阅读说明：本技术 一种顺序遗传算法的双电耦合燃料电池汽车能效优化方法 (Method for optimizing energy efficiency of double-electric-coupling fuel cell automobile by sequential genetic algorithm ) 是由 王佳 张盛龙 胡侠 姚建红 于 2019-11-11 设计创作，主要内容包括：本发明公开了一种顺序遗传算法的双电耦合燃料电池汽车能效优化方法,双电耦合燃料电池动力系统主要由氢燃料电池发电机组、锂离子电池组共同为车辆提供驱动电能。本发明根据汽车的运行情况实时优化燃料电池和锂电池放电功率的大小,燃料电池的放电功率P<Sub>fc</Sub>和锂离子电池的放电功率为P<Sub>L</Sub>,然后以这两功率参数作为优化设计变量,采用顺序遗传算法优化两个参数功率大小,最终获得总效率最高的功率输出方案,为双电耦合燃料电池电动汽车的能效优化控制提供必要的技术支持,解决现有双电耦合系统的控制方法没有将汽车的能效发挥到最优的问题。(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 cell fc And the discharge power of the lithium ion battery is P L Then, 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.)

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

step 3, initializing the population, and adopting a decimal coding method to carry out hydrogen consumption speed on the fuel cellRate of change

Step 3, randomly generating N individuals to form an initial population V₁＝{v₁,v₂,...,v_i,...,v_NWherein the ith individual is v_i＝(v_i,1，v_i,2)，v_i,1Representing the hydrogen consumption rate of the fuel cell at the kth time of the ith individual

step 4, calculating V₁Each individual v in_iUseful power P of fuel cell output at k-th moment_fc(k)_iAnd the output useful power P of the lithium battery at the kth moment_L(k)_iOperation efficiency η of fuel cell at time k_fc(k)_iAnd discharge power η of lithium battery at time k_L(k)_i；

Step 5, the total efficiency η (k) of the i individual at the kth time of automobile implementation_iSize is defined as per individual v_iAdaptive value of (3), η (k)_i＝η_fc(k)_i×η_L(k)_i；

Step 6, judging whether the current optimization algebra T is equal to T or not_maxIf yes, stopping calculation, and taking the individual with the maximum fitness value, namely the k-th time real-timeTotal efficiency η (k)_iHighest individual v_iAs a result of the finding and in accordance therewith

Wherein the content of the first and second substances,

if t is<T_maxEntering step 7;

step 7, according to the size of the fitness value, corresponding individuals v are subjected to fitness_iSorting 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 is_iThe selection probability of (2) is:

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 P_cObtaining a new population V₂；

Step 9, according to the variation rate P_mObtaining 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 in_iUseful power P of fuel cell output at k-th moment_fc(k)_i

Wherein, V_fcRepresenting the voltage of the fuel cell; f_fcRepresents the faradaic constant of the fuel cell;

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 in_iThe useful output power P of the lithium battery at the kth moment_L(k)_i

Wherein:

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 in_iOperating efficiency η of fuel cell at time k_fc(k)_iAnd discharge power η of lithium battery at time k_L(k)_i

Wherein the content of the first and second substances,

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 in_iDischarge power η of lithium battery at the k-th moment_L(k)_i

Wherein the content of the first and second substances,

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 P_cObtaining a new population V₂The method is carried out according to the following formula,

v_i′(k)＝θv_i(k)+(1-θ)v_j(k)

v_j′(k)＝θv_j(k)+(1-θ)v_i(k)

wherein θ represents a random crossing position between 0 and 1; i ∈ {1,2, …, N }, j ∈ {1,2, …, N }, v ∈_i' (k) and v_j' (k) denotes a new individual crossed at the k-th time, v_i(k) And v_j(k) Indicating the individuals that need to be crossed at time k.

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

Hydrogen consumption rate equivalent to lithium battery

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

Hydrogen consumption rate equivalent to lithium batteryEncoding is carried out, the population size is defined as N, and the crossing rate is P_cThe rate of variation is P_mThe best individual selection probability is q and the iterative maximum algebra is T_max；

Step 3, randomly generating N individuals to form an initial population V₁＝{v₁,v₂,...,v_i,...,v_NWherein the ith individual is v_i＝(v_i,1，v_i,2)，v_i,1Representing the hydrogen consumption rate of the fuel cell at the kth time of the ith individual

Size, v_i,2Represents the equivalent hydrogen consumption rate of the lithium battery at the kth moment of the ith individual

Setting the current optimization algebra as t as 1;

step 4, calculating V₁Each individual v in_iUseful power P of fuel cell output at k-th moment_fc(k)_iAnd the output useful power P of the lithium battery at the kth moment_L(k)_iOperation efficiency η of fuel cell at time k_fc(k)_iAnd discharge power η of lithium battery at time k_L(k)_i；

Step 5, the total efficiency η (k) of the i individual at the kth time of automobile implementation_iSize is defined as per individual v_iAdaptive value of (3), η (k)_i＝η_fc(k)_i×η_L(k)_i；

Step 6, judging whether the current optimization algebra T is equal to T or not_maxIf yes, stopping calculation, and taking the individual with the maximum fitness value, namely the k-th real-time total efficiency η (k)_iHighest individual v_iAs a result of the finding and in accordance therewith

And

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 individual_Q(k)_i，

Wherein the content of the first and second substances,

is the power factor of the fuel cell and,

denotes the molar mass of hydrogen, F, of the fuel cell_fcExpressing the Faraday constant, V, of the fuel cell_fcDenotes the voltage of the fuel cell, phi is the power coefficient of the lithium battery pack, H_LRepresents the calorific value of hydrogen, s_lfIs the equivalent coefficient of discharge of the battery,

η_batfor efficiency of lithium battery system η_fccFor fuel cell system efficiency η_DCDCDCDC converter efficiency for fuel cells;

if t is<T_maxEntering step 7;

step 7, according to the size of the fitness value, corresponding individuals v are subjected to fitness_iSorting 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 is_iThe selection probability of (2) is:

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 P_cObtaining a new population V₂；

Step 9, according to the variation rate P_mObtaining 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 in_iUseful power P of fuel cell output at k-th moment_fc(k)_i

Wherein, V_fcRepresenting the voltage of the fuel cell; f_fcRepresents the faradaic constant of the fuel cell;

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.

Further, calculate V₁Each individual v in_iThe useful output power P of the lithium battery at the kth moment_L(k)_i

Wherein:

the equivalent hydrogen consumption rate of the lithium battery at the kth moment of the ith individual; h_LRepresents the heating value of hydrogen; s_lfIs the equivalent coefficient of battery discharge.

Further, calculate V₁Each individual v in_iOperating efficiency η of fuel cell at time k_fc(k)_iAnd discharge power η of lithium battery at time k_L(k)_i

Wherein the content of the first and second substances,

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 in_iDischarge power η of lithium battery at the k-th moment_L(k)_i

Wherein the content of the first and second substances,

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 P_cObtaining a new population V₂The method is carried out according to the following formula,

v_i′(k)＝θv_i(k)+(1-θ)v_j(k)

v_j′(k)＝θv_j(k)+(1-θ)v_i(k)

wherein θ represents a random crossing position between 0 and 1; i ∈ {1,2, …, N }, j ∈ {1,2, …, N }, v ∈_i' (k) and v_j' (k) denotes a new individual crossed at the k-th time, v_i(k) And v_j(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 P_fc≤P_Q≤P_fc+P_L。

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

Indicating that the power distribution coefficient is greater than 30% when the fuel cell hydrogen storage capacity is greater than

Is 1; when the hydrogen storage capacity is less than 30% and more than 10%, the power distribution coefficient

Is 0.7; when the hydrogen storage capacity is lower than 10%, the power distribution coefficient

Is 0.5.

Power coefficient of lithium battery pack

Indicating that the power distribution coefficient is greater than 30% when the battery charge is greater than

Is 1; when the battery electric quantity is less than 30% and more than 10%, the power distribution coefficient

Is 0.7; when the SOC of the battery is lower than 10%, the power distribution coefficient

Is 0.5.

Wherein, P_QReal-time discharge power for the vehicle; p_fcDischarging power for the fuel cell; p_LDischarging 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:

in the formula η_fcThe operating efficiency of the fuel cell; p_fcUseful power for the output of the fuel cell;

the power (calculated from chemical reaction theory) generated by the complete reaction of hydrogen at the current flow rate of the fuel cell.

In the formula (I), the compound is shown in the specification,

represents the rate of hydrogen consumption by the fuel cell,denotes the molar mass of hydrogen, F, of the fuel cell_fcExpressing the Faraday constant, V, of the fuel cell_fcRepresenting 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 battery_L：

Wherein:

the equivalent hydrogen consumption rate of the lithium battery; h_LRepresents the heating value of hydrogen; s_lfAn 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.

Wherein, η_batFor efficiency of lithium battery system η_fccFor fuel cell system efficiency η_DCDCDCDC converter efficiency for fuel cells.

The operating efficiency of a lithium battery can be equivalently expressed as:

wherein, η_LThe 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

Hydrogen consumption rate equivalent to lithium battery

(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

Hydrogen consumption rate equivalent to lithium battery

(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

Encoding is carried out, the population size is defined as N, and the crossing rate is P_cThe rate of variation is P_mThe best isThe selection probability of an individual is q, and the iteration maximum algebra is T_max；

Step two, randomly generating N individuals to form an initial population V₁＝{v₁,v₂,...,v_i,...,v_NWherein the ith individual is v_i＝(v_i,1，v_i,2)，v_i,1Representing the hydrogen consumption rate of the fuel cell at the kth time of the ith individual

Size, v_i,2Represents the equivalent hydrogen consumption rate of the lithium battery at the kth moment of the ith individual

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。

Wherein, V_fcRepresenting the voltage of the fuel cell; f_fcRepresents the faradaic constant of the fuel cell;

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.

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。

Wherein:

the equivalent hydrogen consumption rate of the lithium battery at the kth moment of the ith individual; h_LRepresents the heating value of hydrogen; s_lfThe 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).

Wherein, η_batFor efficiency of lithium battery system η_fccFor fuel cell system efficiency η_DCDCDCDC 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。

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 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。

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 v_iI.e. the overall efficiency η (k) of the vehicle implementation at the kth time of the ith individual_iSize;

η(k)_i＝η_fc(k)_i×η_L(k)_i(6)

step eight, judging whether the current optimization algebra T is equal to T or not_maxIf yes, stopping calculation, and taking the individual with the maximum fitness value, namely the k-th real-time total efficiency η (k)_iHighest individual v_iAs a result of the finding and in accordance therewith

And

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<T_maxAnd entering the step nine.

Wherein the content of the first and second substances,

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

Is 1; when the hydrogen storage capacity is less than 30% and more than 10%, the power distribution coefficient

Is 0.7; when the hydrogen storage capacity is lower than 10%, the power distribution coefficient

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

Is 1; when the battery electric quantity is less than 30% and more than 10%, the power distribution coefficient

Is 0.7; when the SOC of the battery is lower than 10%, the power distribution coefficient

Is 0.5.

Step nine, according to the size of the fitness value, corresponding individuals v_iSorting is carried out;

step ten, defining the selection probability of the best individual as q, and sequencing the ith individual at the k time v_iThe selection probability of (2) is:

then, the regeneration individuals are selected according to the selected opportunity determined by the probability.

Eleven step, according to the crossing rate P_cObtaining a new population according to the formula (9).

Wherein θ represents a random crossing position between 0 and 1; i ∈ {1,2, …, N }, j ∈ {1,2, …, N }, v ∈_i' (k) and v_j' (k) denotes a new individual crossed at the k-th time, v_i(k) And v_j(k) Indicating the individuals that need to be crossed at time k.

Step twelve, according to the variation rate P_mNew 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.