CN109842342B - Anti-interference intelligent controller of hub motor for pure electric vehicle - Google Patents
Anti-interference intelligent controller of hub motor for pure electric vehicle Download PDFInfo
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Abstract
The invention discloses an anti-interference intelligent controller of a hub motor for a pure electric vehicle, which consists of a load compensation controller, a state feedback controller, a voltage decoupling controller, a controller parameter optimization module and a voltage limiting controller, wherein the input of the load compensation controller is load torque TlAnd speed omega, output is voltage ud1、uq1(ii) a The state feedback controller input is a reference speed omegarefReference current id refOptimal controller gain matrix Kbestω and current id、iqOutput is voltage ud2、uq2(ii) a Voltage decoupling controller inputs are ω and idThe output is a voltage ud3、uq3(ii) a The controller parameter optimization module inputs are ω and idAnd omegarefAnd id refThe output is Kbest(ii) a The voltage limiting controller inputs are omega, iqAnd voltage
And
obtaining global optimum parameter K of state feedback controller by gray wolf optimization algorithmbestThe maximum voltage supplied to the motor when the vehicle starts, accelerates and climbs a slope is increased.
Description
Technical Field
The invention belongs to the field of control of pure electric vehicles, and particularly relates to an intelligent controller of a hub motor system for a pure electric vehicle.
Background
With the increasing exacerbation of the problems of energy crisis, environmental pollution and the like, the improvement of the energy utilization efficiency becomes increasingly important. Compared with the traditional internal combustion engine automobile, the pure electric automobile has lower emission and higher energy utilization rate. For a conventional vehicle, a clutch, a transmission, a propeller shaft, a differential and even a transfer case are indispensable components, but the components are heavy and complicated, and are disadvantageous for improving the economy and the dynamic performance of the vehicle. The use of an in-wheel motor solves these problems well. Adopt in-wheel motor not only to make automobile structure simpler, for a lot of spaces that the vehicle had saved moreover, transmission efficiency also greatly improves simultaneously. The permanent magnet synchronous motor has the remarkable characteristics of high dynamic performance, high efficiency, light weight and the like, so that the permanent magnet synchronous motor is widely applied to new energy pure electric vehicles at present.
The exploration of more intelligent and high-performance motor controllers is an important direction for the development of pure electric vehicles in the future. The existing permanent magnet synchronous hub motor control technology is mainly adopted, and the following two types are adopted:
(1) controlling the open-loop constant-pressure frequency ratio of the rotating speed: the control method is mainly used for a motor group speed regulating system, and is found by observing a steady-state characteristic curve. The constant-voltage frequency ratio control has an obvious defect that the problems of rotor oscillation and step loss are not solved, and the defects of low stability, unsatisfactory dynamic performance, difficult parameter design and the like are obvious due to the adoption of the control of a single-variable system.
(2) And (3) double closed-loop vector control based on a PI controller: a double closed-loop control system is used, an inner ring is a current ring, an outer ring is a rotating speed ring, and the rotating speed and the current are respectively regulated through negative feedback of the rotating speed and the current. The control method has the obvious defects that the whole control system is provided with three PI controllers, the number of the parameters of the controllers needing to be set is at least six, and the actual application brings great workload; and the dynamic characteristics of the system are limited due to the inherent drawbacks of the dual closed loop control architecture.
The Chinese patent application No. 201110358020.2, entitled "double-hub motor electronic differential and speed regulation integrated controller", discloses differential control under working conditions such as automobile turning, the Chinese patent application No. 201210098727.9, entitled "a hub motor hybrid drive control system and control method thereof", and the Chinese patent application No. 201220144616.2, entitled "a hub motor hybrid drive control system", discloses conversion control of single-shaft drive and four-wheel drive under different working conditions, all emphasizes on the cooperation between different motors, and does not relate to improving the control performance control of a single hub motor.
Disclosure of Invention
The invention aims to provide an anti-interference intelligent controller of a hub motor system for a pure electric vehicle, which can effectively improve various control performance indexes of the hub motor system and increase the voltage utilization rate aiming at the common load characteristics of the hub motor.
The technical scheme adopted by the invention is as follows: the output end of the anti-interference intelligent controller of the hub motor for the pure electric vehicle is connected with the input end of a hub motor system, and the input end of the hub motor system is a control voltage ud、uqThe outputs are the rotation speed omega and the current id、iqThe anti-interference intelligent controller consists of a load compensation controller, a state feedback controller, a voltage decoupling controller, a controller parameter optimization module and a voltage limiting controller, wherein the input of the load compensation controller is load torque TlAnd speed omega, output being control voltage ud1、uq1(ii) a The input to the state feedback controller is a reference rotation speed omegarefReference current id refOptimal controller gain matrix KbestAnd the rotational speed omega and the current id、iqThe output is a control voltage ud2、uq2(ii) a The inputs of the voltage decoupling controller are the speed omega and the current idThe output is a control voltage ud3、uq3(ii) a The inputs to the controller parameter optimization module are speed ω and current idAnd a reference rotational speed omegarefAnd a reference current id refThe output is the optimal controller gain matrix Kbest(ii) a The input of the voltage-limiting controller is the rotating speed omega and the current iqAnd voltage
And
the output being said control voltage ud、uq。
Further, the controller parameter optimization module randomly generates N groups of weight matrixes Q and R, calculates a gain matrix K and outputs the gain matrix K to the state feedback controller, calculates the fitness value F of the current weight matrix, determines three groups of weight matrixes with the best fitness in the N groups of weight matrixes in the iteration, updates all weight matrixes by utilizing a wolf optimization algorithm, and outputs a weight matrix [ QR (quick response) matrix ] in the next iteration]i+1By [ QR]i+1Computing a gain matrix K in the next iterationi+1Will gain matrix Ki+1Outputting to a state feedback controller, repeating the steps until reaching the maximum iteration number, and obtaining the weight matrix [ QR ] with the best fitness]ImaxDetermining the optimal controller gain matrix Kbest。
The invention has the beneficial effects that:
1. the invention replaces a double closed-loop control system by the state feedback controller, effectively overcomes the inherent defects of the double closed-loop system and improves the dynamic performance of the system. The voltage decoupling controller ensures the control precision of the system. The load compensation controller improves the response speed of the hub motor under the working conditions of acceleration, climbing and the like. The three sub-controllers form the anti-interference intelligent controller, the defects of the existing control method of the hub motor system for the pure electric vehicle are effectively overcome, and the anti-interference intelligent controller is simple in design, good in control effect and strong in anti-interference capability.
2. The global optimal parameters of the state feedback controller are obtained through the gray wolf optimization algorithm, and the workload of parameter adjustment is reduced while the controller effect is improved.
3. The invention adopts a novel dynamic voltage constraint treatment combined with the current motor running state to further improve the dynamic property of the vehicle. Compared with the traditional fixed voltage amplitude limiting mode, the invention can effectively improve the maximum voltage supplied to the motor when the vehicle starts, accelerates and climbs a slope and shorten the adjusting time on the premise of ensuring the safe operation of the hub motor system.
4. The required control variable and the input variable of the invention are measurable and easily measurable variables, and the control algorithm of the controller is realized only by modular software programming without adding extra instruments and equipment, thereby effectively improving the control quality of the controller on the premise of not increasing the control cost and being beneficial to engineering realization.
Drawings
FIG. 1 is a structural block diagram of an anti-interference intelligent controller of a hub motor for a pure electric vehicle and input and output of the anti-interference intelligent controller;
fig. 2 is a structural equivalent block diagram of the in-wheel motor system 1 in fig. 1;
fig. 3 is a flow chart of parameter optimization of the controller parameter optimization module 3 of fig. 1.
In the figure: 1. a hub motor system; 2. an anti-interference intelligent controller; 3. a controller parameter optimization module; 11.2r/2s coordinate transformation module; 12, SVPWM; 13. an inverter module; 14. a hub motor; 15.3s/2r coordinate transformation module; 21. a load compensation controller; 22. a state feedback controller; 23. a voltage decoupling controller; 24. a voltage limiting controller; 41. a rotation speed setting module; 42. a current setting module; 43. the torque is loaded.
Detailed Description
As shown in fig. 1: the anti-interference intelligent controller 2 of the hub motor for the pure electric vehicle is connected in series with the front end of a hub motor system 1, the output end of the anti-interference intelligent controller 2 is connected with the input end of the hub motor system 1, and the control voltage u is outputd、uqThe in-wheel motor system 1 outputs a rotating speed omega and a current id、iq。
The anti-interference intelligent controller 2 is composed of a load compensation controller 21, a state feedback controller 22, a voltage decoupling controller 23, a controller parameter optimization module 3 and a voltage limiting controller 24.
The input to the load compensation controller 21 is the load torque TlAnd a rotation speed omega, the output of the load compensation controller 21 being a control voltage ud1、uq1。
The input to the state feedback controller 22 is a reference rotational speed ωrefReference current id refOptimal controller gain matrix KbestAnd the rotating speed omega and the current i output by the in-wheel motor system 1d、iqThe output of the state feedback controller 22 is a control voltage ud2、u q2。
The input of the voltage decoupling controller 23 is the rotating speed omega and the current i output by the hub motor system 1dThe output of the voltage decoupling controller 23 is the control voltage ud3、uq3。
The input of the controller parameter optimization module 3 is the rotating speed omega and the current i output by the hub motor system 1dAnd a reference rotational speed omegarefAnd a reference current id refThe output of the controller parameter optimization module 3 is the optimal controller gain matrix K for the input state feedback controller 22best。
Will control the voltage ud1、ud2、ud3Combined to obtain a combined voltage
Will control the voltage uq1、uq2、uq3Combined to obtain a combined voltage
Combined voltage
As a first part of the input of the voltage limiting controller 24, a second part of the input of the voltage limiting controller 24 is the rotating speed omega and the current i output by the hub motor system 1q. The output of the voltage limiting controller 24 is a control voltage ud、uqThe control voltage ud、uqIs the output of the anti-interference intelligent controller 2, and controls the voltage ud、uqAnd inputting the hub motor system 1 to realize the control of the hub motor system 1 of the pure electric vehicle.
The anti-interference intelligent controller 2 shown in fig. 1 is constructed by the following ten steps:
the method comprises the following steps: as shown in fig. 2, the in-wheel motor system 1 is constructed. The 2r/2s coordinate transformation module 11, the SVPWM 12, the inverter module 13, the hub motor 14 and the 3s/2r coordinate transformation module 15 are integrated to form the hub motor system 1. The in-wheel motor system 1 is operated at a voltage ud、uqAs input, the rotation speed omega and the current id、iqIs the output.
The 2r/2s coordinate transformation module 11, the SVPWM 12, the inverter module 13 and the hub motor 14 are sequentially connected in series, and the output end of the inverter module 13 is connected with the input end of the 3s/2r coordinate transformation module 15.
The two inputs of the 2r/2s coordinate transformation module 11 are respectively the voltage udAnd uqVoltage udAnd uqThe control voltage u under the two-phase static coordinate system is obtained through the coordinate transformation of the 2r/2s coordinate transformation module 11αAnd uβThe voltage value is used as the input of the SVPWM 12 regulating module, the SVPWM 12 outputs switching pulse signals 0 and 1(0 stands for closed, 1 is on), the switching pulse signals are used as the input of the inverter 13, and the inverter 13 outputs three-phase current i for driving the hub motor 14a、ib、ic. The output of the hub motor 14 is a rotating speed omega, and three-phase current i is outputa、ib、icThe 3s/2r coordinate transformation module 15 outputs a current value i in the synchronous rotation coordinate system as an input of the 3s/2r coordinate transformation module 15d、iq。
Step two: a mathematical model of the in-wheel motor system 1 is established. Through analysis, equivalence and derivation, the mathematical model for establishing the hub motor system 1 is as follows:
in the formula: x ═ id iq ω sw]TAnd u ═ ud uq]TRespectively taking three outputs i of the in-wheel motor system 1 as a state variable and a control variable of the in-wheel motor system 1d、iqω and integral of the rotational speed error swFor the state variables of the system, the control variables are the two inputs u of the in-wheel motor system 1dAnd uq。TlFor load torque, A is the system coefficient matrix, B is the input coefficient matrix, E is the load coefficient matrix:
step three: constructing an analytic expression of the anti-interference intelligent controller of the hub motor system 1, and considering the load sudden change, parameter time variation and other uncertain disturbance characteristics of the hub motor system 11, the output u of the anti-interference intelligent controller of the hub motor system 1 can be obtained as follows:
u＝u1+u2+u3 (2)
in the formula u1、u2、u3Respectively, the outputs of the load compensation controller 21, the state feedback controller 22 and the voltage decoupling controller 23.
Wherein the output voltage u of the load compensation controller 211The method comprises the following steps:
in the formula: k is a radical oftIs the torque coefficient of the in-wheel motor 14, BωIs the coefficient of friction, T, of the in-wheel motor 14qIs the time constant of the in-wheel motor 14, J is the moment of inertia, kt＝1.32，Bω＝0.006，Tq＝0.05，J＝0.004kgm2，
Second and first derivatives, T, of the speed ωlIs negativeAnd carrying torque.
Output u of state feedback controller 222The method comprises the following steps:
in the formula: k is a 2 × 4 gain matrix whose values directly affect the response characteristics of the system. In the linear quadratic optimization theory, the designed controller should give consideration to both control performance and energy loss, and the gain matrix K of the controller should be selected to minimize the following equation:
the first part in equation (5) represents the tracking performance of the controller, the second part represents the control energy, and Q and R are weight matrices representing the relative importance of the control performance and the energy loss, which have a direct effect on the performance of the tamper resistant intelligent controller 2.
After obtaining the weight matrices Q and R, the gain matrix K can be obtained by:
K＝lqr(A,B,Q,R) (6)
lqr () is a linear quadratic optimization function.
Output u of voltage decoupling controller 233The method comprises the following steps:
in the formula: k is a radical of1、k2And k3Is the voltage coupling coefficient, k1＝17，k2＝37，k3＝103.2。
Step four: referring to fig. 3, the controller parameter optimization module 3 is utilized to obtain a gain matrix K of the state feedback controller 22 for achieving global optimization of the system, and the optimal controller gain matrix is denoted as Kbest。
The external input of the controller parameter optimization module 3 is the output of the hub motor system 1 at different timesSpeed of rotation omega and current idAnd the reference rotation speed ω output by the rotation speed setting module 41 and the current setting module 42refAnd a reference current id ref. The output of the controller parameter optimization module 3 is an optimal gain matrix K assigned to the state feedback controllerbest。；
The controller parameter optimization module 3 obtains the optimal gain matrix K according to the following stepsbest：
Step 1: initializing weight matrix, randomly generating N groups of weight matrices Q and R, and recording as [ QR]0，[QR]iThe weight matrices Q and R during the ith iteration are shown, with N being 30.
Step 2: the gain matrix K of the state feedback controller 22 is calculated from equation (6).
And step 3: the obtained gain matrix K is output to the state feedback controller 22.
And 4, step 4: the anti-interference intelligent controller 2 of the in-wheel motor is used for driving the in-wheel motor system 1 to obtain the discrete motor output rotating speed omega under the gain matrix K of the feedback controller 22 at the current statenAnd current id nAnd n represents the sampling instant.
And 5: calculating the fitness value F of the current weight matrix by using the formula (8):
in the formula: w is a1、w2As a weight value, w1＝5，w2＝2。eω、eidIs the error of the actual rotation speed and the actual current relative to the reference value. n represents the sampling instant, Ts the sampling time.
Step 6: determining three groups with the best fitness in the N groups of weight matrixes in the iteration, and marking as [ QR]i 1,[QR]i 2,[QR]i 3。
And 7: based on the determined three groups of weight matrixes [ QR]i 1,[QR]i 2,[QR]i 3Optimization calculation using gray wolfUpdating all weight matrixes by the method, and outputting the weight matrix [ QR ] in the next iteration]i+1. A grayish optimization algorithm is utilized to approach a globally optimal state feedback controller, and the difficulty in selecting a gain matrix in state feedback control is effectively solved.
And 8: according to equation (6) from [ QR]i+1Computing a state feedback controller coefficient gain matrix K in a next iterationi+1。
And step 9: repeating the steps 3 to 8 until the maximum iteration number I is reachedmax,Imax10. The gain matrix obtained in the ith iteration process is Ki。
Step 10: the output reaches the maximum iteration number ImaxThe post-channel weight matrix [ QR ] with the best fitness]ImaxDetermined optimal controller gain matrix Kbest：
Step five: the load compensation controller 21 is constructed using equation (3). For a pure electric vehicle, the load torque TlDivided into acceleration loads TaAnd a climbing load Tθ：
In the formula: m is the total vehicle mass, r is the tire radius, and theta is the road surface inclination angle.
The actual output speed omega and the load torque T of the in-wheel motor system 1 are comparedlAs input to the load compensation controller 21, its output is a voltage ud1、uq1。
Step six: the state feedback controller 22 is constructed using equation (4). The optimal solution finally obtained in the step four, namely the optimal controller gain matrix KbestAnd into the state feedback controller 22. Setting the reference speed ω output by the module 41 at the rotational speedrefThe reference current i output by the current setting module 42d refWheel hubOutputs ω, i of the motor system 1dAnd iqFor input, the output of the state feedback controller 22 is a control voltage ud2、uq2。
Step seven: the voltage decoupling controller 23 is constructed using equation (7). With the output omega, i of the in-wheel motor system 1dAs an input of the voltage decoupling controller 23, the output of the voltage decoupling controller 23 is obtained as ud3、uq3。
Step eight: in order to improve the voltage utilization rate on the premise of ensuring the safe operation of the hub motor system 1, the invention abandons the traditional fixed voltage amplitude limiting method and adopts a novel constraint processing mode which combines the current motor operation condition. The first part of the input of the voltage limiting controller 24, which is formed by combining the control voltages output by the load compensation controller 21, the state feedback controller 22 and the voltage decoupling controller 23, is:
simultaneously outputs omega and i of the in-wheel motor system 1qAs a second partial input to the voltage limiting controller 24. The discrete expression of the q-axis current of the in-wheel motor system 1 is:
iq(n+1)＝kαuq(n)+kβiq(n)-kEq(n) (10)
in the formula, Eq(n) is the back electromotive force at time n, Eq(n)＝ωψf，ψfIs a permanent magnet flux linkage; k is a radical ofαIs the voltage coefficient, kβ-Is the current coefficient, kIs the back electromotive force coefficient, kα＝0.31，kβ＝9.56，k＝0.03。
The purpose of the voltage limiting controller 24 is to ensure the current i of the hub motor system 1 at the next momentq(n +1) not exceeding the rated phase current I of the machineNNamely, ensuring that:
iq(n+1)≤IN (11)
the q-axis can be obtained from the following formulas (13) and (14)Voltage udThe constraint conditions of (1) are:
|ud(n)|≤uup (13)
in the formula: u. ofupIs the rated voltage of the inverter. The pressure-limiting controller 24 can be constructed by the formulas (12) and (13). The output of the voltage limiting controller 24 is a control voltage ud、uqAs an input to the in-wheel motor system 1.
Step nine: the controller parameter optimization module 3 is connected with the state feedback controller 22 in series, and after the state feedback controller 22, the load compensation controller 21 and the voltage decoupling controller 23 are connected in parallel, the state feedback controller 22, the load compensation controller 21 and the voltage decoupling controller 23 are connected in series with the voltage limiting controller 24 to form the anti-interference intelligent controller 2 of the hub motor. The anti-interference intelligent controller 2 gives the reference rotating speed omega generated by the rotating speed setting unit 41refA reference current i generated by the current setting unit 42d refActual rotating speed omega and actual current i output by the in-wheel motor system 1d、iqAnd load torque TlAs input, with a voltage ud、uqThe control method is used for outputting, so that high-performance robust control of the hub motor system 1 of the pure electric vehicle is achieved.
Claims (6)
1. The output end of the anti-interference intelligent controller of the hub motor for the pure electric vehicle is connected with the input end of a hub motor system (1), and the input end of the hub motor system (1) is a control voltage ud，uqThe outputs are the rotation speed omega and the current id、iqThe method is characterized in that: the load decoupling control circuit is composed of a load compensation controller (21), a state feedback controller (22), a voltage decoupling controller (23), a controller parameter optimization module (3) and a voltage limiting controller (24), wherein the input of the load compensation controller (21) is load torque TlAnd speed of rotation omega, the output being the control voltage ud1、uq1(ii) a The input to the state feedback controller (22) is a reference rotation speed omegarefReference current
Optimal controller gain matrix KbestAnd the rotational speed omega and the current id、iqOutput is a control voltage ud2、uq2(ii) a The input of the voltage decoupling controller (23) is a rotating speed omega and a current idThe output being a control voltage ud3、uq3(ii) a The input of the controller parameter optimization module (3) is the rotating speed omega and the current idAnd a reference rotational speed omegarefAnd a reference current
The output is said optimal controller gain matrix Kbest(ii) a The input of the voltage limiting controller (24) is a rotating speed omega and a current iqAnd voltage
And
the output being said control voltage ud、uq。
2. The anti-interference intelligent controller for the hub motor of the pure electric vehicle as claimed in claim 1, wherein: the controller parameter optimization module (3) randomly generates N groups of weight matrixes Q and R, calculates a gain matrix K and outputs the gain matrix K to the state feedback controller (22), calculates the fitness value F of the current weight matrix, determines three groups of weight matrixes with the best fitness in the N groups of weight matrixes in the iteration, updates all weight matrixes by utilizing a gray wolf optimization algorithm, and outputs the weight matrix [ QR (quick response) in the next iteration]i+1By [ QR]i+1Computing a gain matrix K in the next iterationi+1(ii) a Then the gain matrix Ki+1Output to a state feedback controller (22); repeating the steps until the maximum iteration number is reached, and obtaining the weight matrix [ QR ] with the best fitness]ImaxDetermining the optimal controller gain matrix Kbest。
3. The anti-interference intelligent controller for the hub motor of the pure electric vehicle as claimed in claim 1, wherein:
ktis the torque coefficient of the in-wheel motor, BωIs the friction coefficient, T, of the in-wheel motorqIs the time constant of the in-wheel motor, J is the moment of inertia, kt＝1.32，Bω＝0.006，Tq＝0.05，J＝0.004kgm2，
Second and first derivatives of the rotational speed ω, respectively.
4. The anti-interference intelligent controller for the hub motor of the pure electric vehicle as claimed in claim 1, wherein:
k is a 2 × 4 gain matrix, K is lqr (a, B, Q, R), lqr () is a linear quadratic optimization function, a is a system coefficient matrix, B is an input coefficient matrix, Q and R are weight matrices, and x is a state variable of the in-wheel motor system (1).
5. The anti-interference intelligent controller for the hub motor of the pure electric vehicle as claimed in claim 1, wherein:
k1、k2and k3Is the voltage coupling coefficient.
6. The anti-interference intelligent controller for the hub motor of the pure electric vehicle as claimed in claim 2, wherein: the fitness value of the current weight matrix
w1、w2As a weight value, w1＝5，w2＝2，eω、eidThe error between the actual rotation speed and the actual current with respect to the reference value is shown, N is the sampling time, Ts is the sampling time, and N is 30.
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| CN110376884B (en) * | 2019-06-26 | 2022-12-16 | 江苏大学 | Construction method of dynamic anti-interference controller of driving motor of new energy automobile |
| CN110481339B (en) * | 2019-07-26 | 2022-11-18 | 江苏大学 | Intelligent composite controller for hub motor of electric automobile |
| CN111510045B (en) * | 2020-03-19 | 2023-05-05 | 江苏大学 | Construction method of position-sensor-free controller of hub motor |
| CN111934586B (en) * | 2020-07-07 | 2023-11-10 | 江苏大学 | Electric automobile in-wheel motor disturbance attenuation controller |
| CN112713830B (en) * | 2020-12-21 | 2022-04-08 | 华中科技大学 | Permanent magnet synchronous motor speed regulation system and multi-target optimal state feedback control method |
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Application publication date: 20190604 Assignee: Honeycomb drive system (Jiangsu) Co.,Ltd. Assignor: JIANGSU University Contract record no.: X2023320000079 Denomination of invention: An Anti-interference Intelligent Controller of Hub Motor for Pure Electric Vehicle Granted publication date: 20201120 License type: Common License Record date: 20230206 |
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