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Based on the analysis of the overall structure of the existing hub motor used for electric bicycles, aiming at the problems of instability and large noise when the inner rotor of the hub motor adopts the left and right half shaft structure, a hub motor structure of the through shaft inner rotor and reduction device is improved and designed. Based on the optimization design theory, the minimum total volume of the reduction device is taken as the objective function to optimize. Creo software is used to draw the optimized 3D model of the reducer, and finite element analysis is carried out by Ansys Workbench software to check the strength and stiffness of the structure. The results show that the new design overcomes the defects of the left and right half shaft in the electric bicycle hub motor, the volume of the reducer is reduced by 8.6% after optimization, and the structural design strength and stiffness meet the requirements, which verifies the rationality of the structure, and provides a new idea and theoretical basis for the electric bicycle hub motor structure design and optimization.

A hub motor is integrated into an electric wheel. The external excitation is complex and the heat dissipation conditions are poor. The working temperature of the hub motor easily becomes too high, resulting in large fluctuations in the output torque, which affect its service life. Taking a four-wheel hub-driven electric vehicle as the research object and aiming to resolve the issue of inaccurate prediction of the output torque of the hub motor in the real operating environment of the vehicle, a method for analyzing the temperature rise and torque characteristics of the hub motor considering multisource excitation and magnetic–thermal bidirectional coupling is proposed. First, the multisource excitation transmission path of the hub motor and the coupling principle of the road-electric wheel-vehicle body suspension system are analyzed from three aspects: the electromagnetic effect of the hub motor itself, the tire-ground effect, and the interaction between suspension (body) and electric wheel. We concluded that the load torque and air gap change in the motor are the key factors of its torque characteristics. On this basis, a dynamic model of the road-electric wheel-suspension-vehicle body system, an electromagnetic field model of the hub motor, and a temperature field model are established, and the influence of load torque and air gap change on the loss of in-wheel motor under multisource excitation is analyzed. Furthermore, based on the magnetic–thermal bidirectional coupling method, the motor loss under the combined action of load torque and air gap change is introduced into the temperature field model, and combined with the electromagnetic field model of the hub motor, the temperature distribution law and torque characteristics of the hub motor are accurately predicted. Finally, the accuracy and effectiveness of the calculation results of the temperature and torque characteristics of the hub motor are verified via an electric wheel bench test. [ABSTRACT FROM AUTHOR]

Flexible operation and high handling stability are often required in the distributed electric drive mobile platform in special scenarios of facility horticulture. This study aims to design a mobile platform with distributed electric driven independently by four-wheel hub motors. An adaptive anti-slip control strategy was also proposed to improve the steering flexibility and stability. The dynamic model and Ackermann differential steering model were constructed for the distributed electric drive mobile platform. The target steering speed of each wheel was determined to combine the instantaneous center of speed and tire side deflection angle. Then, the time-varying adhesion coefficient was selected to improve the adaptability of the distributed electric drive mobile platform. A complex road identifier was designed using strong tracking adaptive untracked Kalman filter, in order to accurately estimate the road adhesion coefficient. Finally, an anti-slip controller was obtained with adaptive sliding mode. The optimal wheel slip rate was determined, according to the estimated road adhesion coefficient and control the wheel slip rate in real time, Finally, the self-adaptive anti-slip control was realized for the drive wheel of the distributed electric drive mobile platform in the facility horticulture scene. Both Carsim-MATLAB/Simulink co-simulation and vehicle tests of the distributed horticultural electric drive mobile platform were carried out to verify the effectiveness of the control strategy. The simulation results show that the estimated errors of the wheel to road adhesion coefficient were 0.009 and 0.033 on the unchanged and docked road surfaces, respectively, while 0.01 and 0.007 on the opposite road surfaces for left and right wheel, respectively; When using anti-slip control, the slip rate errors of the left wheel were 0.031, 0.015, and 0.038, respectively, and the slip rate errors of the right wheel were 0.026, 0.005, and 0.028, respectively. The test results show that the maximum slip rates of the left front, right front, left rear, and right rear wheels without anti-slip control were 0.80, 0.85, 0.90, and 0.93, respectively, indicating severe wheel slip. Under the same road conditions, there was a significant deviation in the wheel slip rate between adjacent moments, resulting in the slipping at all times unsuitable for the stable driving of the mobile platform. The adaptive anti-skid control with road recognition can be expected to accurately estimate the wheel road adhesion coefficient on complex roads, thus reducing the error of wheel slip rate. The overall estimated values were around 0.44 and 0.47 for the four-wheel road adhesion coefficient after the stable starting of the mobile platform. The maximum slip rates of the controlled wheel under two tested road conditions were approximately 0.69 and 0.68, respectively. The tire slip was greatly reduced during turning, in order to effectively improve the driving stability of the mobile platform. [ABSTRACT FROM AUTHOR]

Electric vehicles with hub motors have integrated the motor into the wheel, which increase the unsprung mass of the vehicle, and intensifies the vibration of the underspring components. The motor excitation during driving also intensifies the wheel vibration. The coupling effect between the two makes the performance of electric vehicles deteriorate. The article employed a disc-type permanent-magnet motor as the hub motor, taking into consideration the increase in sprung mass caused by the hub motor and the adverse effects of vertical vibration from motor excitation. Based on random road-surface excitation, and considering the secondary excitation caused by wheel motor drive and vehicle-road coupling, a coupled-dynamics model of a semi-active-suspension vehicle-road system for vertical vehicle motion is investigated under multiple excitations. Using body acceleration, suspension deflection, and dynamic tire load as evaluation indicators, a BP neural network PID controller based on the sparrow search algorithm optimization is proposed for the semi-active-suspension system. Compared with PID control and particle swarm optimization (PSO-BPNN-PID), the research findings indicate that the optimized semi-active suspension significantly improves the ride comfort of hub-motor electric vehicles, and meets the requirements for control performance under different vehicle driving conditions. [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.)

Due to the narrow working space of an in-wheel motor, the heat generated by the motor loss is difficult to dissipate. This makes it easier for the in-wheel motor to demagnetize the permanent magnet due to the mega-temperature, which affects the output efficiency. To solve this problem, an external rotor hub motor is studied. First, in accordance with the theory of magnetic field modulation, the in-wheel motor to be studied is designed. By analyzing the electromagnetic characteristics of the motor, the correctness of the motor design is verified, and the losses of the motor under different working conditions are calculated. To acquire a more rigorous temperature increase record, the magnetic-thermal bidirectional coupling method is utilized to analyze the temperature field under different load conditions. The mechanism of the demagnetization of permanent magnets is analyzed, and demagnetization at different temperatures is obtained by magneto-thermal two-way coupling. Research shows that when the motor is overloaded for a long time, the temperature can reach a maximum of 220 °C. At this temperature, the permanent magnet undergoes irreversible demagnetization, which results in a 93.44% decrease in torque. Finally, temperature increase tests of a permanent magnet motor are carried out to verify the validity of the magneto-thermal two-way coupling analysis. [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.)

Temperature rise of the hub motor in distributed drive electric vehicles (DDEVs) under long-time and overload operating conditions brings parameter drift and degrades the performance of the motor. A novel online parameter identification method based on improved teaching-learning-based optimization (ITLBO) is proposed to estimate the stator resistance, ��-axis inductance, ��-axis inductance, and flux linkage of the hub motor with respect to temperature rise. The effect of temperature rise on the stator resistance, ��-axis inductance, ��-axis inductance, and magnetic flux linkage is analysed. The hub motor parameters are identified offline. The proposed ITLBO algorithm is introduced to estimate the parameters online. The Gaussian perturbation function is employed to optimize the TLBO algorithm and improve the identification speed and accuracy. The mechanisms of group learning and low-ranking elimination are established. After that, the proposed ITLBO algorithm for parameter identification is employed to identify the hub motor parameters online on the test bench. Compared with other parameter identification algorithms, both simulation and experimental results show the proposed ITLBO algorithm has rapid convergence and a higher convergence precision, by which the robustness of the algorithm is effectively verified. Keywords: parameters identification, teaching–learning-based optimization, hub motor, temperature rise.

Copyright of Journal of Shanghai University of Engineering Science / Shanghai Gongcheng Jishu Daxue Xuebao is the property of Journal of Shanghai University of Engineering Science 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.)

In this study, E-bike, called pedelec due to being driven via a pedal and electric excitation is investigated experimentally based on energy consumption at defined urban road profiles under full electrical mode without pedal usage. The digital twin (DT) model is created based on the powertrain system model to estimate the consumed instant electrical power dependend on voltage, current, torque, and road type classifications. To achieve these targets, a data logging system is designed and programmed to measure consumed electrical power as current and voltage inputs. Road profiles are divided into the asphalt and graded gravel at different slope levels. As next step, the obtained energy consumption data is used in powertrain modeling to create physical E-bike model, which is the input for neural network model representing the DT model. This novel test methodology provides the approaches of energy consumption and driving range calculation in E-bike powertrain system modelling. [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]

Electric vehicles with hub motors have integrated the motor into the wheel, which increase the unsprung mass of the vehicle, and intensifies the vibration of the underspring components. The motor excitation during driving also intensifies the wheel vibration. The coupling effect between the two makes the performance of electric vehicles deteriorate. The article employed a disc-type permanent-magnet motor as the hub motor, taking into consideration the increase in sprung mass caused by the hub motor and the adverse effects of vertical vibration from motor excitation. Based on random road-surface excitation, and considering the secondary excitation caused by wheel motor drive and vehicle-road coupling, a coupled-dynamics model of a semi-active-suspension vehicle-road system for vertical vehicle motion is investigated under multiple excitations. Using body acceleration, suspension deflection, and dynamic tire load as evaluation indicators, a BP neural network PID controller based on the sparrow search algorithm optimization is proposed for the semi-active-suspension system. Compared with PID control and particle swarm optimization (PSO-BPNN-PID), the research findings indicate that the optimized semi-active suspension significantly improves the ride comfort of hub-motor electric vehicles, and meets the requirements for control performance under different vehicle driving conditions.

The design and development of a rear-mounted power assist system specifically made for manual wheelchairs are the main objectives of this project. This invention's main goal is to reduce physical strain on people who have restricted upper body strength or movement, improving their independent mobility and general quality of life. The power assist wheels are designed to make maneuvering through difficult environments such as inclines and uneven surfaces easier. These types of terrain provide challenges for traditional manual wheelchairs. The goal of using this technology is to provide a solution that enhances the mobility of the disabled while simultaneously fostering exclusivity and accessibility, which benefits society as a whole.

An adaptive cruise system was proposed based on hierarchical control strategy with an electric vehicle driven by hub motor as research object. Combined with the upper model predictive controller and the lower proportion integral differential（PID） controller, the driving torque output by the hub motor was precisely controlled under complex longitudinal following conditions. Based on the front vehicle acceleration, the variable headway of vehicle spacing strategy was proposed, and the model predictive control（MPC） algorithm was used to solve the expected acceleration of the upper controller. By PID algorithm, the vehicle front axle driving moment was solved to be entered into the lower wheel hub motor controller for realizing front and driving moment distribution, and finally realizing the vehicle longitudinal adaptive cruise. A co-simulation model was established to verify the proposed adaptive cruise hierarchical control strategy for scenarios of uniform forward speed, emergency braking and urban cycle conditions. The results show that the proposed adaptive cruise system control strategy has good car following effect under longitudinal complex driving conditions with small vehicle spacing error, and the acceleration change and motor drive torque change can be synchronized and responded well. An adaptive cruise system was proposed based on hierarchical control strategy with an electric vehicle driven by hub motor as research object. Combined with the upper model predictive controller and the lower proportion integral differential（PID） controller, the driving torque output by the hub motor was precisely controlled under complex longitudinal following conditions. Based on the front vehicle acceleration, the variable headway of vehicle spacing strategy was proposed, and the model predictive control（MPC） algorithm was used to solve the expected acceleration of the upper controller. By PID algorithm, the vehicle front axle driving moment was solved to be entered into the lower wheel hub motor controller for realizing front and driving moment distribution, and finally realizing the vehicle longitudinal adaptive cruise. A co-simulation model was established to verify the proposed adaptive cruise hierarchical control strategy for scenarios of uniform forward speed, emergency braking and urban cycle conditions. The results show that the proposed adaptive cruise system control strategy has good car following effect under longitudinal complex driving conditions with small vehicle spacing error, and the acceleration change and motor drive torque change can be synchronized and responded well. [ABSTRACT FROM AUTHOR]

A hub motor is an effective drive system for Battery Electric Vehicles (BEVs). However, due to limitations on packaging and cost, there are few applications in which hub motors are taken as the only actuators for a brake vehicle. Most applications involve a Regenerative Braking System (RBS) combined with a Hydraulic Braking System (HBS). In this paper, a top hierarchy Advanced Emergency Braking System (AEBS) controller is designed in Matlab/Simulink and State-flow, including functionalities of basic emergency braking, brake force distribution between front and rear wheels, anti-lock braking and coordination between RBS and HBS based on Model Predictive Control (MPC); a Seven Degrees of Freedom (DOF) BEV chassis model is constructed and rear-end crash test scenarios are created in Carsim with a high and low road adhesion coefficient. A series of comparison tests show that not only are the stopping distances between the ego vehicle and target vehicle shorter, but also the braking torques, longitudinal slip ratio and rotation speed of each wheel are well controlled without wheel locking. To sum up, in addition to meeting the AEBS requirements of avoiding a rear-end collision, the control strategy developed in this paper also levels up braking performance and enhances vehicle stability on both high-mu and low-mu roads for BEVs driven by a hub motor independently. [ABSTRACT FROM AUTHOR]

The effect of CO2 emission on the environment and climate change and the scarcity of fossil fuels is a global issue nowadays. To resolve this issue, the world is trying to switch from fuel vehicles to electric vehicles. But it is taking too long due to many limitations of electric vehicles. So, to resolve the limitations, various research projects are currently being conducted, on different parts of the electric vehicle. For best performance and high efficiency and torque, BLDC motors are used. The outer rotor BLDC motors provide very high efficiency. Still, the bikes available in the market have quite low speed or are very costly. So, it is important to increase the speed at a cheaper price. And for that, the In-Wheel motor is the best option. The In-Wheel bike with a motor is significantly different from a petrol (or center motor drive) motorbike as the engine (or center motor) is separate from the wheel as any heating of the engine/ motor would not heat the wheels but in the case of the In-Wheel motor, heating of the motor would produce a similar type of heating in the wheels. If the temperatures and the total magnetic flux density of the In-Wheel motor go beyond normal values, then there would be a chance that the electric bike will be heated at a very high temperature and may catch fire too. Also, the pressure on the wheel will affect the performance of the motor. So, it is essential to properly analyze and calculate the temperatures and pressures at different points of the motor. Hence, in this work, a novel approach of the E-Magnetic model, Lab model, Thermal model, and Mechanical model are analyzed properly using Ansys Motor -CAD. The Thermal model is used for getting the best temperatures of various parts of the In-Wheel motor. And the mechanical model is used to get the best pressure at the different points of the motor. [ABSTRACT FROM AUTHOR]

Aiming at the negative vibration effect caused by the increased unsprung mass of the hub motor-air suspension (HM-AS) vehicle and the unbalanced magnetic force (UMF) of the hub motor, a linear quadratic regulator (LQR) air suspension control strategy based on a genetic algorithm is proposed. Considering the coupling effect of road surface excitation and UMF, the model of the HM-AS vehicle is established. Then, according to optimal control theory and the genetic algorithm, the LQR controller is designed to suppress the vibration of the HM-AS vehicle, and the weight matrix of the LQR controller is optimized through the genetic algorithm. The simulation results show that the proposed LQR control strategy effectively improves the ride comfort and motor safety of the HM-AS vehicle. [ABSTRACT FROM AUTHOR]

When a four-wheel-drive hybrid electric vehicle (HEV) with dual clutch transmission (DCT) is decelerating while turning, the vehicle often transits from front-wheel hybrid mode to electric mode of the rear hub motors, in order to improve the energy efficiency. However, the mode transition process is accompanied by the driving torque front-to-rear axis conversion and the wheel load transfer. And the mode transition process involves not only the torque coordinated control of the multi-power sources, but also the differential control of the left and right rear hub motors. If not properly controlled, it will cause a large jerk and the vehicle to be unstable. In this paper, a seven degrees of freedom (DOF) of vehicle model integrated with DCT powertrain system is introduced to reflect its lateral and longitudinal dynamic characteristics in the mode transition process. The optimal control strategy considering the differential distribution of the rear hub motors' torque is proposed based on sliding-mode control (SMC). Simulation and hardware-in-the-loop (HIL) test results show that the proposed mode transition control strategy can not only achieve smooth front- to rear-end driving torque switching and reduce the jerk 5.7%, but also improve the tracking performance of side slip angle and the yaw rate. [ABSTRACT FROM AUTHOR]

A hub motor is an effective drive system for Battery Electric Vehicles (BEVs). However, due to limitations on packaging and cost, there are few applications in which hub motors are taken as the only actuators for a brake vehicle. Most applications involve a Regenerative Braking System (RBS) combined with a Hydraulic Braking System (HBS). In this paper, a top hierarchy Advanced Emergency Braking System (AEBS) controller is designed in Matlab/Simulink and State-flow, including functionalities of basic emergency braking, brake force distribution between front and rear wheels, anti-lock braking and coordination between RBS and HBS based on Model Predictive Control (MPC); a Seven Degrees of Freedom (DOF) BEV chassis model is constructed and rear-end crash test scenarios are created in Carsim with a high and low road adhesion coefficient. A series of comparison tests show that not only are the stopping distances between the ego vehicle and target vehicle shorter, but also the braking torques, longitudinal slip ratio and rotation speed of each wheel are well controlled without wheel locking. To sum up, in addition to meeting the AEBS requirements of avoiding a rear-end collision, the control strategy developed in this paper also levels up braking performance and enhances vehicle stability on both high-mu and low-mu roads for BEVs driven by a hub motor independently.

The energy-efficient, clean, and quiet attributes of electric vehicles offer solutions to conventional challenges related to resource scarcity and environmental pollution. Consequently, thorough research into harmonizing energy recuperation during braking, enhancing vehicle stability, and ensuring occupant comfort in electric vehicles is imperative for their effective advancement. The study introduces a regenerative braking control strategy for electric vehicles founded on game theory optimization to enhance braking performance and optimize braking energy utilization. Develop a regenerative braking control approach based on the dynamic model of an electric vehicle equipped with hub motors. Employing game theory, we establish participants, control variables, strategy sets, benefit functions, and constraints to optimize the coefficient K for regenerative braking. The efficacy and superiority of the control strategy model are validated through joint simulations using Matlab/Simulink and AVL Cruise. Research findings indicate: (1) Speed tracking error remains below 3% in both NEDC and CLTC-P simulations, underscoring the effectiveness of the dynamic model and control strategy devised in this study. (2) The energy recovery rate achieved by the game theory-based optimization strategy surpasses that of the Cruise self-contained strategy and fuzzy control strategy by 18.06% and 4.5% in the NEDC simulation, and by 13.48% and 3.85% in the CLTC-P simulation, respectively. The adhesion coefficient curves implemented on the front and rear axles, derived from the game theory optimization control strategy, closely approximate the ideal adhesion coefficient curve, leading to a substantial enhancement in the car's braking stability. The degree of jerk magnitude regulated by the game theory optimization strategy consistently falls within the ±3 m/s³ threshold, resulting in a considerable enhancement in the comfort of vehicle occupants. These outcomes underscore the efficacy of the game theory-based optimized control strategy in enhancing energy recovery, braking stability, and comfort throughout the braking process of the vehicle., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Due to the insulation aging, demagnetization and other problems of the permanent magnet and insulating material in the permanent magnet synchronous hub motor under high temperature, the numerical simulation of the heat dissipation of surface mounted permanent magnet synchronous hub motor is proposed. According to the heat transfer of hub motor, the effect degree of heat conduction, heat convection and heat radiation is obtained, the heat transfer coefficient of each part is calculated, and the influence of motor insulation material on temperature rise is analyzed. The experimental results show that the heat dissipation of hub motor under natural cooling condition is poor, and the internal oil cooling method can effectively improve the heat dissipation of hub motor and reduce the temperature difference. When operating at high speed, this reduces the potential safety hazard. [ABSTRACT FROM AUTHOR]

Nowadays, Electric powered vehicles are the most efficient mode of transport. Electric motors are rotating the shaft of any vehicle or in the case of the hub motor the wheel itself. In this project, we are working on a single electric-powered wheel that will be used at multiple places with some easy adjustments in clamping. Our electrically powered wheel can improve the mobility of people with disabilities, as an electric scooter and for various other purposes. An electric hub motor is used in a wheel with which the battery and all the electric circuits are placed around the handlebar for easy accessibility. This will reduce man efforts, to make all these operations work through one mechanism. When this wheel is clamped to its other attachments, a new functional use of this electric powered wheel can be put to use. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control 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.)

Aiming at the negative vibration effect caused by the increased unsprung mass of the hub motor-air suspension (HM-AS) vehicle and the unbalanced magnetic force (UMF) of the hub motor, a linear quadratic regulator (LQR) air suspension control strategy based on a genetic algorithm is proposed. Considering the coupling effect of road surface excitation and UMF, the model of the HM-AS vehicle is established. Then, according to optimal control theory and the genetic algorithm, the LQR controller is designed to suppress the vibration of the HM-AS vehicle, and the weight matrix of the LQR controller is optimized through the genetic algorithm. The simulation results show that the proposed LQR control strategy effectively improves the ride comfort and motor safety of the HM-AS vehicle.

In hub-motor electric vehicles (HM-EVs), the unbalanced electromagnetic force generated by the HM will further deteriorate the dynamic performance of the electric vehicle. In this paper, a semiactive suspension control method is proposed for HM-EVs. A quarter HM-EV model with an electromechanical coupling effect is established.The model consists of three parts: a motor model, road excitation model and vehicle model. A hybrid model predictive controller (HMPC) is designed based on the developed model, taking into account the nonlinear constraints of damping force. The focus is on improving the vertical performance of the HM-EV. Then, a Kalman filter is designed to provide the required state variables for the controller. The proposed control algorithm and constrained optimal control (COC) algorithm are simulation compared under random road excitation and bump road excitation, and the results show that the proposed control algorithm can improve ride comfort, reduce motor vibration, and improve handling stability more substantially. [ABSTRACT FROM AUTHOR]

This paper presents a new design of omnidirectional automatic guided vehicle based on a hub motor, and proposes a joint controller for path tracking. The proposed controller includes two parts: a fuzzy controller and a multi-step predictive optimal controller. Firstly, based on various steering conditions, the kinematics model of the whole vehicle and the pose (position, angle) model in the global coordinate system are introduced. Secondly, based on the modeling, the joint controller is designed. Lateral deviation and course deviation are used as the input variables of the control system, and the threshold value is switched according to the value of the input variable to realise the correction of the large range of posture deviation. Finally, the joint controller is implemented by using the industrial PC and the self-developed control system based on the Free scale minimum system. Path tracking experiments were made under the straight and circular paths to test the ability of the joint controller for reducing the pose deviation. The experimental results show that the designed guided vehicle has excellent ability to path tracking, which meets the design goals [ABSTRACT FROM AUTHOR]

Congested road network and traffic jam lead to vehicle idling and fuel wastage. An E-bike which is a solution to this is often available with Thumb Throttle Mechanism and single speed operation. A motor controller with multiple output voltage levels and Click Throttle Mechanism is presented in this paper which provides variable speed operation, better power utilization and is highly economical. Proposed modified KY converter utilizes the turns ratio of an On-Line Tap Changer (OLTC) to generate multiple output voltages for variable speed commutation. This possess a stable continuous conduction mode throughout its operation and reduced switching transients when compared to the existing topologies. Mutually independent control variables namely turns ratio and duty ratio, are used here for voltage conversion, which facilitates the flexible transition between multiple output voltage levels. A 15 W prototype of the proposed modified inductor coupled KY converter is realized using Arduino Uno Board. [ABSTRACT FROM AUTHOR]

The optimal control strategy for the decoupling of drive torque is proposed for the problems of runaway and driving stability in straight-line driving of electric vehicles driven by four-wheel hub motors. The strategy uses a hierarchical control logic, with the upper control logic layer being responsible for additional transverse moment calculation and driving anti-slip control; the middle control logic layer is responsible for the spatial motion decoupling for the underlying coordinated distribution of the four-wheel drive torque, on the basis of which the drive anti-skid control of a wheel motor-driven electric vehicle that takes into account the transverse motion of the whole vehicle is realized; the lower control logic layer is responsible for the optimal distribution of the driving torque of the vehicle speed following control. Based on the vehicle dynamics software Carsim2019.0 and MATLAB/Simulink, a simulation model of a four-wheel hub motor-driven electric vehicle control system was built and simulated under typical operating conditions such as high coefficient of adhesion, low coefficient of adhesion and opposing road surfaces. The research shows that the wheel motor drive has the ability to control the stability of the whole vehicle with large intensity that the conventional half-axle drive does not have. Using the proposed joint decoupling control of the transverse pendulum motion and slip rate as well as the optimal distribution of the drive force with speed following, the transverse pendulum angular speed and slip rate can be effectively controlled with the premise of ensuring the vehicle speed, thus greatly improving the straight-line driving stability of the vehicle.

In hub-motor electric vehicles (HM-EVs), the unbalanced electromagnetic force generated by the HM will further deteriorate the dynamic performance of the electric vehicle. In this paper, a semiactive suspension control method is proposed for HM-EVs. A quarter HM-EV model with an electromechanical coupling effect is established.The model consists of three parts: a motor model, road excitation model and vehicle model. A hybrid model predictive controller (HMPC) is designed based on the developed model, taking into account the nonlinear constraints of damping force. The focus is on improving the vertical performance of the HM-EV. Then, a Kalman filter is designed to provide the required state variables for the controller. The proposed control algorithm and constrained optimal control (COC) algorithm are simulation compared under random road excitation and bump road excitation, and the results show that the proposed control algorithm can improve ride comfort, reduce motor vibration, and improve handling stability more substantially.

The compound position sensorless control technology of hub motor is studied, and the switching method of the speed transition interval is improved to increase the reliability of drive system in electric drive armored vehicle. A permanent magnet synchronous motor （PMSM）control system based on double closed loops of torque and current was built, for which the maximum torque per ampere （MTPA）control below the base speed and the flux-weakening control above the base speed are used. A simplified high frequency pulse injection method is used at low speed, and a model reference adaptive system （MRAS） is used at medium and high speeds to realize the rotor position identification in the full speed range. The variable weight weighted control switching method is adopted, and the working interval selection of the two algorithms is improved in the process of algorithm switching. The system hardware and software resources are greatly saved while assuring the identification accuracy. The simulated and experimental results show that the proposed sensorless algorithm can identify the speed and rotor position accurately in the full speed range, and the new switching method enables the motor to achieve smooth transition in the speed switch interval and ensures the reliable operation of motor. [ABSTRACT FROM AUTHOR]

The lateral stability control of tractor semi-trailer plays a vital role for enhancing its driving safety, and the distributed electric drive structure of a hub motor creates opportunities and challenges for realising the lateral stability accurately. Based on the dynamics simulation software TruckSim, a nonlinear dynamic tractor semi-trailer model is established, and a MATLAB/Simulink linear three-degree-of-freedom monorail reference model is established. The upper controller adopts fuzzy proportional–integral–derivative control to export active yaw torque values of the tractor and semi-trailer. The lower controller outputs the driving/braking torque of each wheel according to the target wheel driving/braking rules and torque distribution rules. The tractor produce an active yaw torque through conventional differential braking the hub motor is installed on both sides of the semi-trailer, and the active yaw torque is produced by the coordinated control of the driving/braking torque of the hub motor and the differential braking of the mechanical braking system. To prevent wheel locking, the slip rate of each wheel is controlled. Finally, based on the TruckSim–MATLAB/Simulink cosimulation platform, cosimulation is performed under typical working conditions. The simulation results show that the control strategy proposed in this report is superior to the conventional differential braking control (ESP). It can not only improve the lateral stability of the vehicle more effectively, but also improve the roll stability.

Ａｉｍｉｎｇ ａｔ ｓｏｌｖｉｎｇ ｔｈｅ ｐｒｏｂｌｅｍ ｏｆ ｔｈｅ ｓｔａｂｉｌｉｔｙ ｃｏｎｔｒｏｌ ｏｆ ｄｉｓｔｒｉｂｕｔｅｄ ｅｌｅｃｔｒｉｃ ｖｅｈｉｃｌｅｓ ｕｎｄｅｒ ｈｉｇｈ￣ｓｐｅｅｄ ｄｒｉｖｉｎｇ ｃｏｎｄｉｔｉｏｎｓꎬ ｔｈｉｓ ｐａｐｅｒ ｓｔｕｄｉｅｄ ｔｈｅ ｒｅｆｅｒｅｎｃｅ ｍｏｄｅｌ ｏｆ ｄｉｓｔｒｉｂｕｔｅｄ ｄｒｉｖｅ ｅｌｅｃｔｒｉｃ ｖｅｈｉｃｌｅꎬ ｆｕｚｚｙ ＰＩ ｃｏｎｔｒｏｌꎬ ｖｅｈｉｃｌｅ ｄｙｎａｍｉｃｓ ｐｌａｎｎｉｎｇꎬ ｃｏｏｒｄｉｎａｔｅｄ ｃｏｎｔｒｏｌ ｓｔｒａｔｅｇｙ ｏｆ ｍｏｔｏｒ/ ｈｙｄｒａｕｌｉｃ ｓｙｓｔｅｍꎬ ｏｐｔｉｍａｌ ｃｏｎｔｒｏｌ ｄｉｓｔｒｉｂｕｔｉｏｎ ｍｅｔｈｏｄ ａｎｄ ｓｏ ｏｎ. Ｍｏｒｅｏｖｅｒꎬ ｔｈｅ ｃｏｎｔｒｏｌ ｓｔｒａｔｅｇｉｅｓ ｏｆ ｅｌｅｃｔｒｏ￣ｈｙｄｒａｕｌｉｃ ｓｔａｂｉｌｉｔｙ ｏｆ ｄｉｓｔｒｉｂｕ￣ ｔｅｄ ｄｒｉｖｅ ｅｌｅｃｔｒｉｃ ｖｅｈｉｃｌｅｓ ｗｅｒｅ ｓｕｍｍａｒｉｚｅｄ ａｎｄ ａ ｖｅｈｉｃｌｅ ｓｔａｂｉｌｉｔｙ ｃｏｎｔｒｏｌ ｓｙｓｔｅｍ ｂａｓｅｄ ｏｎ ｔｈｅ ｃｏｏｐｅｒａｔｉｖｅ ｃｏｎｔｒｏｌ ｏｆ ｈｕｂ ｍｏｔｏｒ/ ｈｙｄｒａｕｌｉｃ ｂｒａｋｅ ｓｙｓｔｅｍ ｗａｓ ｐｒｏｐｏｓｅｄ. Ｉｎ ａｄｄｉｔｉｏｎꎬ ｔｈｅ ｄｙｎａｍｉｃｓ ｍｏｄｅｌ ｏｆ ｄｉｓｔｒｉｂｕｔｅｄ ｄｒｉｖｅ ｅｌｅｃｔｒｉｃ ｖｅｈｉｃｌｅ ｗａｓ ｅｓｔａｂｌｉｓｈｅｄ ｕｓｉｎｇ Ｃａｒｓｉｍ ａｎｄ ｔｈｅ ｃｏｏｐｅｒａｔｉｖｅ ｃｏｎｔｒｏｌ ｓｔｒａｔｅｇｙ ｏｆ ｍｏｔｏｒ/ ｈｙｄｒａｕｌｉｃ ｗａｓ ｄｅｓｉｇｎｅｄ ｂｙ Ｓｉｍｕｌｉｎｋ. Ａｎｄ ｔｈｅｎ ｔｈｅ ｓｉｎｕｓｏｉｄａｌ ｓｔａｇｎａｔｉｏｎ ｓｔｅｅｒｉｎｇ ｔｅｓｔ ｗａｓ ｃａｒｒｉｅｄ ｏｕｔ ｏｎ ｔｈｅ ＣａｒＳｉｍ/ Ｓｉｍｕｌｉｎｋ ｃｏ￣ｓｉｍｕｌａｔｉｏｎ ｐｌａｔｆｏｒｍ. Ｔｈｅ ｒｅｓｕｌｔｓ ｉｎｄｉｃａｔｅ ｔｈａｔ ｖｅｈｉｃｌｅｓ ｗｉｔｈｏｕｔ ｃｏｎｔｒｏｌ ｏｒ ｏｎｌｙ ｗｉｔｈ ｍｏｔｏｒ ｃｏｎｔｒｏｌ ｕｎａｂｌｅ ｔｏ ｍａｉｎｔａｉｎ ｓｔａｂｉｌｉｔｙ ｕｎｄｅｒ ｅｘｔｒｅｍｅ ｃｏｎｄｉｔｉｏｎｓꎬ ｗｈｉｌｅ ｔｈｅ ｓｔｅｅｒｉｎｇ ｓｔａｂｉｌｉｔｙ ｏｆ ｔｈｅ ｖｅｈｉｃｌｅ ｃａｎ ｂｅ ｅｎｓｕｒｅｄ ｗｉｔｈ ｔｈｅ ｃｏｏｐｅｒａｔｉｖｅ ｃｏｎｔｒｏｌ ｏｆ ｍｏｔｏｒ/ ｈｙｄｒａｕｌｉｃ ｂｒａｋｅ ｓｙｓｔｅｍ. [ABSTRACT FROM AUTHOR]

The typical motor has poor heat dissipation conditions that are limited by the installation space and working environment, and the high operation temperature increase has been a bottleneck to improve the power density and torque density. The inner rotor motor is considered to be the research object, and an oil-cooling structure for end winding and stator core is proposed. The heat inside the motor is mainly carried away through the lubricating oil in the form of heat conduction and convection heat transfer. The 3d motor model was built using the ANSYS software. The temperature field of the motor was simulated to analyze the temperature distribution inside the motor under rated and peak working conditions. The low-speed high-torque test and one-hour temperature-rise test of the motor prototype were performed on a bench built in the laboratory. The comparison between the test results with water-cooled motor shows that the temperature-rise rate of oil-cooled motor with the same electromagnetic structure is slower than that of water-jacketed cooled motor, and the temperature difference between the front and back of the motor decreases by 18 °C in half an hour. The oil-cooled method has a good cooling effect on the stator core and works for longer time under rated conditions.