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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 article presents a cooperative controller that is specifically designed to enhance the stability of a distributed-drive vehicle during steering. The controller focuses on improving lateral stability during steering and achieving optimal torque allocation to meet numerous objectives. The article proposes a novel approach to improve the performance of the sliding mode controller for transverse stability control during steering. This is achieved by designing a fractional-order non-singular fast terminal sliding mode surface function, a fractional-order double-power exponential convergence law, and introducing a weighted integration term. Furthermore, the vehicle's torque was fine-tuned by employing an ant colony optimization (ACO) technique within the acceptable range defined by the lateral and longitudinal control requirements. To prevent the ACO algorithm from being stuck in local optima, a pseudo-random rule was implemented based on the original state transfer probability. This rule helps accelerate the convergence of the algorithm. Additionally, an elite approach and a dynamic change strategy for pheromone concentration were devised. Ultimately, the performance of the co-controller that was built is evaluated by simulation experiments conducted under both accelerated and decelerated driving situations. The test findings indicate that the technique effectively improves the lateral stability, tracking control, and energy economy of electric cars, with promising potential for practical use. [ABSTRACT FROM AUTHOR]

In response to the problems of excessive greenhouse-gas and particulate emissions and the low traction efficiency of conventional diesel tractors in the field, a purely electric wheel-side drive tractor was studied, including an electric motor drive system, a battery ballast system, and an electro–hydraulic suspension system. This paper develops a dynamics model of an electric tractor-ploughing unit under complex soil conditions, leading to the proposal of an active control method for drive wheel torque and a joint control method for the traction force of the suspension system and the front- and rear-axle loads of a tractor. Finally, the tractor is prototyped and assembled, and ploughing tests are carried out. The ploughing results show that the active torque-distribution control method proposed in this study reduces the tractor slip by 14.83% and increases the traction efficiency by 10.28% compared with the average torque-distribution mode. Compared with the conventional traction control mode, the joint control method for traction and ballast proposed in this paper results in a 3.7% increase in traction efficiency, a 15.05% decrease in slip, and a 4.9% reduction in total drive motor energy consumption. This study will help to improve the operation quality and traction efficiency of electric tractors in complex soil conditions.

Direct yaw moment control (DYC) can effectively improve the yaw stability of four-wheel distributed drive electric vehicles (4W-DDEVs) under extreme conditions, which has become an indispensable part of active safety control for 4W-DDEVs. This study proposes a novel hierarchical DYC architecture for 4W-DDEVs to enhance vehicle stability during ever-changing road conditions. Firstly, a vehicle dynamics model is established, including a two-degree-of-freedom (2DOF) vehicle model for calculating the desired yaw rate and sideslip angle as the control target of the upper layer controller, a DDEV model composed of a seven-degree-of-freedom (7DOF) vehicle model, a tire model, a motor model and a driver model. Secondly, a hierarchical DYC is designed combining the upper layer yaw moment calculation and low layer torque distribution. Specifically, based on Matlab/Simulink, improved linear quadratic regulator (LQR) with weight matrix optimization based on inertia weight cosine-adjustment particle swarm optimization (IWCPSO) is employed to compute the required additional yaw moment in the upper-layer controller, while quadratic programming (QP) is used to allocate four motors' torque with the optimization objective of minimizing the tire utilization rate. Finally, a comparative test with double-lane-change and sinusoidal conditions under a low and high adhesion road surface is conducted on Carsim and Matlab/Simulink joint simulation platform. With IWCPSO-LQR under double-lane-change (DLC) condition on a low adhesion road surface, the yaw rate and sideslip angle of the DDEV exhibits improvements of 95.2%, 96.8% in the integral sum of errors, 94.9%, 95.1% in the root mean squared error, and 78.8%, 98.5% in the peak value compared to those without control. Simulation results indicate the proposed hierarchical control method has a remarkable control effect on the yaw rate and sideslip angle, which effectively strengthens the driving stability of 4W-DDEVs. [ABSTRACT FROM AUTHOR]

To improve the handling stability of distributed drive electric vehicles, this paper introduces an electronic differential control strategy based on the stability criterion of the phase plane method. The strategy first plots the distributed electric vehicle's center of mass side angle and center of mass angular speed on the phase plane, and then it analyzes the vehicle's stability under various working conditions to determine the parameters that ensure the stability performance. Subsequently, an adaptive fuzzy control strategy is employed to achieve fast and accurate distribution of the torque to each wheel, thereby enhancing the vehicle's stability. A joint simulation platform is constructed using MATLAB/Simulink and CarSim. A comparison with the traditional electronic differential strategy demonstrates that the proposed distribution strategy based on phase plane stability exhibited excellent stability. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office 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 electric wheel loader is a new prototype in powertrains and drivetrains that saves energy consumption and diminishes emissions as earthmoving machinery. Dual-motor drive in the front and rear axles of electric wheel loaders helps the distribution of drive torque. However, challenges arise during shoveling conditions, particularly when one motor generates torque exceeding the ground's adhesion force, leading to tire slippage. This study thoroughly examines the mechanical structure of the working unit and elucidates the correlation between wheel load and hydraulic pressure in the base chamber of the tilt cylinder. This analysis is accomplished through a combination of theoretical derivations and experimental tests. The experiments involve a 5 ton rated load electric wheel loader tested across five running cases as well as weighing tests on a 15 ton rated load electric wheel loader. Based on the experiment discoveries, a dual-motor drive electric wheel loader is designed with specific transmission ratios for the front and rear drivetrains, and a torque distribution strategy is proposed based on wheel load during shoveling. Running condition tests demonstrate sufficient drive force for the new electric wheel loader, and shoveling tests reveal a significant reduction in tire slippage when employing the proposed torque distribution strategy compared to evenly distributed torque in the front and rear axles. Moreover, the driving force during the shoveling process remains undiminished. This indicates that the newly designed loader, in conjunction with the proposed strategy, exhibits excellent shoveling efficiency. [ABSTRACT FROM AUTHOR]

The eco‐driving strategy is of great significance in driving cost for plug‐in hybrid electric vehicles in driving trips, especially at signalized intersections. To address the issue of further energy saving, this study proposes an ecological approach and departure‐driving strategy by using syncretic learning with trapezoidal collocation algorithm. First, a syncretic learning‐based speed predictor is built by merging back propagation neural networks and radial basis function neural networks. Second, the syncretic learning‐based speed predictor and trapezoidal collocation algorithm are combined to optimize the speed trajectory. Third, the torque between the engine and the motor is distributed by the dynamic programming algorithm. Then, model predictive control optimizes torque output in the control time domain. Finally, the driving interval optimization method is designed to avoid mixed‐integer programming problems and redundant constraints, which make vehicles cross intersections without stopping. The numerical verification results show that the trapezoidal collocation algorithm with syncretic learning has more advantages than other methods in speed trajectory planning. Compared with the original trajectory, the driving time through the intersection is reduced and the total driving cost is lowered by 19.82%. Validation results confirm the effectiveness of the proposed strategy in energy consumption management at signalized intersections. [ABSTRACT FROM AUTHOR]

According to the independent and controllable advantage of the torque of each wheel for four- wheel independent driving electric off-road vehicle, to improve the traction and off-road ability of off-road vehicle, a coordinated control strategy of driving torque was proposed. The torque of the driving motor was pre-distributed according to the load of each axis. The optimal estimation of road slip rate was realized by the extreme value searching algorithm based on wheel slip rate and road adhesion coefficient. Proportional-integral-differential (PID) control-sliding mode control (SMC) and state-based driving force redistribution method were used to achieve the coordinated distribution of wheel drive torque for restraining the wheelslip under bad road conditions. Through the co-simulation of CarSim/MATLAB and hardware-in-the-loop (HIL) test, the experimental verification of climbing steep slope, single adhesion road surface, split road and continuous unrolling road was carried out. The results show that by the proposed control strategy, the torque of the driving wheel can be distributed according to the actual working conditions, and the slip rate of wheels is reduced to realize the optimization of the driving force of whole vehicle. [ABSTRACT FROM AUTHOR]

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.)

In this paper, an optimal nonlinear attitude controller is designed and implemented as hardware in the loop for a spacecraft simulator under various failures of reaction wheels. The proposed controller is developed in the closed form based on predicting the nonlinear continuous responses of spacecraft simulator. The special case of the controller when all uncertainties are ignored leads to feedback linearization. However, the stability of the controller in the presence of parametric uncertainties and unmodeled dynamics of the platform is analyzed, and the effect of the prediction time on the boundedness of system responses is presented. A redundant reaction wheel is located in the platform to compensate for failures of three main reaction wheels. How to distribute torque between healthy wheels under different types of failure including stuck and oscillatory failures is addressed and experimentally implemented. The laboratory results that are consistent with computer simulations show the accuracy and validity of the designed controller. It is seen that the spacecraft attitude converges in a limited time in the presence of system uncertainties and actuator failures. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & 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 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.

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office 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.)

Dedicated hybrid transmission (DHT) has attracted wide attention because of its light weight and small overall size. The hybrid system has a variety of different power sources, which can change the operation mode to respond to different operating conditions, thus improving the fuel economy. The DHT torque distribution control strategy model is built on Matlab/Simulink platform to control the optimal output torque of the engine, so as to ensure the engine operating in the economic zone. Off-line simulation is carried out based on the test cycle conditions of light passenger vehicles in China and light vehicle in the world. The results show that the matching accuracy of the expected speed and the actual speed is high, and the simulation model is accurate and reliable. Under CLTC_P condition, the fuel consumption of 100 km is 5.74 L, saving 36.33% than that of traditional fuel vehicles.

To improve the handling stability of distributed drive electric vehicles, this paper introduces an electronic differential control strategy based on the stability criterion of the phase plane method. The strategy first plots the distributed electric vehicle’s center of mass side angle and center of mass angular speed on the phase plane, and then it analyzes the vehicle’s stability under various working conditions to determine the parameters that ensure the stability performance. Subsequently, an adaptive fuzzy control strategy is employed to achieve fast and accurate distribution of the torque to each wheel, thereby enhancing the vehicle’s stability. A joint simulation platform is constructed using MATLAB/Simulink and CarSim. A comparison with the traditional electronic differential strategy demonstrates that the proposed distribution strategy based on phase plane stability exhibited excellent stability.

Direct yaw moment control (DYC) can effectively improve the yaw stability of four-wheel distributed drive electric vehicles (4W-DDEVs) under extreme conditions, which has become an indispensable part of active safety control for 4W-DDEVs. This study proposes a novel hierarchical DYC architecture for 4W-DDEVs to enhance vehicle stability during ever-changing road conditions. Firstly, a vehicle dynamics model is established, including a two-degree-of-freedom (2DOF) vehicle model for calculating the desired yaw rate and sideslip angle as the control target of the upper layer controller, a DDEV model composed of a seven-degree-of-freedom (7DOF) vehicle model, a tire model, a motor model and a driver model. Secondly, a hierarchical DYC is designed combining the upper layer yaw moment calculation and low layer torque distribution. Specifically, based on Matlab/Simulink, improved linear quadratic regulator (LQR) with weight matrix optimization based on inertia weight cosine-adjustment particle swarm optimization (IWCPSO) is employed to compute the required additional yaw moment in the upper-layer controller, while quadratic programming (QP) is used to allocate four motors’ torque with the optimization objective of minimizing the tire utilization rate. Finally, a comparative test with double-lane-change and sinusoidal conditions under a low and high adhesion road surface is conducted on Carsim and Matlab/Simulink joint simulation platform. With IWCPSO-LQR under double-lane-change (DLC) condition on a low adhesion road surface, the yaw rate and sideslip angle of the DDEV exhibits improvements of 95.2%, 96.8% in the integral sum of errors, 94.9%, 95.1% in the root mean squared error, and 78.8%, 98.5% in the peak value compared to those without control. Simulation results indicate the proposed hierarchical control method has a remarkable control effect on the yaw rate and sideslip angle, which effectively strengthens the driving stability of 4W-DDEVs.

The traditional traction control system (TCS) based on hydraulic braking only works when the wheels are slipping, which will cause the problem of slow response to extreme slip. In addition, the TCS of four‐wheel‐independent‐drive electric vehicle (4WIDEV) is often based on road adhesion characteristics identification or optimal slip ratio identification to implement active control, which is difficult to achieve in engineering. Aiming at this problem, a practical active TCS is proposed in this paper. Firstly, according to the wheel slip state of the front and rear axles, the dynamic transfer of torque between axles is realized to maintain the vehicle propulsion power. Second, the adhesion conditions between road and tire are classified, and two sets of target slip ratio thresholds are formulated for high and low adhesion pavement, respectively. Then the current road adhesion coefficient is estimated by using the advantage that the in‐wheel motor torque can be obtained in real‐time. Thirdly, the overall framework of the control strategy is established, the logic threshold control algorithm is adopted for tracking the wheel target slip ratio. Finally, the simulation results show that the proposed active TCS can improve the vehicle power and avoid excessive wheel slipping. [ABSTRACT FROM AUTHOR]

A novel direct yaw-moment control scheme is proposed to improve the lateral stability of distributed-drive electric vehicles (DDEV). The scheme adopts a hierarchical control structure that consists of an upper controller and a lower controller. An enhanced sliding mode controller (ESMC) with improved exponential reaching law is designed to calculate the additional yaw-moment to maintain the lateral stability of the vehicle in the upper controller. By adding the function about the sliding mode surface, the approach speed is increased when it is far away from the sliding mode surface, and the approach speed is reduced when it is close to the sliding mode surface, so as to suppress chattering. An optimized method of torque distribution is proposed in the lower controller, which uses a multi-verse optimizer (MVO) to minimize the tire utilization rate as the objective function to calculate the torque and optimally distribute it to each in-wheel motor. The co-simulation of Carsim and Simulink shows that under the conditions of high speed and high-adhesion road, the tracking error of the yaw rate is reduced from 6.4% to 2.7%, and the sideslip angle is within 0.018 rad; under the conditions of low speed and low-adhesion road, the tracking error of the yaw rate is reduced from 5.2% to 3.8%, and the sideslip angle is within 0.008 5 rad. The proposed strategy has better stability compared with the rule-based distribution strategy. [ABSTRACT FROM AUTHOR]

Wheel torques of vehicles driven by hub motors are independently controllable with rapid dynamic response and redundant configuration of drive system, which have significant advantages in integrated vehicle dynamics control. However, the complex driving conditions and high power demand are likely to cause temperature-rise fault of the motor, which can ultimately affect driving efficiency. To investigate the temperature fault diagnosis of hub motor and the torque distribution strategy of each wheel under the temperature expiration condition, and ensure the stability and power performance of the vehicle, the fuzzy logic detection algorithm was designed to diagnose the temperature fault of the motor, and the temperature failure factor was proposed to quantify the failure state of the hub motor. The longitudinal and lateral force requirements were calculated according to the expected speed and front wheel angle, and the fault tolerant control strategy was designed to achieve the optimal distribution of torque reconstruction. The proposed algorithm was verified on a prototype vehicle. The results show that the fault diagnosis module and the solution strategy can judge the motor failure state in time and reconstruct the torque distribution, which improves the driving safety and achieves the coordinated control of the vehicle dynamics and stability. [ABSTRACT FROM AUTHOR]

Based on the analysis of the operating conditions of the tractor, a Hybrid four-wheel drive tractor is proposed, and formulate the torque distribution control strategy based on fuzzy control, to control the driving wheel slip rate of the Hybrid four-wheel drive tractor in the high traction efficiency operating range of the tractor. The vehicle model of the Hybrid four-wheel drive tractor is established in AVL-CRUISE software, and the torque distribution control strategy based on fuzzy control is established in MATLAB/Simulink software. The AVL-CRUISE and MATLAB/Simulink co-simulation was carried out based on the plowing condition of the tractor. The simulation results show that the torque distribution control strategy based on fuzzy control can control the driving wheel slip rate of the Hybrid four-wheel drive tractor in the high traction efficiency operating range, the power performance of the Hybrid four-wheel drive tractor is improved, while the engine runs smoothly and is always in the high-efficiency range of engine operation, and the economy is better. [ABSTRACT FROM AUTHOR]

The electric wheel loader is a new prototype in powertrains and drivetrains that saves energy consumption and diminishes emissions as earthmoving machinery. Dual-motor drive in the front and rear axles of electric wheel loaders helps the distribution of drive torque. However, challenges arise during shoveling conditions, particularly when one motor generates torque exceeding the ground’s adhesion force, leading to tire slippage. This study thoroughly examines the mechanical structure of the working unit and elucidates the correlation between wheel load and hydraulic pressure in the base chamber of the tilt cylinder. This analysis is accomplished through a combination of theoretical derivations and experimental tests. The experiments involve a 5 ton rated load electric wheel loader tested across five running cases as well as weighing tests on a 15 ton rated load electric wheel loader. Based on the experiment discoveries, a dual-motor drive electric wheel loader is designed with specific transmission ratios for the front and rear drivetrains, and a torque distribution strategy is proposed based on wheel load during shoveling. Running condition tests demonstrate sufficient drive force for the new electric wheel loader, and shoveling tests reveal a significant reduction in tire slippage when employing the proposed torque distribution strategy compared to evenly distributed torque in the front and rear axles. Moreover, the driving force during the shoveling process remains undiminished. This indicates that the newly designed loader, in conjunction with the proposed strategy, exhibits excellent shoveling efficiency.

High-speed and complex road conditions make it easy for vehicles to reach limit conditions, increasing the risk of instability. Consequently, there is an urgent need to solve the problem of vehicle stability and safety. In this paper, adaptive stability control is studied in BEVs driven by in-wheel motors. Based on the sliding model algorithm, a joint weighting control of the yaw rate and sideslip angle is carried out, and a weight coefficient is designed using a fuzzy algorithm to realize adaptive direct yaw moment control. Next, optimal torque distribution is designed with the minimum sum of four tire load rates as the optimization objective. Then, combined with the road adhesion coefficient and the maximum motor torque constraint, the torque distribution problem is transformed into a functionally optimal solution problem with constraints. The simulation results show that the direct yaw moment controller based on the adaptive sliding mode algorithm has a good control effect on the yaw rate and sideslip angle, and it can effectively improve vehicle adaptive stability control. In the optimal torque distributor based on road surface recognition, the estimated error of road adhesion is within 10%, and has a greater margin to deal with vehicle instability, which can effectively improve vehicle adaptive stability control. [ABSTRACT FROM AUTHOR]

For multi-axle distributed drive (MADD) vehicles, the complexity of the longitudinal dynamics control system increases with the number of driven wheels, which presents a huge challenge to control the multi-motor drive vehicle with more than four wheels. To reduce the control system complexity, this paper proposes a coordinated slip control algorithm using the hierarchical linear quadratic regulator (HLQR) scheme for a 12 × 12 MADD vehicle. The 12-wheel driving system is decoupled based on the wheel load and simplified to a double local subsystem. First, the 12 × 12 MADD vehicle dynamics model is established. Then, the optimal slip ratio is obtained on the basis of the road friction coefficient estimation through a fuzzy control algorithm when the wheel slips. Afterwards, the wheel slip ratio is controlled based on the HLQR program for anti-slip regulation. Furthermore, the driving torque control allocation based on quadratic programming (QR) is coordinated with the anti-slip control. Simulink results show that the proposed coordinated slip control based on HLQR can improve slip control accuracy by more than 30% and greatly reduce the calculation load. The torque control allocation is also limited by the slip control results to ensure wheel dynamic stability and smoothly satisfy the driver's demand. [ABSTRACT FROM AUTHOR]

Recently, there has been significant attention given to the electrification of transportation due to concerns about fossil fuel depletion and environmental pollution. Conventional drive systems typically include a clutch, reduction gear, and mechanical differential, which results in power loss, noise, vibration, and additional maintenance. However, in-wheel motor drive technology eliminates the need for these components, providing benefits such as higher system efficiency, improved wheel control, and increased passenger comfort. This article offers a comprehensive review of the technology and development of in-wheel motor drives. It begins with an overview of in-wheel motor drives in electric vehicles, followed by an exploration of the types of electric motors suitable for in-wheel motor drives. The paper then presents an industrial state of the art of in-wheel motors, comparing them with conventional motor drives, and reviews the implemented power electronics, control system, and cooling systems. Finally, the paper concludes by providing an outlook on the challenges and future trends of in-wheel drive systems. [ABSTRACT FROM AUTHOR]

Aiming to improve the distance per charge of four in-wheel independent motor-drive electric vehicles in intelligent transportation systems, a hierarchical energy management strategy that weighs their computational efficiency and optimization performance is proposed. According to the information of an intelligent transportation system, a method combining reinforcement learning with receding horizon optimization is proposed at the upper level, which solves the cruising velocity for eco-driving in a long predictive horizon based on the online construction of a velocity planning problem. At the lower level, a multi-objective optimal torque allocation method that considers energy saving and safety is proposed, where an analytical solution based on the state feedback control was obtained with the vehicle following the optimal speed of the upper level and tracking the centerline of the target path. The energy management strategy proposed in this study effectively reduces the complexity of the intelligent energy-saving control system of the vehicle and achieves a fast solution to the whole vehicle energy optimization problem, integrating macro-traffic information while considering both power and safety. Finally, an intelligent, connected hardware-in-the-loop (HIL) simulation platform is built to verify the method formulated in this study. The simulation results demonstrate that the proposed method reduces energy consumption by 12.98% compared with the conventional constant-speed cruising strategy. In addition, the computational time is significantly reduced. [ABSTRACT FROM AUTHOR]

A multi-layer controller of direct yaw moment for electric vehicles is developed in this study. In the upper layer, the yaw moment are obtained using Adaptive Sliding Mode Control (ASMC) with adaptation gain to track the desired vehicle yaw rate. The corrective yaw moments are applied by four in-wheel electric motors. The lower layer controller consists of a torque distribution algorithm and in-wheel motor torque controllers as well. The proposed torque distribution algorithm is intended to distribute the reference torques of each in-wheel motor controller appropriately based on both total longitudinal force and corrective yaw moment. To elucidate the effectiveness and robustness of the above control method, the simulation under various manoeuvres was carried out. A 7-DOF non-linear vehicle model is used for simulations and their results signify that the proposed control algorithm accomplishes a proper distribution of longitudinal force among four individual wheels, in turn, enhancing the yaw stability of the vehicle.

Active suspension control and direct yaw-moment control (DYC) are widely used in the vehicle control field. To solve the coupling between those two controllers, a coordinated control of active suspension and DYC is proposed to further improve the vehicle roll and yaw stability. To enhance the adaptive ability of the active suspension, a proportional integral control optimized by the genetic fuzzy algorithm is introduced. DYC is proposed based on the sliding mode control. To restrain the chattering, the parameters of the sliding mode control is optimized by a genetic algorithm. Finally, a coordinated controller is presented based on the adaptive distribution of the anti-roll torque in the front and rear suspension. The simulation results show that the proposed active suspension and DYC can greatly improve the roll and yaw stability, respectively. The expected vehicle status can be well tracked. In addition, the coordinated control is compared by simply using two independent controllers under a different tire–road friction coefficient and different steering maneuver. The results show that the coordinated control has an even better performance under each working condition. [ABSTRACT FROM AUTHOR]

This study proposes a method based on reinforcement learning (RL) for point-to-point and dynamic reference position tracking control of a planar cable-driven parallel robots, which is a multi-input multi-output system (MIMO). The method eliminates the use of a tension distribution algorithm in controlling the system's dynamics and inherently optimizes the cable tensions based on the reward function during the learning process. The deep deterministic policy gradient algorithm is utilized for training the RL agents in point-to-point and dynamic reference tracking tasks. The performances of the two agents are tested on their specifically trained tasks. Moreover, we also implement the agent trained for point-to-point tasks on the dynamic reference tracking and vice versa. The performances of the RL agents are compared with a classical PD controller. The results show that RL can perform quite well without the requirement of designing different controllers for each task if the system's dynamics is learned well. [ABSTRACT FROM AUTHOR]

The improvement of both the stability and economy of the four in-wheel motor drive (4IWMD) electric vehicle under complex drive cycles is currently a difficult problem in this field. A torque distribution method with the comprehensive goals of optimal torque distribution and energy efficiency, considering economy through energy efficiency for the 4IWMD electric vehicle, is proposed in this paper. Each component of the 4IWMD electric vehicle is modelled. The dynamic programming (DP) control algorithm is utilized for torque distribution between the front and rear in-wheel motors to obtain optimal torque distribution and energy efficiency in the 4IWMD electric vehicle. The simulation is performed on a co-simulation platform with the software of AVL Cruise and MATLAB/Simulink, considering a straight road. Compared to the fuzzy logic control algorithm, the simulation results are very promising, as the energy consumption of the electric vehicle was reduced by 22.68%, 20.73% and 21.84% under the WLTC, NEDC and customized IM240 driving cycle conditions, respectively, with the proposed DP control algorithm. The hardware-in-the loop (HIL) experimental results also indicate that the effectiveness of the proposed DP algorithm is verified under the NEDC, WLTC and IM240 driving cycles, when a straight road is considered. The proposed DP control algorithm not only reduces the vehicle energy consumption and guarantees the optimization of torque distribution, but also increases the driving range of the vehicle. [ABSTRACT FROM AUTHOR]

Regenerative braking is one of the most important methods for improving energy utilization in electric vehicles. Electric vehicles with front-rear, individually driven configurations exhibit significant potential and flexibility for recovering braking energy. To improve the system’s high-power impact tolerance, a high-power density hydraulic energy storage system can be incorporated to facilitate a full-drive dual-motor electric–hydraulic hybrid (DMEHH) powertrain. The DMEHH system is composed of an independently driven electric–hydraulic hybrid front axle and a purely electric rear axle. In this study, a method for distributing braking torque to minimize energy loss was devised based on the proposed DMEHH powertrain. Power loss models for both the electric and hydraulic subsystems have been developed. Loss minimization control was adapted for the power loss model of the permanent magnet synchronous motors in the powertrain, and the front-rear and front electric–hydraulic torque braking distribution maps were calculated using the energy-loss minimization rule. The application of torque distribution maps resulted in energy losses less than those of the general ideal torque distribution for all braking conditions. The energy loss decreased by 27.2% when the loss minimization control method was used in the WLTC cycle, and decreased by 29.1%, 25.5%, and 21.6% in UDDS, NEDC, and US06 driving cycles, respectively.

Based on the analysis of the operating conditions of the tractor, a Hybrid four-wheel drive tractor is proposed, and formulate the torque distribution control strategy based on fuzzy control, to control the driving wheel slip rate of the Hybrid four-wheel drive tractor in the high traction efficiency operating range of the tractor. The vehicle model of the Hybrid four-wheel drive tractor is established in AVL-CRUISE software, and the torque distribution control strategy based on fuzzy control is established in MATLAB/Simulink software. The AVL-CRUISE and MATLAB/Simulink co-simulation was carried out based on the plowing condition of the tractor. The simulation results show that the torque distribution control strategy based on fuzzy control can control the driving wheel slip rate of the Hybrid four-wheel drive tractor in the high traction efficiency operating range, the power performance of the Hybrid four-wheel drive tractor is improved, while the engine runs smoothly and is always in the high-efficiency range of engine operation, and the economy is better.

To solve the problem of lateral instability of the vehicle caused by insufficient lateral force of the tires due to the insufficient torque provided by the motor to the tire when the vehicle turns sharply or avoids obstacles in an emergency, a layered control method is used to design a lateral stability control system. The upper decision layer selects the yaw rate and the sideslip angle of the center of mass as the control variables and uses the joint state deviation of the yaw rate and the sideslip angle of the center of mass and the rate of change of the deviation as the input of the sliding mode variable structure controller to calculate the additional yaw moment required to maintain vehicle stability. The lower torque distribution layer realizes the distribution of torque through the electro-hydraulic coordinated control method: the torque distribution rule based on real-time load transfer calculates the torque corresponding to the control wheel and generates the torque through the hub motor and transmits it to the wheel. When the torque output from the motor cannot provide sufficient torque for the vehicle, hydraulic braking is used as a compensating control, and the difference between the required yaw torque and the motor-generated yaw torque is used as the required torque for hydraulic control to calculate the wheel cylinder pressure required to brake the wheels. Based on the joint simulation model of MATLAB/Simulink and Carsim, the sine and double shift line working condition are selected for stability simulation experiments. From the simulation results, it can be seen that the yaw rate and sideslip angle of the center of mass of the vehicle with sliding mode control and electro-hydraulic coordinated control almost coincide with the ideal value curve, which are both smaller than the output parameters of the uncontrolled vehicle. From the perspective of the motor output torque, compared with pure motor control, the effect of electro-hydraulic coordinated control is better, and the hydraulic system can compensate for the braking torque in time and enhance the lateral stability of the vehicle. The designed control strategy can make the yaw rate and the sideslip angle of the center of mass of the vehicle follow the reference value better, which can effectively avoid the vehicle sideslip and instability and improve the vehicle yaw stability and driving safety. However, due to the limitations of experimental equipment, the proposed method could not be applied to the real vehicle test. The real vehicle test can better test the control effect of the proposed method. [ABSTRACT FROM AUTHOR]

For multi-axle distributed drive (MADD) vehicles, the complexity of the longitudinal dynamics control system increases with the number of driven wheels, which presents a huge challenge to control the multi-motor drive vehicle with more than four wheels. To reduce the control system complexity, this paper proposes a coordinated slip control algorithm using the hierarchical linear quadratic regulator (HLQR) scheme for a 12 × 12 MADD vehicle. The 12-wheel driving system is decoupled based on the wheel load and simplified to a double local subsystem. First, the 12 × 12 MADD vehicle dynamics model is established. Then, the optimal slip ratio is obtained on the basis of the road friction coefficient estimation through a fuzzy control algorithm when the wheel slips. Afterwards, the wheel slip ratio is controlled based on the HLQR program for anti-slip regulation. Furthermore, the driving torque control allocation based on quadratic programming (QR) is coordinated with the anti-slip control. Simulink results show that the proposed coordinated slip control based on HLQR can improve slip control accuracy by more than 30% and greatly reduce the calculation load. The torque control allocation is also limited by the slip control results to ensure wheel dynamic stability and smoothly satisfy the driver’s demand.

The drive torque for each motor can be performed independently for the distributed drive electric wheel loader (DDEWL). On the shovelling condition, the torque distribution can be optimised according to the tire load. Then the parasitic power and wheel slippage can be reduced, and the tractive force and efficiency are improved. In this study, the singular value decomposition unscented Kalman filter is adopted to estimate the shovelling load. Based on the estimated shovelling load, the vertical tire force is calculated. The tire load rate is used to build the optimisation objective. The drive antiskid is set as the boundary condition. And a modified particle swarm optimisation is applied as the optimisation algorithm. A wheel loader is utilised for the testing data acquisition. Based on the acquired testing data, the comparative study about the optimisation and un-control strategies is operated. The results show that the slippage occurs less in the optimisation strategy, and the tractive force and efficiency are higher than the un-control strategy. In this study, the feasibility of the shovel load estimator is verified. The advantages of the optimisation strategy of distributed drive electric wheel loaders in shovelling process are analysed. [ABSTRACT FROM AUTHOR]

A multi-layer controller of direct yaw moment for electric vehicles is developed in this study. In the upper layer, the yaw moment are obtained using Adaptive Sliding Mode Control (ASMC) with adaptation gain to track the desired vehicle yaw rate. The corrective yaw moments are applied by four in-wheel electric motors. The lower layer controller consists of a torque distribution algorithm and inwheel motor torque controllers as well. The proposed torque distribution algorithm is intended to distribute the reference torques of each in-wheel motor controller appropriately based on both total longitudinal force and corrective yaw moment. To elucidate the effectiveness and robustness of the above control method, the simulation under various manoeuvres was carried out. A 7-DOF non-linear vehicle model is used for simulations and their results signify that the proposed control algorithm accomplishes a proper distribution of longitudinal force among four individual wheels, in turn, enhancing the yaw stability of the vehicle. [ABSTRACT FROM AUTHOR]

This paper addresses the design of a torque vectoring architecture to control the four electrical machines in a four-wheel-drive (4WD) formula-type competition vehicle. The scheme includes a new yaw-rate controller and a novel optimal torque distribution algorithm. Two yaw-rate controllers are proposed: one based on $\mathcal {H}_\infty$ optimal control and another based on linear parameter varying (LPV) system concepts. Both controllers are designed using an extended bicycle model validated with experimental data. Simulation results shown the effectiveness of the proposed overall control scheme in terms of energy efficiency, cornering speed and stability no matter the high-demanding working conditions. Such an effectiveness is quantitatively demonstrated by means of several key performance indicators chosen to ease the comparison of the proposed approach with respect to other reported works. [ABSTRACT FROM AUTHOR]

The market for electric vehicles is growing rapidly. Among them, the demand for a dual motor type 4 WD (Four -Wheel Driving) system is increasing. In this paper, we present the Torque Matching Strategy (TMS) method to select the optimal torque distribution ratio for dual motors. The TMS controller operates to set the optimal efficiency point by linearizing the drive efficiency combination of the two motors. Driving simulation and testing were performed through five drive cycles in the driver model interworking environment implemented in MATLAB and Carsim. The optimal distribution ratio was derived according to the front and rear gear ratios under the load condition, and the driving was verified by comparing it with the TMS control method. The efficiency was numerically verified by comparing the power loss of the driving motor. It reduced up to 34% in Urban Dynamometer Driving Schedule and up to 56.3% in Highway fuel efficiency test. The effectiveness of the TMS control method was demonstrated through the distribution rate trend based on the operation cycle and power loss. [ABSTRACT FROM AUTHOR]

Since the existing control strategy for electro-hydraulic composite braking (EHB) system is essentially "vehicle-centered", it is liable to cause incompatibility with current driving behavior, torque fluctuation and inconsistent braking feeling occur, which affects braking safety. In view of the above issue, a personalized MPC control strategy in accordance with the design methodology of characteristic E is presented. To do this, the data collection platform for characterizing driving behavior is constructed under typical vehicle-following conditions. Then, a generalized radial basis function (GRBF) neural network is adopted to accurately identify braking intensity of different driving behaviors. Next, an optimization model for the maximum energy recovery of EHB system is established in terms of required braking torque and motor speed, the distribution coefficient of braking torque is optimized by applying the adaptive particle swarm optimization (APSO) algorithm. Finally, the proposed personalized MPC control strategy is verified under different driving behaviors, the results display that: (1) the personalized MPC controller possess superiority of acquiring stable braking feeling, the torque tracking error is decreased by 96.8 %; (2) energy recovery for EHB system with optimized torque distribution is increased by 34.47 % under FTP-75 cycle conditions, and the response amplitude of braking feeling is increased by 5.6 %. • Braking torque distribution coefficients of EHB system is optimized for different driving behaviors. • The parameter setting issue of driving behaviors characteristics and MPC controller is addressed. • A personalized MPC controller in accordance with driving behavior characteristic E is presented. [ABSTRACT FROM AUTHOR]

Recently, there has been significant attention given to the electrification of transportation due to concerns about fossil fuel depletion and environmental pollution. Conventional drive systems typically include a clutch, reduction gear, and mechanical differential, which results in power loss, noise, vibration, and additional maintenance. However, in-wheel motor drive technology eliminates the need for these components, providing benefits such as higher system efficiency, improved wheel control, and increased passenger comfort. This article offers a comprehensive review of the technology and development of in-wheel motor drives. It begins with an overview of in-wheel motor drives in electric vehicles, followed by an exploration of the types of electric motors suitable for in-wheel motor drives. The paper then presents an industrial state of the art of in-wheel motors, comparing them with conventional motor drives, and reviews the implemented power electronics, control system, and cooling systems. Finally, the paper concludes by providing an outlook on the challenges and future trends of in-wheel drive systems.

To solve the coordination problem between the driving anti-skid strategy and the optimal torque distribution strategy, based on the real-time particle swarm optimization algorithm, a comprehensive torque distribution strategy with the consideration of stability and economy of vehicles was proposed. The driving anti-skid strategy with slip rate as control target could output the front and rear axle torque constraints. By the particle swarm optimization with real-time optimization as optimization method and with front and rear axle torque constraints as dynamic constraint condition, the optimal torque distribution strategy could run normally when the anti-skid strategy took effect. Based on AVL-Cruise and Simulink platform, a joint simulation model of vehicle economy was established to analyze the optimization effect. The results show that under the three selected cycle conditions, the power consumption per hundred kilometers is averagely decreased by 4. 79%, and the driving mileage is averagely increased by 5. 49%. The comprehensive torque distribution strategy can improve the fuel economy on the premise of ensuring the longitudinal stability of bus. [ABSTRACT FROM AUTHOR]

A dual-layer energy-efficient control strategy is proposed for distributed drive electric vehicles to reduce energy consumption during cornering maneuvers and improve vehicle stability. The hierarchical control strategy consists of two energy-saving schemes. The first-layer energy-efficient scheme combines an active front steering (AFS) system and direct yaw moment control (DYC) to improve vehicle stability and reduce total energy consumption. An extended Kalman filter observer is proposed to estimate lateral forces and side-slip angle. The second-layer energy-efficient scheme adopts a multiobjective driving/braking torque allocation algorithm to achieve minimum energy consumption while maintaining vehicle stability. A friction ellipse constraint and an actuator constraint are considered. Furthermore, the effect of the actuator restraint on the vehicle's yaw stability in a high-speed cornering maneuver is studied. Finally, the proposed control strategy is evaluated in MATLAB and the CarSim platform. The results demonstrate that the actuator constraint has a great influence on vehicle stability during high-speed cornering maneuvers. In addition, compared with a conventional control strategy, the dual-layer energy-efficient control strategy can reduce energy consumption by 29.5 % and 28.8 % for the dual lane change maneuver and high-speed snake steering maneuver, respectively. The proposed control strategy can effectively enhance the vehicle's handing performance during high-speed cornering maneuvers. [ABSTRACT FROM AUTHOR]

Active suspension control and direct yaw-moment control (DYC) are widely used in the vehicle control field. To solve the coupling between those two controllers, a coordinated control of active suspension and DYC is proposed to further improve the vehicle roll and yaw stability. To enhance the adaptive ability of the active suspension, a proportional integral control optimized by the genetic fuzzy algorithm is introduced. DYC is proposed based on the sliding mode control. To restrain the chattering, the parameters of the sliding mode control is optimized by a genetic algorithm. Finally, a coordinated controller is presented based on the adaptive distribution of the anti-roll torque in the front and rear suspension. The simulation results show that the proposed active suspension and DYC can greatly improve the roll and yaw stability, respectively. The expected vehicle status can be well tracked. In addition, the coordinated control is compared by simply using two independent controllers under a different tire–road friction coefficient and different steering maneuver. The results show that the coordinated control has an even better performance under each working condition.

To improve the steering sensitivity of four-wheel steering vehicle at high speed and enhance the stability of four-wheel steering vehicle on low friction road, the coordinated control system of active rear wheel steering（ARS） and four-wheel torque distribution（4 WTD） was designed based on the hierarchical coordinated closed-loop control strategy for ARS/4 WID vehicle. The ARS controller with feedforward and feedback was designed with vehicle centroid sideslip angle as control objective. The 4 WTD controller was designed with vehicle yaw rate and desired longitudinal speed as control objectives. The rule-based coordination controller was designed to distribute the working range of each sub controller reasonably. Establishing the CarSim/Simulink co-simulation platform, the designed coordinated control system was verified by simulation. The simulation results show that the designed coordinated control system can improve the performance of four-wheel steering vehicle. [ABSTRACT FROM AUTHOR]

The improvement of both the stability and economy of the four in-wheel motor drive (4IWMD) electric vehicle under complex drive cycles is currently a difficult problem in this field. A torque distribution method with the comprehensive goals of optimal torque distribution and energy efficiency, considering economy through energy efficiency for the 4IWMD electric vehicle, is proposed in this paper. Each component of the 4IWMD electric vehicle is modelled. The dynamic programming (DP) control algorithm is utilized for torque distribution between the front and rear in-wheel motors to obtain optimal torque distribution and energy efficiency in the 4IWMD electric vehicle. The simulation is performed on a co-simulation platform with the software of AVL Cruise and MATLAB/Simulink, considering a straight road. Compared to the fuzzy logic control algorithm, the simulation results are very promising, as the energy consumption of the electric vehicle was reduced by 22.68%, 20.73% and 21.84% under the WLTC, NEDC and customized IM240 driving cycle conditions, respectively, with the proposed DP control algorithm. The hardware-in-the loop (HIL) experimental results also indicate that the effectiveness of the proposed DP algorithm is verified under the NEDC, WLTC and IM240 driving cycles, when a straight road is considered. The proposed DP control algorithm not only reduces the vehicle energy consumption and guarantees the optimization of torque distribution, but also increases the driving range of the vehicle.

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