CN109878480B - Regenerative braking control method for switching friction coefficient prediction modes of electric automobile - Google Patents
Regenerative braking control method for switching friction coefficient prediction modes of electric automobile Download PDFInfo
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- CN109878480B CN109878480B CN201910168009.6A CN201910168009A CN109878480B CN 109878480 B CN109878480 B CN 109878480B CN 201910168009 A CN201910168009 A CN 201910168009A CN 109878480 B CN109878480 B CN 109878480B
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Abstract
A regenerative braking control method for electric vehicle friction coefficient prediction mode switching belongs to the technical field of electric power; inputting the states of wheels and the whole vehicle and the braking intention into a fuzzy rule base; detecting velocity v in longitudinal directionxBraking torque TBrakeAs input, the coefficient of friction mujsPredicting the friction coefficient for the coefficient measurement fuzzy control model of target output; outputting an instruction according to the control rule, sending the instruction to a brake system, and driving an actuating mechanism after processing; the braking force of the front wheel brake and the rear wheel brake is distributed within a reasonable friction coefficient range; when the friction coefficient is lower than the safety limit value mu of the friction coefficientThldIn the switching control mode, the friction coefficient predicted value is compensated by the modified friction coefficient neural network controller for delta mu, the friction braking force is increased, and the friction braking force and the regenerative braking force share are redistributed; the invention effectively reduces the braking distance and reduces the probability of traffic accidents caused by low friction coefficient between the wheels and the ground.
Description
Technical Field
The invention belongs to the technical field of electric power, and particularly relates to a method for controlling regenerative braking by switching friction coefficient prediction modes of an electric vehicle.
Background
A low road surface coefficient of static friction (i.e., the coefficient of adhesion) is a dangerous situation. Shortly after raining, when only a small amount of rainwater is on the road surface, the rainwater is mixed with dust and oil dirt to form water liquid with high viscosity, and the rolling tire cannot extrude a water liquid film between the tire tread and the road surface; the adhesion performance is greatly reduced due to the lubricating action of the water film; as the water layer deepens, the water slipping phenomenon can occur; in cold regions such as northeast and the like, the average adhesion coefficient of a road surface compacted or iced by snow in winter is only about 0.1, the total braking force of the ground is suddenly reduced, the front wheels and the rear wheels slip, and the braking distance of the automobile is prolonged.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides the method for controlling the friction coefficient prediction mode switching and the regenerative braking of the electric automobile.
A friction coefficient prediction mode switching regenerative braking control method for an electric vehicle comprises the following steps:
step a, inputting the states of wheels and the whole vehicle and the braking intention into a fuzzy rule base;
step b, detecting the speed v in the longitudinal directionxBraking torque TBrakeAs input, the coefficient of friction mujsPredicting the friction coefficient for the coefficient measurement fuzzy control model of target output;
step c, outputting an instruction according to the control rule, sending the instruction to a brake system, and driving an actuating mechanism after processing;
d, distributing the braking force of the front and rear wheel brakes within a reasonable friction coefficient range;
step e, when the friction coefficient is lower than the safety limit value mu of the friction coefficientThldAnd in the switching control mode, the friction coefficient predicted value is compensated by the neural network controller for the correction friction coefficient by delta mu, the friction braking force is increased, and the friction braking force and the regenerative braking force share are redistributed.
Further, the braking torque TBrakeFuzzification is carried out, a Gaussian function distribution is used, and the interval range is [ -6,0 [)]The domain is divided into 8 subsets, including QB, VVB, VB, B, S, VS, VVS, and QS, representing respectively, substantially large, small, substantially small, and substantially small。
Further, the longitudinal detection speed vxWorking Range [0,11]The domain of discourse is divided into 7 subsets, including VS, S, M, MB, B, VB, and VVB, representing very small, medium, large, and very large, respectively.
Further, the coefficient of friction μjsThe defined interval is [0.2,0.8 ]]The domain of discourse is divided into 9 subsets, including QH, VVH, VH, H, L, VL, VVL, QL and QQL, representing considerably high, very high, low, very low, considerably low and considerably low, respectively.
Further, the control rules include partial fuzzy rules as follows:
if T isBrakeIs QB, and vxIs VVS, then μjsIs VVH;
if T isBrakeIs VVB, and vxIs VS, then μjsIs VVH;
if T isBrakeIs QB, and vxIs VVB, then μjsIs L.
Furthermore, an electric booster valve optimized braking system is additionally arranged, and a booster type regenerative braking system structure is constructed. When the friction coefficient is lower than the safety limit value mu of the friction coefficientThldIn the switching control mode, the friction coefficient predicted value is compensated by the friction coefficient neural network controller to be delta mu, a BP neural network is established according to a mu-slip curve by adopting a double-hidden layer network predictor according to different values of the friction coefficient, and a corresponding mu is searched in a slip rate lambda safety areasafeAnd predicting 500 groups of data in the friction coefficient distribution area, and encrypting data acquisition in a local area with low friction coefficient. The electric booster valve is started to increase pressure and prevent the wheel from sideslipping. During the brake process of the booster-type electro-hydraulic composite regenerative brake, the BCU regulates the brake pressure of the front wheel and the rear wheel according to the brake pedal signal of a driver, properly reduces the mechanical brake share and effectively improves the front wheel brake share.
Further, the coefficient of friction μWeightFor weighting the friction coefficient, the expression is shown in formula (2), k1And k2Respectively coefficient of friction mujsAnd muABSThe weight factor of (c):
compared with the prior art, the invention has the following beneficial effects:
the invention provides a friction coefficient prediction mode switching regenerative braking control method of an electric vehicle, which effectively reduces the braking distance, reduces the probability of traffic accidents caused by low friction coefficient between wheels and the ground and recovers energy as much as possible by a friction coefficient prediction mode switching regenerative braking control method formed by combining a friction coefficient measurement fuzzy controller and a friction coefficient correction neural network controller; the brake pad has a certain inhibiting effect on the side slip of the wheels, and the safety and the comfort of the automobile are enhanced; is convenient to be popularized and popularized.
Compared with the common brake system in which the anti-skid chain is additionally arranged, the anti-skid chain brake system has remarkable effect, the brake energy is not consumed in the form of heat energy, and the feedback regeneration of the energy is not influenced; compared with the high-grade vehicle equipped with an ESP vehicle body stabilizing system, ASR driving anti-skid control and other equipped intelligent pressure sensors, the cost is relatively low, and the popularization is facilitated; compared with a series regenerative braking system, an ideal energy recovery strategy is not established on an I curve, is not combined with conventional braking, is not compatible with an ABS system, does not generate a hysteresis reaction in the braking process, and reduces the probability of traffic accidents.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is an idealized front and rear brake force distribution graph;
FIG. 3 is a brake torque input graph;
FIG. 4 is a longitudinal detection speed input profile;
FIG. 5 is a graph of coefficient of friction output;
FIG. 6 is a plot of zone division;
FIG. 7 is a schematic view of a coefficient of friction vector;
FIG. 8 is a schematic diagram of a BP neural network modification.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
A friction coefficient prediction mode switching regenerative braking control method for an electric vehicle is shown in FIG. 1, and comprises the following steps:
step a, inputting the states of wheels and the whole vehicle and the braking intention into a fuzzy rule base, wherein the fuzzy rule base is shown as a table 1;
step b, detecting the speed v in the longitudinal directionxBraking torque TBrakeAs input, the coefficient of friction mujsPredicting the friction coefficient for the coefficient measurement fuzzy control model of target output;
step c, obtaining a fuzzy output result according to the control rule, namely the output result corresponding to the table 1, then performing fuzzy solution, sending an instruction to an instruction input end of a brake controller by an ECU (electronic control unit), namely a fuzzy controller in a central control unit, and driving an executing mechanism after processing, wherein the executing mechanism comprises a driving brake block and a braking brake unit;
step d, distributing the braking force of the front wheel brake and the rear wheel brake within a reasonable range of friction coefficient, as shown in figure 2, Fμ1For front wheel brake braking force, Fμ2Braking force for a rear wheel brake;
and e, additionally arranging an electric booster valve optimizing braking system, and constructing a booster type regenerative braking system structure. When the friction coefficient is lower than the safety limit value mu of the friction coefficientThldIn the switching control mode, the friction coefficient predicted value is compensated by the friction coefficient neural network controller to be delta mu, a BP neural network is established according to a mu-slip curve by adopting a double-hidden layer network predictor according to different values of the friction coefficient, tire pressure, braking torque and braking initial speed are selected as the input of the BP neural network predictor, and corresponding mu is searched in a slip rate lambda safety areasafeAnd predicting 500 groups of data in the friction coefficient distribution area, and encrypting data acquisition in a local area with low friction coefficient. Starting upThe electric booster valve increases pressure to prevent the wheel from sideslipping. During the braking process of the booster-type electro-hydraulic composite regenerative braking, the BCU regulates the braking pressure of the front wheel and the rear wheel according to the brake pedal signal of a driver, properly reduces the mechanical braking share and effectively improves the front wheel braking share.
As shown in fig. 8, the BP neural network modification is to first train the network based on the experimental data that has been measured. And aiming at the given real-time input variable, obtaining the corrected friction coefficient value which accords with the braking rule.
With motor braking torque TBrakeLongitudinal detection velocity vxCoefficient of friction μjsA fuzzy controller is created for the output. Since regenerative braking occurs only on the drive wheels, the input amount and output amount are first blurred for the drive wheels as a subject of study.
In particular, the braking torque TBrakeThe input quantity is fuzzified, the Gaussian function distribution is used, the torque range in the driving engineering is wide, and the interval range is [ -6,0 [ -6]As shown in FIG. 3, the domains are divided into 8 subsets, including QB, VVB, VB, B, S, VS, VVS, and QS, representing respectively, substantially large, small, substantially small and substantially small.
In particular, the longitudinal detection speed vxFuzzification is carried out on input quantity, and a working range [0,11 ] is defined]As shown in fig. 4, the domain of discourse is divided into 7 subsets, including VVS, VS, S, B, VB, VVB and QB, representing very small, medium, large, very large and very large, respectively.
In particular, the coefficient of friction μjsThe defined interval is [0.2,0.8 ]]As the coefficient of friction approaches 0.2, the distribution function is relatively tight, as shown in fig. 5, and the universe of discourse is divided into 9 subsets, including QH, VVH, VH, H, L, VL, VVL, QL, and qqql, representing relatively high, very high, low, very low, relatively low, and relatively low, respectively.
In order to brake the automobile stably, a control rule base is required to be established for each condition, and all possible working modes in the braking process are covered. On the basis of road condition actual measurement data, 56 rules are formulated, and a fuzzy rule base is shown as the following table:
TABLE 1
Specifically, the control rules include partial fuzzy rules as follows:
if T isBrakeIs QB, and vxIs VVS, then μjsIs VVH; indicating that when the braking torque is quite large and the longitudinal detected speed is very small, the predicted coefficient of friction is very high;
if T isBrakeIs VVB, and vxIs VS, then μjsIs VVH; indicating that when the braking torque is very large and the longitudinal detected speed is very small, the predicted friction coefficient is very high;
if T isBrakeIs QB, and vxIs VVB, then μjsIs L; indicating that the predicted coefficient of friction is low when the braking torque is quite large and the longitudinal detected speed is very large.
In particular, when the friction coefficient is lower than the safety limit value mu of the friction coefficientThldIn the switching control mode, the friction coefficient predicted value is compensated by the friction coefficient neural network controller by utilizing the correction friction coefficient, a BP neural network is established according to a mu-slip curve of figure 6 by adopting a double-hidden layer network predictor according to different value taking conditions of the friction coefficient, and mu represents the friction coefficient; slip represents slip rate, tire pressure, braking torque and braking initial speed are selected as input of a BP neural network predictor, and corresponding mu is searched in a slip rate lambda safety regionsafeμ denotes the coefficient of friction, μsafeAnd representing a safe friction coefficient, predicting 500 groups of data in a friction coefficient distribution area, and encrypting data acquisition in a local area with a low friction coefficient.
The generation of the friction coefficient during the running of the wheel is not caused by a single factor. For a pure electric bus, the pure electric bus mainly consists of two parts, and the friction coefficient mu detected in the driving processjsAnd anti-lock brake systemSystematically generated muABSThe corresponding relation of (d) is shown in FIG. 7, muABSRepresenting the coefficient of friction generated by the anti-lock braking system. Mu.sjsThe actual stress condition of the wheel is obtained and is the vector sum of the transverse friction coefficient and the longitudinal friction coefficient, and the vector sum is shown as a formula (1). Anti-lock braking systems cause speed fluctuations during braking and also produce hysteresis in the generation of the coefficient of friction as shown in the vector diagram.
Wherein, FxThe frictional resistance in the x direction in the frictional force is shown; fyThe frictional resistance in the y direction in the frictional force is shown; fzVertical load acting on the ground during braking; mu.sxIs the coefficient of friction in the x direction; mu.syThe coefficient of friction in the y-direction.
In summary of the analysis, a weighted friction coefficient is proposed, in particular the friction coefficient μjsFor weighting the friction coefficient, the expression is shown in formula (2), k1And k2Respectively coefficient of friction mujsAnd muABSThe weight factor of (c):
wherein, muWeightThe weighting friction coefficient is the friction coefficient obtained by integrating the friction coefficient detected in the driving process and the friction coefficient generated by an anti-lock braking system; as shown in FIGS. 6 and 7, the x-axis λ increases, and the x-axis λ can be roughly divided into 3 regions, wherein the 1 st region is an approximately linear region, μxThe major effect is in the longitudinal direction, with a smaller λ, μABSNeglect, k2Is approximately 0; the 2 nd middle area is a relative safety area, the automobile can simultaneously have the conditions of driving turning and braking turning, and the lambda is in a reasonable space, and the transverse and longitudinal friction coefficient mu isxAnd muyAre all bigger; in the 3 rd region muyDecrease very rapidly, muxThe trend of the change is relatively gentle and is simultaneously towards muThe influence is great, and the brake is in the process of advancing or decelerating.
Claims (7)
1. A friction coefficient prediction mode switching regenerative braking control method for an electric vehicle is characterized by comprising the following steps:
step a, inputting the states of wheels and the whole vehicle and the braking intention into a fuzzy rule base;
step b, detecting the speed v in the longitudinal directionxBraking torque TBrakeAs input, the coefficient of friction mujsPredicting the friction coefficient for the coefficient measurement fuzzy control model of target output;
step c, outputting an instruction according to the control rule, sending the instruction to a brake system, and driving an actuating mechanism after processing;
d, distributing the braking force of the front and rear wheel brakes within a reasonable friction coefficient range;
step e, when the friction coefficient is lower than the safety limit value mu of the friction coefficientThldAnd in the switching control mode, the friction coefficient predicted value is compensated by the neural network controller for correcting the friction coefficient, namely delta mu, and the friction braking force and the regenerative braking force share are redistributed to increase the regenerative braking force.
2. The electric vehicle friction coefficient prediction mode switching regenerative braking control method according to claim 1, characterized in that the braking torque TBrakeFuzzification is carried out, a Gaussian function distribution is used, and the interval range is [ -6,0 [)]The domain of discourse is divided into 8 subsets, including QB, VVB, VB, B, S, VS, VVS, and QS, representing respectively, substantially large, small, substantially small, and substantially small.
3. The electric vehicle friction coefficient prediction mode switching regenerative braking control method according to claim 2, characterized in that the longitudinal detection speed vxWorking Range [0,11]The domain of discourse is divided into 7 subsets, including VVS, VS, S, B, VB, VVB, and QB, representing very small, medium, large, and very large, respectively。
4. The electric vehicle friction coefficient prediction mode switching regenerative braking control method according to claim 3, characterized in that the friction coefficient μjsThe defined interval is [0.2,0.8 ]]The domain of discourse is divided into 9 subsets, including QH, VVH, VH, H, L, VL, VVL, QL and QQL, representing considerably high, very high, low, very low, considerably low and considerably low, respectively.
5. The electric vehicle friction coefficient prediction mode switching regenerative braking control method according to claim 4, characterized in that the control rule comprises a partial fuzzy rule as follows:
if T isBrakeIs QB, and vxIs VVS, then μjsIs VVH;
if T isBrakeIs VVB, and vxIs VS, then μjsIs VVH;
if T isBrakeIs QB, and vxIs VVB, then μjsIs L.
6. The method as claimed in claim 5, wherein the regenerative braking control is performed when the friction coefficient is lower than the safety limit μThldIn the switching control mode, the friction coefficient predicted value is compensated by the friction coefficient neural network controller to be delta mu, a BP neural network is established according to a mu-slip curve by adopting a double-hidden layer network predictor according to different values of the friction coefficient, and a corresponding mu is searched in a slip rate lambda safety areasafe，μsafeAnd representing a safe friction coefficient, predicting 500 groups of data in a friction coefficient distribution area, and encrypting data acquisition in a local area with a low friction coefficient.
7. The method for controlling regenerative braking with mode switching for predicting friction coefficient of electric vehicle according to claim 6, wherein the friction coefficient μWeightTo weight the coefficient of friction, the expression is shown in equation (2), μABSRepresenting the coefficient of friction, k, generated by an anti-lock braking system1And k2Respectively coefficient of friction mujsAnd muABSThe weight factor of (c):
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| CN113830042A (en) * | 2020-06-24 | 2021-12-24 | 广州汽车集团股份有限公司 | Automobile brake control method and system |
| DE102020215398A1 (en) | 2020-12-07 | 2022-06-09 | Robert Bosch Gesellschaft mit beschränkter Haftung | Braking torque control device, braking system and method for controlling braking torque |
| CN113085806B (en) * | 2021-04-06 | 2022-04-08 | 南京航空航天大学 | Composite regenerative braking control method based on giant magnetostrictive braking system |
| JP7392680B2 (en) * | 2021-04-06 | 2023-12-06 | トヨタ自動車株式会社 | Vehicle braking system |
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