CN110816303A - Method for optimizing energy efficiency of double-electric-coupling fuel cell automobile by sequential genetic algorithm - Google Patents

Method for optimizing energy efficiency of double-electric-coupling fuel cell automobile by sequential genetic algorithm Download PDF

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CN110816303A
CN110816303A CN201911093294.6A CN201911093294A CN110816303A CN 110816303 A CN110816303 A CN 110816303A CN 201911093294 A CN201911093294 A CN 201911093294A CN 110816303 A CN110816303 A CN 110816303A
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fuel cell
power
lithium battery
value
individual
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王佳
张盛龙
胡侠
姚建红
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Changshu Institute of Technology
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    • 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
    • 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
    • B60L50/70—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by fuel cells
    • 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
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • 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
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • 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
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/30—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling fuel cells
    • 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
    • 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
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/14—Plug-in electric vehicles
    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/40—Application of hydrogen technology to transportation, e.g. using fuel cells

Abstract

The invention discloses a method for optimizing the energy efficiency of a double-electric coupling fuel cell automobile by a sequential genetic algorithm. The invention optimizes the discharge power of the fuel cell and the lithium battery in real time according to the running condition of the automobile, and the discharge power P of the fuel cellfcAnd the discharge power of the lithium ion battery is PLThen, thenThe two power parameters are used as optimization design variables, the power of the two parameters is optimized by adopting a sequential genetic algorithm, and finally, a power output scheme with the highest total efficiency is obtained, so that necessary technical support is provided for energy efficiency optimization control of the double-electric coupling fuel cell electric automobile, and the problem that the energy efficiency of the automobile is not optimally exerted by the existing control method of the double-electric coupling system is solved.

Description

Method for optimizing energy efficiency of double-electric-coupling fuel cell automobile by sequential genetic algorithm
Technical Field
The invention relates to a fuel cell automobile energy efficiency optimization method, in particular to a double-electric coupling fuel cell automobile energy efficiency optimization method based on a sequential genetic algorithm.
Background
The hydrogen fuel cell automobile is rapidly developed in recent years, the hydrogen fuel cell has the characteristics of high energy density and high hydrogenation speed, and the problems of short endurance mileage and long charging time of the pure electric automobile are well solved. At present, a double-electric coupling type fuel cell automobile is increasingly paid attention, because the fuel cell automobile can fully exert the technical characteristics of a fuel cell and a lithium battery, the characteristic of high discharge rate of the lithium ion battery is fully exerted under the working condition of accelerating climbing of the automobile, and the dynamic property of the automobile is improved; during the normal driving stage of the vehicle, the fuel cell system works, if the output power of the fuel cell is larger than the power requirement of the vehicle, the fuel cell can charge the lithium ion battery through the double-power DCDC converter. The braking energy recovery technology can effectively prolong the endurance mileage of the electric automobile, converts kinetic energy into electric energy in the braking process of the automobile and charges a power battery, and the introduced super capacitor bank can well solve the problems because the braking efficiency has the characteristics of instantaneous large current and unstable voltage.
The existing energy efficiency optimization method of the double-electric coupling fuel cell power system is a technical scheme which is difficult to obtain the highest total efficiency by setting the output power of a fuel cell and a lithium ion battery under the working conditions of starting, accelerating, normally running, braking and the like of a vehicle according to the rules formulated by a strategy formulated according to the working conditions of the vehicle and combining the SOC state of the lithium battery.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a double-electric-coupling fuel cell automobile energy efficiency optimization method of a sequential genetic algorithm, and a power matching scheme with the lowest energy consumption rate is obtained.
The technical scheme of the invention is as follows: a method for optimizing the energy efficiency of a double-electric coupling fuel cell automobile based on a sequential genetic algorithm is realized on the basis of a double-electric coupling fuel cell power system, and the double-electric coupling fuel cell power system comprises the following steps: the hydrogen generation system comprises a lithium ion power battery pack, a fuel cell engine, a fuel cell DCDC converter, a hydrogen supply system, a lithium ion battery system, a super capacitor pack, a driving motor controller and a transmission, wherein the driving motor is mechanically connected with the transmission, the transmission is connected with a vehicle half shaft, the driving motor is electrically connected with the driving motor controller, the fuel cell engine is electrically connected with the fuel cell DCDC converter, the fuel cell DCDC converter is respectively electrically connected with the lithium ion power battery pack and the super capacitor pack, and the driving motor controller is respectively electrically connected with the fuel cell DCDC converter, the lithium ion power battery pack and the super capacitor pack; the optimization method comprises the following steps:
step 1, monitoring fuel cell system information, power battery pack state information, super capacitor pack state information, vehicle running speed and driver intention in real time, calculating system power demand according to the vehicle running speed and the opening degree of an accelerator pedal, and setting a working mode of a power system; when the power requirement of the system is larger than the maximum output power of the fuel cell and smaller than the sum of the output powers of the fuel cell and the lithium ion battery, the system enters a full-driving mode, the fuel cell system, the lithium cell system and the super capacitor system all work, and the step 2 is entered;
step 2, optimizing the rate of hydrogen consumption of the fuel cell by using the variable
Figure BDA0002267516890000021
Hydrogen consumption rate equivalent to lithium battery
Figure BDA0002267516890000022
The system is optimized for a single target, and the optimization target is that the real-time efficiency of the electric vehicle with the double electric coupling fuel cells is highest;
step 3, initializing the population, and adopting a decimal coding method to carry out hydrogen consumption rate on the fuel cell
Figure BDA0002267516890000023
Hydrogen consumption rate equivalent to lithium batteryEncoding is carried out, the population size is defined as N, and the crossing rate is PcThe rate of variation is PmThe best individual selection probability is q and the iterative maximum algebra is Tmax；
Step 3, randomly generating N individuals to form an initial population V1＝{v1,v2,...,vi,...,vNWherein the ith individual is vi＝(vi,1，vi,2)，vi,1Representing the hydrogen consumption rate of the fuel cell at the kth time of the ith individual
Figure BDA0002267516890000025
Size, vi,2Represents the equivalent hydrogen consumption rate of the lithium battery at the kth moment of the ith individual
Figure BDA0002267516890000026
Setting the current optimization algebra as t as 1;
step 4, calculating V1Each individual v iniUseful power P of fuel cell output at k-th momentfc(k)iAnd the output useful power P of the lithium battery at the kth momentL(k)iOperation efficiency η of fuel cell at time kfc(k)iAnd discharge power η of lithium battery at time kL(k)i；
Step 5, the total efficiency η (k) of the i individual at the kth time of automobile implementationiSize is defined as per individual viAdaptive value of (3), η (k)i＝ηfc(k)i×ηL(k)i；
Step 6, judging whether the current optimization algebra T is equal to T or notmaxIf yes, stopping calculation, and taking the individual with the maximum fitness value, namely the k-th real-time total efficiency η (k)iHighest individual viAs a result of the finding and in accordance therewith
Figure BDA0002267516890000027
And
Figure BDA0002267516890000028
respectively used as the hydrogen consumption rates of a fuel cell and a lithium battery, and calculating the real-time discharge power P of the automobile at the kth moment of the ith individualQ(k)i，
Wherein the content of the first and second substances,
Figure BDA0002267516890000032
is the power factor of the fuel cell and,
Figure BDA0002267516890000033
denotes the molar mass of hydrogen, F, of the fuel cellfcExpressing the Faraday constant, V, of the fuel cellfcDenotes the voltage of the fuel cell, phi is the power coefficient of the lithium battery pack, HLRepresents the calorific value of hydrogen, slfIs the equivalent coefficient of discharge of the battery,
Figure BDA0002267516890000034
ηbatfor efficiency of lithium battery system ηfccFor fuel cell system efficiency ηDCDCDCDC converter efficiency for fuel cells;
if t is<TmaxEntering step 7;
step 7, according to the size of the fitness value, corresponding individuals v are subjected to fitnessiSorting is carried out, the selection probability of the best individual is defined as q, and then the k time v of the ith individual after sorting isiThe selection probability of (2) is:
Figure BDA0002267516890000035
wherein, i is 1, 2., N,
then, selecting a regeneration individual according to the selection opportunity determined by the probability;
step 8, according to the crossing rate PcObtaining a new population V2；
Step 9, according to the variation rate PmObtaining new group V according to basic single point variation method3；
Step 10, grouping the V group3As a new generation population, use V3Replace V and get another t ═ t +1, and return to step 4.
Further, the power coefficient of the fuel cell is determined by the size of the hydrogen storage of the fuel cell, when the hydrogen storage of the fuel cell is greater than a first limit value, the power coefficient of the fuel cell is a first value, when the hydrogen storage of the fuel cell is less than or equal to the first limit value and greater than a second limit value, the power coefficient of the fuel cell is a second value, and when the hydrogen storage of the fuel cell is less than or equal to the second limit value, the power coefficient of the fuel cell is a third value; the first limit > the second limit, the third value < the second value < the first value ≦ 1.
Further, the power coefficient of the lithium battery pack is determined by the magnitude of the electric quantity of a lithium battery, when the electric quantity of the lithium battery is greater than a first limit value, the power coefficient of the lithium battery pack is a first value, when the electric quantity of the lithium battery is less than or equal to the first limit value and greater than a second limit value, the power coefficient of the lithium battery pack is a second value, and when the electric quantity of the lithium battery is less than or equal to the second limit value, the power coefficient of the lithium battery pack is a third value; the first limit > the second limit, the third value < the second value < the first value ≦ 1.
Further, the first limit value is 30% -35%, the first limit value is 10% -15%, the first value is 0.95-1, the second value is 0.7-0.75, and the third value is 0.5-0.6.
Further, said calculating V1Each individual v iniUseful power P of fuel cell output at k-th momentfc(k)i
Figure BDA0002267516890000041
Wherein, VfcRepresenting the voltage of the fuel cell; ffcRepresents the faradaic constant of the fuel cell;
Figure BDA0002267516890000042
represents the molar mass of the fuel cell hydrogen;
Figure BDA0002267516890000043
representing the rate of hydrogen consumption by the fuel cell at the ith individual time instant k.
Further, calculate V1Each individual v iniThe useful output power P of the lithium battery at the kth momentL(k)i
Figure BDA0002267516890000044
Wherein:
Figure BDA0002267516890000045
the equivalent hydrogen consumption rate of the lithium battery at the kth moment of the ith individual; hLRepresents the heating value of hydrogen; slfIs the equivalent coefficient of battery discharge.
Further, calculate V1Each individual v iniOperating efficiency η of fuel cell at time kfc(k)iAnd discharge power η of lithium battery at time kL(k)i
Figure BDA0002267516890000046
Wherein the content of the first and second substances,
Figure BDA0002267516890000047
the power generated by the complete reaction of the hydrogen at the current flow rate of the fuel cell at the kth moment of the ith individual.
Further, calculate V1Each individual v iniDischarge power η of lithium battery at the k-th momentL(k)i
Figure BDA0002267516890000048
Wherein the content of the first and second substances,
Figure BDA0002267516890000049
the power generated by the complete reaction of the hydrogen at the current flow rate of the lithium battery at the kth moment of the ith individual.
Further, according to the crossing rate PcObtaining a new population V2The method is carried out according to the following formula,
vi′(k)＝θvi(k)+(1-θ)vj(k)
vj′(k)＝θvj(k)+(1-θ)vi(k)
wherein θ represents a random crossing position between 0 and 1; i ∈ {1,2, …, N }, j ∈ {1,2, …, N }, v ∈i' (k) and vj' (k) denotes a new individual crossed at the k-th time, vi(k) And vj(k) Indicating the individuals that need to be crossed at time k.
The technical scheme provided by the invention has the advantages that the method can obtain the power matching scheme with the lowest energy consumption rate, fully exert the advantages of the fuel cell and the lithium battery, obtain the highest output efficiency while ensuring the dynamic property of the vehicle, and provide necessary technical support for the energy efficiency optimization control of the double-electric coupling fuel cell vehicle.
Drawings
FIG. 1 is a schematic flow chart of a sequential selection genetic algorithm according to the present invention.
Fig. 2 is a layout schematic diagram of a power battery pack and a fan of a pure electric vehicle.
FIG. 3 is a schematic diagram of a vehicle execution system operating mode determination process.
Detailed Description
The present invention is further illustrated by the following examples, which are not to be construed as limiting the invention thereto.
Referring to fig. 2, the method for optimizing energy efficiency of a dual electric coupling fuel cell vehicle by using a sequential genetic algorithm according to the present invention is implemented based on a dual electric coupling fuel cell power system, and the dual electric coupling fuel cell power system includes: the system comprises a lithium ion power battery pack 7, a fuel cell engine 5, a fuel cell DCDC converter 4, a hydrogen supply system 8, a whole vehicle control system 9, a lithium ion battery system, a super capacitor pack 6, a driving motor 2, a driving motor controller 3 and a transmission 1, wherein a is a left front wheel, b is a right front wheel, c is a left rear wheel, d is a right rear wheel, a and b are connected with the transmission 1, and the hydrogen supply system 8 is connected with the fuel cell engine 5.
The driving motor 2 is mechanically connected with the transmission 1, the transmission 1 is connected with a vehicle half shaft, the driving motor 2 is electrically connected with the driving motor controller 3, the fuel cell engine 5 is electrically connected with the fuel cell DCDC converter 4, and the fuel cell DCDC converter 4 is electrically connected with the lithium ion power battery pack 7 and the super capacitor pack 6 respectively.
As shown in fig. 3, in the driving process of the dual-electric coupling fuel cell automobile, the system information of the fuel cell, the state information of the power battery pack, the state information of the super capacitor pack, the driving speed of the automobile and the intention of a driver are monitored in real time, the power requirement of the system is calculated according to the driving speed of the automobile and the opening degree of an accelerator pedal, and the working mode of the power system is set; when the system power demand is larger than the maximum output power of the fuel cell and smaller than the sum of the output power of the fuel cell and the output power of the lithium ion battery, namely Pfc≤PQ≤Pfc+PL。
And the execution system enters a full-driving mode, the fuel cell system, the lithium battery system and the super capacitor system all work, and the output power of the fuel cell system and the output power of the lithium battery system are optimized according to an optimization algorithm.
Otherwise, the fuel cell system single working mode is entered. Judging whether the vehicle is in a braking mode, if so, changing the motor into a power generation mode, preferentially charging the lithium battery pack, and if the recovered electric energy does not meet the recovery condition of the lithium battery, charging the super capacitor; the fuel cell system and the generator preferentially charge the lithium battery pack, and if the recovered electric energy does not meet the recovery condition of the lithium battery, the super capacitor is charged;
if the vehicle is not in the braking mode, the fuel cell system works, the residual electric energy is preferentially used for charging the lithium battery, and if the recovered electric energy does not meet the recovery condition of the lithium battery, the super capacitor is charged.
To be noted:
(1) the super capacitor system only works in a braking recovery mode and an emergency acceleration working condition, and does not participate in the patent optimization.
(2) And whether the super capacitor works or not is finished by sending an instruction by a finished automobile control system.
This patent the two electric coupling driving system models comprise fuel cell, lithium cell group, ultracapacitor system group, because ultracapacitor system group is as auxiliary energy, the energy that can save is less, and this patent is not as the optimization object. Therefore, the optimization object of the patent is a fuel cell and a lithium ion battery pack. In view of the limited nature of the onboard energy sources, power coefficients are set for both energy sources.
Power factor of fuel cell
Figure BDA0002267516890000061
Indicating that the power distribution coefficient is greater than 30% when the fuel cell hydrogen storage capacity is greater than
Figure BDA0002267516890000062
Is 1; when the hydrogen storage capacity is less than 30% and more than 10%, the power distribution coefficient
Figure BDA0002267516890000063
Is 0.7; when the hydrogen storage capacity is lower than 10%, the power distribution coefficient
Figure BDA0002267516890000064
Is 0.5.
Power coefficient of lithium battery pack
Figure BDA0002267516890000065
Indicating that the power distribution coefficient is greater than 30% when the battery charge is greater than
Figure BDA0002267516890000066
Is 1; when the battery electric quantity is less than 30% and more than 10%, the power distribution coefficient
Figure BDA0002267516890000067
Is 0.7; when the SOC of the battery is lower than 10%, the power distribution coefficient
Figure BDA0002267516890000068
Is 0.5.
Figure BDA0002267516890000069
Wherein, PQReal-time discharge power for the vehicle; pfcDischarging power for the fuel cell; pLDischarging power for the lithium battery;is the power coefficient of the fuel cell; phi is the power coefficient of the lithium battery pack.
The relationship between the efficiency and the discharge power of the fuel cell system and the lithium cell system is as follows:
the operating efficiency of a fuel cell can be expressed as:
Figure BDA00022675168900000611
in the formula ηfcThe operating efficiency of the fuel cell; pfcUseful power for the output of the fuel cell;
Figure BDA00022675168900000612
the power (calculated from chemical reaction theory) generated by the complete reaction of hydrogen at the current flow rate of the fuel cell.
Figure BDA0002267516890000071
In the formula (I), the compound is shown in the specification,
Figure BDA0002267516890000072
represents the rate of hydrogen consumption by the fuel cell,denotes the molar mass of hydrogen, F, of the fuel cellfcExpressing the Faraday constant, V, of the fuel cellfcRepresenting the voltage of the fuel cell.
Since the output power of the fuel cell can be expressed as a relation with the hydrogen consumption rate, and the output efficiency of the lithium battery can also be expressed as a corresponding relation with the hydrogen consumption rate for the convenience of system optimization, the following relation is introduced:
discharge power P of lithium batteryL：
Figure BDA0002267516890000074
Wherein:
Figure BDA0002267516890000075
the equivalent hydrogen consumption rate of the lithium battery; hLRepresents the heating value of hydrogen; slfAn equivalence coefficient for battery discharge that takes into account the average energy path of storage and release of chemical energy of hydrogen to the lithium battery.
Figure BDA0002267516890000076
Wherein, ηbatFor efficiency of lithium battery system ηfccFor fuel cell system efficiency ηDCDCDCDC converter efficiency for fuel cells.
The operating efficiency of a lithium battery can be equivalently expressed as:
Figure BDA0002267516890000077
wherein, ηLThe discharge efficiency of the lithium battery;the power (calculated by chemical reaction theory) generated by the complete reaction of the hydrogen at the current flow rate of the lithium battery.
Referring to fig. 1, the design steps of the method for optimizing the energy efficiency of a dual electric coupling fuel cell vehicle by using a sequential genetic algorithm are as follows:
(1) determining an optimal design variable: the design variables include a total of two parameters, which are: rate of hydrogen consumption by fuel cell
Figure BDA0002267516890000079
Hydrogen consumption rate equivalent to lithium battery
Figure BDA00022675168900000710
(2) Determining an optimization design target: the system is optimized for a single target, and the optimization target is that the real-time efficiency of the double-electric coupling fuel cell automobile is the highest.
(3) Determining an optimization limiting condition: fuel cell hydrogen consumption rate
Figure BDA0002267516890000081
Hydrogen consumption rate equivalent to lithium battery
Figure BDA0002267516890000082
(the hydrogen consumption rate range is determined at the time of hydrogen fuel cell generator set design, and is related to vehicle design criteria).
(4) The energy efficiency optimization method of the double-electric coupling fuel cell comprises the following steps:
step one, initializing a group, and adopting a decimal coding method to carry out hydrogen consumption rate on a fuel cellHydrogen consumption rate equivalent to lithium battery
Figure BDA0002267516890000084
Encoding is carried out, the population size is defined as N, and the crossing rate is PcThe rate of variation is PmThe best isThe selection probability of an individual is q, and the iteration maximum algebra is Tmax；
Step two, randomly generating N individuals to form an initial population V1＝{v1,v2,...,vi,...,vNWherein the ith individual is vi＝(vi,1，vi,2)，vi,1Representing the hydrogen consumption rate of the fuel cell at the kth time of the ith individual
Figure BDA0002267516890000085
Size, vi,2Represents the equivalent hydrogen consumption rate of the lithium battery at the kth moment of the ith individual
Figure BDA0002267516890000086
The size is set as that the current optimization algebra is T ═ 1(T ≦ T)max)；
Step three, calculating the output useful power P of the fuel cell at the kth moment of the ith individual by adopting a formula (1)fc(k)i。
Figure BDA0002267516890000087
Wherein, VfcRepresenting the voltage of the fuel cell; ffcRepresents the faradaic constant of the fuel cell;
Figure BDA0002267516890000088
represents the molar mass of the fuel cell hydrogen;
Figure BDA0002267516890000089
representing the rate of hydrogen consumption by the fuel cell at the ith individual time instant k.
Step four, calculating the output useful power P of the lithium battery of the ith individual at the kth moment by adopting a formula (2)L(k)i。
Figure BDA00022675168900000810
Wherein:
Figure BDA00022675168900000811
the equivalent hydrogen consumption rate of the lithium battery at the kth moment of the ith individual; hLRepresents the heating value of hydrogen; slfThe method is characterized in that the method is an equivalent coefficient of battery discharge, the equivalent coefficient considers an average energy path from chemical energy of hydrogen to storage and release of a lithium battery, and the specific calculation is obtained by adopting a formula (3).
Figure BDA00022675168900000812
Wherein, ηbatFor efficiency of lithium battery system ηfccFor fuel cell system efficiency ηDCDCDCDC converter efficiency for fuel cells.
Step five, calculating the working efficiency η of the fuel cell at the kth time of the ith individual by adopting a formula (4)fc(k)i。
Figure BDA0002267516890000091
Wherein the content of the first and second substances,
Figure BDA0002267516890000092
the power (calculated by chemical reaction theory) generated by the complete reaction of the hydrogen at the current flow rate of the fuel cell at the kth moment of the ith individual.
Step six, calculating the discharge power η of the lithium battery of the ith individual at the kth moment by adopting a formula (5)L(k)i。
Figure BDA0002267516890000093
Wherein the content of the first and second substances,the power (calculated by chemical reaction theory) generated by the complete reaction of the hydrogen at the current flow rate of the lithium battery at the kth moment of the ith individual.
Step seven, taking the formula (6) as a fitness function, and adoptingThe formula calculates each individual viI.e. the overall efficiency η (k) of the vehicle implementation at the kth time of the ith individualiSize;
η(k)i＝ηfc(k)i×ηL(k)i(6)
step eight, judging whether the current optimization algebra T is equal to T or notmaxIf yes, stopping calculation, and taking the individual with the maximum fitness value, namely the k-th real-time total efficiency η (k)iHighest individual viAs a result of the finding and in accordance therewith
Figure BDA0002267516890000095
And
Figure BDA0002267516890000096
respectively used as hydrogen consumption rates of a fuel cell and a lithium battery, and calculating the real-time automobile discharge power P at the kth moment of the ith individual by adopting a formula (7)Q(k)iIf t is<TmaxAnd entering the step nine.
Wherein the content of the first and second substances,
Figure BDA0002267516890000098
the power distribution coefficient is the power coefficient of the fuel cell when the hydrogen storage capacity of the fuel cell is more than 30 percent
Figure BDA0002267516890000099
Is 1; when the hydrogen storage capacity is less than 30% and more than 10%, the power distribution coefficient
Figure BDA00022675168900000910
Is 0.7; when the hydrogen storage capacity is lower than 10%, the power distribution coefficient
Figure BDA00022675168900000911
Is 0.5.
Wherein phi is the power coefficient of the lithium battery pack whenWhen the electric quantity of the lithium battery is more than 30%, the power distribution coefficient
Figure BDA00022675168900000912
Is 1; when the battery electric quantity is less than 30% and more than 10%, the power distribution coefficient
Figure BDA0002267516890000101
Is 0.7; when the SOC of the battery is lower than 10%, the power distribution coefficient
Figure BDA0002267516890000102
Is 0.5.
Step nine, according to the size of the fitness value, corresponding individuals viSorting is carried out;
step ten, defining the selection probability of the best individual as q, and sequencing the ith individual at the k time viThe selection probability of (2) is:
Figure BDA0002267516890000103
then, the regeneration individuals are selected according to the selected opportunity determined by the probability.
Eleven step, according to the crossing rate PcObtaining a new population according to the formula (9).
Figure BDA0002267516890000104
Wherein θ represents a random crossing position between 0 and 1; i ∈ {1,2, …, N }, j ∈ {1,2, …, N }, v ∈i' (k) and vj' (k) denotes a new individual crossed at the k-th time, vi(k) And vj(k) Indicating the individuals that need to be crossed at time k.
Step twelve, according to the variation rate PmNew populations were obtained according to the basic single point mutation method.
And step thirteen, taking the population obtained in the step twelve as a new generation population, and returning to the step three, wherein t is t + 1.

Claims (9)

1. A double-electric coupling fuel cell automobile energy efficiency optimization method based on a sequential genetic algorithm is characterized by being realized based on a double-electric coupling fuel cell power system, wherein the double-electric coupling fuel cell power system comprises the following steps: the hydrogen generation system comprises a lithium ion power battery pack, a fuel cell engine, a fuel cell DCDC converter, a hydrogen supply system, a lithium ion battery system, a super capacitor pack, a driving motor controller and a transmission, wherein the driving motor is mechanically connected with the transmission, the transmission is connected with a vehicle half shaft, the driving motor is electrically connected with the driving motor controller, the fuel cell engine is electrically connected with the fuel cell DCDC converter, the fuel cell DCDC converter is respectively electrically connected with the lithium ion power battery pack and the super capacitor pack, and the driving motor controller is respectively electrically connected with the fuel cell DCDC converter, the lithium ion power battery pack and the super capacitor pack; the optimization method comprises the following steps:
step 1, monitoring fuel cell system information, power battery pack state information, super capacitor pack state information, vehicle running speed and driver intention in real time, calculating system power demand according to the vehicle running speed and the opening degree of an accelerator pedal, and setting a working mode of a power system; when the power requirement of the system is larger than the maximum output power of the fuel cell and smaller than the sum of the output powers of the fuel cell and the lithium ion battery, the system enters a full-driving mode, the fuel cell system, the lithium cell system and the super capacitor system all work, and the step 2 is entered;
step 2, optimizing the rate of hydrogen consumption of the fuel cell by using the variable
Figure FDA0002267516880000011
Hydrogen consumption rate equivalent to lithium battery
Figure FDA0002267516880000012
The system is optimized for a single target, and the optimization target is that the real-time efficiency of the electric vehicle with the double electric coupling fuel cells is highest;
step 3, initializing the population, and adopting a decimal coding method to carry out hydrogen consumption speed on the fuel cellRate of change
Figure FDA0002267516880000013
Hydrogen consumption rate equivalent to lithium battery
Figure FDA0002267516880000014
Encoding is carried out, the population size is defined as N, and the crossing rate is PcThe rate of variation is PmThe best individual selection probability is q and the iterative maximum algebra is Tmax；
Step 3, randomly generating N individuals to form an initial population V1＝{v1,v2,...,vi,...,vNWherein the ith individual is vi＝(vi,1，vi,2)，vi,1Representing the hydrogen consumption rate of the fuel cell at the kth time of the ith individual
Figure FDA0002267516880000015
Size, vi,2Represents the equivalent hydrogen consumption rate of the lithium battery at the kth moment of the ith individual
Figure FDA0002267516880000016
Setting the current optimization algebra as t as 1;
step 4, calculating V1Each individual v iniUseful power P of fuel cell output at k-th momentfc(k)iAnd the output useful power P of the lithium battery at the kth momentL(k)iOperation efficiency η of fuel cell at time kfc(k)iAnd discharge power η of lithium battery at time kL(k)i；
Step 5, the total efficiency η (k) of the i individual at the kth time of automobile implementationiSize is defined as per individual viAdaptive value of (3), η (k)i＝ηfc(k)i×ηL(k)i；
Step 6, judging whether the current optimization algebra T is equal to T or notmaxIf yes, stopping calculation, and taking the individual with the maximum fitness value, namely the k-th time real-timeTotal efficiency η (k)iHighest individual viAs a result of the finding and in accordance therewith
Figure FDA0002267516880000021
And
Figure FDA0002267516880000022
respectively used as the hydrogen consumption rates of a fuel cell and a lithium battery, and calculating the real-time discharge power P of the automobile at the kth moment of the ith individualQ(k)i，
Figure FDA0002267516880000023
Wherein the content of the first and second substances,
Figure FDA0002267516880000024
is the power factor of the fuel cell and,
Figure FDA0002267516880000025
denotes the molar mass of hydrogen, F, of the fuel cellfcExpressing the Faraday constant, V, of the fuel cellfcDenotes the voltage of the fuel cell, phi is the power coefficient of the lithium battery pack, HLRepresents the calorific value of hydrogen, slfIs the equivalent coefficient of discharge of the battery,
Figure FDA0002267516880000026
ηbatfor efficiency of lithium battery system ηfccFor fuel cell system efficiency ηDCDCDCDC converter efficiency for fuel cells;
if t is<TmaxEntering step 7;
step 7, according to the size of the fitness value, corresponding individuals v are subjected to fitnessiSorting is carried out, the selection probability of the best individual is defined as q, and then the k time v of the ith individual after sorting isiThe selection probability of (2) is:
Figure FDA0002267516880000027
wherein, i is 1, 2., N,
then, selecting a regeneration individual according to the selection opportunity determined by the probability;
step 8, according to the crossing rate PcObtaining a new population V2；
Step 9, according to the variation rate PmObtaining new group V according to basic single point variation method3；
Step 10, grouping the V group3As a new generation population, use V3Replace V and get another t ═ t +1, and return to step 4.
2. The method for optimizing the energy efficiency of the dual-electric-coupled fuel cell automobile through the sequential genetic algorithm according to claim 1, wherein the power coefficient of the fuel cell is determined by the hydrogen storage capacity of the fuel cell, when the hydrogen storage capacity of the fuel cell is greater than a first limit value, the power coefficient of the fuel cell is a first value, when the hydrogen storage capacity of the fuel cell is less than or equal to the first limit value and greater than a second limit value, the power coefficient of the fuel cell is a second value, and when the hydrogen storage capacity of the fuel cell is less than or equal to the second limit value, the power coefficient of the fuel cell is a third value; the first limit > the second limit, the third value < the second value < the first value ≦ 1.
3. The method for optimizing energy efficiency of a dual-electric-coupling fuel cell vehicle of a sequential genetic algorithm of claim 1, wherein the power coefficient of the lithium battery pack is determined by the magnitude of the electric quantity of the lithium battery, the power coefficient of the lithium battery pack is a first value when the electric quantity of the lithium battery is greater than a first limit, the power coefficient of the lithium battery pack is a second value when the electric quantity of the lithium battery is less than or equal to the first limit and greater than a second limit, and the power coefficient of the lithium battery pack is a third value when the electric quantity of the lithium battery is less than or equal to the second limit; the first limit > the second limit, the third value < the second value < the first value ≦ 1.
4. The method for optimizing the energy efficiency of the dual-electric-coupling fuel cell vehicle by the sequential genetic algorithm according to claim 2 or 3, wherein the first limit value is 30% to 35%, the first limit value is 10% to 15%, the first value is 0.95 to 1, the second value is 0.7 to 0.75, and the third value is 0.5 to 0.6.
5. The method for optimizing energy efficiency of a dual electric coupling fuel cell vehicle of a sequential genetic algorithm according to claim 1, wherein the calculating V is1Each individual v iniUseful power P of fuel cell output at k-th momentfc(k)i
Figure FDA0002267516880000031
Wherein, VfcRepresenting the voltage of the fuel cell; ffcRepresents the faradaic constant of the fuel cell;
Figure FDA0002267516880000032
represents the molar mass of the fuel cell hydrogen;representing the rate of hydrogen consumption by the fuel cell at the ith individual time instant k.
6. The method for optimizing energy efficiency of a dual electric coupling fuel cell vehicle of a sequential genetic algorithm according to claim 1, wherein the calculating V is1Each individual v iniThe useful output power P of the lithium battery at the kth momentL(k)i
Figure FDA0002267516880000034
Wherein:
Figure FDA0002267516880000035
the equivalent hydrogen consumption rate of the lithium battery at the kth moment of the ith individual; hLRepresents the heating value of hydrogen; slfIs the equivalent coefficient of battery discharge.
7. The method for optimizing energy efficiency of a dual electric coupling fuel cell vehicle of a sequential genetic algorithm according to claim 1, wherein the calculating V is1Each individual v iniOperating efficiency η of fuel cell at time kfc(k)iAnd discharge power η of lithium battery at time kL(k)i
Figure FDA0002267516880000041
Wherein the content of the first and second substances,
Figure FDA0002267516880000042
the power generated by the complete reaction of the hydrogen at the current flow rate of the fuel cell at the kth moment of the ith individual.
8. The method for optimizing energy efficiency of a dual electric coupling fuel cell vehicle of a sequential genetic algorithm according to claim 1, wherein the calculating V is1Each individual v iniDischarge power η of lithium battery at the k-th momentL(k)i
Figure FDA0002267516880000043
Wherein the content of the first and second substances,
Figure FDA0002267516880000044
the power generated by the complete reaction of the hydrogen at the current flow rate of the lithium battery at the kth moment of the ith individual.
9. Dual electric coupling fuel cell of sequential genetic algorithm according to claim 1The method for optimizing the energy efficiency of the automobile is characterized in that the method is based on the cross rate PcObtaining a new population V2The method is carried out according to the following formula,
vi′(k)＝θvi(k)+(1-θ)vj(k)
vj′(k)＝θvj(k)+(1-θ)vi(k)
wherein θ represents a random crossing position between 0 and 1; i ∈ {1,2, …, N }, j ∈ {1,2, …, N }, v ∈i' (k) and vj' (k) denotes a new individual crossed at the k-th time, vi(k) And vj(k) Indicating the individuals that need to be crossed at time k.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111459025A (en) * 2020-03-26 2020-07-28 河南科技大学 Composite power supply electric vehicle power distribution strategy optimized by particle algorithm
CN111845462A (en) * 2020-07-06 2020-10-30 无锡沃尔福汽车技术有限公司 Fuel cell power distribution control method
CN111845462B (en) * 2020-07-06 2022-02-22 无锡沃尔福汽车技术有限公司 Fuel cell power distribution control method
CN112677779A (en) * 2020-12-24 2021-04-20 北京理工大学 Information physical fusion system for hybrid energy storage
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