CN115071449B - Composite power supply energy management method based on multi-fuzzy controller - Google Patents
Composite power supply energy management method based on multi-fuzzy controller Download PDFInfo
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- 238000007726 management method Methods 0.000 title claims abstract description 24
- 239000002131 composite material Substances 0.000 title claims abstract description 19
- 239000003990 capacitor Substances 0.000 claims abstract description 46
- 230000006870 function Effects 0.000 claims abstract description 26
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims abstract description 19
- 229910052744 lithium Inorganic materials 0.000 claims abstract description 19
- 238000007599 discharging Methods 0.000 claims abstract description 8
- 238000000034 method Methods 0.000 claims abstract description 6
- 150000001875 compounds Chemical class 0.000 abstract description 4
- 238000007689 inspection Methods 0.000 description 12
- 238000010586 diagram Methods 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L50/00—Electric propulsion with power supplied within the vehicle
- B60L50/40—Electric propulsion with power supplied within the vehicle using propulsion power supplied by capacitors
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L50/00—Electric propulsion with power supplied within the vehicle
- B60L50/50—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells
- B60L50/60—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by batteries
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Abstract
The invention provides a compound power supply energy management method based on a multi-fuzzy controller, which comprises the following steps: the charging and discharging trend of the super capacitor is changed through the switching of the multi-fuzzy controller, so that the charging and discharging balance of the super capacitor is improved; to improve the energy management performance of the composite power supply; the method comprises the following steps: selecting fuzzy controllers A-D according to the switching signal alpha; calculating power ratios beta and delta USOC (k-1); inputting the power ratio beta into a selected fuzzy controller, and obtaining a power ratio beta language variable value according to a set power ratio beta membership function; inputting delta USOC (k-1) into a selected fuzzy controller, and obtaining delta USOC (k-1) language variable values according to a set delta USOC (k) membership function; according to the power ratio beta language variable value and the delta USOC (K-1) language variable value, obtaining a scaling factor K p language variable value by checking a fuzzy rule table; calculating a specific value of K p; and calculating the supercapacitor distribution current and the lithium battery distribution current.
Description
Technical Field
The invention relates to the technical field of energy management of inspection robots, in particular to a multi-fuzzy controller-based composite power source energy management method for a lithium battery and a super capacitor.
Background
With the continuous consumption of non-renewable energy sources such as petroleum, natural gas and the like and the increasingly severe ecological environment, the inspection robot is rapidly developed. The composite power supply is a key technology for solving the problems of insufficient energy supply, low instantaneous power, continuous voyage and the like of a single power supply system of the inspection robot, the characteristics of a lithium battery and a super capacitor in the composite power supply on energy density and instantaneous power are just opposite, the lithium battery belongs to an energy storage device and can output low-frequency stable power, and the super capacitor belongs to a power storage device and can output high-frequency variable power. In addition, the super capacitor can also reduce impact damage of high-current discharge of the lithium battery to the super capacitor. In order to fully exert the advantages of the two, a composite power supply energy management strategy is designed according to the power requirements in the actual road conditions of the inspection robot, and the use of two energy sources, namely a lithium battery and a super capacitor, is reasonably distributed.
In order to fully exert the advantages of different power sources in the hybrid power system and avoid the disadvantages, the working modes of the inspection robot under different working conditions are analyzed:
(1) When the inspection robot starts or needs to accelerate in a short time, the driving motor needs to quickly increase the running speed to an expected value, and the super capacitor has the characteristic of instantaneous heavy current, can provide short-time heavy power for the motor, and meets the power requirement of the inspection robot for starting.
(2) The inspection robot is started to a constant speed, and in the continuous running process, the motor is required to be kept in stable and continuous running, the required power is small, and the lithium battery in the hybrid power system can provide stable and continuous power for the inspection robot.
(3) When the inspection robot accelerates in a short time or goes up a slope, the power requirement of the whole vehicle is increased, the two power sources work simultaneously, the lithium battery can provide continuous and stable power output for the driving motor, the super capacitor group can provide instant high power, the super capacitor group supplements each other, and the running requirement of the inspection robot is met.
(4) In the deceleration running process of the inspection robot, a driving motor can be rapidly switched into a generator, and the generator generates electric power through braking movement to store and redistribute the stored energy in a hybrid power system, so that the energy management system classifies and sorts the stored energy. In the energy distribution, the charging of the super capacitor is considered preferentially, and when the charging value of the super capacitor is high, the residual electric energy is transmitted to the lithium battery SOC.
Some terms related to the present application are as follows:
SOC: state of charge.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a multi-fuzzy controller-based composite power supply energy management method which is used for improving the composite power supply management performance of a super capacitor and a lithium battery. In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the invention is as follows:
The embodiment of the invention provides a compound power supply energy management method based on a multi-fuzzy controller, which comprises the following steps: the charging and discharging trend of the super capacitor is changed through the switching of the multi-fuzzy controller, so that the charging and discharging balance of the super capacitor is improved.
Further, the method specifically comprises the following steps:
defining a power ratio beta reflecting the relation of the working condition information of adjacent time periods, which is expressed as:
wherein P Flat plate (k-1) is the average required power of the robot in the period from the moment of k-1 to the moment of k, and P (k) is the required power of the robot at the moment of k;
defining a switching signal α, expressed as:
α＝MSOC+2Mp+1 (4)
Wherein USOC (k) represents the instantaneous value of the supercapacitor SOC at time k; USOC opt is the reference value of the super capacitor SOC; alpha is a fuzzy controller switching signal; the switching signals α=i (i=1, 2,3, 4) correspond to the fuzzy controllers a to D, respectively;
when P Flat plate (k-1) < 0, the linguistic variable of the power ratio β is set to { NB, NS, PS, PB } for the fuzzy controllers A and B; when P Flat plate (k-1) > 0, setting the power ratio beta language variable as { NB, NS, PS, PMS, PM, PMB, PB } for the fuzzy controllers C and D, wherein NB, NS, PS, PMS, PM, PMB and PB represent negative big, negative small, positive middle and small, median big and positive big respectively; selecting a power ratio beta domain, and setting a power ratio beta membership function;
Setting the language variable of delta USOC (k) as { NB, NS, ZE, PS, PB }, which is respectively represented as negative large, negative small, zero, positive small and positive large; selecting a domain of delta USOC (k), and setting a delta USOC (k) membership function;
Defining the output quantity of four fuzzy controllers A-D, namely that the language variable of the scaling factor K p is { NB, NM, NS, ZE, PS, PM, PB }, which respectively represents negative big, negative medium, negative small, zero, positive small, medium and positive big; selecting a discourse domain of a scale factor K p, and setting a membership function of the scale factor K p;
the prediction formula of the super capacitor SOC variation delta USOC (k) in the period from the moment k to the moment k+1 is set as follows:
ΔUSOC(k)＝ΔUSOC(k-1)+Kp(USOCopt-USOC(k)) (5)
ΔUSOC(k-1)＝USOC(k)-USOC(k-1) (6)
Wherein USOC (k-1) represents the instantaneous value of the super capacitor SOC at the time of k-1, delta USOC (k-1) and delta USOC (k) respectively represent the variation of the super capacitor SOC in the period from the time of k-1 to the time of k and in the period from the time of k to the time of k+1;
step S10, calculating a switching signal alpha according to formulas (2) - (4), and selecting fuzzy controllers A-D according to the switching signal alpha;
step S20, calculating a power ratio beta according to a formula (1), and calculating delta USOC (k-1) according to a formula (6);
Step S30, inputting the power ratio beta into the fuzzy controller selected in the step S10, and obtaining a power ratio beta language variable value according to the set power ratio beta membership function;
Step S40, inputting delta USOC (k-1) into the fuzzy controller selected in the step S10, and obtaining delta USOC (k-1) language variable value according to the set delta USOC (k) membership function;
step S50, obtaining a proportional factor K p language variable value by checking a fuzzy rule table according to the power ratio beta language variable value and the delta USOC (K-1) language variable value;
Step S60, calculating a specific numerical value of K p according to a membership function of the set scale factor K p;
Step S70, inputting a scale factor K p into a formula (5), obtaining a predicted value of delta USOC (K), and calculating a super capacitor distribution current i c (K) according to a formula i c(k)＝-ΔUSOC(k).Qc, wherein Q c is the maximum charge amount of the super capacitor;
Step S80, subtracting the super capacitor distribution current i c (k) from the total demand current i Total (S) (k) to obtain a lithium battery distribution current i b (k).
Further, the power ratio β has the argument of [ -10,10].
Further, the argument of ΔUSOC (k) is [ -4,4].
Further, the argument of the scaling factor K p is [ -8,8].
The technical scheme provided by the embodiment of the invention has the beneficial effects that:
1) And the charge and discharge balance of the super capacitor is realized through four fuzzy controllers, and the energy management performance of the composite power supply is improved.
2) Under the condition of meeting the power requirement of the robot in running, the energy utilization rate is improved, meanwhile, the use of lithium batteries is reduced, and the life cycle of the composite power supply system is improved.
Drawings
FIG. 1 is a schematic diagram of an energy management method in an embodiment of the invention.
FIG. 2 is a graph showing the power ratio β membership function of different fuzzy controllers according to an embodiment of the present invention.
FIG. 3 is a graph showing the ΔUSOC (k) membership function in an embodiment of the present invention.
FIG. 4 is a graph showing the membership function of the scaling factor K p according to an embodiment of the present invention.
Fig. 5 is a comparison diagram of SOC of a lithium battery with various strategies under integrated test conditions in an embodiment of the present invention.
FIG. 6 is a graph showing comparison of the SOC of the super capacitor with various strategies under the comprehensive test conditions in the embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a compound power supply energy management method based on a multi-fuzzy controller, which comprises the following steps: the charging and discharging trend of the super capacitor is changed through the switching of the multi-fuzzy controller, so that the charging and discharging balance of the super capacitor is improved; to improve the energy management performance of the composite power supply;
a compound power supply energy management method based on a multi-fuzzy controller is shown in fig. 1, and specifically comprises the following steps:
defining a power ratio beta reflecting the relation of the working condition information of adjacent time periods, which is expressed as:
wherein P Flat plate (k-1) is the average required power of the robot in the period from the moment of k-1 to the moment of k, and P (k) is the required power of the robot at the moment of k;
defining a switching signal α, expressed as:
α＝MSOC+2Mp+1 (4)
Wherein USOC (k) represents the instantaneous value of the supercapacitor SOC at time k; USOC opt is the reference value of the super capacitor SOC; alpha is a fuzzy controller switching signal; the switching signals α=i (i=1, 2,3, 4) correspond to the fuzzy controllers a to D, respectively;
When P Flat plate (k-1) < 0, the linguistic variable of the power ratio β is set to { NB, NS, PS, PB } for the fuzzy controllers A and B; when P Flat plate (k-1) > 0, setting the power ratio beta language variable as { NB, NS, PS, PMS, PM, PMB, PB } for the fuzzy controllers C and D, wherein NB, NS, PS, PMS, PM, PMB and PB represent negative big, negative small, positive middle and small, median big and positive big respectively; the power ratio beta is selected as the argument of [ -10,10], and the membership function of the power ratio beta is set; the power ratio β membership function in this embodiment is shown in fig. 2;
Setting the language variable of delta USOC (k) as { NB, NS, ZE, PS, PB }, which is respectively represented as negative large, negative small, zero, positive small and positive large; selecting the argument of delta USOC (k) as [ -4,4], and setting delta USOC (k) membership function; the Δusoc (k) membership function in this example is shown in fig. 3;
Defining the output quantity of four fuzzy controllers A-D, namely that the language variable of the scaling factor K p is { NB, NM, NS, ZE, PS, PM, PB }, which respectively represents negative big, negative medium, negative small, zero, positive small, medium and positive big; the domain of the selected scale factor K p is [ -8,8], and a scale factor K p membership function is set; in this embodiment, the membership function of the scaling factor K p is shown in fig. 4;
the prediction formula of the super capacitor SOC variation delta USOC (k) in the period from the moment k to the moment k+1 is set as follows:
ΔUSOC(k)＝ΔUSOC(k-1)+Kp(USOCopt-USOC(k)) (5)
ΔUSOC(k-1)＝USOC(k)-USOC(k-1) (6)
Wherein USOC (k-1) represents the instantaneous value of the super capacitor SOC at the time of k-1, delta USOC (k-1) and delta USOC (k) respectively represent the variation of the super capacitor SOC in the period from the time of k-1 to the time of k and in the period from the time of k to the time of k+1;
step S10, calculating a switching signal alpha according to formulas (2) - (4), and selecting fuzzy controllers A-D according to the switching signal alpha;
step S20, calculating a power ratio beta according to a formula (1), and calculating delta USOC (k-1) according to a formula (6);
Step S30, inputting the power ratio beta into the fuzzy controller selected in the step S10, and obtaining a power ratio beta language variable value according to the set power ratio beta membership function;
Step S40, inputting delta USOC (k-1) into the fuzzy controller selected in the step S10, and obtaining delta USOC (k-1) language variable value according to the set delta USOC (k) membership function;
step S50, obtaining a proportional factor K p language variable value by checking a fuzzy rule table according to the power ratio beta language variable value and the delta USOC (K-1) language variable value;
Step S60, calculating a specific numerical value of K p according to a membership function of the set scale factor K p;
Step S70, inputting a scale factor K p into a formula (5), obtaining a predicted value of delta USOC (K), and calculating a super capacitor distribution current i c (K) according to a formula i c(k)＝-ΔUSOC(k).Qc, wherein Q c is the maximum charge amount of the super capacitor;
Step S80, subtracting the super capacitor distribution current i c (k) from the total demand current i Total (S) (k) to obtain a lithium battery distribution current i b (k);
The fuzzy rule table in this example is shown in table 1:
TABLE 1
In order to verify the superiority of the multi-Fuzzy controller-based composite power supply energy management method provided by the embodiment compared with other energy management strategies, the multi-Fuzzy controller-based composite power supply energy management method is subjected to a comparison test with a Logic threshold (Logic) and a Fuzzy control strategy (Fuzzy) under a comprehensive test working condition based on an automobile simulation software ADVISOR; the strategy of the method proposed in this embodiment is abbreviated as Fuzzy-M in FIGS. 5 and 6;
Under the comprehensive working condition test, the SOC of the lithium battery under the Fuzzy-M strategy and the SOC of the lithium battery under the other two strategies basically keep synchronous decline, the SOC of the lithium battery under the Fuzzy-M strategy declines more slowly in the initial stage of the test, and is slightly lower than the other two strategies, but the SOC difference is not more than 1%, and basically keeps consistent; however, the super capacitor SOC of the Fuzzy-M strategy is higher than that of the other two strategies, the consumed frequency is high, the power output is more but the super capacitor is in a higher state, and the fact that the super capacitor in the Fuzzy-M strategy recovers more energy in a braking state is explained;
The verification result shows that the energy management method based on the Fuzzy-M strategy can fully absorb the energy released by the robot in the braking state under the condition that the power requirement of the robot running is met, so that the energy utilization rate is improved, meanwhile, the use of a lithium battery is reduced, and the life cycle of a composite power supply system is improved.
Finally, it should be noted that the above-mentioned embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same, and although the present invention has been described in detail with reference to examples, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention, and all such modifications and equivalents are intended to be encompassed in the scope of the claims of the present invention.
Claims (4)
1. A multi-fuzzy controller-based composite power supply energy management method, comprising: the charging and discharging trend of the super capacitor is changed through the switching of the multi-fuzzy controller, so that the charging and discharging balance of the super capacitor is improved;
The method specifically comprises the following steps:
defining a power ratio beta reflecting the relation of the working condition information of adjacent time periods, which is expressed as:
wherein P Flat plate (k-1) is the average required power of the robot in the period from the moment of k-1 to the moment of k, and P (k) is the required power of the robot at the moment of k;
defining a switching signal α, expressed as:
α＝MSOC+2Mp+1 (4)
Wherein USOC (k) represents the instantaneous value of the supercapacitor SOC at time k; USOC opt is the reference value of the super capacitor SOC; alpha is a fuzzy controller switching signal; the switching signals α=i (i=1, 2,3, 4) correspond to the fuzzy controllers a to D, respectively;
when P Flat plate (k-1) < 0, the linguistic variable of the power ratio β is set to { NB, NS, PS, PB } for the fuzzy controllers A and B; when P Flat plate (k-1) > 0, setting the power ratio beta language variable as { NB, NS, PS, PMS, PM, PMB, PB } for the fuzzy controllers C and D, wherein NB, NS, PS, PMS, PM, PMB and PB represent negative big, negative small, positive middle and small, median big and positive big respectively; selecting a power ratio beta domain, and setting a power ratio beta membership function;
Setting the language variable of delta USOC (k) as { NB, NS, ZE, PS, PB }, which is respectively represented as negative large, negative small, zero, positive small and positive large; selecting a domain of delta USOC (k), and setting a delta USOC (k) membership function;
Defining the output quantity of four fuzzy controllers A-D, namely that the language variable of the scaling factor K p is { NB, NM, NS, ZE, PS, PM, PB }, which respectively represents negative big, negative medium, negative small, zero, positive small, medium and positive big; selecting a discourse domain of a scale factor K p, and setting a membership function of the scale factor K p;
the prediction formula of the super capacitor SOC variation delta USOC (k) in the period from the moment k to the moment k+1 is set as follows:
ΔUSOC(k)＝ΔUSOC(k-1)+Kp(USOCopt-USOC(k)) (5)
ΔUSOC(k-1)＝USOC(k)-USOC(k-1) (6)
Wherein USOC (k-1) represents the instantaneous value of the super capacitor SOC at the time of k-1, delta USOC (k-1) and delta USOC (k) respectively represent the variation of the super capacitor SOC in the period from the time of k-1 to the time of k and in the period from the time of k to the time of k+1;
step S10, calculating a switching signal alpha according to formulas (2) - (4), and selecting fuzzy controllers A-D according to the switching signal alpha;
step S20, calculating a power ratio beta according to a formula (1), and calculating delta USOC (k-1) according to a formula (6);
Step S30, inputting the power ratio beta into the fuzzy controller selected in the step S10, and obtaining a power ratio beta language variable value according to the set power ratio beta membership function;
Step S40, inputting delta USOC (k-1) into the fuzzy controller selected in the step S10, and obtaining delta USOC (k-1) language variable value according to the set delta USOC (k) membership function;
step S50, obtaining a proportional factor K p language variable value by checking a fuzzy rule table according to the power ratio beta language variable value and the delta USOC (K-1) language variable value;
Step S60, calculating a specific numerical value of K p according to a membership function of the set scale factor K p;
Step S70, inputting a scale factor K p into a formula (5), obtaining a predicted value of delta USOC (K), and calculating a super capacitor distribution current i c (K) according to a formula i c(k)＝-ΔUSOC(k).Qc, wherein Q c is the maximum charge amount of the super capacitor;
Step S80, subtracting the super capacitor distribution current i c (k) from the total demand current i Total (S) (k) to obtain a lithium battery distribution current i b (k).
2. The multi-fuzzy controller based composite power supply energy management method of claim 1,
The power ratio β has the argument of [ -10,10].
3. The multi-fuzzy controller based composite power supply energy management method of claim 1,
The argument of ΔUSOC (k) is [ -4,4].
4. The multi-fuzzy controller based composite power supply energy management method of claim 1,
The argument of the scaling factor K p is [ -8,8].
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