, Carsim, and the Matlab/Simulink, show the effectiveness of the control algorithm

    Optimal Direct Yaw Moment Control of a 4WD Electric Vehicle

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    This thesis is concerned with electronic stability of an all-wheel drive electric vehicle with independent motors mounted in each wheel. The additional controllability and speed permitted using independent motors can be exploited to improve the handling and stability of electric vehicles. In this thesis, these improvements arise from employing a direct yaw moment control (DYC) system that seeks to adapt the understeer gradient of the vehicle and achieve neutral steer by employing a supervisory controller and simultaneously tracking an ideal yaw rate and ideal sideslip angle. DYC enhances vehicle stability by generating a corrective yaw moment realized by a torque vectoring controller which generates an optimal torque distribution among the four wheels. The torque allocation at each instant is computed by finding a solution to an optimization problem using gradient descent, a well-known algorithm that seeks the minimum cost employing the gradient of the cost function. A cost function seeking to minimize excessive wheel slip is proposed as the basis of the optimization problem, while the constraints come from the physical limitations of the motors and friction limits between the tires and road. The DYC system requires information about the tire forces in real-time, so this study presents a framework for estimating the tire force in all three coordinate directions. The sideslip angle is also a crucial quantity that must be measured or estimated but is outside the scope of this study. A comparative analysis of three different formulations of sliding mode control used for computation of the corrective yaw moment and an evaluation of how successfully they achieve neutral steer is presented. IPG Automotive’s CarMaker software, a high-fidelity vehicle simulator, was used as the plant model. A custom electric powertrain model was developed to enable any CarMaker vehicle to be reconfigured for independent control of the motors. This custom powertrain, called TVC_OpenXWD uses the torque/speed map of a Protean Pd18 implemented with lookup tables for each of the four motors. The TVC_OpenXWD powertrain model and controller were designed in MATLAB and Simulink and exported as C code to run them as plug-ins in CarMaker. Simulations of some common maneuvers, including the J-turn, sinusoidal steer, skid pad, and mu-split, indicate that employing DYC can achieve neutral steer. Additionally, it simultaneously tracks the ideal yaw rate and sideslip angle, while maximizing the traction on each tire[CB1] . The control system performance is evaluated based on its ability to achieve neutral steer by means of tracking the reference yaw rate, stabilizing the vehicle by means of reducing the sideslip angle, and to reduce chattering. A comparative analysis of sliding mode control employing a conventional switching function (CSMC), modified switching function (MSMC), and PID control (HSMC) demonstrates that the MSMC outperforms the other two methods in addition to the open loop system

    A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

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    Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme

    A fast model predictive control allocation of distributed drive electric vehicles for tire slip energy saving with stability constraints

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    Abstract 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

    Actuators for Intelligent Electric Vehicles

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    This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

    Torque Vectoring Predictive Control of a Four In-Wheel Motor Drive Electric Vehicle

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    The recent integration of vehicles with electrified powertrains in the automotive sector provides higher energy efficiency, lower pollution levels and increased controllability. These features have led to an increasing interest in the development of Advanced Driver- Assistance Systems (ADAS) that enhance not only the vehicle dynamic behaviour, but also its efficiency and energy consumption. This master’s thesis presents some contributions to the vehicle modeling, parameter estimation, model predictive control and reference generation applied to electric vehicles, paying particular attention to both model and controller validation, leveraging offline simulations and a real-time driving simulator. The objective of this project is focused on the Nonlinear Model Predictive Controller (NMPC) technique developing torque distribution strategies, specifically Torque Vectoring (TV) for a four-in wheel motor drive electric vehicle. A real-time TV-NMPC algorithm will be implemented, which maximizes the wheels torque usage and distribution to enhance vehicle stability and improve handling capabilities. In order to develop this control system, throughout this thesis the whole process carried out including the implementation requirements and considerations are described in detail. As the NMPC is a model-based approach, a nonlinear vehicle model is proposed. The vehicle model, the estimated parameters and the controller will be validated through the design of open and closed loop driving maneuvers for offline simulations performed in a simulation plant (VI-CarRealTime) and by means of a real-time driving simulator (VI-Grade Compact Simulator) to test the vehicle performance through various dynamic driving conditions

    Torque Vectoring Predictive Control of a Four In-Wheel Motor Drive Electric Vehicle

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    The recent integration of vehicles with electrified powertrains in the automotive sector provides higher energy efficiency, lower pollution levels and increased controllability. These features have led to an increasing interest in the development of Advanced Driver- Assistance Systems (ADAS) that enhance not only the vehicle dynamic behaviour, but also its efficiency and energy consumption. This master’s thesis presents some contributions to the vehicle modeling, parameter estimation, model predictive control and reference generation applied to electric vehicles, paying particular attention to both model and controller validation, leveraging offline simulations and a real-time driving simulator. The objective of this project is focused on the Nonlinear Model Predictive Controller (NMPC) technique developing torque distribution strategies, specifically Torque Vectoring (TV) for a four-in wheel motor drive electric vehicle. A real-time TV-NMPC algorithm will be implemented, which maximizes the wheels torque usage and distribution to enhance vehicle stability and improve handling capabilities. In order to develop this control system, throughout this thesis the whole process carried out including the implementation requirements and considerations are described in detail. As the NMPC is a model-based approach, a nonlinear vehicle model is proposed. The vehicle model, the estimated parameters and the controller will be validated through the design of open and closed loop driving maneuvers for offline simulations performed in a simulation plant (VI-CarRealTime) and by means of a real-time driving simulator (VI-Grade Compact Simulator) to test the vehicle performance through various dynamic driving conditions

    Energy recovery strategy for regenerative braking system of intelligent four-wheel independent drive electric vehicles

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    Regenerative braking system can recovery energy in various electric vehicles. Considering large computation load of global optimization methods, most researches adopt instantaneous or local algorithms to optimize the recuperation energy, and incline to study straight deceleration processes. However, uncertain drivers' intentions limit the potential exploration of economy improvement, and simple test conditions do not reflect the complexity of actual driving cycles. Herein, an innovative control architecture is designed for intelligent vehicles to overcome these challenges to some extent. Compared with traditional vehicles, driverless ones would eliminate drivers' interferences, and have more freedoms to optimize energy recovery, route tracking and dynamics stability. Specifically, a series regenerative braking system is designed, and then a three‐level control architecture is first proposed to coordinate three performances. In the top layer, some rules with maximum recuperation energy is exploited off‐line for optimising the velocity and control commands on‐line. In the middle layer, local algorithm is used to track the commands and complex routes for optimal energy from a global perspective. In the bottom layer, hydraulic and regenerative toques are allocated. Tests are conducted to demonstrate the effectiveness of the design and control schemes
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