CN114906014A - Fuel cell automobile energy management method and system based on gull optimization algorithm - Google Patents
Fuel cell automobile energy management method and system based on gull optimization algorithm Download PDFInfo
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Abstract
The invention discloses a fuel cell automobile energy management method and a system based on a gull optimization algorithm, wherein the method comprises the following steps: constructing a fuzzy controller, wherein the fuzzy controller takes the required power of a vehicle and the state of charge of a power battery in the vehicle as input variables and takes the output power of a fuel battery in the vehicle as an output variable; establishing an equivalent hydrogen consumption mathematical model of the vehicle, and defining an expression of the equivalent hydrogen consumption mathematical model as a fitness function utilized by a gull optimization algorithm; and acquiring all membership functions contained in the fuzzy controller, and optimizing all membership functions by utilizing a gull optimization algorithm to acquire a reasonable fuzzy energy management strategy. The invention obtains the energy management strategy of the fuel cell automobile by introducing the fuzzy controller and optimizing the parameters by utilizing the gull optimization algorithm, and is beneficial to improving the energy utilization rate of the fuel cell automobile.
Description
Technical Field
The invention relates to the technical field of new energy vehicles, in particular to a fuel cell vehicle energy management method and system based on a gull optimization algorithm.
Background
The fuel cell is widely applied to the automobile industry as a renewable energy source, although the fuel cell has higher energy density, the dynamic characteristic of the fuel cell is poor, and the fuel cell is difficult to independently supply power to the automobile, most fuel cell automobiles usually adopt a combined structure of the fuel cell and a power battery to supply energy to the whole automobile, but the difficulty of vehicle energy management is increased, and a reasonable fuel cell automobile energy management strategy is proposed to become the current research key point for the purpose of greatly improving the energy utilization rate of the automobile.
Disclosure of Invention
The invention provides a fuel cell automobile energy management method and system based on a gull optimization algorithm, which are used for solving one or more technical problems in the prior art and at least provide a beneficial selection or creation condition.
The embodiment of the invention provides a fuel cell automobile energy management method based on a gull optimization algorithm, which comprises the following steps:
constructing a fuzzy controller, wherein the fuzzy controller takes the required power of a vehicle and the state of charge of a power battery in the vehicle as input variables and takes the output power of a fuel battery in the vehicle as an output variable;
establishing an equivalent hydrogen consumption mathematical model of the vehicle, and defining an expression of the equivalent hydrogen consumption mathematical model as a fitness function utilized by a gull optimization algorithm;
and acquiring all membership functions contained in the fuzzy controller, and optimizing all membership functions by utilizing a gull optimization algorithm to acquire a reasonable fuzzy energy management strategy.
Further, the calculation formula of the vehicle required power is as follows:
wherein, P req For the power demand of the vehicle, u is the vehicle speed, η t For transmission system efficiency, m is vehicle weight, g is gravitational acceleration, f is rolling resistance coefficient, α is road slope angle, C D The coefficient is an air resistance coefficient, A is a windward area of the vehicle, delta is a rotating mass conversion coefficient, and a is vehicle acceleration.
Further, the calculation formula of the state of charge of the power battery in the vehicle is as follows:
wherein, the SOC is the state of charge value of the power battery, SOC 0 Is the power battery state of charge value at the beginning of charging and discharging, C N The rated capacity of the power battery is shown, eta is the charge-discharge efficiency, I is the current of the power battery, and t is the charge-discharge time.
Further, the construction process of the fuzzy controller is as follows:
carrying out interval fuzzification on the required power of the vehicle to obtain a plurality of first fuzzy subsets in a set first discrete domain range;
carrying out interval fuzzification on the state of charge of the power battery to obtain a plurality of second fuzzy subsets in a set second discrete universe range;
interval fuzzification is carried out on the output power of the fuel cell to obtain a plurality of third fuzzy subsets within a set third discrete discourse range;
and empirically reasoning the first fuzzy subsets, the second fuzzy subsets and the third fuzzy subsets to form a fuzzy rule base.
Further, the expression of the mathematical model of the equivalent hydrogen consumption is as follows:
wherein,
is the equivalent hydrogen consumption of the vehicle,
is the equivalent hydrogen consumption of the fuel cell,
the equivalent hydrogen consumption due to stack decay during fuel cell operation,
is the equivalent hydrogen consumption of the power cell, N cell The number of cells contained in the fuel cell, I fc Is the output current of the fuel cell and,
is the molar mass of hydrogen, F is the Faraday constant, P 1 The rate of degradation of fuel cell performance, n, due to load change cycles 1 The number of cyclic load change cycles, P, while the vehicle is running 2 The rate of degradation of fuel cell performance due to start-stop cycling, n 2 Number of start-stop cycles, P, while the vehicle is running 3 Is the rate of fuel cell performance degradation caused by idle load, t 1 For idle time duration per hour, P 4 The rate of degradation of the fuel cell performance due to a high load condition, t 2 For high power load operation duration, alpha fc Is the price of the stack inside the fuel cell,
for hydrogen price, P bat Is the output power of the power battery, alpha ele Is an electricity price per degree, η bat And t is the running time for the energy conversion efficiency of the power battery.
Further, the obtaining all membership functions included in the fuzzy controller includes:
determining a first membership function corresponding to the vehicle required power based on the plurality of first fuzzy subsets;
determining a second membership function corresponding to the state of charge of the power battery based on the plurality of second fuzzy subsets;
and determining a third membership function corresponding to the output power of the fuel cell based on the plurality of third fuzzy subsets.
Further, the first membership function and the third membership function are both trapezoidal membership functions, and the second membership function is a triangular membership function.
Further, the optimizing all membership functions by using a gull optimization algorithm to obtain a reasonable fuzzy energy management strategy includes:
acquiring a plurality of first unknown variables contained in the first membership function, a plurality of second unknown variables contained in the second membership function and a plurality of third unknown variables contained in the third membership function according to the piecewise linear curve crossing condition of the first membership function, the second membership function and the third membership function;
setting basic parameters of a gull optimization algorithm, wherein the basic parameters comprise gull population quantity, maximum iteration times and a boundary interval of a target variable, and the target variable comprises the first unknown variables, the second unknown variables and the third unknown variables;
initializing the current position of each gull used in the gull optimization algorithm based on the boundary interval of the target variable, so that the target variables indicated by the current position of each gull are different;
based on the operating condition data of the vehicle in the last week, combining with a gull optimization algorithm to iteratively update the current position of each gull, and acquiring the optimal target variable referred by the optimal gull position;
and optimizing all membership functions by using the optimal target variable, so that the fuzzy controller is optimized to output a reasonable fuzzy energy management strategy.
In addition, an embodiment of the present invention further provides a fuel cell vehicle energy management system based on a gull optimization algorithm, where the system includes:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor implements any of the gull optimization algorithm-based fuel cell vehicle energy management methods described above.
The invention has at least the following beneficial effects: the fuzzy controller is introduced, the gull optimization algorithm is used for optimizing parameters, and an equivalent hydrogen consumption mathematical model considering the recession cost of the fuel cell is introduced in the optimization process as a fitness function utilized by the gull optimization algorithm, so that the optimized fuzzy controller can output a reasonable fuzzy energy management strategy, the energy utilization rate of a fuel cell automobile is improved, and the service life of the fuel cell can be prolonged to a certain extent.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a schematic flow chart of a fuel cell vehicle energy management method based on a gull optimization algorithm according to an embodiment of the present invention;
FIG. 2 is a schematic representation of a first membership function in an embodiment of the invention;
FIG. 3 is a diagram of a second membership function in an embodiment of the invention;
FIG. 4 is a diagram of a third membership function in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It is noted that while a division of functional blocks is depicted in the system diagram, and logical order is depicted in the flowchart, in some cases the steps depicted and described may be performed in a different order than the division of blocks in the system or the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Referring to fig. 1, fig. 1 is a schematic flow chart of a fuel cell vehicle energy management method based on a gull optimization algorithm according to an embodiment of the present invention, where the method includes the following steps:
and S101, constructing a fuzzy controller, wherein the fuzzy controller takes the required power of the vehicle and the state of charge of a power battery in the vehicle as input variables, and takes the output power of a fuel battery in the vehicle as an output variable.
In the above step S101, the calculation formula of the vehicle required power is:
the calculation formula of the state of charge of the power battery in the vehicle is as follows:
in the formula: p req For the power demand of the vehicle, u is the vehicle speed, η t For transmission system efficiency, m is vehicle weight, g is gravitational acceleration, f is rolling resistance coefficient, α is road slope angle, C D The coefficient is an air resistance coefficient, A is a windward area of the vehicle, delta is a rotating mass conversion coefficient, a is vehicle acceleration, SOC is a state of charge value of the power battery, and SOC is 0 Is the power battery state of charge value at the beginning of charging and discharging, C N The rated capacity of the power battery is shown, eta is the charge-discharge efficiency, I is the current of the power battery, and t is the charge-discharge time.
It should be noted that, during the running of the vehicle, the output power of the fuel cell and the output power of the power cell should meet the power demand of the vehicle, the fuel cell should work in its efficient operation region as much as possible to avoid the scenario of frequent and large power changes, and the state of charge of the power cell should be at its desired output and perform energy recovery when the vehicle is braking.
In the above step S101, the construction process of the fuzzy controller includes the following steps:
(1) power P required for the vehicle req Interval fuzzification is carried out to obtain a range of [ -1,1] in a given first discrete domain]A number of first blur subsets, wherein the number of first blur subsets in particular comprises a first blur subset NB (negative large), a first blur subset NM (negative medium), a first blur subset NS (negative small), a first blur subset ZE (zero), a first blur subset PS (positive small), a first blur subset PM (positive medium) and a first blur subset PB (positive large).
(2) And carrying out interval fuzzification on the state of charge SOC of the power battery to obtain a plurality of second fuzzy subsets within a set second discrete domain range [0,1], wherein the plurality of second fuzzy subsets specifically comprise a second fuzzy subset VL (very low), a second fuzzy subset L (low), a second fuzzy subset M (medium), a second fuzzy subset H (high) and a second fuzzy subset VH (very high).
(3) For the output power P of the fuel cell fc Performing interval fuzzification to obtainDetermining a third discrete discourse domain range [0,1]A number of third blur subsets, wherein the number of third blur subsets in particular comprises a third blur subset VS (small), a third blur subset S (small), a third blur subset M (medium), a third blur subset B (large) and a third blur subset VB (large).
(4) Empirically reasoning the first fuzzy subsets in the step (1), the second fuzzy subsets in the step (2) and the third fuzzy subsets in the step (3) to form a fuzzy rule base, as shown in table 1.
TABLE 1 fuzzy rule base
It should be noted that, in the operation process of the fuzzy controller, firstly, fuzzification processing is performed on the input vehicle required power and the power battery state of charge to obtain a vehicle required power fuzzy amount and a power battery state of charge fuzzy amount; then, acquiring corresponding fuzzy quantity of the output power of the fuel cell by inquiring a fuzzy rule base in the fuzzy controller; and finally, performing defuzzification processing on the fuzzy quantity of the output power of the fuel cell to obtain the final output power of the fuel cell.
And S102, establishing an equivalent hydrogen consumption mathematical model of the vehicle, and defining an expression of the equivalent hydrogen consumption mathematical model as a fitness function utilized by a gull optimization algorithm.
In step S102, the expression of the mathematical equivalent hydrogen consumption model established by the present invention is:
wherein,
is the equivalent hydrogen consumption of the vehicle,
is the equivalent hydrogen consumption of the fuel cell,
the equivalent hydrogen consumption due to stack decay during fuel cell operation,
is the equivalent hydrogen consumption of the power cell, N cell The number of cells contained in the fuel cell, I fc Is the output current of the fuel cell and,
is the molar mass of hydrogen, F is the Faraday constant, P 1 The rate of degradation of fuel cell performance, n, due to load change cycles 1 The number of cyclic load change cycles, P, while the vehicle is running 2 The rate of degradation of fuel cell performance due to start-stop cycling, n 2 Number of start-stop cycles, P, while the vehicle is running 3 Is the rate of fuel cell performance degradation caused by idle load, t 1 For idle time duration per hour, P 4 The rate of degradation of the fuel cell performance due to a high load condition, t 2 For high power load operation duration, alpha fc As the price of the stack inside the fuel cell,
for hydrogen price, P bat Is the output power of the power battery, alpha ele Is an electricity price per degree, η bat And t is the running time for the energy conversion efficiency of the power battery.
And S103, acquiring all membership functions contained in the fuzzy controller, and optimizing all the membership functions by using a gull optimization algorithm to acquire a reasonable fuzzy energy management strategy.
In step S103, the obtaining process of all membership functions included in the fuzzy controller includes: firstly, based on the first fuzzy subsets, determining a first membership function corresponding to the required power of the vehicle in the given first discrete domain range [ -1,1], wherein the first membership function is a trapezoidal membership function, as shown in fig. 2; secondly, determining a second membership function corresponding to the state of charge of the power battery in the established second discrete domain range [0,1] based on the plurality of second fuzzy subsets, wherein the second membership function is a triangular membership function, and is shown in fig. 3; and finally, determining a third membership function corresponding to the output power of the fuel cell in the given third discrete domain range [0,1] based on the plurality of third fuzzy subsets, wherein the third membership function is a trapezoidal membership function, as shown in fig. 4.
Before performing the optimization of all membership functions, the following steps should preferably be performed:
(1) according to the piecewise linear curve crossing condition of the first membership function shown in fig. 2, obtaining a plurality of first unknown variables included in the first membership function, where the plurality of first unknown variables specifically include x 1 、x 2 、x 3 、x 4 、x 5 、x 6 、x 7 、x 8 、x 9 、x 10 The ten first unknown variables are constrained to have value ranges respectively as follows: x is the number of 1 ∈(-1,-0.8)、x 2 ∈(-0.8,-0.6)、x 3 ∈(-0.6,-0.4)、x 4 ∈(-0.4,-0.2)、x 5 ∈(-0.2,0)、x 6 ∈(0,0.2)、x 7 ∈(0.2,0.4)、x 8 ∈(0.4,0.6)、x 9 ∈(0.6,0.8)、x 10 ∈(0.8,1)。
(2) According to the piecewise linear curve crossing condition of the second membership function shown in fig. 3, obtaining a plurality of second unknown variables included in the second membership function, where the plurality of second unknown variables specifically include y 1 、y 2 、y 3 、y 4 、y 5 The value ranges of the five second unknown variables are respectively constrained as：y 1 ∈(0,0.2)、y 2 ∈(0.2,0.4)、y 3 ∈(0.4,0.6)、y 4 ∈(0.6,0.8)、y 5 ∈(0.8,1)。
(3) Obtaining a plurality of third unknown variables included in the third membership function according to the piecewise linear curve crossing condition of the third membership function shown in fig. 4, wherein the plurality of third unknown variables specifically include z 1 、Z 2 、z 3 、z 4 、z 5 、z 6 、z 7 、z 8 The eight third unknown variables are simultaneously constrained to have value ranges respectively as follows: z is a radical of 1 ∈(0.1,0.2)、z 2 ∈(0.2,0.3)、z 3 ∈(0.3,0.4)、z 4 ∈(0.4,0.5)、z 5 ∈(0.5,0.6)、z 6 ∈(0.6,0.7)、z 7 ∈(0.7,0.8)、z 8 ∈(0.8,0.9)。
Before the optimization of all membership functions is performed, a brief description is preferably given to the implementation principle of the gull optimization algorithm adopted in the embodiment of the present invention, where the gull optimization algorithm mainly simulates the migration behavior and the attack behavior of gulls, and the details are as follows:
when the gull only migrates, the gull should sequentially satisfy the three conditions of avoiding collision, moving towards the direction of the optimal position, and moving to the position close to the optimal position, so the formula for moving the gull from the current position to the new position is as follows:
wherein D is s (t) is the new position reached by the gull after moving from the current position, C s (t) is the position of the gull after collision with other adjacent gulls, M s (t) is the direction in which the optimum position is located, P s (t) is the current position of the seagull, P best (t) is the optimal position, A denotes the gull's motion behavior in a given search space, B is a random number for balancing global and local searches, f c Is a frequency for controlling A and has a value of from 2Linear decrease to 0, t being the current iteration number, MAX iteration Is the maximum number of iterations, r d Is a random number and its value falls within 0,1]Within the range.
When a seagull attacks during migration, the seagull makes spiral motion in the air and the motion angle and the motion speed of the seagull change, so that the calculation formula for moving the seagull from the current position to the final position (namely, the attack position) is as follows:
P o (t)＝D s (t)×(r×cosθ)×(r×sinθ)×(r×θ)+P best (t)
wherein, P o (t) is the final position of the gull after moving from the current position, r is the radius of the spiral formed by the spiral motion, and r is u × e θv U and v are constants related to the spiral, which are set by the technician, and theta is a spiral angle formed by the spiral motion.
In step S103, the optimizing all membership functions by using the gull optimization algorithm specifically includes the following steps:
step 2, initializing the current position of each gull used in the gull optimization algorithm based on the boundary interval of the target variables, so that the target variables indicated by the current position of each gull are different;
step 3, obtaining an optimal target variable referred by the optimal gull position after iteratively updating the current position of each gull based on the operating condition data of the vehicle in a near week and combining a gull optimization algorithm;
and 4, optimizing all membership functions by using the optimal target variable, and further optimizing the fuzzy controller to output a reasonable fuzzy energy management strategy.
It should be noted that, in step 1, the target variables include the first unknown variables, the second unknown variables, and the third unknown variables, that is, the dimension of the target variable is 23, and therefore, the boundary interval of the target variable should include the value range of the unknown variable corresponding to each dimension.
More specifically, the operation condition data actually covers all parameter values related to the expression of the vehicle required power, the state of charge of the power battery and the mathematical model of the equivalent hydrogen consumption, and in addition, a target variable denoted by the current position of each gull in the initialization stage and the t-th iteration may actually form a virtual fuzzy controller, and the embodiment of the present invention provides that the iteration is executed from t ═ 1, and at this time, the specific execution process for the step 3 includes the following steps:
step a, analyzing the operation condition data by combining an expression of the equivalent hydrogen consumption mathematical model and a virtual fuzzy controller formed by each gull in an initialization stage to obtain the fitness corresponding to the current position of each gull in the initialization stage;
b, obtaining the gull with the minimum fitness in the gull population, and designating the current position of the gull in the initialization stage as the optimal gull position;
c, executing the t iteration, and updating and acquiring the current positions of the remaining other seagulls in the t iteration according to the migration behavior and the attack behavior of the seagulls;
d, analyzing the operation condition data by combining the expression of the equivalent hydrogen consumption mathematical model and a virtual fuzzy controller formed by the rest of the gulls in the t-th iteration to obtain the fitness corresponding to the current positions of the rest of the gulls in the t-th iteration;
step e, obtaining the gull with the minimum fitness among the rest gulls, and judging whether the fitness corresponding to the current position of the gull in the t-th iteration is smaller than the fitness corresponding to the optimal gull position; if so, re-designating the current position of the gull in the tth iteration as the optimal gull position utilized in the next iteration; if not, continuing to execute the step f;
step f: judging whether t is the maximum iteration number; if yes, continuing to execute the step g; if not, assigning t +1 to t and returning to execute the step c;
step g: and outputting the finally specified optimal gull position after iterative updating.
In the step a, the virtual fuzzy controller formed by each gull in the initialization stage is used to calculate the output power of the fuel cell associated with each gull, and further convert the output power into the output current of the fuel cell associated with each gull, and then the output current and the operation condition data are input into the expression of the mathematical equivalent hydrogen consumption model to calculate the fitness corresponding to the current position of each gull in the initialization stage.
In the embodiment of the invention, the fuzzy controller is introduced, the gull optimization algorithm is used for parameter optimization, and the equivalent hydrogen consumption mathematical model considering the recession cost of the fuel cell is introduced in the optimization process as the fitness function used by the gull optimization algorithm, so that the optimized fuzzy controller can output a reasonable fuzzy energy management strategy, the energy utilization rate of a fuel cell automobile is improved, and the service life of the fuel cell can be prolonged to a certain extent.
In addition, an embodiment of the present invention further provides a fuel cell vehicle energy management system based on a gull optimization algorithm, where the system includes:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor implements the method for fuel cell vehicle energy management based on gull optimization algorithm of any of the embodiments described above.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are the same as those in the above method embodiment.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor is the control center of the gull optimization algorithm-based fuel cell vehicle energy management system, and various interfaces and lines are utilized to connect the various parts of the entire gull optimization algorithm-based fuel cell vehicle energy management system operational device.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the gull optimization algorithm-based fuel cell vehicle energy management system by running or executing the computer programs and/or modules stored in the memory, and by invoking the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein: the storage program area is used for storing an operating system, application programs (such as a sound playing function and an image playing function) required by at least one function and the like; the storage data area is used for storing data (such as audio data, a phone book and the like) created according to the use of the mobile phone. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart-Media-Card (SMC), a Secure-Digital (SD) Card, a Flash-memory Card (Flash-Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the description of the present application has been made in considerable detail and with particular reference to a few illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed that the present application effectively covers the intended scope of the application by reference to the appended claims, which are interpreted in view of the broad potential of the prior art. Further, the foregoing describes the present application in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial changes from the present application, not presently foreseen, may nonetheless represent equivalents thereto.
Claims (9)
1. A method for fuel cell vehicle energy management based on a gull optimization algorithm, the method comprising:
constructing a fuzzy controller, wherein the fuzzy controller takes the required power of a vehicle and the state of charge of a power battery in the vehicle as input variables and takes the output power of a fuel battery in the vehicle as an output variable;
establishing an equivalent hydrogen consumption mathematical model of the vehicle, and defining an expression of the equivalent hydrogen consumption mathematical model as a fitness function utilized by a gull optimization algorithm;
and acquiring all membership functions contained in the fuzzy controller, and optimizing all membership functions by utilizing a gull optimization algorithm to acquire a reasonable fuzzy energy management strategy.
2. The gull optimization algorithm-based fuel cell vehicle energy management method of claim 1, wherein the vehicle power demand is calculated by the following formula:
wherein, P req For the power demand of the vehicle, u is the vehicle speed, η t For transmission system efficiency, m is vehicle weight, g is gravitational acceleration, f is rolling resistance coefficient, α is road slope angle, C D The coefficient is an air resistance coefficient, A is a windward area of the vehicle, delta is a rotating mass conversion coefficient, and a is vehicle acceleration.
3. The gull optimization algorithm-based fuel cell vehicle energy management method of claim 1, wherein the power battery state of charge in the vehicle is calculated by the following formula:
wherein, the SOC is the state of charge value of the power battery, SOC 0 Is the power battery state of charge value at the beginning of charging and discharging, C N The rated capacity of the power battery is shown, eta is the charge-discharge efficiency, I is the current of the power battery, and t is the charge-discharge time.
4. The gull optimization algorithm-based fuel cell vehicle energy management method of claim 1, wherein the fuzzy controller is constructed by:
carrying out interval fuzzification on the required power of the vehicle to obtain a plurality of first fuzzy subsets in a set first discrete domain range;
carrying out interval fuzzification on the state of charge of the power battery to obtain a plurality of second fuzzy subsets in a set second discrete universe range;
interval fuzzification is carried out on the output power of the fuel cell to obtain a plurality of third fuzzy subsets within a set third discrete discourse range;
and empirically reasoning the first fuzzy subsets, the second fuzzy subsets and the third fuzzy subsets to form a fuzzy rule base.
5. The gull optimization algorithm-based fuel cell vehicle energy management method of claim 1, wherein the mathematical model of the equivalent hydrogen consumption is expressed as:
wherein,
is the equivalent hydrogen consumption of the vehicle,
is the equivalent hydrogen consumption of the fuel cell,
the equivalent hydrogen consumption due to stack decay during fuel cell operation,
is the equivalent hydrogen consumption of the power cell, N cell The number of cells contained in the fuel cell, I fc Is the output current of the fuel cell and,
is the molar mass of hydrogen, F is the Faraday constant, P 1 The rate of degradation of fuel cell performance, n, due to load change cycles 1 The number of cyclic load change cycles, P, during vehicle travel 2 The rate of degradation of fuel cell performance due to start-stop cycling, n 2 Number of start-stop cycles, P, while the vehicle is running 3 Is the rate of fuel cell performance degradation caused by idle load, t 1 For idle time duration per hour, P 4 The rate of degradation of the fuel cell performance due to a high load condition, t 2 For high power load operation duration, alpha fc Is the price of the stack inside the fuel cell,
for hydrogen price, P bat Is the output power of the power cell, alpha ele Eta electric price per degree bat And t is the running time for the energy conversion efficiency of the power battery.
6. The gull optimization algorithm-based fuel cell vehicle energy management method of claim 4, wherein the obtaining all membership functions contained within the fuzzy controller comprises:
determining a first membership function corresponding to the vehicle required power based on the plurality of first fuzzy subsets;
determining a second membership function corresponding to the state of charge of the power battery based on the plurality of second fuzzy subsets;
and determining a third membership function corresponding to the output power of the fuel cell based on the plurality of third fuzzy subsets.
7. The gull optimization algorithm-based fuel cell vehicle energy management method of claim 6, wherein the first and third membership functions are both trapezoidal membership functions and the second membership function is a triangular membership function.
8. The gull optimization algorithm-based fuel cell vehicle energy management method of claim 6, wherein the optimizing all membership functions using the gull optimization algorithm to obtain a reasonable fuzzy energy management strategy comprises:
acquiring a plurality of first unknown variables contained in the first membership function, a plurality of second unknown variables contained in the second membership function and a plurality of third unknown variables contained in the third membership function according to the piecewise linear curve crossing condition of the first membership function, the second membership function and the third membership function;
setting basic parameters of a gull optimization algorithm, wherein the basic parameters comprise gull population quantity, maximum iteration times and a boundary interval of a target variable, and the target variable comprises the first unknown variables, the second unknown variables and the third unknown variables;
initializing the current position of each gull used in the gull optimization algorithm based on the boundary interval of the target variable, so that the target variables indicated by the current position of each gull are different;
based on the operating condition data of the vehicle in the near week, and combining with a gull optimization algorithm to iteratively update the current position of each gull, acquiring an optimal target variable referred to by the optimal gull position;
and optimizing all membership functions by using the optimal target variable, so that the fuzzy controller is optimized to output a reasonable fuzzy energy management strategy.
9. A fuel cell vehicle energy management system based on a gull optimization algorithm, the system comprising:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the gull optimization algorithm-based fuel cell vehicle energy management method of any of claims 1 to 8.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116278987A (en) * | 2023-03-16 | 2023-06-23 | 佛山仙湖实验室 | Hydrogen fuel cell automobile energy management method and system based on pigeon optimization algorithm |
| CN116341395A (en) * | 2023-05-29 | 2023-06-27 | 西北工业大学 | Energy management method, system, equipment and terminal for multi-stack fuel cell aircraft |
| CN116834613A (en) * | 2023-08-29 | 2023-10-03 | 北京永氢储能科技有限责任公司 | Power battery assisted hydrogen fuel cell automobile system energy management method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116278987A (en) * | 2023-03-16 | 2023-06-23 | 佛山仙湖实验室 | Hydrogen fuel cell automobile energy management method and system based on pigeon optimization algorithm |
| CN116278987B (en) * | 2023-03-16 | 2024-04-23 | 佛山仙湖实验室 | Hydrogen fuel cell automobile energy management method and system based on pigeon optimization algorithm |
| CN116341395A (en) * | 2023-05-29 | 2023-06-27 | 西北工业大学 | Energy management method, system, equipment and terminal for multi-stack fuel cell aircraft |
| CN116341395B (en) * | 2023-05-29 | 2023-08-01 | 西北工业大学 | Energy management method, system, equipment and terminal for multi-stack fuel cell aircraft |
| CN116834613A (en) * | 2023-08-29 | 2023-10-03 | 北京永氢储能科技有限责任公司 | Power battery assisted hydrogen fuel cell automobile system energy management method |
| CN116834613B (en) * | 2023-08-29 | 2023-11-10 | 北京永氢储能科技有限责任公司 | Power battery assisted hydrogen fuel cell automobile system energy management method |
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