Independent-drive Electric Vehicle Research Articles

Adaptive Sliding Mode Control for Yaw Stability of Four-Wheel Independent-Drive EV Based on the Phase Plane

Aiming at the yaw stability problem of a four-wheel independent-drive electric vehicle (EV) during steering, this paper proposes an adaptive sliding mode control strategy (ASMC) for yaw stability based on the phase plane. The control strategy adopts hierarchical control. The upper layer is the ASMC controllers based on particle swarm optimization (PSO). Aiming at the chattering problem of sliding mode controller, the approach law is designed as the adaptive approach law, which changes with the change of system state by using the adaptive control principle; to minimize the response delay and tracking error, the control system is taken as the object to find a set of optimal parameters for a constant velocity approach rate based on PSO. The middle level is a joint controller, which uses the established β−β˙ phase plane stability region boundary model to control the upper-level controllers jointly. When the vehicle is in the stable region, the ASMC controller for the yaw rate is used to determine the yaw moment; when the vehicle is outside the stable region, the final yaw moment is determined by the ASMC controller for the yaw rate and the ASMC controller for the sideslip angle, to restore the stability of the vehicle. The lower layer is a torque optimal distribution controller, which converts the yaw moment into torque and optimally distributes it to four wheels. Finally, Simulink and CarSim platforms are used for joint simulation. The results prove that the proposed control strategy can effectively reduce the error between the actual and the ideal value of control parameters and improve the vehicle’s stability when steering.

Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy and Safety

This paper presents a novel torque vectoring control (TVC) method for four in-wheel-motor independent-drive electric vehicles that considers both energy-saving and safety performance using deep reinforcement learning (RL). Firstly, the tire model is identified using the Fibonacci tree optimization algorithm, and a hierarchical torque vectoring control scheme is designed based on a nonlinear seven-degree-of-freedom vehicle model. This control structure comprises an active safety control layer and a torque allocation layer based on RL. The active safety control layer provides a torque reference for the torque allocation layer to allocate torque while considering both energy-saving and safety performance. Specifically, a new heuristic random ensembled double Q-learning RL algorithm is proposed to calculate the optimal torque allocation for all driving conditions. Finally, numerical experiments are conducted under different driving conditions to validate the effectiveness of the proposed TVC method. Through comparative studies, we emphasize that the novel TVC method outperforms many existing related control results in improving vehicle safety and energy savings, as well as reducing driver workload.

An Anti-Skid Control System Based on the Energy Method for Decentralized Electric Vehicles

Anti-slip control, as a fundamental technique of vehicle stability control, prevents loss of control of vehicles, especially under extreme driving conditions. However, current control methods fail to suppress vehicle slippage when steering. Therefore, a new anti-slip control approach for four-wheel independent-drive electric vehicles (EVs) based on the energy method is proposed. This approach makes full use of the distribution of motor energy between the body and the wheels during vehicle turning, being able to adjust the driving torque of each wheel. Simulation results validate that the proposed approach can prevent wheel slip when the vehicle steers on slippery roads. Furthermore, simulations also show that the proposed control strategy can maintain high control performance when the motor flux linkage varies.

A Hierarchical Control Strategy for FWID-EVs Based on Multi-Agent with Consideration of Safety and Economy

In this study, a hierarchical chassis control strategy is designed to enhance vehicle economy and safety for four-wheel independent-drive electric vehicles (FWID-EVs). In the upper-level, a vehicle dynamics model based on multiple agents is proposed, and a distributed model predictive control (DMPC) method is designed to dimensionally solve the problem of tracking the center-of-mass torque of the demanded velocity trajectory and stability parameters. In the bottom-level, a multi-objective torque distribution strategy that weighs safety, dynamics and economy based on multi-agent theory is designed by comprehensively considering the motor efficiency and tire energy loss. Finally, a hardware-in-the-loop (HIL) simulation platform is built to verify the method formulated in this paper. The results show that the method in this paper is effective in tracking the desired trajectory and further enhancing the stability of the vehicle under various conditions. Compared with other algorithms, while guaranteeing safety and dynamics, the energy consumption of the powertrain is reduced by 9.51%.

Chassis coordinated control based on ideal roll angle to improve vehicle stability

The current active control system for vehicles mainly focuses on following the reference trajectory constituted by the ideal yaw rate and sideslip angle, without considering the influence of the body roll angle on the vehicle stability performance. To improve the vehicle stability, in this study, a method of determining the three-dimensional stability region of ‘lateral speed–yaw rate–roll angle’ was proposed, the variation of the stability region with respect to the front steering angle was studied, and the limit steering angle boundary and vehicle ideal roll angle were obtained. Subsequently, a hierarchical coordinated control framework was proposed for actively controlling the front steering angle, suspension force, and motor torque of a four-wheel independent-drive electric vehicle with active front steering and active suspension systems. Finally, considering the effects of different motor torque distribution algorithms on the control performance, the performance of several control strategies was compared for high and low adhesion roads. The results indicated that the control strategy considering the body roll angle under different working conditions can improve the vehicle stability. On this basis, the torque distribution strategy based on minimum tire utilisation has the best following and stability performances among the strategies examined in this study.

MPC-Based Integrated Control of Trajectory Tracking and Handling Stability for Intelligent Driving Vehicle Driven by Four Hub Motor

Aiming at the problem of trajectory tracking and handling stability of intelligent four hub-motor independent-drive electric vehicle at high speed, an integrated control strategy based on model predictive control (MPC) is proposed. Firstly, a kinematics preview model is established considering the adaptive change of preview distance with longitudinal vehicle speed. Secondly, combined with the proposed preview model and considering the vehicle dynamic characteristics, an upper level controller of MPC is created to track the lateral deviation, heading angle deviation, sideslip angle and yaw rate to obtain the optimal solution of front wheel angle and additional yaw moment, which realize the control of handling stability while ensuring the trajectory tracking accuracy at same time. Then considering the varying road adhesion and the influence of longitudinal slip at the four tires, a lower level controller composed of torque optimal control and sliding mode controller is designed to realize the optimal torque distribution of four hub motors. Finally, through the co-simulation of MATLAB / Simulink and CarSim and the hardware-in-the-loop experiment, it is verified that the proposed integrated control strategy can effectively improve the path tracking accuracy and ensure the vehicle handling stability at high speed. Compared with the general MPC strategy, who does not control the sideslip angle and yaw rate with no preview model, the cumulative lateral tracking error of the integrated control is reduced by 51.9% and 87.7% respectively.

Two-level optimal torque distribution for handling stability control of a four hub-motor independent-drive electric vehicle under various adhesion conditions

In order to increase the handling stability performance of the four hub-motor independent-drive electric vehicle (4MIDEV) under various road conditions such as joint and bisectional road, this paper present an effective two-level optimal torque distribution control method. This method can calculate torque command to each hub-motor according to the desired longitudinal force and desired extra yaw moment. The first level allocation control algorithm has small calculation cost and fast response, and can achieve the maximum stability margin of vehicle under good road adhesion condition. Because the adhesion coefficients of each wheel are different, the weight coefficient of objective function is adjusted in real time in the second level allocation control strategy, so as to ensure the vehicle’s handling stability under various adhesion conditions, especially when four wheels have different road adhesion coefficients. Finally, the actual torque of the hub-motor is adjusted according to the slip rate by the sliding mode controller. The proposed distribution strategy is verified in CarSim-Matlab/Simulink joint simulation platform and hardware in the loop verification platform. Through double lane shift simulation on joint and bisectional road conditions, compared to the “general control” strategy, it is verified that the proposed distribution strategy can reduce the error of yaw rate and sideslip angle up to 47.1% and 50.3% compare to the general controller, and the calculation cost is reduced so that effectively improve the handling stability and real-time performance when facing different roads.

A Post Impact Stability Control for Four Hub-Motor Independent-Drive Electric Vehicles

This article presents a post-impact lateral stability control strategy integrated for Four Hub-Motor Independent-Drive Electric Vehicles (4MIDEV) to address the issue of loss of vehicle stability control due to vehicle skidding and saturated tire forces upon light vehicle collision using active front steering (AFS) and direct yaw moment control (DYC). Upon a light vehicle collision, vehicle experiences lateral and yaw motions due to collision impact-induced yaw moment. A multiple-objective hierarchical control strategy to attenuate vehicle yaw moment and regain stability control is proposed. Two potential sets of control reference states for the control strategy motion control are considered: desired DYC states and the drift equilibrium. A direct yaw moment controller based on sliding mode control (SMC) theory is designed to track the desired yaw rate. For the AFS, the tracking of desired DYC sideslip angle is performed using a SMC based controller, whereas the tracking of drift equilibrium state values employs a state feedback controller. An SMC longitudinal controller is designed for deceleration control of the vehicle after collision. Upon compensation of the yaw moment, a multiple sliding surface control theory-based lane controller is employed for lateral path deviation and heading angle control. The effectiveness of the proposed control strategy is validated on Carsim and Matlab/Simulink joint simulation platform. Simulation results showed that the proposed control strategy is effective in improving the post-impact stability control of the 4MIDEV on a high-velocity condition, and the control strategy that tracks the drift equilibrium showed better control performance on low adhesion coefficient surface.

Handling stability control strategy for four‐wheel hub motor‐driven vehicle based on adaptive control for road adhesion

Handling stability control can improve the safety of four in-wheel motor independent-drive electric vehicle (4MIDEV). An adaptive motion state control strategy based on improved sliding mode control for 4MIDEV is proposed to improve the steering stability control of the vehicle under different adhesion conditions. This adaptive control strategy uses a multi-layer structure, which mainly includes a motion reference layer, an upper-layer motion controller, and a lower-layer torque distribution controller. Aiming at the non-linear dynamic characteristics of 4MIDEV under different adhesion conditions, a dynamically parameter-varying motion reference layer is constructed to improve the accuracy of the reference values of sideslip angle and yaw rate, and realize the joint control of them. Considering the adhesion coefficient, construct the comprehensive yaw rate state variable, and use the improved sliding mode control algorithm to calculate the expected yaw moment. The lower-level controller uses the optimal distribution method to distribute the torque of each drive wheel in combination with the adhesion coefficient. By constructing a joint simulation platform of Carsim and Simulink, simulations under different road adhesion conditions are realized, verifying that the proposed improved strategy can effectively adapt to different adhesion conditions, while improve the handling stability of 4MIDEV.

Parameter influence analysis in an optimized fuzzy stability control for a four-wheel independent-drive electric vehicle

This paper presents an optimized fuzzy logic control (FLC) applied to four-wheel independent-drive electric vehicles (FWID-EVs). The controller is developed to act as an Electronic Differential (ED), which changes the in-wheel motors torques to correct the vehicle trajectory during some specific maneuvers. The simulation determines the rotation, the lateral and longitudinal displacement of the vehicle, in which the nonlinear Magic Formula is applied to define the tires slip, associated with the load transfer during curves or drive/break situations and with the steering input. According to the literature, several parameters are used to provide stability controls of similar vehicles, for example, the sideslip angle and yaw rate. Therefore, a parameter influence analysis is carried out to find out the most relevant parameters to be monitored in the investigated FWID-EV. Once the relevant input parameters are defined, an optimum ED FLC is developed by means of a genetic algorithm multi-objective optimization with the objective of minimizing the trajectory error of the FWID-EV under a combination of the Sine with Dwell and constant steering standard maneuvers. The results show the best control configuration is a combination of steer and sideslip angles, with a reduced number of rules and less processing time (0.71 s). Besides, the proposed control presents a reduction in the trajectory error, improvement up to 95.1%, emphasizing that the parameter influence analysis allows finding an optimum, more efficient, and time-saving control solution. Therefore, a simple adjustment in the variables inputs can decrease the control process, without overshadowing the dynamic behavior.

Torque Vectoring Control of RWID Electric Vehicle for Reducing Driving-Wheel Slippage Energy Dissipation in Cornering

The anxiety of driving range and inconvenience of battery recharging has placed high requirements on the energy efficiency of electric vehicles. To reduce driving-wheel slip energy consumption while cornering, a torque vectoring control strategy for a rear-wheel independent-drive (RWID) electric vehicle is proposed. First, the longitudinal linear stiffness of each driving wheel is estimated by using the approach of recursive least squares. Then, an initial differential torque is calculated for reducing their overall tire slippage energy dissipation. However, before the differential torque is applied to the two side of driving wheels, an acceleration slip regulation (ASR) is introduced into the overall control strategy to avoid entering into the tire adhesion saturation region resulting in excessive slip. Finally, the simulations of typical manoeuvring conditions are performed to verify the veracity of the estimated tire longitudinal linear stiffness and effectiveness of the torque vectoring control strategy. As a result, the proposed torque vectoring control leads to the largest reduction of around 17% slip power consumption for the situations carried out above.

Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle

The four-wheel independent-drive electric vehicle (FWID-EV) stands out among the electrically-propelled vehicles due to its higher efficiency. This EV configuration faces an energy management challenge, needing a controller to define the best power split among the motors, decreasing the energy consumption while keeping the EV performance. Aiming to overcome this challenge, this paper presents a fuzzy logic control (FLC) strategy to perform the power split among the electric motors to improve vehicle energy efficiency and dynamic performance under real driving conditions. The implemented controller is developed hierarchically, ensuring the speed follows a driving cycle and acting as an electronic differential to correct the vehicle trajectory along the path. Multi-objective optimization is presented using a particle swarm algorithm (MOPSO) to minimize the mass of the electrical components (motors and battery) and extend the driving range throu gh the maximization of the battery state of charge (SoC). Moreover, the optimization process also aims to enhance vehicle handling by minimizing the driver steering input during the cycle. The best trade-off solution was able to reach a 124.2 km drive range with a 235.8 kg battery (387.8 V and 91.2 Ah), presenting improved handling performance with 78.5% decrease in the driver steering action.

A New Torque Distribution Control for Four-Wheel Independent-Drive Electric Vehicles

Torque distribution control is a key technique for four-wheel independent-drive electric vehicles because it significantly affects vehicle stability and handling performance, especially under extreme driving conditions. This paper, which focuses on the global yaw moment generated by both the longitudinal and the lateral tire forces, proposes a new distribution control to allocate driving torques to four-wheel motors. The proposed objective function not only minimizes the longitudinal tire usage, but also make increased use of each tire to generate yaw moment and achieve a quicker yaw response. By analysis and a comparison with prior torque distribution control, the proposed control approach is shown to have better control performance in hardware-in-the-loop simulations.

Wheel torque distribution optimization of four-wheel independent-drive electric vehicle for energy efficient driving

With the help of several in-wheel motors, four-wheel independent-drive electric vehicle (4WIDEV) has tremendous potential to improve vehicle performance. Except for the theme of stability enhancement during the past two decades, energy saving topic becomes more attractive recently. However, it is difficult to achieve stability and economy performance simultaneously. Given the commonly used control method of rigid yaw rate tracking may limit the energy saving potential of 4WIDEV in cornering. Therefore, be different from previous studies which often focus on a sole target, in this paper, a driving energy management strategy for 4WIDEV based on multi-objective online optimization of four-wheel torque distribution is proposed in this paper, which includes weighted yaw rate tracking error into its parameterized control objectives besides electric drive system efficiency, tire slip losses and wheel torque ripple. Meanwhile, for better coordination of stability and economy in different situation, fuzzy logic control is adopted to dynamically adjust the weight of the control objective indexes of the proposed multi-objective online optimization function. Then, a particle swarm optimization algorithm is adopted to solve the optimization function. Finally, the proposed strategy for energy-efficient driving is verified based on the built Simulink and CarSim co-simulation model. The results show that the proposed four-wheel torque distribution strategy based on the multi-objective online optimization is more effective than average distribution strategy and offline optimization strategy.

Trajectory tracking algorithm in a hierarchical strategy for electric vehicle driven by four independent in-wheel motors

ABSTRACT Trajectory tracking is a key technology for precisely controlling autonomous vehicles. The characteristics of an independent drive electric vehicle (IDEV) are used as the basis for designing a trajectory tracking algorithm using a hierarchical strategy. While the upper controller tracks the desired trajectory based on model predictive control (MPC), the middle controller adjusts the speed using the fuzzy theory to optimize the parameters of the proportional integral (PI) controller and generates an additional yaw control moment based on sliding mode control (SMC). Finally, the lower controller distributes the torque to each wheel according to stability and adhesion conditions. A co-simulation platform is established to verify the performance of the proposed algorithm under typical scenarios. Results indicate that the proposed algorithm can provide good dynamic driving ability at high speeds and on low-adhesion roads. The accuracy of trajectory tracking using the proposed algorithm is better than that of the traditional preview–follower driver model and the performance of the proposed torque distribution algorithm is superior to that of the traditional even torque distribution algorithm.

Path Tracking and Direct Yaw Moment Coordinated Control Based on Robust MPC With the Finite Time Horizon for Autonomous Independent-Drive Vehicles

It is a striking fact that the characteristics of parametric uncertainties, external disturbance, time-varying and nonlinearities are available in the constructed model of autonomous independent-drive vehicles; therefore, in this paper, the robust model predictive control (MPC) with the finite time horizon is proposed to realize the coordinated path tracking and direct yaw moment control (DYC) for autonomous four in-wheel motor independent-drive electric vehicles (AMIDEV). Firstly, considering the time-varying and uncertain feature of the tire cornering stiffness and the vehicle velocity in the state space equation constructed by 2 degrees of freedom (DoF) vehicle model and the path tracking preview model, the linear parameter varying (LPV) discrete model with four polytypic vertexes is constructed. Then, based on the linear matrix inequality (LMI) method, the novel robust MPC theory with the finite time horizon is put forward to solve the min-max optimization problem after updating four polytypic vertexes in real time, which could deal with the inevitable model mismatch problem caused by the time-varying, uncertain vehicle dynamic characteristics and external disturbance. Finally, the simulation and experimental results have verified that the proposed novel robust MPC theory could emerge from the stranglehold exercised by the conservativeness of the traditional robust MPC theory with the infinite time horizon, which strengthens the robustness of this control system as well as achieves better path tracking accuracy and handling ability of AMIDEV.

Robust Performance Improvement of Lateral Motion in Four-Wheel Independent-Drive Electric Vehicles

In this article a robust two-layer control scheme is proposed to improve the performance of Four-Wheel Independent-Drive (4-WID) electric vehicles. The main objective is to enhance the lateral motion performance of electric vehicle under various manoeuvres and in the existence of parameter uncertainties and exterior disturbances. The upper-layer control law is proposed according to a novel terminal sliding mode approach formulated using the longitudinal velocity and yaw rate dynamics of the electric vehicle. It aims at generating the desired driving forces of each tire along with the vehicle's yaw momentum while guaranteeing chattering-free motion and finite-time stability in the existence of parametric uncertainties and disturbances. The lower-layer control law is formulated in the light of a constrained optimization problem to generate the optimal forces and torques for the in-wheel electric motors and achieve the desired longitudinal force and yaw momentum. The efficacy of the suggested approach is confirmed using CARSIM; a high-fidelity simulation package. Its performance analysis considered three standard driving manoeuvres and included a comparative analysis of other designs. The obtained results show that the offered methodology ensures the finite-time stability of the vehicle and improves its manoeuvrability and tracking performance under various driving conditions.

Disturbance Compensation and Torque Coordinated Control of Four In-Wheel Motor Independent-Drive Electric Vehicles

Functional overlaps and conflicts between Electronic Power Steering (EPS) and Torque Vectoring Control (TVC) for Distributed Drive Electric Vehicles (DDEVs) are issues that need to be addressed urgently. This paper deals with the interaction between EPS and TVC from the view of the influence mechanism and control objectives. The disturbance steering torque caused by the differential torque is estimated and a unified steering characteristic is proposed. To validate the analysis results, a torque compensation strategy is modified based on the exiting control system. And a robust vehicle state estimator is built thanks to the aligning torque estimation to provide the necessary information. Typical ground test results show that the torque oscillation on the steering wheel is suppressed. A faster and more linear steering response can be seen, which means that the proposed disturbance compensation strategy can comfort the contradiction between EPS and TVC, also improve the handling performance of the vehicle.

Electronic Differential Control of Rear-Wheel Independent-Drive Electric Vehicle

Electronic Differential Control of Rear-Wheel Independent-Drive Electric Vehicle

Torque Coordinated Control of Four In-Wheel Motor Independent-Drive Vehicles With Consideration of the Safety and Economy

How to reasonably coordinate and control the torque of four wheels to enhance vehicle performance is an important research problem of the four in-wheel-motor independent-drive electric vehicle (4MIDEV). In this paper, a coordinated hierarchical control strategy for the dynamic wheel torque of 4MIDEV steering is proposed, which considers both safety and energy-saving performance. The innovation of this paper lies in two new valuable algorithms in the top and bottom level controller, respectively. Besides the optimal control method on the basis of the 2-degree of freedom (DoF) ideal model in the top level controller, a novel model predictive control method based on the 7-DoF dynamic model is put forward to obtain two virtual generalized forces required during vehicle driving. In the bottom level controller, a novel optimal allocation (OA) algorithm is brought forward to improve the safety and energy-saving performance. The tire stability margin and the electric power of driving system balancing the driving energy consumption and regenerative braking energy recovery are considered in the quadratic objective function of OA algorithm at the same time. Carsim-Simulink joint simulation results under open-loop and closed-loop steering conditions illustrate that, compared with the 2-DoF LQR control method, the 7-DoF MPC method could effectively improve vehicle handling stability and safety. The bottom optimal allocation algorithm can not only improve the vehicle handling stability, but also make the driving system more energy-saving.