CN113093708B - Multi-signal fusion hub motor automobile torque distribution test system and prospective control method - Google Patents
Multi-signal fusion hub motor automobile torque distribution test system and prospective control method Download PDFInfo
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Abstract
The invention relates to a multi-signal fusion hub motor automobile torque distribution test system and a prospective control method. The invention comprises a hub motor automobile, a torque distribution controller, a road condition simulation belt, a load motor, a data acquisition system and a road condition simulation platform. During the test, the road condition simulation belt is loaded on the road condition simulation platform and is driven by the load motor, the wheel hub motor automobile is placed on the road condition simulation platform, the torque distribution controller is connected with the controller of the wheel hub motor, the state signal measured by the data acquisition system is transmitted to the upper computer for monitoring, and the upper computer adjusts the load motor and simulates the driving resistance. The vehicle torque distribution controller is preset with a prospective control method, a vehicle control algorithm can be modified, and a tester can test and optimize a vehicle control strategy according to test records. The invention solves the problems of low simulation verification accuracy of the control algorithm and high verification cost of the test vehicle, reduces the development cost of the control algorithm and has comprehensive test functions.
Description
Technical Field
The invention relates to the field of hub motor automobiles, in particular to a torque distribution algorithm test system and a torque distribution control method.
Background
In recent years, the problem of air pollution has become more serious, and electric vehicles have been developed as a means of transportation using clean energy. The four-wheel hub motor driven automobile has the obvious technical advantages of simple chassis arrangement, flexible control method and the like.
The torque output of four driving wheels of the hub motor automobile can be independently adjusted, and is different from the traditional two-wheel drive and four-wheel drive automobiles, so that the active safety control algorithm of the traditional automobile cannot be directly used for controlling the hub motor automobile, and the development of the hub motor automobile control algorithm is one of the difficulties in the field of hub motor automobiles. In the development process of the control algorithm, the verification of the control algorithm is a crucial step, and the traditional control algorithm verification mostly adopts simulation verification or a mode of verifying a test vehicle with an open interface in a test field. The simulation verification cost is low, but the simulation verification cost is not completely consistent with the actual vehicle condition, the validity of the vehicle control algorithm cannot be comprehensively verified, the verification of the test vehicle is high in accuracy, but the requirement on a test site is high, and the repeated test cost is high. The hub motor torque distribution test system designed by the invention has the advantages that the torque distribution control algorithm is verified through the indoor bench type test system, the problems of high test cost and high field requirement of a test vehicle are solved, and the accuracy is higher than that of simulation verification. Meanwhile, a vehicle torque distribution method based on multi-signal fusion and prospective control is preinstalled in the test system, accurate and anti-interference current vehicle state information is obtained through a multi-signal fusion module in the method, a torque distribution scheme in a future period is formulated by combining collected forward road surface images, a vehicle prospective control effect is achieved, and a control target is completed.
In conclusion, the method and the device solve the problem of verification of the control algorithm of the hub motor automobile, have important practical significance for development of the control algorithm of the hub motor automobile, and can improve the development of the control algorithm of the hub motor automobile to a certain extent.
Disclosure of Invention
The invention provides a hub motor automobile torque distribution test system based on multi-signal fusion, and aims to solve the problems that in the existing vehicle torque distribution algorithm verification, a simulation verification model cannot comprehensively represent an actual vehicle, the validity of a vehicle control algorithm cannot be comprehensively verified, the requirement on a verification field of a test vehicle is high, and the test cost is high.
In order to achieve the purpose, the invention adopts the following technical scheme: the hub motor torque distribution test experimental system based on multi-signal fusion comprises a road condition simulation platform, a torque distribution controller, a hub motor automobile and accessories thereof, a road condition simulation block, image acquisition equipment, a data acquisition system, an upper computer and a load motor. The road condition simulation platform is provided with a road condition simulation belt with the road condition simulation block and is driven by the load motor, the hub motor vehicle is placed on the road condition simulation platform, the torque distribution controller is communicated with a motor controller of the hub motor, a vehicle state signal measured by the data acquisition system is transmitted to the torque distribution controller for control decision, the vehicle state is transmitted to the upper computer for monitoring, and meanwhile, the upper computer adjusts the torque of the load motor according to the test working condition setting to simulate the driving resistance.
Furthermore, the road condition simulation platform is provided with four types of road condition simulation blocks which are respectively used for simulating four types of road surfaces of a gravel road section, a pebble road section, a wading road section and a cement road section.
Furthermore, a groove position for installing and fixing the iron blocks is formed in the frame of the hub motor automobile, and the load of the hub motor automobile to be tested can be adjusted by increasing or decreasing the number of the iron blocks, so that the hub motor automobile is suitable for tests under more conditions.
Furthermore, the vehicle controller is connected with the motor controller through a CAN bus to carry out information interaction. The motor controller controls the permanent magnet synchronous motor by adopting Field Oriented Control (FOC) and Space Vector Pulse Width Modulation (SVPWM), and the motor controller inputs 200V direct current power and outputs modulated three-phase current to the permanent magnet synchronous motor.
Furthermore, the load motor drives the road condition simulation belt to rotate, and the direction of the road condition simulation belt is opposite to the advancing direction of the vehicle. The long-time test can be carried out on the premise that the hub motor automobile does not slide down on the road condition simulation device.
Furthermore, the four wheels are respectively provided with an in-wheel motor, each in-wheel motor is independently controlled by a corresponding motor controller, and the motor controllers control the in-wheel motors after receiving instructions of the vehicle controllers.
Furthermore, the image acquisition equipment adopts a camera to acquire road surface images within 5 meters in front of the contact point between the wheels and the ground, the acquired image data are transmitted to the vehicle controller and processed by the image processing module, and the adhesion coefficient of the road surface within 3 meters in front is obtained.
Preferably, the four hub motors are permanent magnet synchronous motors which are cooled in a water cooling mode in the wheel.
Drawings
FIG. 1 is a schematic diagram of the apparatus of the present invention.
Fig. 2 is a functional block diagram of a vehicle torque distribution controller in the present invention.
Fig. 3 is a control flow chart of a look-ahead control algorithm preset in the controller according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.
The invention provides a hub motor automobile torque distribution experiment system which comprises a road condition simulation platform, a vehicle torque distribution controller, a hub motor automobile and accessories thereof, a road condition simulation block, image acquisition equipment, a data acquisition system, an upper computer and a load motor, wherein the hub motor automobile torque distribution experiment system is shown in figure 1. The road condition simulation platform comprises a bottom rack and a road condition simulation belt and is used for bearing the hub motor vehicle above, the road condition simulation blocks are four in number, each road condition simulation block is composed of a plurality of strip blocks, the road condition simulation blocks are installed and fixed on the road condition simulation belt at intervals and are in direct contact with the wheels of the hub motor vehicle, and the road condition simulation belt can rotate together under the driving of a load motor. And the upper computer receives the data of the acquisition system and controls the load motor. The hub motor automobile comprises a vehicle torque distribution controller, a motor controller, a frame, hub motor wheels, a loading iron block, a power supply and the like.
In this embodiment, the load motor drives the road condition simulation belt on the road condition simulation platform with a certain torque, so as to simulate rolling resistance, air resistance and gradient resistance during the driving process of the vehicle. The rotation linear speed of the road condition simulation belt is consistent with the vehicle speed, the road condition simulation belt advances in the opposite direction of the vehicle, and the road condition simulation belt is synchronously opened and closed, so that the hub motor vehicle is ensured not to slide down the road condition simulation belt, and a long-time vehicle test is carried out in a limited space. The image acquisition equipment is a camera, the camera for acquiring the front image is arranged in the middle of the bottom of the frame, and the image of the road surface 5 meters ahead of the wheel touch point is acquired.
In this embodiment, the in-wheel motor automobile runs on the road condition simulation belt according to the working condition input in the upper computer, and the stable speed of the automobile is realized through the adjustment of the rotating speed of the in-wheel motor. The four wheels of the hub motor are all provided with torque sensors, the suspension is provided with force sensors for measuring the load of each wheel, and the bottom of the frame is provided with a grating displacement sensor for measuring the speed of the vehicle relative to a road condition simulation belt.
In this embodiment, the driving parameters measured by the sensors include wheel torque, wheel load, vehicle speed, and a front road image, and these data are transmitted to the upper computer through the CAN bus for monitoring, and are transmitted to the vehicle torque distribution controller on the in-wheel motor vehicle through the CAN bus, and after being calculated by each module of the vehicle torque distribution controller, the wheel torque is adjusted, so as to achieve the purpose of controlling the slip ratio to be optimal.
In the embodiment, the person who carries out the test selects the test working condition through the human-computer interaction interface of the upper computer, and can also customize the test working condition according to the speed of each moment of the input vehicle. A torque distribution method based on look-ahead control is preset in the main control chip, a tester can write the torque distribution algorithm of the tester into a program frame of the main control chip, an interface between a program and hardware is well defined, and only a control algorithm part needs to be changed. And when the main control module detects that the corresponding hardware can carry out a corresponding test, starting the test bed. The main control module sends an operation instruction of the upper computer to the hub motor controller to control the rotating speed of the hub motor, the upper computer calculates the vehicle load at each moment, and the load simulation module is controlled to simulate the rolling resistance, the air resistance and the gradient resistance of the whole vehicle through controlling the exciting current of the load motor. Various vehicle operation parameters are collected by various sensors on the hub motor automobile, the vehicle operation parameters are transmitted to a vehicle controller for decision making, an optimal torque distribution signal is sent to a motor controller, and the hub motor torque is controlled through the motor controller. The main control module can acquire the operation signals of all the modules through the data acquisition module, and the upper computer performs real-time data display and data recording on the whole test process. The control strategy of the whole vehicle can be well tested and optimized by a tester according to the test record.
In this embodiment, as shown in fig. 2, the torque distribution controller of the distributed drive vehicle according to the embodiment of the present invention includes a core processor, a memory unit, an external interface, a switching element, a resistor capacitor, and a digital-to-analog/analog-to-digital converter in hardware, and the processor used by the controller is S32K1xx. Besides the self-writing control algorithm, a torque distribution control program is packaged in the processor, and the acquired image signals within 5 meters in front, the wheel rotating speed, the wheel torque, the wheel advancing speed (relative platform) and the wheel load information acquired by the data acquisition system are input and output to the wheel torques calculated by the controller. The control program software comprises a forward road condition analysis module, a multi-signal fusion processing module, a forward-looking control module and other auxiliary function modules. Each module bears different functions and jointly completes the look-ahead control based on look-ahead signals and multi-signal fusion.
In this embodiment, the wheel rotation speed, the torque, the forward speed (relative platform), and the load information acquired by the input data bit acquisition system of the control program are output as the torque of each wheel, and the control target is that the vehicle slip ratio is optimal, that is, the slip ratio is within a range of 10% to 20%. In each time step, the controller calculates the optimal torque of each wheel. The road condition analysis module obtains an adhesion coefficient of a road surface within 3 meters in front according to the transmitted road surface image within 5 meters in front; the multi-information fusion module is used for calculating and making decisions according to a plurality of transmitted signals to obtain an accurate current road surface adhesion coefficient and a current tire slip rate; the foresight control module obtains the best torque of each wheel at a plurality of moments in the future based on the foresight control algorithm according to the current road adhesion coefficient, the current tire slip ratio and the front road adhesion coefficient obtained by the two modules, and then sends the best torque at the current moment to the motor controller for execution.
In this embodiment, the front road condition analysis module obtains the adhesion coefficient of the two-dimensional road surface within 3 meters in front of the grounding point of the wheel hub motor vehicle through processing the image information of the road surface within 5 meters in front of the vehicle, which is transmitted from the image acquisition device. The step of obtaining the road surface adhesion coefficient information from the image information is as follows.
1. The method specifically comprises the following steps of intercepting acquired image signals within 5 meters to obtain image signals within 3 meters, and extracting road surface characteristic parameters from image information through a depth-first traversal algorithm: processing pixel points of the image, selecting a vertex in the image as an initial point of traversal, and performing recursive calling, wherein the depth-first search recursive calling comprises the following operations:
(1) Accessing the searched non-accessed adjacent points;
(2) Changing the access flag value of the vertex to be accessed;
(3) And (4) continuing to execute the operation (1), and if the adjacent point is marked as not accessed, starting to perform the same access and search from the adjacent point.
2. And comparing the extracted road surface characteristic parameters with a road surface information database to obtain road surface adhesion coefficient information within 3 meters ahead of the ground point of the vehicle.
In this embodiment, the multi-information fusion module receives signals of each sensor on the vehicle and performs unified processing. The multi-sensor signal fusion system achieves better functionality than the sum of the functions that can be achieved by the individual sensors processing independently. Different sensor types can be used for additionally providing certain redundancy under the condition that all one sensor fails, and the slip rate state information of the vehicle can still be measured. Through many signal fusion modules, based on a plurality of sensor signals, obtain accurate reliable in-wheel motor car's current wheel slip rate information.
In this embodiment, the multi-source signal fusion algorithm is a distributed parallel structure, and the signal fusion steps are as follows.
1. The used speed, moment and pressure sensors (different types) collect and observe the data of the detected hub motor automobile, and all the collected data are sent to the multi-signal fusion module.
2. The multi-signal fusion module performs feature extraction transformation on output data (discrete time function data) of the sensor by adopting a Principal Component Analysis (PCA) method, and extracts a feature vector representing observation data. The process of feature extraction is
(1) Carrying out standardization processing on an original d-dimensional data set;
(2) Constructing a covariance matrix of the sample;
(3) Calculating an eigenvalue of the covariance matrix and a corresponding eigenvector;
(4) Selecting the eigenvectors corresponding to the first k largest eigenvalues, wherein k is less than or equal to d;
(5) Constructing a mapping matrix W through the first k eigenvectors;
(6) The d-dimensional raw data is converted into k-dimensional feature subspace by means of a mapping matrix W.
3. The characteristic data of each sensor about the target are grouped, namely associated, according to the same target.
4. And processing the feature vectors based on a Bayesian algorithm, and fusing the data of each sensor of each target. The method comprises the following specific steps
(1) Taking the feature vector of each sensor as Bayesian estimation to obtain the associated probability distribution of each sensor data
(2) Synthesizing a combined posterior probability distribution function of fused data by correlating probability distributions
(3) And solving a fusion signal which enables the likelihood function of the joint distribution function to be minimum, namely a final fusion value of the multi-sensor information, namely an accurate current vehicle wheel slip rate parameter.
In this embodiment, the look-ahead control module is shown in fig. 3, and can predict future output according to an initial state of the vehicle and optimize a control quantity sequence of a whole stage from the present time to the future, and the steps are as follows.
1. Predictive model-prediction of output over a period of time in the future. In the present embodiment, the prediction model is based on a non-linear expression describing a function of the slip ratio with the state quantity (road surface adhesion coefficient, vehicle speed, front road surface adhesion coefficient) and the control quantity (torque of each wheel). The next-time slip ratio output is predicted from the control amount (each wheel torque) and the previous-time slip ratio output. Meanwhile, the present embodiment not only performs prediction based on the current state quantity, but also obtains a road adhesion coefficient in the front (i.e., in the future) in an image manner, and uses the road adhesion coefficient for predicting the slip ratio at each moment. And parameterizing the road adhesion coefficient in the prediction model based on the front road adhesion coefficient to obtain a variable self-adaptive prediction model, and obtaining a more accurate prediction result based on the self-adaptive prediction model. And forming closed loop optimization by using the feedback information.
In this embodiment, the time step of the prediction model is set to 0.02s, the prediction time domain is 100, the control time domain is also 100, at the initial time, the control quantity sequence at the next 100 times is input through the current initial time state prediction, and the slip ratio output sequence at the next 100 times (i.e., 2 s) is predicted based on the prediction model.
2. Rolling optimization-rolling performs finite time domain online optimization (optimal control). In this embodiment, in the rolling optimization process, an objective function is actually established with the optimal slip ratio as a target, so as to obtain an optimal control sequence that minimizes the objective function. The optimization objective function can be divided into two parts, the first part is the accumulated error, which is the norm of the difference between the predicted value and the target value at each moment, and the second part is the terminal error, which is the norm of the difference between the predicted final state and the target value. And adjusting the importance ratio of the two targets through the weight coefficient, and adjusting the control effect.
In this embodiment, the boundary problem solved by the optimization problem is considered in a constraint manner, and specific constraints include a maximum torque constraint and a constraint of a torque change rate (i.e., a torque difference between two moments in a prediction time domain). The constraint form is an inequality constraint, wherein the torque constraint is a hard constraint and the torque change rate constraint is a soft constraint, and the objective function is added in a penalty function form, and exceeding the constraint can enable the objective function value to obtain a maximum value.
In this example. The optimization procedure is as follows, firstly, an initial control quantity sequence is given, and therefore a corresponding slip rate output prediction sequence can be obtained. And then, iteration is carried out based on an optimization solving algorithm, and a group of control quantity sequences which enable the objective function to be minimum, namely an optimal torque distribution sequence, is found out. The torque in the sequence respectively represents the optimal torque in a control time domain (100 time steps) from the initial time, but only the first control quantity in the sequence is adopted in the control process, and the control sequence in the control time domain is obtained by re-optimizing the control sequence at the next time, and the first control quantity in the sequence is adopted for control.
3. Feedback correction-the prediction model is modified by the feedback of prediction error, so that the prediction precision is improved. And according to the difference value between the feedback of the controlled object and the prediction result, correcting the prediction result to obtain an accurate prediction value for optimization, obtaining a better torque distribution sequence and realizing closed-loop optimization.
In summary, by the prospective control method, the current torque distribution is obtained by solving a finite time domain optimal control problem at each sampling time, the prospective control method has the function of displaying the future dynamic behavior of the process, can realize arbitrary future control strategies, and observe the output changes of different control strategies in the process, thereby providing a basis for comparing the advantages and disadvantages of the control strategies, and obtaining the optimal control effect in the whole period from the current time to the predicted time. Through feedback correction, the accuracy of the whole prediction system can be improved, and errors in the prediction process can be eliminated. Through rolling optimization, global optimal control can be obtained under the influence of external interference, and a control target is completed.
Through the foresight control method in the embodiment, the vehicle control system can predict a severe slipping condition which may occur in advance by 2S according to the current sensor signal and the front road surface signal obtained by image acquisition and post-processing, and make control measures in advance. Meanwhile, the prediction can be fed back to the driver, so that the driver can conveniently make corresponding reactions such as braking, deceleration and the like in advance, and the safety of automobile driving is effectively improved. The prospective control algorithm belongs to the category of active safety control of the automobile. The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (6)
1. The multi-signal fused hub motor torque distribution test system comprises a road condition simulation platform (1), a torque distribution controller (2), a hub motor automobile and accessories (3) thereof, a road condition simulation block (4), image acquisition equipment (5), a data acquisition system (6), an upper computer (7) and a load motor (8), wherein a road condition simulation belt with the road condition simulation block (4) is arranged on the road condition simulation platform (1) and is driven by the load motor (8), the hub motor automobile (3) is placed on the road condition simulation platform (1), the torque distribution controller (2) is connected with a motor controller of the hub motor in a wheel, a state signal measured by the data acquisition system (6) is transmitted to the torque distribution controller (2) for control, and data is transmitted to the upper computer (7) for monitoring, and meanwhile, the upper computer (7) adjusts the working condition of the load motor (8) according to the input test working condition to simulate resistance;
the method is characterized in that:
the torque distribution controller (2) comprises a core processor, a storage unit, an external interface, an external crystal oscillator, a switching element, a resistance capacitor, a digital-analog/analog-digital converter, a processor used by the controller is S32K1xx, a torque distribution control program embedded and packaged in the processor is input into an image signal collected within 3 meters ahead, and wheel rotating speed, torque, forward speed and load information collected by the data collection system (6), wherein the forward speed is relative to the forward speed of a platform and is output to each wheel torque calculated by the controller, the control program comprises a forward road condition analysis module, a multi-signal fusion processing module and a forward-looking control module, the external interface receives road surface images from the image collection device within 5 meters ahead of a wheel grounding point, and information such as the wheel rotating speed and the torque, the forward road condition analysis module obtains an adhesion coefficient of a two-dimensional road surface within 5 meters ahead of the wheel grounding point through processing of the image information of the road surface, the multi-signal fusion processing module receives signals on the vehicle for uniform processing, the multi-signal fusion processing module can obtain an accurate and reliable state information of the forward-ground point, and obtains an optimal road surface adhesion coefficient by a next-time distribution control method based on the next-round rolling-time distribution control sequence, and the optimal road surface distribution control method for obtaining an optimal forward-time distribution control sequence;
the prospective control method can predict future output and optimize the control quantity sequence of the whole current to future stage, and comprises the following steps: (1) Prediction model-prediction of output over a period of time in the future; (2) Rolling optimization-rolling performs finite time domain online optimization; (3) Feedback correction-by predicting error feedback, modifying the prediction model to improve the prediction accuracy;
obtaining automobile operation parameters through each sensor, and carrying out comprehensive decision and calculation on data of each sensor by adopting a multi-source signal fusion algorithm based on a Bayesian fusion theory to obtain current slip rate state parameters of the automobile, wherein the method comprises the following steps: the method comprises the steps of (1) acquiring data for observing a tested hub motor vehicle by using speed, moment and pressure sensors, wherein the pressure sensors comprise all sensors of different types, (2) performing feature extraction transformation on output data of the sensors, and the output data of the sensors are discrete time function data, and extracting feature vectors representing observation data, (3) grouping, namely associating description data of the sensors about targets according to the same target, and (4) processing the feature vectors based on a Bayesian algorithm, and synthesizing the sensor data of each target to obtain consistency explanation and description of the target.
2. The in-wheel motor torque distribution testing system of claim 1, wherein: the four wheels of the wheel hub motor automobile (3) are all provided with wheel hub motors, the four wheel hub motors are controlled and driven by four independent motor controllers, the wheel hub motor automobile is provided with front and rear suspension frames, a loading frame is connected above the suspension frames, and the vertical load of the automobile can be adjusted by increasing or decreasing iron blocks on the loading frame.
3. The in-wheel motor torque distribution testing system of claim 1, wherein: road conditions simulation platform (1) surface cover road conditions simulation area, inside has the piece that pulls that is used for the drive, the outside is the road conditions simulation block that is used for simulating the road surface that has different attachment rates, road conditions simulation block (4), its characterized in that: different materials are adhered to the surface of the road condition simulation block (4), and the simulated road conditions of the blocks comprise a gravel road section, a pebble road section, a wading road section and a smooth cement road section.
4. The in-wheel motor torque distribution testing system of claim 1, wherein: the rotating speed and the torque of the load motor (8) are controlled according to signals sent by the upper computer, the rotating speed of the load motor (8) drives the road condition simulation belt to rotate, so that the speed of the road condition simulation belt is equal to or slightly slower than the advancing speed of the hub motor automobile, and the load motor (8) and the hub motor realize synchronous switching, so that long-time test can be carried out on the premise that the hub motor automobile does not slide down on the road condition simulation device.
5. The in-wheel motor torque distribution testing system of claim 1, wherein: data acquisition system (6) are including wheel angular velocity sensor, torque sensor, data transmission line, camera, through fixing in-wheel motor car the place ahead the camera acquires 5 meters interior road surface image signal in the place ahead, through torque, rotational speed, the speed of moving, the load parameter signal of vehicle operation are gathered to the sensor, spread into torque distribution controller (2) and are used for control to it monitors the signal to spread into host computer (7).
6. The in-wheel motor torque distribution testing system of claim 1, wherein: the road condition analysis module comprises an image processing program and an adhesion rate estimation program, the image acquisition equipment acquires a front road surface image, converts an optical signal into an electric signal firstly and further into a digital signal capable of being processed by a computer, the image processing equipment extracts road surface characteristic parameters from image information through a depth-first traversal algorithm, and estimates a road surface adhesion coefficient by comparing a database to obtain the road surface adhesion coefficient in front of the grounding point of the wheel by 3 meters.
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| CN113791598B (en) * | 2021-07-29 | 2024-04-26 | 哈尔滨理工大学 | Four-wheel moment distribution ring testing device under extreme working condition and torque optimizing method |
| CN113910919B (en) * | 2021-10-27 | 2023-06-06 | 东风越野车有限公司 | Wheel end synchronous step target torque control method, system and medium for hub motor vehicle |
| CN116945913B (en) * | 2023-07-18 | 2024-03-12 | 石家庄铁道大学 | Synchronous differential drive control algorithm for hub motor of railway vehicle |
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