JPH04251502A - Controller for electric vehicle - Google Patents

Abstract

PURPOSE:To utilize adhesion to the maximum by employing a slip rate of dynamic wheel, a time differentiation and a speed difference of circumferential adhesion, determined based on the circumferential speed of each dynamic wheel and a ground speed of vehicle, as preceding part variables whereas employing a torque command value of an induction motor as following part conditions and then subjecting the torque to fuzzy processing. CONSTITUTION:Induction motors 2A-2D of an electric vehicle 1 are driven through an inverter 101 to rotate dynamic wheels 3A-3D and then rotational speed thereof are detected 4A-4D thus operating 5A-5D a motor frequency. A ground speed Vo of vehicle operated from a driven wheel 14, phase current of each motor, voltage phase theta of the inverter 101 and the motor frequency are then operated 6A-6D. The operating units 6A-6D operate slip rate, adhesion and creep speed for each dynamic wheel, subjecting them to fuzzy inference 8A-8D, weighing 9A-9D, max combination 11 and non-fuzzy operation. An integrated value Ip' is then added to a torque current pattern Ip and current control 107 is carried out to drive the inverter 101. According to the invention, adhesion of all dynamic wheels can be utilized to the maximum.

Description

[Detailed description of the invention]

0001

[Industrial Application Field] The present invention is an electric vehicle control device with improved adhesive performance for an electric vehicle that controls a plurality of induction motors using a variable voltage variable frequency inverter (hereinafter referred to as a VVVF inverter), a so-called VVVF inverter vehicle. It is related to.

0002

2. Description of the Related Art A method shown in FIG. 9 is known as a method for improving the adhesive performance of electric vehicles. FIG. 9 shows a control circuit of a VVVF inverter system employing a typical conventional readhesion control method.

[0003] 1 is an electric car and represents one vehicle. 101 is an inverter device. 2A, 2B,
2C and 2D (hereinafter referred to as 2A to 2D) are induction motors, and 3A to 3D are driving wheels indicated by letters corresponding to the induction motors 2A to 2D, respectively. 4A to 4D are motor rotation speed detectors similarly indicated by corresponding alphabetical characters,
This example shows a pulse generator (PG), which generates a pulse train with a frequency proportional to the rotational speed of the motor. Further, 5A to 5D are motor rotational frequency calculation means indicated by corresponding alphabetical letters, which convert each PG signal into rotational frequencies fMA to fMD of each electric motor. Furthermore, 7A to 7D are differentiators which input each motor rotation frequency and output the time differentials dfMA/dt to dfMD/dt. Reference numeral 108 denotes a motor frequency selection means that determines a reference motor frequency for frequency control of a group of motors driven by the same inverter 101. Algorithms for selecting the reference motor frequency include a method of fixing it to a predetermined motor frequency (a method of determining the motor frequency from the PG signal of the shaft that is least likely to idle during power running or braking), and a method of dynamically switching it. (method of setting the frequency of each motor to the minimum value during power running, and setting the frequency of each motor to the maximum value during braking).

The output of the motor frequency selection means 108 is
This is a reference motor frequency fM, which is added to a slip frequency command fSS, which will be described later, at an addition point 109 to obtain an inverter frequency command fI. In addition, in order to supply alternating current with a constant voltage/frequency ratio to the electric motor group, the inverter output voltage value is the value obtained by multiplying the inverter frequency command fI by the V/f ratio by a V/f ratio device indicated by 110. The value is divided by the filter capacitor voltage detection value VC by the divider 111, and the value further passed through the first-order lag filter 112 is set as the transformation ratio command α. The first-order lag filter 112 is provided for stabilization.

The inverter 101 determines the switching timing by determining the intersection of a carrier having a switching frequency suitable for the inverter frequency command fI and a three-phase sine wave signal having an amplitude corresponding to the transformation ratio command α. Three-phase alternating current is applied to each motor using pulse width modulation.

The torque current pattern generating means 104 is
A current pattern Ip proportional to the motor torque corresponding to the required acceleration is generated based on a notch command from the driver for acceleration/braking and a signal from the load adjustment device that detects the load on the vehicle. Hereinafter, the effective current proportional to the motor torque will be referred to as torque current. Torque current command IIS, which is the difference between torque current pattern Ip and torque current correction pattern Ip' described later, and detected value I of torque current
The deviation from I is input to the current control means 107, and the output of this current control means 107 is the slip frequency command fS.
It becomes S.

Reference numeral 113 denotes a slip detecting means, and the slip detecting means 113 detects a comparator means 114 when the maximum value of the motor frequency differentials dfMA/dt to dfMD/dt, which are the outputs of the differentiators 7A to 7D, exceeds a predetermined value. to drive. This comparator 114 is an on/off signal generating means with a delay of a fixed time T, and when the state of the comparator changes and is maintained for a fixed time, the slip detection signal SLIP is turned on. Also,
When readhesion control is performed and the maximum value of the motor frequency differential falls below the above-mentioned predetermined value and this state is maintained for a certain period of time T, the slip detection signal SLIP is returned to the OFF state. Here, the delay time T is provided to avoid false detection due to noise mixed into the motor frequency differential.

Reference numeral 115 denotes a torque current correction pattern generating means, which generates a torque current correction pattern Ip' in response to the slip detection signal SLIP. The torque current correction pattern generating means 115 increases the torque current correction pattern at a steep gradient when the slip detection signal SLIP is turned on, and gradually increases the torque current correction pattern when the slip is finished and the slip detection signal SLIP is turned off. decrease with a slope.

The torque current pattern Ip has an addition point of 10
5, the torque current correction pattern Ip' is subtracted to obtain the torque current command IIS. Therefore, when it is determined that slipping has occurred, the motor torque is rapidly reduced, and when it is determined that readhesion has occurred, the motor torque is gradually increased.

Reference numeral 102 denotes an effective current detection means, which detects each phase current of the motor group using a current sensor and calculates the effective current. Further, 103 is a torque current calculating means, which calculates the torque current II from the effective current IM which is the output of the effective current detecting means 102 and the output voltage phase θ of the inverter.

[0011] In this example, a method of narrowing down the torque current pattern is shown. In addition to this, various readhesion control methods have been proposed, such as methods that directly narrow down the slip frequency, but they are essentially the same as the above example in the sense that they are switched when the amount of slip is above or below a predetermined value. It has become a thing.

0012

[Problems to be Solved by the Invention] The conventional adhesion control system as described above does not work unless slipping or skidding actually occurs, exceeds a predetermined value, and elapses for a predetermined period of time. The correction amount also uses a preset fixed value, and does not depend on the degree of idling. Furthermore, the method of correcting the motor torque involves repeating decreases and increases, and cannot be said to fully utilize the characteristics of the VVVF inverter vehicle, which can continuously adjust the motor torque. Furthermore, as a method of detecting slippage, if at least one of multiple electric motor frequencies exceeds a predetermined value for a certain period of time, it is considered that slipping has occurred, and to what extent is the slipping occurring in other driving wheels other than the one determined to have occurred? The system does not take into consideration whether or not the adhesive strength is obtained. Therefore, there are two cases in which one driving wheel is idling due to dirt on the rail surface (hereinafter referred to as uniaxial idling), and a case in which all the driving wheels are idling at the same time due to a decrease in adhesion due to rain, etc. (hereinafter referred to as single-axis idling). The amount of throttling of the electric motor torque is the same in the case of shaft idling (referred to as shaft idling), and it is easily expected that the torque will be throttled too much in the former situation.

[0013] Adhesive force is a physical phenomenon determined by the condition of the contact surface between the wheel and the rail, and strictly speaking, it must be considered that it differs for each driving wheel. Therefore, the ideal adhesion control system is one that can independently control the driving force around the driving wheels so that the maximum adhesion force can be obtained depending on the adhesion state of each driving wheel. Some electric locomotives using a VVVF inverter are provided with an inverter device for each induction motor of each driving wheel so that the driving force around the driving wheels of each driving wheel can be controlled independently. However, in VVVF inverter trains that adopt a distributed power system, due to implementation constraints or cost constraints of the inverter device, a method is generally adopted in which four to eight induction motors are controlled by one inverter device. It has become a target. Therefore, adhesion control in a VVVF inverter train depends on the adhesion state of each driving wheel shaft.
It should be thought of as a multi-purpose control system that controls the adhesion of the entire train set to be maximized as much as possible.

[0014] The applicant of this application previously filed Japanese Patent Application No. 2-328269.
The device described in the specification of the "Electric Vehicle Control Device" for which a patent application was filed in No. 1 detects and calculates the differential slip rate, differential adhesive force, and creep speed of each driving wheel, determines the reference driving wheel from these values, and controls the device. It was not intended to comprehensively evaluate the adhesion of an entire train. In contrast, the present invention detects and calculates the state quantities of all the driving wheels, determines a torque current correction pattern for each drive wheel based on the state quantities, fuzzy-synthesizes them, and then performs defuzzification processing. The system evaluates the adhesion state of each driving wheel comprehensively and controls the electric motor torque so that the total adhesion force for the entire train is always maximized. It is what makes it possible.

Now, FIG. 10 shows the relationship between the slip rate and adhesive force between the driving wheels and the rail (hereinafter referred to as adhesive properties).
This is what is shown. Here, the slip rate is the value obtained by dividing the difference speed between the driving wheel circumferential speed and the ground vehicle speed by the driving wheel circumferential speed.
It is defined by the following formula. λ=(VM-VO)/VM

(1) In the above equation, VO represents the ground vehicle speed and VM represents the driving wheel circumferential speed. Also, the difference speed between the driving wheel circumferential speed and the ground vehicle speed is called the creep speed, and is expressed as VS, VS = VM - VO

Defined in (2).

As is well known, the adhesive force is proportional to the axle load, but as shown in FIG. 10, in a range where the slip rate is small, it is also approximately proportional to the slip rate. The value of the slip rate and adhesive force at a certain point determines the position in terms of adhesive properties, which will be referred to as the operating point.

Now, the adhesion force is F, the driving force transmitted from the electric motor to the driving wheels (hereinafter referred to as driving wheel circumferential driving force) is FM, and the rotating system drive that accelerates the rotational inertial system such as the motor rotor, reduction gear, and driving wheels. The force (hereinafter simply referred to as rotation system driving force) is F
When R, the following formula holds true. FM=F+FR

(3) The above equation shows that the sum of the adhesive force and the rotation system driving force is the driving force around the driving wheels, and the adhesive force is a component force that contributes to the acceleration of the vehicle body.

The adhesive force F has an upper limit FO, which is called the adhesive limit. When no wheel circumferential driving force is applied (static state or coasting state), the operating point is at the origin, and as the wheel circumferential driving force is applied, it climbs the slope on the left side of the adhesion characteristic. Then, a point P is reached where the adhesive force reaches a maximum value FO at a certain slip rate λO. If the driving force around the driving wheels continues to increase, the slope on the right side of the adhesion characteristic will start to fall, and the adhesion force F will decrease.As can be seen from equation (3), the rotational driving force FR will increase, and the rotational speed of the driving wheels will decrease. will rapidly increase. This phenomenon is called idling. The area where the slip rate is smaller than the adhesive force limit point P will be called the creep area, and the area where it is larger will be called the slip area.

It is known that the adhesive properties vary depending on the weather and the condition of the rail surface, but as a general tendency, the upper limit of the adhesive force F in the dry state is as shown in FIG.
O is large, and the slip rate that gives FO is small. On the other hand, in a wet state, the upper limit of adhesive force F is low as shown in Figure 11.
As O 2 decreases, the characteristics become flat as a whole.

[0020] Normally, the upper limit of adhesive strength in a dry state F
Since a torque current pattern is set that allows running with a lower adhesive force than O2, stable running is possible in the creep region during drying. However, due to rain, oil stains on the rail surface, etc., the upper limit of adhesion force FO may become lower than the required adhesion force as shown in Fig. 11, and even when normal driving force is applied to the circumference of the wheels, The operating point passes through the point P that gives the upper limit value FO and enters the idling region.

In the conventional readhesion control method using slip detection, after entering this slip region, the driving force around the driving wheels is suddenly reduced so as to return the operating point in the direction of arrow b, and the operating point is brought into the creep region. Pull back. Then, when readhesion is determined, the driving force around the driving wheels is gradually increased, but at this time, the operating point moves in the direction of arrow a, passes through point P again, and enters the idling region. Thereafter, the operation of reciprocating left and right around point P is repeated.

Up to this point, we have described the adhesion characteristics and movement of the operating point when focusing on one driving wheel, but in reality there are multiple driving wheels, and the adhesion characteristics and operating point for each of them are as follows: As shown in FIG. 12, they are naturally different. This is because the adhesion characteristics vary depending on the physical conditions of the contact surface between the driving wheels and the rail, and the driving force around the driving wheels of each driving wheel does not necessarily match due to the characteristics of each induction motor and the difference in driving wheel diameter. This is because there is no guarantee that there will be.

[0023]

[Means for Solving the Problems] FIG. 1 is shown to facilitate understanding of the basic technical idea of the present invention. That is, in principle, the present invention can be realized by optimally utilizing the advantages of fuzzy inference. This will be explained before going into the detailed explanation of FIG. According to fuzzy control, even if a control system is complex and cannot be solved analytically, if an expert has the know-how or knowledge of how to operate for various conditions, this can be expressed in language as a control law, and i
The manipulated variable can be determined using f then -type reasoning. As mentioned above, the adhesive properties vary stochastically due to various factors (season, weather, oil stains on the rails, slope, passing through curves, etc.), and it can be said that it is difficult to model them using mathematical formulas. Therefore, we construct a fuzzy control system by estimating local information on adhesive properties from observable state quantities and using the knowledge of how to control motor torque to maximize adhesive force. This becomes effective.

Fuzzy control also has the characteristic that multi-purpose control can be easily realized. In other words, when different control objectives exist, control that simultaneously satisfies them can be realized. As mentioned earlier,
Controlling multiple induction motors with one inverter
In a VVF inverter electric train, there is an independent control objective for each driving wheel to obtain the maximum adhesion force, and this objective is satisfied by the voltage frequency control of the same inverter device, so multi-purpose control is used. can be considered. Therefore, from this point of view, fuzzy control can be expected to be effective.

Next, a method of calculating antecedent variables that are input to the fuzzy inference block will be explained. As the antecedent variables of the fuzzy inference in the present invention, the time differential dλ/dt of the slip rate λ, the time differential dF/dt of the adhesive force F, and the creep velocity VS are used.
Since dt can be easily obtained by differentiating Equation (1), and creep speed VS can be obtained from Equation (2), only the method of calculating the adhesive force differential dF/dt will be explained below.

Now, if we ignore the torsion of the joint from the electric motor to the pinion of the drive device, the backlash of the gears of the gear system, the torsion of the driving wheel shaft, etc., the driving wheel transmitted from the electric motor to the driving wheel in equation (3) above The circumferential driving force FM and the rotation system driving force FR can be approximated by the following equations. FM=(z/r)KMII

(4) FR=(J/r2)dVM/dt

(5) Here, z is the reduction ratio from the electric motor to the driving wheels, r is the radius of the driving wheels, J is the total moment of inertia of the rotational inertia system (converted value to the driving wheels), KM is the motor torque constant, and II is per motor is the torque current. Also, assuming that the driving wheel circumferential speed VM is proportional to the motor frequency fM, VM=KO・fM

(6) can be used. However, the proportionality coefficient KO is KO=2πr
/(p・z)
(7), where p represents the number of pole pairs of the induction motor.

From equations (4), (5) and equation (2), the adhesive force F is calculated as follows. F=(z/r)KMII−(J/r2)dVM
/dt (8) Here, K1=z・KM/r, K2=J/r2
(9)
Then, equation (8) becomes F=K1II-K2dVM/dt

It can be written as (10). Therefore, the adhesive force differential is obtained by differentiating equation (10) as follows: dF/dt=K1dII/dt−K2d2V/d
t2
(10') is given by.

Next, details of the fuzzy control law will be explained. An example of the fuzzy control law is the slip rate derivative dλ/
The first rule group outputs the torque current correction pattern differential dIp'/dt according to the state of dt and the adhesive force differential dF/dt, and as a brake against the case where the slip gradually grows at a slow speed. , the torque current correction pattern is differentiated by the magnitude of the creep speed dIp'/dt
It can be written in a composite format with a second rule group that outputs.

First, the first rule group will be explained. Here, as shown in ``If the slip rate derivative dλ/dt is positive and large and the adhesive force derivative dF/dt is negative and large, reduce the torque current command greatly'', the slip rate derivative dλ/d
t, the adhesive force differential dF/dt, and the torque current correction pattern differential dIp'/dt, using the membership functions shown in FIGS. 4, 5, and 6, respectively, to express the positive/negative and magnitude relationships in language. This expression uses the following fuzzy labels. PB Positive and large PS Positive and small ZO NS close to zero NB Negative and small (absolute value)
Negative and large (in absolute value) This makes the previous example: if dλ/dt=PB and dF/dt
=NB then dIp'/dt=PB. Here, dIp'/dt=PB is a positive value because
This is because the torque current pattern in FIG. 1 is corrected by subtractive expression, similar to FIG. 9 described above.

Further, the membership function can be expressed using triangles as shown in FIGS. 4 to 6. Now, if a value of [dλ/dt=+0.32] is detected as the slip rate differential dλ/dt, as shown in FIG. 4,
The goodness of fit that dλ/dt=PS is 0.4, dλ/d
The fitness for t=PB is 0.6, and the fitness for other fuzzy labels is 0. Similarly, the adhesive force differential dF/dt
If measured values are also detected for , the goodness of fit for the two fuzzy labels is determined. Here, it is possible to leave it to the designer's discretion as to how much of each input value should be set as PB and set as NS.

Furthermore, a fuzzy control law (hereinafter simply referred to as control law) can be defined using Table 1. However, for simplicity, the vehicle is considered to be powered in only one direction (λ≧0). In FIG. 1, fuzzy inference blocks 8A to 8D
includes such a control law and a fuzzy inference section. This first set of rules, which will be explained in detail later, has the property of controlling the motor torque so that the operating point always reaches the maximum point of the adhesive property, and is the principle of maximizing the use of adhesive force. This is the control law that forms the basis of the invention.

[Table 1]

Next, the purpose and method of the second rule group for using the creep speed VS in fuzzy inference will be explained. FIG. 7 shows the adhesion characteristics when the rail surface is extremely slippery due to early rain or dew condensation. In this case, the adhesion limit point P shown in FIG. 10 and FIG.
This is the case when the trend is not clear and shows an almost flat trend. In this case, when the operating point enters the flat region, there is almost no change in adhesive force, and dF/dt=ZO. If the creep rate grows slowly here, there is almost no change in the slip rate, so dλ/dt = ZO, and the fuzzy inference from the control law table will output almost zero, maintaining the current torque current command. It turns out. As a result, the creep rate increases indefinitely. When the creep speed increases, squeaks between the rail and the driving wheels, vibrations of the bogie, etc. occur, causing maintenance problems and at the same time impairing riding comfort.

To overcome this drawback, creep rate evaluation is incorporated as a backup to the control law described above. The control law for creep rate is as follows. if VS=PS then dIp'/dt=PS
if VS=PB then dIp'/dt=PB
Furthermore, membership functions for these are shown in FIG. As can be seen from Fig. 8, when the creep speed is 5 km/h or less, both the degree of conformance that VS = PS and the degree of conformity that VS = PB become zero, and the output according to this control law,
In other words, torque current correction pattern differential dIp'/dt
becomes zero. Additionally, the creep speed increases to 5 km/h.
In this case, the control law is such that the output is increased according to the magnitude of the creep speed.

[0034] The reason for limiting the creep speed rather than the slip rate is that it is the increase in the creep speed itself that causes the squeaking noise between the rail and the driving wheels and the vibration of the bogie. This is because it is necessary to evaluate the creep rate itself rather than the slip rate.

Up to this point, the fuzzy inference for obtaining a correction pattern of torque current when focusing on one driving wheel has been described. Next, a method of performing this inference for each driving wheel, max-synthesizing the obtained results, and further defuzzifying them to obtain a torque current correction pattern differential will be described in detail.

The above-mentioned fuzzy control law, including the first rule and the second rule, can be described in the following general form. if dλ/dt=A1 dF/dt=B1
VS=C1 then dIp'/dt=D1
if dλ/dt=A2 dF/dt=B2 V
S=C2 then dIp'/dt=D2

:
:

: if dλ/dt=AN dF/dt=B
N VS=CN then dIp'/dt=DN where dλ/dt, dF/dt, VS, dIp'/
dt is the slip rate differential, adhesive force differential,
represents the creep speed and torque current correction pattern differential,
Aj (j=1 to N) represents one of the membership functions NB, NS, ZO, PS, and PB regarding the slip rate differential. Similarly, Bj (j=1~N
), Cj (j=1 to N), and Dj (j=1 to N) represent membership functions of adhesive force differentiation, creep speed, and torque current correction pattern differentiation, respectively. Here, N represents the total number of control laws.

As the fuzzy inference in the present invention, the most commonly used max-min synthesis method is used. Now, the above Aj, Bj, Cj, D
The membership functions corresponding to j are μAj, μBj
, μCj, μDj. The slip rate differential, adhesive force differential, and creep speed at the driving wheel of the A-axis shown in Figure 1 are as follows:
If it is written as dλA/dt, dFA/dt, and VSA, the result of applying the fuzzy control law (hereinafter referred to as fuzzy conclusion) is given as the membership function μOA shown in the following equation. μOA=(ω1∧μD1)∪…∪(ωN∧μD
N) (11) Here, ωj (j=1~N) is the fitness of the antecedent part,
ωj=μAj(dλA/dt)∧μBj(dF
A/dt)∧μCj(VSA) (12) In the above formula, ∧ represents a min operation, and ∪ represents a set sum operation (here, a max operation).

In the first rule group described above, there is no condition regarding the creep velocity VS, while in the second rule group, there is no condition regarding the slip rate differential and the adhesive force differential. The fitness of an antecedent variable with no corresponding condition is set to 1. That is, in the first rule group, μCj (dVSA/dt) = 1, and in the second rule group, μAj (dλA/dt) = μBj (dFA/dt) = 1.
shall be.

Similar to the A-axis, fuzzy inference is also performed on the B-axis, C-axis, and D-axis, and the fuzzy conclusions μOA, μOB
, μOC, μOD are determined. These fuzzy conclusions for each driving wheel obtained in this way are max-combined, and defuzzification is performed by calculating the center of gravity to obtain the final torque current correction pattern differentiation. That is, μ*=μOA∪μOB∪μOC∪μOD

(13), each fuzzy conclusion is max synthesized and dI
p'/dt={∫μ*(dIp'/dt) (dIp'
/dt)d(dIp'/dt)}
/{∫μ*(dIp'/dt) d(dIp'/
dt)} Find the center of gravity of μ* using (14). Here, the integral in equation (14) is an integral over the platform set of membership functions,
Note that this is not a time integral.

[0040] Furthermore, when max-synthesizing the fuzzy conclusions of each driving wheel, instead of simply max-synthesizing,
Weighting can also be performed using weights γA to γD for each driving wheel. That is, the following equation is used instead of equation (13). μ*=γA・μ0A∪γB・μ0B∪γC・μ
0C∪γD・μ0D

(13
′) Here, . represents an algebraic product, and represents an operation that uniformly multiplies the membership value of the fuzzy conclusion by a constant. Here, for example, if the weight for the A-axis driving wheel is 1 and all other weights are 0, equation (13') becomes μ*=
μ0A, and the same result as when the A axis is selected as the reference axis for fuzzy inference can be obtained. That is, equation (13') is
It can be seen as a generalization of the method of setting a reference axis and performing fuzzy inference.

Next, FIG. 1 will be explained in detail. In FIG. 1, the same reference numerals as in FIG. 9 indicate parts having the same functions. The outputs of PG 4A to 4D are calculated by motor frequency calculation means 5A to 5D to determine the motor rotation frequency fM of each axis.
It is converted into A to fMD and becomes input to the arithmetic units 6A to 6D. In addition, the phase current detection values IuA, Iv of each induction motor
A to IuD, IvD, the output voltage phase θ of the inverter device 101 and the ground vehicle speed V0 are also calculated by the calculation device 6A.
~6D is input. The calculation devices 6A to 6D input the above information and calculate the slip rate differential dλA/dt for each driving wheel.
dλD/dt, adhesive force differential dFA/dt ~ dFD
/dt and creep speeds VSA to VSD are calculated and output.

The outputs of the arithmetic units 6A to 6D are input to fuzzy inference blocks 8A to 8D, and fuzzy inference is executed for each driving wheel based on the aforementioned fuzzy control law. The outputs of the fuzzy inference blocks 8A to 8D are membership functions representing fuzzy conclusions, and the algebraic product means 9A to 8D are membership functions representing fuzzy conclusions for each driving wheel.
9D, a max synthesis operation is executed by the max synthesis means 11, and the fuzzy conclusion μ* shown in equation (13) is output. Reference numeral 12 denotes a defuzzifying means, which calculates the center of gravity shown in equation (14) from the fuzzy inference μ*, thereby making it possible to obtain a definite value.

Reference numeral 10 denotes a weight determining means, which outputs weights γA to γD indicating how much respect is given to the fuzzy conclusions μ0A to μ0D of each driving wheel. This may be fixed to a certain constant value for each driving wheel, or may be a quantity that dynamically changes according to each state quantity during driving. As an example, if weighting is performed according to the magnitude of adhesive force in each driving wheel, γA to γD are as follows. γA=FA/(FA+FB+FC+FD)γB=FB/
(FA+FB+FC+FD)γC=FC/(FA+FB
+FC+FD)γD=FD/(FA+FB+FC+FD
) Here, FA to FD represent the adhesive force at each driving wheel. By performing this weighting, it is possible to achieve control in which the fuzzy conclusion of the driving wheel with a large adhesive force is given more importance, and the fuzzy conclusion of the driving wheel with a small adhesive force is not so important.

Now, the momentary torque current command correction pattern differential dIp, which is the output of the defuzzifying means 12
'/dt is integrated by the pseudo-integrator 13 and added to the torque current pattern Ip generated by the torque current command pattern generation means as explained in FIG. 9 as the torque current correction pattern Ip' at the addition point 105', as shown in the figure below Similarly to 9, the frequency and voltage control of the inverter will be performed.

Reference numeral 16 denotes ground vehicle speed detection means, which outputs ground vehicle speed V0. Although this example shows a method of calculating from the signal of the PG 15 attached to the follower wheel 14, the method is not particularly limited to this method.

FIG. 2 shows the internal structure of the arithmetic unit shown at 6A to 6D in FIG. The arithmetic units 6A to 6D have the same internal structure except for input/output variables, and in FIG. 2, the subscripts A to D that distinguish the driving wheels are omitted for simplicity. First, the motor frequency fM is converted by converting means 604 into a driving wheel circumferential speed conversion value VM (hereinafter simply referred to as driving wheel circumferential speed for simplicity). K0 in the figure is defined by the above equation (6). The ground vehicle speed V0 is subtracted from the driving wheel circumferential speed VM at an addition point 605, and the result is divided by the driving wheel driven speed VM itself by a divider 606 to obtain the slip rate λ. That is, the slip rate is calculated according to the above equation (1). Here, the divider 606
When the divisor VM becomes a very small value, that is, during low speed periods, no division is performed and the slip rate is forced to 0.
This shall include the processing of Furthermore, the slip rate is differentiated by a differentiating means 607, and the time derivative of the slip rate dλ/
Find dt. Here, the differentiating means 607 may perform differentiation in a low frequency range sufficient to represent the rotation of the driving wheels, and is integrated with a second-order lag filter that has good attenuation against high frequency noise.

On the other hand, at the addition point 610, the driving wheel peripheral speed VM
The differential speed between the ground vehicle speed V0 and the ground vehicle speed V0 is calculated and passed through a low-pass filter 711 to become the creep speed VS. Further, the effective current calculation means 601 calculates the effective current IM from the phase current detection values Iu and Iv of the corresponding induction motor.
It has the same function as the effective current calculation means shown at 102 in FIGS. 9 and 1. However, phase current detection is performed for each induction motor. Torque current calculation means 602 is an effective current value IM
A current proportional to the motor torque, that is, a torque current I1, is calculated from the output voltage phase θ of the inverter. The torque current I1, which is the output of the torque current calculating means 602, is added to the addition point via the differentiating means 603.
Added to 609. On the other hand, the driving wheel peripheral speed VM is subtracted at an addition point 609 via a second-order differentiator 608. That is, the adhesive force differential is calculated according to equation (10'). Gain K of the differentiating means 603 and the second-order differentiating means 608
1 and K2 are shown in equation (6). Further, the differentiating means 603 and the second-order differentiating means 608, like the above-mentioned differentiating means 607, need only perform a differentiating operation on the low frequency component, and are integrated with a second-order lag filter that has good blocking characteristics against high-frequency noise. has been made into

Here, the filter constants ω1~ω4, ξ1~
ξ4 may be selected so as to sufficiently attenuate high-frequency noise amplified by differentiation or second-order differentiation, and to make the phase delay as small as possible with respect to the driving wheel circumferential speed and the ground vehicle speed. Furthermore, although a second-order lag filter is used as the filter, it is not particularly limited to this as long as it has similar characteristics. Through the above calculations, the slip rate differential dλ/dt, the adhesive force differential dF/dt, and the creep speed VS are determined.

FIG. 3 is a diagram showing the internal structure of the fuzzy inference blocks indicated by 8A to 8D in FIG. 1. Fuzzy inference blocks 8A to 8D differ only in input and output variables,
Since the internal structure is the same, the subscripts A to D that distinguish the driving wheels are omitted in FIG. 3 for the sake of simplicity. The slip rate differential dλ/dt, adhesion force differential dF/dt, and creep velocity VS, which are input to the fuzzy inference block, are each normalized by normalization means 801X to 801Z, and are input to the fuzzy inference means 802. Normalization refers to the range of criteria (e.g. [-1, +
1]), which allows membership functions of the same shape to be shared. The fuzzy inference means 802 applies the fuzzy control law built in the fuzzy rule block 803 to each normalized input to execute fuzzy inference. The fuzzy rule block 803 stores the aforementioned first rule group and second rule group.

[0050] Most common fuzzy control systems have a complete integrator at the subsequent stage of the fuzzy inference block, and are generally called fuzzy PI control systems. In the present invention, pseudo-integration as shown below is used instead of complete integration. This is to prevent the effects of noise superimposed on the antecedent variables of fuzzy inference from being accumulated and retained by complete integration and outputting an unnecessarily large torque current correction pattern. It is preferable to gradually reduce the torque current correction pattern in a state where the output is zero. The pseudo-integrator 13 has such characteristics, and as an example, τ/(1+τ・S)
A first-order delay means expressed as can be used. Here, a relatively long time constant (on the order of several seconds) is used as the time constant τ. Furthermore, the pseudo-integrator 13 has a limiter function that sets the upper limit of the pseudo-integral value to torque current pattern Ip and the lower limit to zero, so that when the torque current command IIS is equal to or higher than torque current pattern Ip, It is prohibited to have a value of zero or less than zero.

[0051]

Each of the means shown in FIGS. 1 to 3, particularly the fuzzy inference blocks 8A to 8D, will be described in conjunction with FIGS. 10 and 11 of the adhesion characteristics shown above and Table 1 defining the control law. Note that in Table 1, ■ to ■ each represent an area surrounded by a broken line.

In a normal adhesion state, the operating point is on the left side of point P in FIG. 10, and a driving force around the driving wheels is generated at a slip rate of λ0 or less. At this time, the creep rate becomes a small value, so as can be seen from the membership function in Figure 8,
The degree of conformity where VS=PS and the degree of conformity where VS=PB are both 0, and the second rule group based on the creep rate does not work.

[0053] Now, due to rain, etc., the adhesive strength decreases, and as shown in Fig.
Suppose that the adhesive properties are as shown in 1. At this time point P
Ideally, you should drive nearby. Now, when the adhesive force decreases, slipping occurs, the slip rate λ increases, and the operating point tends to shift in the direction of arrow d. At this time, dλ/dt>
0, dF/dt<0, so in Table 1 of the control law, the part indicated by ■ corresponds, and dIp'/dt=P
Since S or dIp'/dt=PB, it acts to reduce the motor torque and return it to the direction of arrow b. In other words, if dλ/dt=PS AND dF/dt in the part ■ in Table 1 of the control law
=NS then dIp'/dt=PS i
f dλ/dt=PS AND dF/dt=NB t
hen dIp'/dt=PB if dλ/
dt=PB AND dF/dt=NS then
dIp'/dt=PB if dλ/dt=PB
AND dF/dt=NB then dIp'/
The control law such as dt=PB is the slip rate derivative dλ/dt,
Adhesive force derivative Used according to the value of dF/dt.

Furthermore, when shifting to the mode of arrow b, d
λ/dt<0, dF/dt>0, and Table 1 of the control law
The control law of the part indicated by ■ acts, and dIp'/d
t=NS, that is, it acts to slightly increase the torque current command. This is an empirical control law that anticipates a delay until the electric motor torque actually increases and the wheel circumferential driving force starts to increase again. As a result, the operating point does not pass past the point P and enter the area indicated by the arrow c, but can promote an effect in which the operating point is balanced in the vicinity of the point P.

Next, if the operating point passes point P and returns to the creep region and moves in the direction of arrow c, then
Since dλ/dt<0, dF/dt<0, the control law for the part indicated by ■ in the control law table corresponds, and dIp'/
dt=NS or dIp'/dt=NB, that is, it acts to increase the torque current command. As a result, the motor torque increases and the operating point moves in the direction indicated by arrow a.

When the operating point is in the state of arrow a, d
Since λ/dt>0 and dF/dt>0, the control law for the part indicated by ■ in the control law table corresponds, and dIp'/dt
= NS, that is, it acts to slightly increase the torque current command,
The motor torque is gradually increased to reach point P.

[0057] Since the adhesion limit point P changes from time to time,
The control system repeats the aforementioned mode and operates to constantly track point P. When the adhesive force is stabilized at a constant value, it is balanced near the center of the control law table, and the operating point can be stabilized near the point P.

Furthermore, in a state showing flat adhesion characteristics as shown in FIG. 7, the slip rate differential takes a small value, so the slow growth of the creep rate cannot be captured, and the creep rate gradually increases. However, in this case, the creep speed is limited to a certain value by the second rule group based on the evaluation of the creep speed, and the torque current is modified so that the maximum adhesion force can be obtained within the creep speed limit. Become.

The above is the effect of the fuzzy control law when focusing on one driving wheel. Next, the function of the multi-purpose control of the present invention, which combines fuzzy conclusions for each driving wheel to the max and defuzzifies them, will be explained.

FIGS. 12 and 13 illustrate the adhesion characteristics and operating points of each driving wheel, and the movement of the operating points when the adhesion control according to the present invention is applied. Figure 12
(a) shows a case where only the adhesive property of the A-axis is degraded due to dirt on the rail surface, etc., and only the A-axis is idling. At this time, the operating point on the A-axis enters the idling region, so a fuzzy conclusion can be obtained in the direction of reducing the motor torque, that is, increasing the torque current correction pattern, but the operating point on the other B to D axes creeps. Since it is on the region side, a fuzzy conclusion can be obtained in the direction of increasing the motor torque, that is, decreasing the torque current correction pattern. Then, these two contradictory fuzzy conclusions are combined to the max, and a nearly intermediate conclusion that slightly increases the motor torque, ie, slightly decreases the torque current modification pattern, is obtained. As a result, each operating point moves to a position as shown in FIG. 12(b), and it becomes possible to make maximum use of the entire adhesive force without reducing the motor torque more than necessary.

FIG. 13(a) shows a case where the adhesion characteristics of all the driving wheels are degraded due to rain or the like. At this time, the operating points of all driving wheels enter the idling region, so
The same fuzzy conclusion is obtained in the direction of decreasing motor torque, ie increasing the torque current modification pattern. Even if these are combined to the maximum, the conclusion is that the torque current correction pattern will still increase, so each operating point will be pulled back into the creep region, as shown in FIG. 13(b).

[0062]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Specifically, the control block diagram shown in FIG. 1 can be completely digitalized. That is, 16-bit DSP (digital signal processor)
This can be achieved by adopting such methods. In this example, since a PG is used as the speed detector, digital calculation is possible for speed detection. Furthermore, by digitizing the detected current value using an A/D converter in the effective current detection means 102 or the effective current calculation means 601, all signals to the next stage can be processed by digital calculation. Furthermore, normalization means 801X to 801Z
also serves to quantize each input into 29 steps. Therefore, the membership functions included in the fuzzy inference blocks 8A to 8D can be realized as a staircase waveform in which the gradient from 0 to 1 is approximated by steps. In addition, the inverter control section after addition point 105' is a digital control system using another microprocessor, but it is faster and faster.
If a microprocessor with a high bit number is used, it is also possible to include a fuzzy control section configured with a DSP.

[0063]

As described in detail above, by combining the fuzzy control method and information regarding adhesion, it is possible to realize a control system that has the advantages of a mathematical information processing method and a designer's knowledge. As described above, according to the present invention, it is possible to eliminate the problem of repeating on-off cycles caused by the conventional re-adhesion method based on slip detection, and to utilize the VVVF inverter that continuously changes torque. Furthermore, it is possible to comprehensively evaluate the state quantities of all the driving wheels, to make maximum use of the adhesion force, and to maintain acceleration even when the adhesion characteristics are degraded.

[Brief explanation of the drawing]

FIG. 1 is an overall block diagram of an example of an adhesion control system shown to facilitate the basic technical idea of the present invention.

FIG. 2 is a block diagram of an arithmetic device for calculating a slip rate differential, an adhesion force differential, and a creep rate.

FIG. 3 is a block diagram showing the internal structure of a fuzzy inference block.

FIG. 4 is a graph showing membership functions of slip rate differentials.

FIG. 5 is a graph showing a membership function of adhesive force differentiation.

FIG. 6 is a graph showing membership functions of torque current modification pattern differentiation.

FIG. 7 is a graph showing flat adhesion properties in slippery conditions.

FIG. 8 is a graph showing the membership function of creep rate.

FIG. 9 is a block diagram showing a control circuit of a VVVF inverter using conventional readhesion control.

FIG. 10 is a graph showing adhesive properties when dry.

FIG. 11 is a graph showing adhesive properties in a wet state.

FIG. 12 is a diagram showing the adhesion characteristics and movement of the operating point of each driving wheel when one axis is idling.

FIG. 13 is a diagram showing the adhesion characteristics and movement of the operating point of each driving wheel when all axes are idling.

[Explanation of symbols]

1 Electric vehicle 2A-2D Induction motor 3A-3D Driving wheel 4A-4D, 15 Motor speed detector (PG) 5A-
5D Motor frequency calculating means 6A to 6D Calculating devices 7A to 7D Differentiators 8A to 8D Fuzzy inference blocks 9A to 9D Algebraic product means 10 Weight determining means 11 Max synthesis means 12 Defuzzification means 13 Pseudo integration means 14 Follower wheel 16 Ground vehicle speed Calculating means 101 Inverter 102 Effective current detecting means 103, 602 Torque current calculating means 104 Torque current pattern generating means 107 Current controlling means 108 Motor frequency selecting means 110 V/f ratio 111, 606 Divider 112 First-order lag filter 113 Slipping detecting means 114 Comparator means 115 Torque current correction pattern generation means 601 Effective current calculation means 603, 607 Differentiation means 604 Conversion means 606 Secondary differentiation means 801X, 801Y, 801Z Normalization means 80
2 Fuzzy inference means 803 Fuzzy rule block

Claims (4)

[Claims] 1. An electric vehicle in which a plurality of induction motors are controlled by a variable voltage variable frequency inverter, comprising means for detecting and calculating the driving wheel circumferential speed of each driving wheel, means for detecting and calculating the ground vehicle speed, and the driving wheel circumferential speed. means for calculating the slip rate of each driving wheel from the body and ground vehicle speed, means for calculating the driving wheel circumferential adhesion force of each driving wheel, means for calculating the time differential of the slip rate and the adhesion force, and the slip rate time Differential and Adhesive Force The time differential and the differential speed are used as antecedent variables, and the torque command value of the induction motor is used as a consequent variable, and when the differential speed is small, the slip rate differential and adhesive force differential are used,
A first rule group corrects the electric motor torque command in a direction that can increase the adhesive force, and a second rule group narrows down the electric motor torque according to the magnitude of the differential speed when the differential speed is large. An electric vehicle control device comprising means for executing fuzzy inference, max-synthesizing the obtained results, and further performing defuzzification processing to obtain a final correction pattern of a motor torque command. 2. Claim 1, wherein the torque of the induction motor is command-controlled by a current corresponding to a torque component.
The electric vehicle control device described. 3. The electric vehicle control device according to claim 1, wherein the torque of the induction motor is command-controlled by a torque calculated from an electric signal. 4. The electric vehicle control device according to claim 3, wherein the result inferred from the first rule group is passed through a first-order delay means as the first electric motor torque command correction signal.