CN106026826A - Networked measuring and controlling method for electric vehicle drive motor working condition matching control effectiveness - Google Patents
Networked measuring and controlling method for electric vehicle drive motor working condition matching control effectiveness Download PDFInfo
- Publication number
- CN106026826A CN106026826A CN201610625342.1A CN201610625342A CN106026826A CN 106026826 A CN106026826 A CN 106026826A CN 201610625342 A CN201610625342 A CN 201610625342A CN 106026826 A CN106026826 A CN 106026826A
- Authority
- CN
- China
- Prior art keywords
- sensor
- electric vehicle
- driving motor
- sensors
- networked
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
- H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
- H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention discloses a networked measuring and controlling method for electric vehicle drive motor working condition matching control effectiveness. Traveling paths are simulated, the operation working condition is set, an alternating current-direct current-alternating current power conversion circuit and an SVPWM working condition matching control method are adopted to construct a driving motor closed-loop system with constant power and constant torque output under variable working conditions, and the driving motor closed-loop system is used for simulating the overall dynamic performance in the actual operation process of an electric vehicle; a multisensor fusion and networking technology is adopted to collect and process key information, of electric, mechanical and interference types, for operation of a driving system, a load simulating system and a power source system, a curve model of power, efficiency, rotating speed and mechanical characteristics is set up, dynamic performance of the driving system under different working conditions is analyzed comprehensively, and the system performance of an upper computer is evaluated comprehensively based on the networking form of the CAN bus. The method has a positive effect on improving motor performance and driving system performance and improving control strategies.
Description
Technical field
The invention belongs to vehicle technology field, particularly relate to a kind of driving motor of electric vehicle Conditions Matching and control both effectiveness
Networked Supervising method.
Background technology
Electric motor car, i.e. electrically driven vehicle, have another name called electricity and drive.Electric motor car is divided into alternating current electric vehicle and DC electric car.Logical
The electric motor car often said is using battery as energy source, by the parts such as controller, motor, converts electrical energy into mechanical energy fortune
Dynamic, to control the vehicle of size of current change speed.
Summary of the invention
It is an object of the invention to provide a kind of driving motor of electric vehicle Conditions Matching and control both effectiveness Networked Supervising
Method, it is intended to solve electric motor car in prior art and control the unstability of speed and accuracy not by simple mechanically actuated
Enough problems.Making method for designing become highly versatile, workload is little, convenient and swift.
The present invention is achieved in that driving motor of electric vehicle Conditions Matching controls both effectiveness Networked Supervising method,
Described driving motor of electric vehicle Conditions Matching controls both effectiveness Networked Supervising method simulated driving path and arranges operation
Operating mode, uses power transformation circuit and the SVPWM Conditions Matching control method of AC-DC-AC, builds permanent merit under a variable working condition
Rate and the driving motor closed loop system of permanent torque output, for simulating the overall dynamics in electric motor car actual moving process
Can, use Multi-sensor Fusion and networking technology, to drive system, load simulation system, the electric class of power-supply system operation, machine
Tool class and interference class key message are acquired processing, and set up power, efficiency, rotating speed and mechanical characteristic model,
The comprehensive drive system dynamic property analyzed under different operating modes, and networking form based on CAN, carried out at host computer
The overall merit of systematic function;
Described sensor acquisition parameter includes: specified phase current, specified rotor speed, number of pole-pairs, rotary inertia, stator phase
Resistance;
Described driving motor closed loop system, this system includes that biphase static coordinate is tied to the conversion mould of biphase rotating coordinate system
Block, biphase rotational coordinates are tied to the conversion module of biphase rest frame, the conversion of three phase coordinate systems to biphase rest frame
Module, three inverter modules, SVPWM module, flux observation module, phase current standardization gain, phase current standardization gain,
Rotor rotating speed standardization gain, setting speed standardization gain, setting standardization gain, control voltage increase against standardization
Benefit, control voltage are against standardization gain, PI controller.
Further, described power curve model is:
P is active power, unit kW, and v is driving motor of electric vehicle rotating speed.
Further, described speed curves model is:
yp(k)=ω φ (x (k))+b;
Etching system input quantity when x (k) is k, ypEtching system output when () is k k, ω is weight coefficient;φ () is non-thread
Property function.
Further, the measurement model of described sensor is as follows:
YA(tk-1)、YA(tk)、YA(tk+1) it is respectively sensors A to target at tk-1,tk,tk+1The local Descartes in moment sits
Measuring value under mark system, is respectively as follows:
Wherein, Y'A(tk-1)、Y'A(tk)、Y'A(tk+1) it is respectively sensors A at tk-1,tk,tk+1The local Descartes in moment
Actual position under coordinate system;CAT () is the transformation matrix of error;ξAT () is the systematic error of sensor;For system noise
Sound, it is assumed thatFor zero-mean, separate Gaussian stochastic variable, noise covariance matrix is respectively
For RA(k-1)、RA(k)、RA(k+1)。
Further, described sensor is provided with registration module, and the method for registering of described registration module includes:
In same timeslice, each sensor observation data are carried out increment sequence by certainty of measurement, then by sensors A
Observation data respectively to the time point interpolation of sensor B, extrapolation, to form a series of equally spaced target observation data, adopt
Sensors A is obtained at t with the interpolation extrapolation temporal registration algorithm that carries out of 3 conventional parabolic interpolationsBkTime be engraved in local right angle
Measuring value under coordinate systemFor:
Wherein, tBkFor registration moment, tk-1,tk,tk+1For sensors A distance registration moment nearest three sampling instants,
YA(tk-1),YA(tk),YA(tk+1) be respectively its correspondence the detection data to target;
After deadline registration, according to registration data and the sampled data of sensor B of sensors A, use based on the earth's core
Pseudo-measurement method under body-fixed coordinate system realizes the estimation of the systematic error of sensors A and sensor B;Systematic error based on ECEF
Algorithm for estimating particularly as follows:
K moment target actual position under local rectangular coordinate system is X'1(k)=[x'1(k),y'1(k),z'1(k)]T, polar coordinate
The lower corresponding measuring value of system isIt is respectively distance, azimuth, the angle of pitch;Conversion is sat to local right angle
Mark system is lower for X1(k)=[x1(k),y1(k),z1(k)]T;Sensing system deviation is
It is respectively distance, azimuth and the systematic error of the angle of pitch;Then have
WhereinRepresenting observation noise, average is zero, variance is
Formula (1) is launched by first approximation and is write as matrix form:
Wherein,
Two sensors A and B, then for same public target, if under ECEF coordinate system being X'e=[x'e,y'e,
z'e]T, can obtain:
X'e=XAs+BAX'A1(k)=XBs+BBX'B1(k)\*MERGEFORMAT (4)
BA, BBIt is respectively target position under sensors A with sensor B local coordinate system to be transformed under ECEF coordinate system
Transition matrix during position;
Definition puppet measures and is:
Z (k)=XAe(k)-XBe(k)\*MERGEFORMAT (5)
Wherein, XAe(k)=XAs+BAXA1(k);XBe(k)=XBs+BBXB1(k)
Formula (2), formula (3) are substituted into formula (4) and obtain the pseudo-measurement about sensor bias
Z (k)=H (k) β (k)+W (k) * MERGEFORMAT (6)
Wherein, H (k) [-BACA(k)BBCB(k)], Z (k) is pseudo-measurement vector;H (k) is calculation matrix;β is that sensor is inclined
Difference vector;W (k) is for measuring noise vector;Due to nA(k),nBK () is zero-mean, separate Gaussian stochastic variable, because of
This W (k) is zero-mean gaussian type stochastic variable equally, and its covariance matrix is R (k).
Further, described sensor is provided with covering distributed module, and the mathematical model of described covering distributed module is:
ST:0≤si.start≤l,i∈N (2)
si.end-si.start=bi,i∈N (3)
Wherein C is total effective cover time, and l is each time taken turns, biIt is node siDuring work in each wheel
Between.
The driving motor of electric vehicle Conditions Matching that the present invention provides controls both effectiveness Networked Supervising method, have cured electricity
Motor-car drives the core of controller to control program, and the parameter different controlled motors being had only to revise to controller then can be real
The now control to different motors, the stable operation to driving controller has great benefit, can reduce the repetition of designer
Property work, improve electric machine controller design efficiency.The present invention uses interpolation extrapolation temporal registration algorithm to achieve sensor and adopts
The synchronization of sample data, and establish the pseudo-measurement side unrelated with target state according to the result of interpolation extrapolation temporal registration
Journey, uses spatial registration algorithm based on ECEF to achieve the spatial registration of asynchronous sensor.Foundation due to pseudo-measurement equation
Process is only relevant to target location and unrelated with parameters such as target speeds, and therefore the present invention proposes asynchronous sensor space
Registration Algorithm can effectively solve the asynchronous sensor spatial registration problem under the conditions of target maneuver.
Accompanying drawing explanation
Fig. 1 is that the driving motor of electric vehicle Conditions Matching that the embodiment of the present invention provides controls both effectiveness Networked Supervising side
Law vector Control System Design block diagram.
1, Park conversion module;2, Park inverse transform module;3, Clark conversion module;4, three inverter modules;5、
SVPWM module;6, flux observation module;7, phase current standardization gain;8, phase current standardization gain;9, rotor rotating speed
Standardization gain;10, setting speed standardization gain;11, standardization gain is set;12, voltage is controlled against standardization gain;
13, voltage is controlled against standardization gain;14, PI controller.
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and advantage clearer, below in conjunction with embodiment, to the present invention
It is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not used to
Limit the present invention.
Below in conjunction with the accompanying drawings the application principle of the present invention is explained in detail.
The present invention is with the high-effect control of Conditions Matching of driving motor of electric vehicle and on-line testing and evaluation driveability
Both effectiveness grade is the main starting point of research, by electrical type load simulation travelings such as eddy-current brake, energy feedback load motors
Path and arrange operating condition, uses power transformation circuit and the SVPWM Conditions Matching control method of AC-DC-AC, builds one
Under individual variable working condition, invariable power and the driving motor closed loop system of permanent torque output, be used for simulating electric motor car actual moving process
In overall dynamics performance, use Multi-sensor Fusion and networking technology, to drive system, load simulation system, power supply system
The electric class of the operations such as system, the mechanical and interference key message such as class are acquired processing, and set up power, efficiency, rotating speed with
And the curve model such as mechanical property, the comprehensive drive system dynamic property analyzed under different operating modes is, and net based on CAN
Network form, carries out the overall merit of systematic function, and provides corresponding directiveness suggestion, for improving motor at host computer
Energy, drive system performance and improvement control strategy etc. play a positive role.
Described sensor acquisition parameter includes: specified phase current, specified rotor speed, number of pole-pairs, rotary inertia, stator phase
Resistance.Described driving motor closed loop system, this system include biphase static coordinate be tied to biphase rotating coordinate system conversion module,
Biphase rotational coordinates is tied to the conversion module of biphase rest frame, the conversion mould of three phase coordinate systems to biphase rest frame
Block, three inverter modules, SVPWM module, flux observation module, phase current standardization gain, phase current standardization gain, electricity
Machine rotor rotating speed standardization gain, setting speed standardization gain, set standardization gain, control voltage against standardization gain,
Control voltage against standardization gain, PI controller.
Further, described power curve model is:
P is active power, unit kW, and v is driving motor of electric vehicle rotating speed.
Further, described speed curves model is:
yp(k)=ω φ (x (k))+b;
Etching system input quantity when x (k) is k, ypEtching system output when () is k k, ω is weight coefficient;φ () is non-thread
Property function.
Further, the measurement model of described sensor is as follows:
YA(tk-1)、YA(tk)、YA(tk+1) it is respectively sensors A to target at tk-1,tk,tk+1The local Descartes in moment sits
Measuring value under mark system, is respectively as follows:
Wherein, Y'A(tk-1)、Y'A(tk)、Y'A(tk+1) it is respectively sensors A at tk-1,tk,tk+1The local Descartes in moment
Actual position under coordinate system;CAT () is the transformation matrix of error;ξAT () is the systematic error of sensor;For system noise
Sound, it is assumed thatFor zero-mean, separate Gaussian stochastic variable, noise covariance matrix is respectively
For RA(k-1)、RA(k)、RA(k+1)。
Further, described sensor is provided with registration module, and the method for registering of described registration module includes:
In same timeslice, each sensor observation data are carried out increment sequence by certainty of measurement, then by sensors A
Observation data respectively to the time point interpolation of sensor B, extrapolation, to form a series of equally spaced target observation data, adopt
Sensors A is obtained at t with the interpolation extrapolation temporal registration algorithm that carries out of 3 conventional parabolic interpolationsBkTime be engraved in local right angle
Measuring value under coordinate systemFor:
Wherein, tBkFor registration moment, tk-1,tk,tk+1For sensors A distance registration moment nearest three sampling instants,
YA(tk-1),YA(tk),YA(tk+1) be respectively its correspondence the detection data to target;
After deadline registration, according to registration data and the sampled data of sensor B of sensors A, use based on the earth's core
Pseudo-measurement method under body-fixed coordinate system realizes the estimation of the systematic error of sensors A and sensor B;Systematic error based on ECEF
Algorithm for estimating particularly as follows:
K moment target actual position under local rectangular coordinate system is X'1(k)=[x'1(k),y'1(k),z'1(k)]T, pole
Measuring value corresponding under coordinate system isIt is respectively distance, azimuth, the angle of pitch;Conversion is to local right angle
It is X under coordinate system1(k)=[x1(k),y1(k),z1(k)]T;Sensing system deviation is
It is respectively distance, azimuth and the systematic error of the angle of pitch;Then have
WhereinRepresenting observation noise, average is zero, variance is
Formula (1) is launched by first approximation and is write as matrix form:
X'1(k)=X1(k)+C(k)[ξ(k)+n(k)]\*MERGEFORMAT (3)
Wherein,
Two sensors A and B, then for same public target, if under ECEF coordinate system being X'e=[x'e,y'e,
z'e]T, can obtain:
X'e=XAs+BAX'A1(k)=XBs+BBX'B1(k)\*MERGEFORMAT (4)
BA, BBIt is respectively target position under sensors A with sensor B local coordinate system to be transformed under ECEF coordinate system
Transition matrix during position;
Definition puppet measures and is:
Z (k)=XAe(k)-XBe(k)\*MERGEFORMAT (5)
Wherein, XAe(k)=XAs+BAXA1(k);XBe(k)=XBs+BBXB1(k)
Formula (2), formula (3) are substituted into formula (4) and obtain the pseudo-measurement about sensor bias
Z (k)=H (k) β (k)+W (k) * MERGEFORMAT (6)
Wherein, H (k) [-BACA(k)BBCB(k)], Z (k) is pseudo-measurement vector;H (k) is calculation matrix;β is that sensor is inclined
Difference vector;W (k) is for measuring noise vector;Due to nA(k),nBK () is zero-mean, separate Gaussian stochastic variable, because of
This W (k) is zero-mean gaussian type stochastic variable equally, and its covariance matrix is R (k).
Further, described sensor is provided with covering distributed module, and the mathematical model of described covering distributed module is:
ST:0≤si.start≤l,i∈N (2)
si.end-si.start=bi,i∈N (3)
Wherein C is total effective cover time, and l is each time taken turns, biIt is node siDuring work in each wheel
Between.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all essences in the present invention
Any amendment, equivalent and the improvement etc. made within god and principle, should be included within the scope of the present invention.
Claims (6)
1. a driving motor of electric vehicle Conditions Matching controls both effectiveness Networked Supervising method, it is characterised in that described electricity
Motor-car drives motor Conditions Matching control both effectiveness Networked Supervising method simulated driving path and arrange operating condition, adopts
By power transformation circuit and the SVPWM Conditions Matching control method of AC-DC-AC, build under a variable working condition invariable power and
The driving motor closed loop system of permanent torque output, for simulating the overall dynamics performance in electric motor car actual moving process, uses
Multi-sensor Fusion and networking technology, to drive system, load simulation system, power-supply system run electric class, mechanical with
And interference class key message is acquired processing, and set up power, efficiency, rotating speed and mechanical characteristic model, total score
Analyse the drive system dynamic property under different operating modes, and networking form based on CAN, carry out systematicness at host computer
The overall merit of energy;
Described sensor acquisition parameter includes: specified phase current, specified rotor speed, number of pole-pairs, rotary inertia, stator are mutually electric
Resistance;
Described driving motor closed loop system, this system include biphase static coordinate be tied to biphase rotating coordinate system conversion module,
Biphase rotational coordinates is tied to the conversion module of biphase rest frame, the conversion mould of three phase coordinate systems to biphase rest frame
Block, three inverter modules, SVPWM module, flux observation module, phase current standardization gain, phase current standardization gain, electricity
Machine rotor rotating speed standardization gain, setting speed standardization gain, set standardization gain, control voltage against standardization gain,
Control voltage against standardization gain, PI controller.
2. driving motor of electric vehicle Conditions Matching as claimed in claim 1 controls both effectiveness Networked Supervising method, and it is special
Levying and be, described power curve model is:
P is active power, unit kW, and v is driving motor of electric vehicle rotating speed.
3. driving motor of electric vehicle Conditions Matching as claimed in claim 1 controls both effectiveness Networked Supervising method, and it is special
Levying and be, described speed curves model is:
yp(k)=ω φ (x (k))+b;
Etching system input quantity when x (k) is k, ypEtching system output when () is k k, ω is weight coefficient;φ () is non-linear letter
Number.
4. driving motor of electric vehicle Conditions Matching as claimed in claim 1 controls both effectiveness Networked Supervising method, and it is special
Levying and be, the measurement model of described sensor is as follows:
YA(tk-1)、YA(tk)、YA(tk+1) it is respectively sensors A to target at tk-1,tk,tk+1The local cartesian coordinate system in moment
Under measuring value, be respectively as follows:
Wherein, Y'A(tk-1)、Y'A(tk)、Y'A(tk+1) it is respectively sensors A at tk-1,tk,tk+1The local cartesian coordinate in moment
Actual position under Xi;CAT () is the transformation matrix of error;ξAT () is the systematic error of sensor;For system noise, vacation
IfFor zero-mean, separate Gaussian stochastic variable, noise covariance matrix is respectively RA(k-
1)、RA(k)、RA(k+1)。
5. driving motor of electric vehicle Conditions Matching as claimed in claim 1 controls both effectiveness Networked Supervising method, and it is special
Levying and be, described sensor is provided with registration module, and the method for registering of described registration module includes:
In same timeslice, each sensor observation data are carried out increment sequence by certainty of measurement, then by the sight of sensors A
Survey data are respectively to the time point interpolation of sensor B, extrapolation, to form a series of equally spaced target observation data, often use
3 parabolic interpolations carry out interpolation extrapolation temporal registration algorithm obtain sensors A at tBkTime be engraved in local rectangular coordinate
Measuring value under XiFor:
Wherein, tBkFor registration moment, tk-1,tk,tk+1For three sampling instants that the sensors A distance registration moment is nearest, YA
(tk-1),YA(tk),YA(tk+1) be respectively its correspondence the detection data to target;
After deadline registration, according to registration data and the sampled data of sensor B of sensors A, use based on ground heart solid
Pseudo-measurement method under coordinate system realizes the estimation of the systematic error of sensors A and sensor B;Systematic error estimation based on ECEF
Algorithm particularly as follows:
K moment target actual position under local rectangular coordinate system is X'1(k)=[x'1(k),y'1(k),z'1(k)]T, polar coordinate
The lower corresponding measuring value of system isIt is respectively distance, azimuth, the angle of pitch;Conversion is sat to local right angle
Mark system is lower for X1(k)=[x1(k),y1(k),z1(k)]T;Sensing system deviation is
It is respectively distance, azimuth and the systematic error of the angle of pitch;Then have
WhereinRepresenting observation noise, average is zero, variance is
Formula (1) is launched by first approximation and is write as matrix form:
X'1(k)=X1(k)+C(k)[ξ(k)+n(k)] \*MERGEFORMAT (3)
Wherein,
Two sensors A and B, then for same public target, if under ECEF coordinate system being X'e=[x'e,y'e,z'e
]T, can obtain:
X'e=XAs+BAX'A1(k)=XBs+BBX'B1(k) \*MERGEFORMAT (4)
BA, BBIt is respectively target position under sensors A with sensor B local coordinate system and is transformed into the position under ECEF coordinate system
Time transition matrix;
Definition puppet measures and is:
Z (k)=XAe(k)-XBe(k) \*MERGEFORMAT (5)
Wherein, XAe(k)=XAs+BAXA1(k);XBe(k)=XBs+BBXB1(k)
Formula (2), formula (3) are substituted into formula (4) and obtain the pseudo-measurement about sensor bias
Z (k)=H (k) β (k)+W (k) * MERGEFORMAT (6)
Wherein, H (k) [-BACA(k) BBCB(k)], Z (k) is pseudo-measurement vector;H (k) is calculation matrix;β be sensor bias to
Amount;W (k) is for measuring noise vector;Due to nA(k),nBK () is zero-mean, separate Gaussian stochastic variable, therefore W
K () is zero-mean gaussian type stochastic variable equally, its covariance matrix is R (k).
6. driving motor of electric vehicle Conditions Matching as claimed in claim 1 controls both effectiveness Networked Supervising method, and it is special
Levying and be, described sensor is provided with covering distributed module, and the mathematical model of described covering distributed module is:
ST:0≤si.start≤l,i∈N (2)
si.end-si.start=bi,i∈N (3)
Wherein C is total effective cover time, and l is each time taken turns, biIt is node siWorking time in each wheel.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610625342.1A CN106026826A (en) | 2016-08-03 | 2016-08-03 | Networked measuring and controlling method for electric vehicle drive motor working condition matching control effectiveness |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610625342.1A CN106026826A (en) | 2016-08-03 | 2016-08-03 | Networked measuring and controlling method for electric vehicle drive motor working condition matching control effectiveness |
Publications (1)
Publication Number | Publication Date |
---|---|
CN106026826A true CN106026826A (en) | 2016-10-12 |
Family
ID=57134205
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201610625342.1A Pending CN106026826A (en) | 2016-08-03 | 2016-08-03 | Networked measuring and controlling method for electric vehicle drive motor working condition matching control effectiveness |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN106026826A (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106549617A (en) * | 2016-11-23 | 2017-03-29 | 深圳市道通智能航空技术有限公司 | The control system of permagnetic synchronous motor, control method and unmanned vehicle |
CN111337827A (en) * | 2020-03-16 | 2020-06-26 | 邵阳学院 | Motor measurement and control system and method based on wireless sensor network |
CN112910374A (en) * | 2021-03-10 | 2021-06-04 | 苏州汇川联合动力***有限公司 | Method for optimizing inverter modulation strategy and motor control equipment |
CN113177291A (en) * | 2021-03-29 | 2021-07-27 | 浙江中车电车有限公司 | Electric vehicle efficacy analysis method and system based on cloud data platform |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101729019A (en) * | 2008-10-31 | 2010-06-09 | 发那科株式会社 | Electric motor driving device and motor driving method |
CN102739149A (en) * | 2011-04-08 | 2012-10-17 | 戴政 | Dynamic control method for exciting current during motor vector control |
WO2015003619A1 (en) * | 2013-07-09 | 2015-01-15 | Shenzhen Byd Auto R&D Company Limited | Motor control system of electric vehicle and controlling method for motor control system of electric vehicle and electric vehicle |
EP2890001A1 (en) * | 2012-08-24 | 2015-07-01 | Hitachi Automotive Systems, Ltd. | Motor driving circuit, motor driving system, electric power steering system, electric brake system, and vehicle driving system |
-
2016
- 2016-08-03 CN CN201610625342.1A patent/CN106026826A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101729019A (en) * | 2008-10-31 | 2010-06-09 | 发那科株式会社 | Electric motor driving device and motor driving method |
CN102739149A (en) * | 2011-04-08 | 2012-10-17 | 戴政 | Dynamic control method for exciting current during motor vector control |
EP2890001A1 (en) * | 2012-08-24 | 2015-07-01 | Hitachi Automotive Systems, Ltd. | Motor driving circuit, motor driving system, electric power steering system, electric brake system, and vehicle driving system |
WO2015003619A1 (en) * | 2013-07-09 | 2015-01-15 | Shenzhen Byd Auto R&D Company Limited | Motor control system of electric vehicle and controlling method for motor control system of electric vehicle and electric vehicle |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106549617A (en) * | 2016-11-23 | 2017-03-29 | 深圳市道通智能航空技术有限公司 | The control system of permagnetic synchronous motor, control method and unmanned vehicle |
CN106549617B (en) * | 2016-11-23 | 2019-07-30 | 深圳市道通智能航空技术有限公司 | Control system, control method and the unmanned vehicle of permanent magnet synchronous motor |
CN111337827A (en) * | 2020-03-16 | 2020-06-26 | 邵阳学院 | Motor measurement and control system and method based on wireless sensor network |
CN112910374A (en) * | 2021-03-10 | 2021-06-04 | 苏州汇川联合动力***有限公司 | Method for optimizing inverter modulation strategy and motor control equipment |
CN112910374B (en) * | 2021-03-10 | 2022-12-27 | 苏州汇川联合动力***有限公司 | Method for optimizing inverter modulation strategy and motor control equipment |
CN113177291A (en) * | 2021-03-29 | 2021-07-27 | 浙江中车电车有限公司 | Electric vehicle efficacy analysis method and system based on cloud data platform |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hamida et al. | An adaptive interconnected observer for sensorless control of PM synchronous motors with online parameter identification | |
Alonge et al. | Convergence analysis of extended Kalman filter for sensorless control of induction motor | |
Ortombina et al. | Magnetic modeling of synchronous reluctance and internal permanent magnet motors using radial basis function networks | |
Glumineau et al. | Sensorless AC electric motor control | |
CN106026826A (en) | Networked measuring and controlling method for electric vehicle drive motor working condition matching control effectiveness | |
Wu et al. | Accurate torque modeling with PSO-based recursive robust LSSVR for a segmented-rotor switched reluctance motor | |
CN107276479A (en) | A kind of two-phase orthogonal winding permagnetic synchronous motor rotating speed determines method | |
Zhang et al. | Sliding mode observer for speed sensorless linear induction motor drives | |
Amrane et al. | Unknown input observer design for fault sensor estimation applied to induction machine | |
CN104242744A (en) | Permanent magnet synchronous motor rotating speed control method based on optimized grey prediction compensation | |
Nawae et al. | PMSM torque estimation based on machine learning techniques | |
CN112149274A (en) | Online modeling method for multi-axis engraving machine system with dead zone input nonlinearity | |
CN107681937A (en) | A kind of ultrahigh speed permagnetic synchronous motor speed observation procedure based on neutral net | |
Ibrahim et al. | Conceptual modelling of an EV-permanent magnet synchronous motor digital twin | |
Ghariani et al. | Sliding mode control and neuro-fuzzy network observer for induction motor in EVs applications | |
CN106788053A (en) | Electric motor car permagnetic synchronous motor systematic error compensation control method based on observer | |
Chen et al. | A High‐Performance Control Method of Constant V/f‐Controlled Induction Motor Drives for Electric Vehicles | |
CN103986400A (en) | Model reference self-adaptive system parameter self-tuning method based on two-dimensional fuzzy control | |
CN109194224A (en) | Permanent magnet synchronous motor sensorless strategy method based on extended state observer | |
Montonen et al. | Electric drive dimensioning for a hybrid working machine by using virtual prototyping | |
Chen et al. | High performance control of a permanent magnet synchronous motor for electric vehicle applications | |
Jegan et al. | Simulation and Validation of Permanent Magnet Synchronous Motor Drives Using Reinforcement Learning | |
Hemalatha et al. | Sensorless speed and position control of permanent magnet BLDC motor using particle swarm optimization and ANFIS | |
Praharsena et al. | Stator Flux Estimator Using Feed-Forward Neural Network for Evaluating Hysteresis Loss Curve in Three Phase Induction Motor | |
Li et al. | A novel hybrid control approach for modular automation system: a case study of sensorless interior permanent magnet in-wheel motor |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
WD01 | Invention patent application deemed withdrawn after publication | ||
WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20161012 |