CN106026826A - Networked measuring and controlling method for electric vehicle drive motor working condition matching control effectiveness - Google Patents

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

Driving motor of electric vehicle Conditions Matching controls both effectiveness Networked Supervising method

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:

$P=\left\{\begin{array}{cc}0& (v<3)\\ -1.6736v^4+31.8815v^3-189.0257v^2+511.1903v-538.8190& (3\&le;v<6)\\ 0.0884v^4-7.3241v^3+137.0267v^2-657.5189v+915.7031& (6\&le;v<12)\\ -0.5688v^4+35.7488v^3-839.5314v^2+8732.851v-31944.1486& (12\&le;v<18)\\ -0.0158v^4+1.3898v^3-45.5841v^2+661.8436v-1578.3287& (18\&le;v<25)\\ 0& (v\&GreaterEqual;25)\end{array}\right.$

P is active power, unit kW, and v is driving motor of electric vehicle rotating speed.

Further, described speed curves model is:

y_p(k)=ω φ (x (k))+b；

Etching system input quantity when x (k) is k, y_pEtching 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:

Y_A(t_k-1)、Y_A(t_k)、Y_A(t_k+1) it is respectively sensors A to target at t_k-1,t_k,t_k+1The local Descartes in moment sits Measuring value under mark system, is respectively as follows:

$Y_A\left(t_{k-1}\right)={Y^{\&prime;}}_A\left(t_{k-1}\right)-C_A\left(t_{k-1}\right){\mathrm{\&xi;}}_A\left(t_{k-1}\right)+n_{Y_A\left(t_{k-1}\right)};$

$Y_A\left(t_k\right)={Y^{\&prime;}}_A\left(t_k\right)-C_A\left(t_k\right){\mathrm{\&xi;}}_A\left(t_k\right)+n_{Y_A\left(t_k\right)};$

$Y_A\left(t_{k+1}\right)={Y^{\&prime;}}_A\left(t_{k+1}\right)-C_A\left(t_{k+1}\right){\mathrm{\&xi;}}_A\left(t_{k+1}\right)+n_{Y_A\left(t_{k+1}\right)};$

Wherein, Y'_A(t_k-1)、Y'_A(t_k)、Y'_A(t_k+1) it is respectively sensors A at t_k-1,t_k,t_k+1The local Descartes in moment Actual position under coordinate system；C_AT () is the transformation matrix of error；ξ_AT () 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 R_A(k-1)、R_A(k)、R_A(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 interpolations_BkTime be engraved in local right angle Measuring value under coordinate systemFor:

Wherein, t_BkFor registration moment, t_k-1,t_k,t_k+1For sensors A distance registration moment nearest three sampling instants, Y_A(t_k-1),Y_A(t_k),Y_A(t_k+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:

$\begin{array}{cc}{X^{\&prime;}}_1\left(k\right)=X_1\left(k\right)+C\left(k\right)\&lsqb;\mathrm{\&xi;}\left(k\right)+n\left(k\right)\&rsqb;& \backslash *MERGEFORMAT\end{array}---\left(3\right)$

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=X_As+B_AX'_A1(k)=X_Bs+B_BX'_B1(k)\*MERGEFORMAT (4)

B_A, B_BIt 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)=X_Ae(k)-X_Be(k)\*MERGEFORMAT (5)

Wherein, X_Ae(k)=X_As+B_AX_A1(k)；X_Be(k)=X_Bs+B_BX_B1(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) [-B_AC_A(k)B_BC_B(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 n_A(k),n_BK () 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:

$MaxC=\underset{i\&Element;P}{\&Sigma;}{w}_i\&times;T_i---\left(1\right)$

ST:0≤s_i.start≤l,i∈N (2)

s_i.end-s_i.start=b_i,i∈N (3)

$b_i\&le;B_i\&times;\frac{l}{L},i\&Element;N^4---\left(4\right)$

Wherein C is total effective cover time, and l is each time taken turns, b_iIt is node s_iDuring 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:

$P=\left\{\begin{array}{cc}0& (v<3)\\ -1.6736v^4+31.8815v^3-189.0257v^2+511.1903v-538.8190& (3\&le;v<6)\\ 0.0884v^4-7.3241v^3+137.0267v^2-657.5189v+915.7031& (6\&le;v<12)\\ -0.5688v^4+35.7488v^3-839.5314v^2+8732.851v-31944.1486& (12\&le;v<18)\\ -0.0158v^4+1.3898v^3-45.5841v^2+661.8436v-1578.3287& (18\&le;v<25)\\ 0& (v\&GreaterEqual;25)\end{array}\right.$

P is active power, unit kW, and v is driving motor of electric vehicle rotating speed.

Further, described speed curves model is:

y_p(k)=ω φ (x (k))+b；

Etching system input quantity when x (k) is k, y_pEtching 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:

Y_A(t_k-1)、Y_A(t_k)、Y_A(t_k+1) it is respectively sensors A to target at t_k-1,t_k,t_k+1The local Descartes in moment sits Measuring value under mark system, is respectively as follows:

$Y_A\left(t_{k-1}\right)={Y^{\&prime;}}_A\left(t_{k-1}\right)-C_A\left(t_{k-1}\right){\mathrm{\&xi;}}_A\left(t_{k-1}\right)+n_{Y_A\left(t_{k-1}\right)};$

$Y_A\left(t_k\right)={Y^{\&prime;}}_A\left(t_k\right)-C_A\left(t_k\right){\mathrm{\&xi;}}_A\left(t_k\right)+n_{Y_A\left(t_k\right)};$

$Y_A\left(t_{k+1}\right)={Y^{\&prime;}}_A\left(t_{k+1}\right)-C_A\left(t_{k+1}\right){\mathrm{\&xi;}}_A\left(t_{k+1}\right)+n_{Y_A\left(t_{k+1}\right)};$

Wherein, Y'_A(t_k-1)、Y'_A(t_k)、Y'_A(t_k+1) it is respectively sensors A at t_k-1,t_k,t_k+1The local Descartes in moment Actual position under coordinate system；C_AT () is the transformation matrix of error；ξ_AT () 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 R_A(k-1)、R_A(k)、R_A(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 interpolations_BkTime be engraved in local right angle Measuring value under coordinate systemFor:

Wherein, t_BkFor registration moment, t_k-1,t_k,t_k+1For sensors A distance registration moment nearest three sampling instants, Y_A(t_k-1),Y_A(t_k),Y_A(t_k+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=X_As+B_AX'_A1(k)=X_Bs+B_BX'_B1(k)\*MERGEFORMAT (4)

B_A, B_BIt 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)=X_Ae(k)-X_Be(k)\*MERGEFORMAT (5)

Wherein, X_Ae(k)=X_As+B_AX_A1(k)；X_Be(k)=X_Bs+B_BX_B1(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) [-B_AC_A(k)B_BC_B(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 n_A(k),n_BK () 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:

$MaxC=\underset{i\&Element;P}{\&Sigma;}{w}_i\&times;T_i---\left(1\right)$

ST:0≤s_i.start≤l,i∈N (2)

s_i.end-s_i.start=b_i,i∈N (3)

$b_i\&le;B_i\&times;\frac{l}{L},i\&Element;N---\left(4\right)$

Wherein C is total effective cover time, and l is each time taken turns, b_iIt is node s_iDuring 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:

$P=\left\{\begin{array}{cc}0& \left(v<3\right)\\ -1.6736v^4+31.8815v^3-189.0257v^2+511.1903v-538.8190& \left(3\&le;v<6\right)\\ 0.0884v^4-7.3241v^3+137.0267v^2-657.5189v+915.7031& \left(6\&le;v<12\right)\\ -0.5688v^4+35.7488v^3-839.5314v^2+8732.851v-31944.1486& \left(12\&le;v<18\right)\\ -0.0158v^4+1.3898v^3-45.5841v^2+661.8436v-1578.3287& \left(18\&le;v<25\right)\\ 0& \left(v\&GreaterEqual;25\right)\end{array}\right.$

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:

y_p(k)=ω φ (x (k))+b；

Etching system input quantity when x (k) is k, y_pEtching 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:

Y_A(t_k-1)、Y_A(t_k)、Y_A(t_k+1) it is respectively sensors A to target at t_k-1,t_k,t_k+1The local cartesian coordinate system in moment Under measuring value, be respectively as follows:

$Y_A\left(t_{k-1}\right)={Y^{\&prime;}}_A\left(t_{k-1}\right)-C_A\left(t_{k-1}\right){\mathrm{\&xi;}}_A\left(t_{k-1}\right)+n_{Y_A\left(t_{k-1}\right)};$

$Y_A\left(t_k\right)={Y^{\&prime;}}_A\left(t_k\right)-C_A\left(t_k\right){\mathrm{\&xi;}}_A\left(t_k\right)+n_{Y_A\left(t_k\right)};$

$Y_A\left(t_{k+1}\right)={Y^{\&prime;}}_A\left(t_{k+1}\right)-C_A\left(t_{k+1}\right){\mathrm{\&xi;}}_A\left(t_{k+1}\right)+n_{Y_A\left(t_{k+1}\right)};$

Wherein, Y'_A(t_k-1)、Y'_A(t_k)、Y'_A(t_k+1) it is respectively sensors A at t_k-1,t_k,t_k+1The local cartesian coordinate in moment Actual position under Xi；C_AT () is the transformation matrix of error；ξ_AT () is the systematic error of sensor；For system noise, vacation IfFor zero-mean, separate Gaussian stochastic variable, noise covariance matrix is respectively R_A(k- 1)、R_A(k)、R_A(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 t_BkTime be engraved in local rectangular coordinate Measuring value under XiFor:

Wherein, t_BkFor registration moment, t_k-1,t_k,t_k+1For three sampling instants that the sensors A distance registration moment is nearest, Y_A (t_k-1),Y_A(t_k),Y_A(t_k+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=X_As+B_AX'_A1(k)=X_Bs+B_BX'_B1(k) \*MERGEFORMAT (4)

B_A, B_BIt 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)=X_Ae(k)-X_Be(k) \*MERGEFORMAT (5)

Wherein, X_Ae(k)=X_As+B_AX_A1(k)；X_Be(k)=X_Bs+B_BX_B1(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) [-B_AC_A(k) B_BC_B(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 n_A(k),n_BK () 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:

$\begin{array}{cc}Max& C=\underset{i\&Element;P}{\&Sigma;}{w}_i\&times;T_i\end{array}---\left(1\right)$

ST:0≤s_i.start≤l,i∈N (2)

s_i.end-s_i.start=b_i,i∈N (3)

$b_i\&le;B_i\&times;\frac{l}{L},i\&Element;N---\left(4\right)$

Wherein C is total effective cover time, and l is each time taken turns, b_iIt is node s_iWorking time in each wheel.