Search

For the sake of enhancing the handling and stability of distributed drive electric vehicles (DDEVs) under four-wheel steering (4WS) conditions, this study proposes a novel hierarchical control strategy based on a phase plane analysis. This approach involves a meticulous comparison of the stable region in the phase plane to thoroughly analyze the intricate influence of the front wheel angle, rear wheel angle, road adhesion coefficient, and longitudinal speed on the complex dynamic performances of DDEVs and to accurately determine the critical stable-state parameter. Subsequently, a hierarchical control strategy is presented as an integrated solution to achieve the coordinated control of maneuverability and stability. On the upper control level, a model predictive control (MPC) motion controller is developed, wherein the real-time adjustment of the control weight matrix is ingeniously achieved by incorporating the crucial vehicle stable-state parameter. The lower control level is responsible for the optimal torque allocation among the four wheel motors to minimize the tire load rate, thereby ensuring a sufficient tire grip margin. The optimal torque distribution for the four wheel motors is achieved using a sophisticated two-level allocation algorithm, wherein the friction ellipse is employed as a judgement condition. Finally, this developed control strategy is thoroughly validated through co-simulation utilizing the CarSim 2019 and Simulink 2020b commercial software, demonstrating the validity of the developed control strategy. The comparative results indicate that the presented controller ensures a better tracking capability to the desired vehicle state while exhibiting improved handling stability under both the double lane shifting condition and the serpentine working condition. [ABSTRACT FROM AUTHOR]

This paper presents a centralized disturbance suppression strategy for distributed drive electric vehicles which is based on model predictive direct motion control. This strategy is capable of addressing issues such as parameter uncertainties and external disturbances in vehicles. Firstly, the paper provides a brief introduction to model predictive direct motion control. Secondly, it analyzes the impact of vehicle parameter uncertainties and external disturbances on the mathematical model. Finally, a centralized disturbance suppression strategy based on a sliding mode observer is proposed. Simulation results demonstrate that this strategy exhibits excellent disturbance rejection capabilities. [ABSTRACT FROM AUTHOR]

This paper presents an innovative three-level direct yaw moment control strategy for distributed drive electric vehicles (DDEV) under emergency conditions. The phase plane analysis is used at the supervisory level to design the stability boundary function taking into account the impact of the road adhesion coefficient. To guarantee the performance of finite-time convergence and singularity-free methods, the adaptive nonsingular fast terminal sliding mode control (ANFTSMC) is developed at the decision level to determine the extra yaw moment for tracking the intended side slip angle and yaw rate. Among this, the unstable domain in the phase plane is further separated into moderately and severely unstable according to the degree of vehicle instability, which is defined by the distance between the state phase point and the stability boundary. Meanwhile, the adaptive weight between the handling and stability is obtained. At the executive level, the quadratic programming algorithm is adopted to allocate four-wheel torque with the objective of optimal tire utilization rate. Finally, the co-simulation test is executed in both closed-loop and open-loop circumstances; according to the simulation results, the presented ANFTSMC method outperforms the SMC, and it can decrease the tracking error and improve the handling and stability. [ABSTRACT FROM AUTHOR]

To solve the problems of serious buffeting in traditional sliding mode control and difficulty in obtaining the derivative information of the system sliding mode surface, a distributed drive electric vehicles active front steering (AFS) control method based on adaptive super-twisting sliding mode control (ASTSMC) is proposed. Taking the yaw rate deviation as the state quantity, the stable and convergent sliding mode surface is designed to obtain the equivalent control input of the front wheel angle. The sliding mode function information is substituted into the parameters of the super-twisting algorithm; the discontinuous term is kept in the integrand function to keep the control signal continuous and weaken the system chattering; the adaptive control law is added to design the AFS controller. The co-simulation results of Matlab/Simulink and Carsim show that ASTSMC can reduce the yaw rate by 40.59% compared with no control under step steering condition. Compared with the sliding mode controller, ASTSMC has optimized the sideslip angle by 5.41% under the double line shifting condition. [ABSTRACT FROM AUTHOR]

Intelligentization is the development trend of the future automobile industry. Intelligentization requires that the dynamic control of the vehicle can complete the trajectory tracking according to the trajectory output of the decision planning the driving state of the vehicle and ensure the driving safety and stability of the vehicle. However, trajectory limit planning and harsh road conditions caused by emergencies will increase the difficulty of trajectory tracking and stability control of unmanned vehicles. In view of the above problems, this paper studies the trajectory tracking and stability control of distributed drive unmanned vehicles. This paper applies a hierarchical control framework. Firstly, in the upper controller, an adaptive prediction time linear quadratic regulator (APT LQR) path following algorithm is proposed to acquire the desired front-wheel-steering angle considering the dynamic stability performance of the tires. The lateral stability of the DDAUV is determined based on the phase plane, and the sliding surface, in the improved sliding mode control (SMC), is further dynamically adjusted to obtain the desired additional yaw moment for coordinating the path following and lateral stability. Then, in the lower controller, considering the slip and the working load of four tires, a comprehensive cost function is established to reasonably distribute the driving torque of four in-wheel motors (IWMs) for producing the desired additional yaw moment. Finally, the proposed control algorithm is verified by the hardware-in-the-loop (HIL) experiment platform. The results show the path following and lateral stability can be coordinated effectively under different driving conditions. [ABSTRACT FROM AUTHOR]

A coordinated torque distribution method (CTDM) combined with balanced torque vectoring distribution method (BTVDM) and differential braking distribution method (DBDM) is proposed to enhance vehicle lateral stability and improve ride comfort. The phase plane of sideslip angle and its angular velocity with three regions are used to evaluate vehicle lateral stability. The models of region boundary parameters considering vehicle velocity, steering angle, and road adhesion coefficient are established on the basis of the nonlinear partial least squares regression model (NLPLSRM). Vehicle lateral stability is dynamically evaluated on the basis of the vehicle state position in the phase plane. The distribution ratio of direct yaw moment (DYM) realised by DBDM and BTVDM is determined in accordance with the instability risk of vehicle lateral stability. Compared with BTVDM and DBDM, the proposed CTDM can correctly realise the accuracy of DYM and reduce vehicle velocity, which is beneficial to improving vehicle lateral stability. Numerical simulation results show that the proposed CTDM substantially improves vehicle lateral stability and reduces control intervention for ride comfort. [ABSTRACT FROM AUTHOR]

For the sake of enhancing the handling and stability of distributed drive electric vehicles (DDEVs) under four-wheel steering (4WS) conditions, this study proposes a novel hierarchical control strategy based on a phase plane analysis. This approach involves a meticulous comparison of the stable region in the phase plane to thoroughly analyze the intricate influence of the front wheel angle, rear wheel angle, road adhesion coefficient, and longitudinal speed on the complex dynamic performances of DDEVs and to accurately determine the critical stable-state parameter. Subsequently, a hierarchical control strategy is presented as an integrated solution to achieve the coordinated control of maneuverability and stability. On the upper control level, a model predictive control (MPC) motion controller is developed, wherein the real-time adjustment of the control weight matrix is ingeniously achieved by incorporating the crucial vehicle stable-state parameter. The lower control level is responsible for the optimal torque allocation among the four wheel motors to minimize the tire load rate, thereby ensuring a sufficient tire grip margin. The optimal torque distribution for the four wheel motors is achieved using a sophisticated two-level allocation algorithm, wherein the friction ellipse is employed as a judgement condition. Finally, this developed control strategy is thoroughly validated through co-simulation utilizing the CarSim 2019 and Simulink 2020b commercial software, demonstrating the validity of the developed control strategy. The comparative results indicate that the presented controller ensures a better tracking capability to the desired vehicle state while exhibiting improved handling stability under both the double lane shifting condition and the serpentine working condition.

This paper presents a centralized disturbance suppression strategy for distributed drive electric vehicles which is based on model predictive direct motion control. This strategy is capable of addressing issues such as parameter uncertainties and external disturbances in vehicles. Firstly, the paper provides a brief introduction to model predictive direct motion control. Secondly, it analyzes the impact of vehicle parameter uncertainties and external disturbances on the mathematical model. Finally, a centralized disturbance suppression strategy based on a sliding mode observer is proposed. Simulation results demonstrate that this strategy exhibits excellent disturbance rejection capabilities.

This paper presents an innovative three-level direct yaw moment control strategy for distributed drive electric vehicles (DDEV) under emergency conditions. The phase plane analysis is used at the supervisory level to design the stability boundary function taking into account the impact of the road adhesion coefficient. To guarantee the performance of finite-time convergence and singularity-free methods, the adaptive nonsingular fast terminal sliding mode control (ANFTSMC) is developed at the decision level to determine the extra yaw moment for tracking the intended side slip angle and yaw rate. Among this, the unstable domain in the phase plane is further separated into moderately and severely unstable according to the degree of vehicle instability, which is defined by the distance between the state phase point and the stability boundary. Meanwhile, the adaptive weight between the handling and stability is obtained. At the executive level, the quadratic programming algorithm is adopted to allocate four-wheel torque with the objective of optimal tire utilization rate. Finally, the co-simulation test is executed in both closed-loop and open-loop circumstances; according to the simulation results, the presented ANFTSMC method outperforms the SMC, and it can decrease the tracking error and improve the handling and stability.

For the motion of distributed drive electric vehicles, this paper deigns a novel motion control strategy based on model predictive control, which can control the longitudinal and lateral motion simultaneously. Different with traditional strategies containing one vehicle controller and four motor controllers, this strategy treats the vehicle and its in-wheel motors as one control object and uses only one controller to realize motion control, so that control costs are reduced and the handling performance is improved. First, an improved unified model of the vehicle and in-wheel motors is established to predict future states. Considering different time constants in vehicle dynamic system and motor electrical system, coupling predictions are analyzed in detail and a linear model is proposed to simplify the complex computation. Then, based on the fact that inverters of in-wheel-motor systems have discrete and finite voltage vectors, a model predictive direct motion control strategy is proposed. A cost function is designed to find out optimal voltage vectors which make vehicle follow the desired motion and meet constrains. Finally, to deal with huge amount of calculation and over-actuation problem caused by four in-wheel-motor systems, a deadbeat solution is applied to optimize the optimal voltage vectors calculation. Simulations under the proposed and traditional strategies are carried out, in which that the proposed strategy can realize the vehicle motion control well in different directions, and improve the vehicle handling performance. A practical implementation method is also provided experimentally to realize the strategy on four-motor system.

Intelligentization is the development trend of the future automobile industry. Intelligentization requires that the dynamic control of the vehicle can complete the trajectory tracking according to the trajectory output of the decision planning the driving state of the vehicle and ensure the driving safety and stability of the vehicle. However, trajectory limit planning and harsh road conditions caused by emergencies will increase the difficulty of trajectory tracking and stability control of unmanned vehicles. In view of the above problems, this paper studies the trajectory tracking and stability control of distributed drive unmanned vehicles. This paper applies a hierarchical control framework. Firstly, in the upper controller, an adaptive prediction time linear quadratic regulator (APT LQR) path following algorithm is proposed to acquire the desired front-wheel-steering angle considering the dynamic stability performance of the tires. The lateral stability of the DDAUV is determined based on the phase plane, and the sliding surface, in the improved sliding mode control (SMC), is further dynamically adjusted to obtain the desired additional yaw moment for coordinating the path following and lateral stability. Then, in the lower controller, considering the slip and the working load of four tires, a comprehensive cost function is established to reasonably distribute the driving torque of four in-wheel motors (IWMs) for producing the desired additional yaw moment. Finally, the proposed control algorithm is verified by the hardware-in-the-loop (HIL) experiment platform. The results show the path following and lateral stability can be coordinated effectively under different driving conditions.

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

A lateral motion control scheme for a distributed drive electric vehicle is presented in this paper, which takes into account both in-car network and movement-parameter uncertainty in a synthetic manner. Distributed drive vehicles have obvious advantages in terms of safety and comfort at high speeds due to the well-known E/E architecture, which includes an in-vehicle network, advanced vehicle motion control, and Advanced Driver Assistance System (ADAS) technologies. This is a fundamentally cyber-physical system. However, on the other hand, the application/insertion of in-vehicle network and the dynamic of wide-range varying speeds introduce additional system uncertainties, such as time-varying network induced delays and inevitable system perturbation, making controller design a difficult problem and even making the system unstable. This paper develops a cyber-physical control scheme and under which a two-process perturbation analysis is proposed to illustrate the system uncertainties. A hierarchical control strategy is also devised, with an upper-level gain-scheduling controller dealing with speed perturbation uncertainties and a lower-level $H_{\mathrm {\infty }}$ -LQR controller dealing with in-vehicle network uncertainty. Using real-time hardware in loop testing, the suggested control technique was found to be effective in dealing with both in-vehicle network and system perturbation problems while also ensuring reliable vehicle stability in all three scenarios.

Obtaining accurate vehicle driving state and road adhesion coefficient information is of great significance to many aspects of the vehicle. This paper takes distributed drive electric vehicles as the research object and designs a joint estimation method of vehicle driving state and road adhesion coefficient based on the theory of federated-cubature Kalman filter. The corresponding nonlinear three-degree-of-freedom vehicle dynamics model is established and the state space equation is obtained. Multi-source fusion of low-cost sensor signals is carried out by using information fusion technology, and an algorithm estimator is built by using vehicle dynamics theory. Select typical experimental conditions and apply Simulink to build an algorithm model and co-simulate with CarSim for verification. The experimental results show that the proposed estimation method can improve the accuracy and stability of state estimation.

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]

Slip rate control is important in improving vehicle stability and driving efficiency. In this paper, a robust slip rate control system is designed for distributed drive electric vehicles that consists of two slip rate estimators for multi-driving conditions, a vehicle speed estimator, and an anti-windup robust variable structure slip rate tracking controller. Because there is no driven wheel in a four-in-wheel-motor distributed drive electric vehicle, the estimators for small and large slip rates are designed based on dynamic and kinematic methods, respectively, which can switch according to the slip conditions. The convergence of the estimation error is discussed with the Lyapunov stability law and is less than 2% under the condition of acceleration on a low-friction road. The slip rate tracking controller is designed based on the sliding mode control law and the proportional-integral (PI) control method to handle model nonlinearity, modelling and estimation errors, and disturbances and to control the input chattering and saturation. The asymptotic stability of the tracking error is proven by Lyapunov theory. A joint control variable composed of the wheel angular acceleration and slip rate is designed to improve the robustness of the controller against the slip rate estimation error. Simulations and experiments under various conditions are performed to verify the proposed anti-slip control method. The results show that compared with a distributed drive vehicle without a slip rate controller, the controlled vehicle can prevent serious wheel skid on low-adhesion roads and improve the driving performance.

As one of the active safety technologies, stability control of vehicles has recently received great attention. In order to improve the handling stability of distributed drive electric vehicles under various extreme conditions, a direct yaw moment control (DYC) method based on a novel fuzzy sliding mode control (FSMC) is proposed. First, a linear 2DOF reference vehicle model as ideal value reference, a 7DOF vehicle model used for sideslip angle estimation, an electric-driving wheel model used to provide tire motion parameters based on CarSim platform are established. Then, FSMC is designed as the core decision-making layer of the control method to calculate the required additional yaw moment on the premise of estimating the sideslip angle. Four hub motors are allocated by the distribution method based on axle load proportion. Finally, under two typical working conditions, the four hub motors are allocated. Compared with traditional sliding mode control (SMC), the results show that FSMC can not only maintain vehicle stability more effectively under different working conditions, but also greatly reduce the occurrence of buffeting phenomenon, which has practical significance for engineering applications.

This article proposes an integrated control strategy of autonomous distributed drive electric vehicles. First, to handle the multi-constraints and integrated problem of path following and the yaw motion control, a model predictive control technique is applied to determine optimal front wheels' steering angle and external yaw moment synthetically and synchronously. For ensuring the desired path-tracking performance and vehicle lateral stability, a series of imperative state constraints and control references are transferred in the form of a matrix and imposed into the rolling optimization mechanism of model predictive control, where the detailed derivation is also illustrated and analyzed. Then, the quadratic programming algorithm is employed to optimize and distribute each in-wheel motor's torque output. Finally, numerical simulation validations are carried out and analyzed in depth by comparing with a linear quadratic regulator–based strategy, proving the effectiveness and control efficacy of the proposed strategy. [ABSTRACT FROM AUTHOR]

A vehicle with a four-channel anti-lock braking system (ABS) has poor safety and stability when braking on a low-adhesion road or off-road. In view of this situation, this paper proposes a multi-objective optimization coordinated control method for ABS and AFS based on multi-agent model predictive control (MPC). Firstly, the single-wheel control method is adopted to establish the single-wheel equation based on the slip rate and the stability equation of the centroid yaw based on AFS. The four wheels and the centroid are regarded as agents. The mathematical model of distributed drive electric vehicles based on graph theory and the coordinated control of AFS and ABS is established to reduce the dimension of the model. Secondly, on the basis of the multi-agent theory, an integrated coordinated control method for AFS and ABS based on distributed model predictive control (DMPC) is proposed to realize the ideal values of the vehicle’s slip rate, yaw rate, and sideslip angle, and improve the braking safety and handling stability of the vehicle. Then, to solve the problems of high levels of resource consumption, low real-time performance, and complex implementation in the optimization of the DMPC solution, a prediction solution method using a discrete simplified dual neural network (SDNN) is proposed to balance the computational efficiency and system dynamic performance. Finally, a hardware-in-the-loop (HIL) test bench is built to test the effectiveness of the proposed method under the conditions of a low-adhesion road and an off-road.

In order to improve the extension mileage and ensure the yaw stability of the distributed drive electric vehicle simultaneously, a multi-objective coordination control strategy is presented in this paper, in which the optimal vehicle-state estimation method and the orientated tire force distribution method are designed. The longitudinal force estimation is designed using the unknown input observer Kalman filter, a novel yaw rate and a vehicle sideslip angle estimation scheme is proposed by an observer error compensation method. A multi-objective hierarchical vehicle motion control strategy is developed to monitor the yaw stability and improve the energy efficiency of vehicle in real time. In the upper layer controller design, a sliding mode controller is proposed to track the desired yaw rate. In the lower layer controller design, the orientated tire force distribution scheme is presented to improve the motor efficiency and reduce the energy loss caused by vehicle steering resistance while ensuring the yaw stability of vehicle. Finally, the effectiveness of the proposed estimation method and the global coordination control strategy is validated by simulation in CarSim-Simulink joint platform and road test.

In order to fully explore the potential of distributed drive electric vehicle, an integrated coordination control method consists of model predictive control (MPC) and speed tracking control for trajectory tracking is proposed in this paper. Firstly, the vehicle dynamics model including the slip ratio of each wheel is established. Then, a multi-objective function of the MPC controller is established based on the principle of minimum tracking error, control vector, and control increment. And the optimal control sequences are obtained by optimizing the solution and correcting the feedback of the MPC, which satisfy both the actual kinematic constraints and the actuator performance constraints. The steering angle signal is sent to the steering motor directly, and the torque signal of each wheel which is obtained by superimposing the torque value output by the MPC controller and speed tracking controller is sent to the driving motor simultaneously. Finally, the proposed method is validated by using MATLAB/Simulink-Carsim co-simulation on different roads with conventional and severe pavement conditions. The simulation results show that the designed integrated coordination controller has obvious advantages in terms of tracking accuracy and body attitude stability during the course of lane change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The torque distribution strategy of distributed drive electric vehicle is only aimed at safety or economy. A multi-target coordinated control method considering stability and economy is proposed to solve the problem of single torque distribution target, which consists of a coordination decision controller, a high-level motion controller, and a low-level allocation controller. The coordination decision controller based on the phase plane method determines whether to adopt a stability or economic control strategy. The high-level motion controller consists of a bicycle model with 2 degree of freedom, a speed tracking controller, a stability controller, and an economic controller to calculate the desired direct yaw moment of the four in-wheel motors. The stability controller based on the fuzzy algorithm tracks the desired vehicle side slip angle and yaw rate calculated by the bicycle model with 2 degree of freedom to control vehicle stability. The economical controller based on a multi-motor loss model optimizes the efficiency of the vehicle's drive system. The low-level allocation controller is presented to provide optimally distributed torques for each wheel. Finally, the simulation and hardware-in-the-loop testing show that the coordinated control strategy can effectively improve the stability and economy of the distributed drive electric vehicle. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Distributed drive electric vehicle with four in-wheel motors is widespread with various characteristics, such as performance potentials for independent wheel drive control and energy efficiency. However, in future, one of the biggest obstacles for its success in the automotive industry would be its limited energy storage. This paper proposes a hierarchical control method that involves a high-level motion controller that uses sliding mode control to calculate the total desired force and yaw moment and a low-level allocation controller in which an optimal energy-efficient control allocation scheme is presented to provide optimally distributed torques of four in-wheel motors in all the normal cases. A practicable motor energy efficiency model as a motor actuator is proposed by incorporating the electric motor efficiency map based on measured data into the motor efficiency experiment and a current closed-loop motor model. Moreover, both tracking performance and energy-saving are carried out in this research and evaluated via a co-simulation approach using MATLAB/Simulink and CarSim. A ramp maneuver at a constant speed and New European Driving Cycle and Urban Dynamometer Driving Schedule maneuvers have been conducted. To conclude, it is demonstrated that distributed drive electric vehicle with four in-wheel motors can reduce total power consumption and enhance tracking performance compared with a simple control allocation in which the torques are the fixed ratio distribution. [ABSTRACT FROM AUTHOR]

Reliable estimation of longitudinal force and sideslip angle is essential for vehicle stability and active safety control. This paper presents a novel longitudinal force and sideslip angle estimation method for four-wheel independent-drive electric vehicles in which the cascaded multi-Kalman filters are applied. Also, a modified tire model is proposed to improve the accuracy and reliability of sideslip angle estimation. In the design of longitudinal force observer, considering that the longitudinal force is the unknown input of the electric driving wheel model, an expanded electric driving wheel model is presented and the longitudinal force is obtained by a strong tracking filter. Based on the longitudinal force observer, taking into consideration uncertain interferences of the vehicle dynamic model, a sideslip angle estimation method is designed using the robust Kalman filter and a novel modified tire model is proposed to correct the original tire model using the estimation results of longitudinal tire forces. Simulations and experiments were carried out, and effectiveness of the proposed estimation method was verified. [ABSTRACT FROM AUTHOR]

This paper presents an acceleration slip regulation strategy for distributed drive electric vehicles with two motors on the front axle. The tasks of the strategy include controlling the slip ratio to make full use of the road grip and controlling the yaw rate to eliminate the lateral movement due to the difference between motor torques. The rate of the slip ratio change can be controlled by controlling the motor torque, so that the slip ratio can be controlled by applying a proportional-integral control strategy to control the rate of the slip ratio change. The yaw rate can be controlled to almost zero by applying torque compensation based on yaw rate feedback. A coordination control strategy for the slip ratio control and yaw rate control is proposed based on analysis of the priorities and features of the two control processes. Simulations were carried out using MATLAB/Simulink, and experiments were performed on a hardware-in-loop test bench with actual motors. The results of the simulations and experiments showed that the proposed strategy could improve the longitudinal driving performance and straight line driving stability of the vehicle.

Conventional yaw stability strategy of distributed drive electric vehicle (DDEV) is usually realised by torque distribution strategy. However, the instantaneous variations of four independent tyres slip ratio and the effect of disturbance have not been considered sufficiently. Therefore, it is difficult to realise the robustness of yaw stability for DDEV under various operating conditions. To solve this problem, a novel model predictive controller-based compensation control system (MPC-CCS) is proposed in this paper. The proposed MPC-CCS consists of two parts, an MPC based-feedback controller and a Kalman filter based-feedforward controller. In the feedback controller, a dual torque distribution scheme is adopted to obtain optimal torque values derived from the real-time signals of four independent tyres slip ratio, an MPC is designed to realise optimal torque values for vehicle yaw motion. In the feedforward controller, a Kalman filter is employed to attenuate the effect of the disturbance on yaw performance. In this way, the robustness of yaw stability for DDEV can be guaranteed by the proposed MPC-CCS. The proposed MPC-CCS is evaluated on eight degrees of freedom simulation platform and simulation results of different conditions show the effectiveness of the MPC-CCS. [ABSTRACT FROM AUTHOR]

Exact estimation of longitudinal force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for longitudinal force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for longitudinal force estimation, the longitudinal force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for longitudinal force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated longitudinal force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified. [ABSTRACT FROM AUTHOR]

For a distributed drive electric vehicle (DDEV) which is equipped with redundant actuators, allocation control is a key technique. Three different allocation control algorithms are designated with fixed efficiency matrix, dynamic efficiency matrix, and direct yaw moment distribution, respectively. All these algorithms are applied in a vehicle stability control system with hierarchical control structure and evaluated from three aspects, namely, control precision, real-time characteristics, and control energy. Comparison results demonstrate that the algorithm with dynamic efficiency matrix has the best comprehensive performance, which is also validated in field tests based on a DDEV equipped with four motors. [ABSTRACT FROM AUTHOR]

The effect of vehicle active safety systems is subject to the accurate knowledge of vehicle states. Therefore, it is of great importance to develop a precise and robust estimation approach so as to deal with nonlinear vehicle dynamics systems. In this paper, a planar vehicle model with a simplified tire model is established first. Two advanced model-based estimation algorithms, an unscented Kalman filter and a moving horizon estimation, are developed for distributed drive electric vehicles. Using the proposed algorithms, vehicle longitudinal velocity, lateral velocity, yaw rate as well as lateral tire forces are estimated based on information fusion of standard sensors in today's typical vehicle and feedback signals from electric motors. Computer simulations are implemented in the environment of CarSim combined with Matlab/Simulink. The performance of both estimators regarding convergence, accuracy, and robustness against an incorrect initial estimate of longitudinal velocity is compared in detail. The comparison results demonstrate that both estimation approaches have favourable coincidence with the corresponding reference values, while the moving horizon estimation is more accurate and robust, and owns faster convergence. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

In this paper, a novel direct yaw control method based on driver operation intention for stability control of a distributed drive electric vehicle is proposed. It was discovered that the vehicle loses its stability easily under an emergency steering alignment (EA) problem. An emergent control algorithm is proposed to improve vehicle stability under such a condition. A driver operation intention recognition module is developed to identify the driving conditions. When the vehicle enters into an EA condition, the module can quickly identify it and transfer the control method from normal direct yaw control to emergency control. Two control algorithms are designed. The emergency control algorithm is applied to an EA condition while the adaptive control algorithm is applied to other conditions except the EA condition. Both simulation results and real vehicle results show that: The driver module can accurately identify driving conditions based on driver operation intention. When the vehicle enters into EA condition, the emergent control algorithm can intervene quickly, and it has proven to outperform normal direct yaw control for better stabilization of vehicles. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For a distributed drive electric vehicle (DDEV) driven by four in-wheel motors, advanced vehicle dynamic control methods can be realized easily because motors can be controlled independently, quickly and precisely. And direct yaw-moment control (DYC) has been widely studied and applied to vehicle stability control. Good vehicle handling performance: quick yaw rate transient response, small overshoot, high steady yaw rate gain, etc, is required by drivers under normal conditions, which is less concerned, however. Based on the hierarchical control methodology, a novel control system using direct yaw moment control for improving handling performance of a distributed drive electric vehicle especially under normal driving conditions has been proposed. The upper-loop control system consists of two parts: a state feedback controller, which aims to realize the ideal transient response of yaw rate, with a vehicle sideslip angle observer; and a steering wheel angle feedforward controller designed to achieve a desired yaw rate steady gain. Under the restriction of the effect of poles and zeros in the closed-loop transfer function on the system response and the capacity of in-wheel motors, the integrated time and absolute error (ITAE) function is utilized as the cost function in the optimal control to calculate the ideal eigen frequency and damper coefficient of the system and obtain optimal feedback matrix and feedforward matrix. Simulations and experiments with a DDEV under multiple maneuvers are carried out and show the effectiveness of the proposed method: yaw rate rising time is reduced, steady yaw rate gain is increased, vehicle steering characteristic is close to neutral steer and drivers burdens are also reduced. The control system improves vehicle handling performance under normal conditions in both transient and steady response. State feedback control instead of model following control is introduced in the control system so that the sense of control intervention to drivers is relieved. [ABSTRACT FROM AUTHOR]

A vehicle with a four-channel anti-lock braking system (ABS) has poor safety and stability when braking on a low-adhesion road or off-road. In view of this situation, this paper proposes a multi-objective optimization coordinated control method for ABS and AFS based on multi-agent model predictive control (MPC). Firstly, the single-wheel control method is adopted to establish the single-wheel equation based on the slip rate and the stability equation of the centroid yaw based on AFS. The four wheels and the centroid are regarded as agents. The mathematical model of distributed drive electric vehicles based on graph theory and the coordinated control of AFS and ABS is established to reduce the dimension of the model. Secondly, on the basis of the multi-agent theory, an integrated coordinated control method for AFS and ABS based on distributed model predictive control (DMPC) is proposed to realize the ideal values of the vehicle's slip rate, yaw rate, and sideslip angle, and improve the braking safety and handling stability of the vehicle. Then, to solve the problems of high levels of resource consumption, low real-time performance, and complex implementation in the optimization of the DMPC solution, a prediction solution method using a discrete simplified dual neural network (SDNN) is proposed to balance the computational efficiency and system dynamic performance. Finally, a hardware-in-the-loop (HIL) test bench is built to test the effectiveness of the proposed method under the conditions of a low-adhesion road and an off-road. [ABSTRACT FROM AUTHOR]

For tackling the problem of the reduction in longitudinal driving torque and acceleration as well as the occurrence of undesired yaw moment and yaw angular velocity when the driving force control system of distributed drive electric vehicle enters the working mode of acceleration slip regulation, two multi-wheel driving torque coordination control strategies are proposed. Among them the strategy for power performance is aiming at raising longitudinal driving torque and acceleration, while the strategy for stability is targeted at enhancing the lateral stability of vehicle. The results of real vehicle test show that the proposed coordination control scheme effectively achieves the design objectives and improves the performance of vehicle. [ABSTRACT FROM AUTHOR]