Energy management method for hydrogen-lithium hybrid power generation system based on power following and fuzzy control

Abstract

The invention discloses an energy management method for a hydrogen-lithium hybrid power generation system based on power following and fuzzy control, and belongs to the technical field of thermotechnical automatic control, the hydrogen-lithium hybrid power generation system comprises a fuel cell and a storage battery, and the fuel cell is connected to one end of a one-way DC / DC converter; the other end of the one-way DC / DC converter is connected with a load; the storage battery and the super capacitor are connected to one end of the bidirectional DC / DC converter, and the other end of the bidirectional DC / DC converter is connected with the other end of the unidirectional DC / DC converter; the method comprises the following steps: step 1, modeling a fuel cell and a storage battery; step 2, obtaining the power required by the current load and the charge state of the storage battery; and step 3, outputting an output power value of the fuel cell according to fuzzy control. The demanded power of the load is combined with the fuzzy control technology, so that the power of the fuel cell and the power of the storage battery are reasonably distributed, and the dynamic performance and safety of the system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of thermal automatic control, and in particular to an energy management method for a hydrogen-lithium hybrid power generation system based on power following and fuzzy control. Background Art

[0002] Fuel cell vehicles are considered to be an alternative to fossil fuel internal combustion engine vehicles, which can reduce the impact of transportation on the environment. Compared with traditional internal combustion engine vehicles, fuel cell vehicles have higher reversible thermodynamic efficiency at room temperature, and the water generated by the reaction will not pollute the environment. They have the advantage of sustainable development and can alleviate the problem of insufficient energy supply. At the same time, fuel cells are no noise compared to internal combustion engines. Compared with pure electric vehicles, fuel cell vehicles do not have the disadvantages of short driving range and long charging time, which makes them closer to internal combustion engine vehicles in terms of function than pure electric vehicles.

[0003] Proton Exchange Membrane Fuel Cells (PEMFC) are widely used in electric vehicles and other fields due to their high specific energy and low operating temperature. However, PEMFC is limited in practical applications due to its slow dynamic response and inability to recover braking energy. Lithium-ion batteries are also used in automobiles and other fields due to their advantages such as fast dynamic response and rapid charging and discharging, but their application is also limited due to their disadvantages such as long charging time and limited driving range. Therefore, connecting the two in parallel to form a hybrid power system can effectively combine the advantages of fuel cells and lithium-ion batteries to make up for their shortcomings.

[0004] Fuel cells have the problem of slow dynamic response to load changes and inability to recover braking energy. Therefore, fuel cell vehicles generally have other energy sources in addition to the main fuel cell energy source. For multiple energy sources, energy management issues will arise, which plays an important role in ensuring the energy efficiency of the vehicle, maintaining the SOC of the battery, improving the economy of the vehicle, and the durability of the battery. It has also become a key and difficult point in research. In order to better control the power distribution of fuel cell hybrid systems, various energy management strategies have been developed in previous studies. However, the current energy management strategy design is mostly based on a series of fuzzy rules based on engineering experience and intuition, fuzzy or deterministic, to allocate power requirements, such as fuzzy logic and thermostat control. These methods have low computational complexity and are easy to implement in real time, but their performance is overly dependent on predefined rules and calibration parameters. In addition, frequent startup of fuel cells not only reduces the service life of the fuel cell, but also the accumulated heat during frequent startup causes safety issues for the fuel cell. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes an energy management method for a hydrogen-lithium hybrid power generation system based on power following and fuzzy control, which combines the required power of the load with fuzzy control technology to reasonably distribute the power of the fuel cell and the battery to meet the dynamic performance requirements of the system.

[0006] A method for energy management of a hydrogen-lithium hybrid power generation system based on power following and fuzzy control, comprising a fuel cell and a storage battery, wherein the fuel cell is connected to one end of a unidirectional DC / DC converter; the other end of the unidirectional DC / DC converter is connected to a load; the storage battery and a supercapacitor are connected to one end of a bidirectional DC / DC converter, and the other end of the bidirectional DC / DC converter is connected to the other end of the unidirectional DC / DC converter; the method comprises the following steps:

[0007] Step 1, modeling the fuel cell and battery;

[0008] Step 2, obtaining the current load power required and the battery charge state;

[0009] Step 3: output the fuel cell output power value according to the fuzzy control.

[0010] Preferably, step 1 comprises:

[0011] Fuel Cell Modeling:

[0012] Voltage V of a single fuel cell electrochemical model fc for:

[0013] V fc =E Nernst -V act -V ohm -V con

[0014] Among them, E Nernst Represents the ideal thermodynamic voltage under non-standard conditions; V act Represents the voltage of activation polarization loss; V ohm The voltage representing the ohmic polarization loss; V con The voltage representing the concentration polarization losses;

[0015] The voltage E0 of a single fuel cell under standard conditions:

[0016]

[0017] Where ΔG represents the change in Gibbs free energy; n represents the number of electrons transferred; F represents the Faraday constant 96485;

[0018] Ideal thermodynamic voltage of a single fuel cell:

[0019]

[0020] Where R represents the gas constant; T represents the thermodynamic temperature; p H2 and p O2 represent the partial pressures of hydrogen and oxygen respectively.

[0021] Preferably, step 1 also includes:

[0022] Battery Modeling:

[0023] Battery terminal voltage V bat Expressed in the form:

[0024] V bat =V oc -I bat R o

[0025] Among them, V oc Represents the open circuit voltage of the battery; R o Represents the ohmic internal resistance of the battery; I bat Represents the output current of the battery;

[0026] The required power of the battery P bat Expressed in the form:

[0027] P bat =V oc I bat -I bat 2 R o

[0028] Battery current I bat :

[0029]

[0030] The battery state of charge SOC is defined as follows:

[0031]

[0032] Where SOC0 represents the initial value of the battery state of charge; η represents the coulomb efficiency; Δt represents the sampling time; Q bat Represents the actual maximum charge of the battery.

[0033] Preferably, step 2 further comprises:

[0034] By judging the current battery SOC state and the required power of the load, the switching strategies under different situations are formulated, specifically:

[0035] 1) SOC<SOCmi n When the charging switch is turned on,

[0036] If charging is required, mot <P eff max When the fuel cell operates at the maximum efficiency power of the fuel cell, the excess power is used to charge the battery; when the required power P mot Greater than the maximum efficiency power P eff max But it is less than the maximum power P of the fuel cell max When the fuel cell is used alone to supply power, the power requirement P mot Greater than the maximum power of the fuel cell P max When the power is on, the fuel cell and the battery are used together for power supply;

[0037] 2) SOC>SOC max When the charging switch is turned off,

[0038] When charging is not required, the required power P mot Lower than the minimum power P of the fuel cell low When the fuel cell does not work, the battery is used to supply power. mot Greater than the maximum power P of the fuel cell max When P low and P max In between, it is powered solely by the fuel cell;

[0039] Among them, SOC min The battery SOC low critical point is specified. If the lithium battery SOC is lower than SOCmin, the charging switch will be turned on; SOC max The battery SOC high critical point is specified. If the battery SOC is higher than SOC max , the charging switch will be turned off; P mot is the load power requirement, P FC is the output power of the fuel cell, P low With P max Represent the minimum working output power and maximum output power of the fuel cell, P eff max Indicates the maximum efficiency power of the fuel cell.

[0040] Preferably, the battery is a lithium battery.

[0041] Preferably, the fuel cell is a hydrogen proton exchange membrane fuel cell.

[0042] Preferably, the step 3 comprises:

[0043] Step 3-1: Dimensional transformation of fuzzy controller input parameters, including:

[0044] When the fuel cell supplies power to the load and the lithium battery at the same time, the fuzzy controller input parameters are the battery state of charge SOC and the load demand power P mot ;

[0045] When only fuel cells are used for power supply, the input parameter of the fuzzy controller is the load demand power P mot ;

[0046] When only lithium batteries are used for power supply, the fuzzy controller has no input parameters and does not work;

[0047] When the fuel cell and lithium battery are used to supply power to the battery at the same time, the input parameters of the fuzzy controller are the battery state of charge SOC and the load demand power P mot .

[0048] Preferably, the step 3 further comprises:

[0049] Step 3-2: Set the load power requirement P mot The fuzzy subsets are divided into five {VL, L, M, H, VH}, representing very low, low, medium, high, and very high;

[0050] The battery state of charge SOC fuzzy subset is divided into five parts: {VL1, L1, M1, H1, VH1}, representing very low SOC area, low SOC area, medium SOC area, high SOC area and very high SOC area;

[0051] Fuel cell output power P FC The fuzzy subsets are divided into {NB, NM, NS, Z, PS, PM, PB}, which are represented by NB (Negative Big) very low, NM (Negative Medium) medium low, NS (Negative Small) low, Z (Zero) medium, PS (Positive Small) slightly high, PM (Positive Medium) medium high and PB (Positive Big) very high respectively.

[0052] Preferably, the step 3 further comprises:

[0053] Step 3-3: Set the membership function according to the required power of the load and the state of charge of the battery;

[0054] Step 3-4: Obtain the input value through the membership function, set the fuzzy rules about the load demand power and battery charge state, and obtain the fuzzy parameters of the fuel cell output power through the fuzzy rules.

[0055] Preferably, the step 3 further comprises:

[0056] Step 3-5: Use the variable weight method to clarify the fuzzy parameters of the fuel cell output power obtained in step 3-3, specifically:

[0057]

[0058] u i (x i )=F(a m a(x i ), b m b(x i ))

[0059] Output is the output power value of the fuel cell in this cycle, x i represents the output power of the fuel cell, μ i (x i ) represents the fuzzy parameter corresponding to the output power of the fuel cell, and n represents the number of output power fuzzy parameters obtained after the fuzzy subset of the controller is mapped through the fuzzy rule; a m and b m Indicates the battery state of charge SOC input and the load power requirement P mot The weight in the energy supply mode, a(x i ) represents the fuzzy subset element of the battery state of charge SOC, b(x i ) represents the load power requirement P mot ; F(a, b) represents the fuzzy parameter output value corresponding to the fuzzy rule with parameters a and b as input;

[0060] The weights are adjusted to:

[0061] 1) When the fuel cell supplies power to the load and the battery at the same time, a m =0.3, b m =0.7;

[0062] 2) When only the fuel cell is used for power supply, a m =1, b m =0;

[0063] 3) When using fuel cells and batteries to power the load at the same time, a m =0.6, b m =0.4;

[0064] Finally, the fuel cell output power value of this cycle is output, the required power of the load and the charge state of the battery in the next time are obtained, and the next cycle control is performed.

[0065] Beneficial effects:

[0066] First, by combining the load power demand with fuzzy control technology, the power of the fuel cell and battery can be reasonably distributed to meet the dynamic performance requirements of the system.

[0067] Second, it reduces the number of starts and stops of the fuel cell, avoids the fuel cell working under low load conditions and frequent starts and stops, and improves the life and safety of the fuel cell.

[0068] Third, the battery's state of charge (SOC) has a buffer zone to ensure that the battery always operates in a safe area, thereby increasing the safety margin of the fuel cell system.

[0069] Fourth, the load charge state fluctuation of the battery is controlled within a very small range, ensuring that the load charge state of the battery is in an efficient working range, achieving stability and reliability of the battery operation, and is also beneficial to the service life and safety of the battery, with better economy. Under the power following strategy and fuzzy control, the fuel cell output power is more stable and the fuel cell has a longer service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of a hydrogen fuel cell and lithium battery hybrid system for realizing the present invention.

[0071] Figure 2 The figure is a flow chart of an embodiment of the present invention.

[0072] Figure 3 Schematic diagram of the membership function of the battery charge state, load demand power and fuel cell output power of the automobile hydrogen-lithium hybrid power generation system in an embodiment of the present invention.

[0073] Figure 4 It is a simulation model of the energy management method of the hydrogen-lithium hybrid power generation system based on power following and fuzzy control in the example of the present invention.

[0074] Figure 5 This is a graph showing changes in the output power of a fuel cell under the control strategy of an embodiment of the present invention.

[0075] Figure 6 This is a graph showing changes in the state of charge of a lithium battery under the control strategy of an embodiment of the present invention.

[0076] Figure 7 This is a battery model according to an embodiment of the present invention.

[0077] Figure 8 This is a schematic diagram of the battery charge state and load power demand in the present invention. DETAILED DESCRIPTION

[0078] The technical solution provided by the present invention will be described in detail below in conjunction with specific embodiments. It should be understood that the following specific implementation methods are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0079] In this example, the fuel cell uses a hydrogen proton exchange membrane fuel cell (PEMFC), the storage battery uses a lithium battery, and the fuel cell is connected to a DC / DC converter, and then connected to the load. The lithium battery and supercapacitor are connected to a bidirectional DC / DC converter, and then connected to the upper DC / DC converter. The detailed connection is as follows Figure 1 As shown. The initial state of charge of the lithium battery is set to 80%. The controller continuously obtains the current state of charge of the battery and the power demand of the load, performs fuzzy processing on the two input parameters, obtains the fuzzy parameters of the output power of the fuel cell by setting fuzzy rules, and finally performs clarification processing through the centroid method to obtain the output power value of the fuel cell.

[0080] Based on the above system, the energy management method of the hydrogen-lithium hybrid power generation system based on power following and fuzzy control provided by the present invention is as follows: Figures 2 to 7 As shown, the following steps are included:

[0081] Step 1: Digital modeling of the fuel cell and battery module is carried out. The detailed steps are as follows:

[0082] 1. Fuel cell modeling:

[0083] Under no-load conditions, the output voltage of PEMFC is high; in actual operation, the actual output voltage decreases due to the change in current density. This difference is called overpotential. There are three reasons for overpotential, namely activation polarization, ohmic polarization and concentration polarization. Therefore, the voltage V of a single fuel cell electrochemical model is fc for:

[0084] V fc =E Nernst -V act -V ohm -V con

[0085] In the formula, E Nernst Represents the ideal thermodynamic voltage under non-standard conditions; V act Represents the voltage of activation polarization loss; V ohm The voltage representing the ohmic polarization loss; V con Voltage representing concentration polarization losses.

[0086] The voltage E0 of a single fuel cell under standard conditions can be calculated from the change in Gibbs free energy:

[0087]

[0088] Where ΔG represents the change in Gibbs free energy; n represents the number of electrons transferred; and F represents the Faraday constant of 96485.

[0089] The ideal thermodynamic voltage of a single fuel cell under non-standard conditions can be calculated from the Nemst equation:

[0090]

[0091] Where R represents the gas constant; T represents the thermodynamic temperature; pH2 and pO2 represent the partial pressures of hydrogen and oxygen, respectively.

[0092] 2. Battery modeling:

[0093] The Ri nt model is used to model the battery, and its battery structure is as follows Figure 7 shown.

[0094] V oc Represents the open circuit voltage of the battery; R o Represents the ohmic internal resistance of the battery; I bat Represents the output current of the battery.

[0095] Battery terminal voltage V bat Expressed in the form:

[0096] V bat =V oc -I bat R o

[0097] The required power P of the battery bat Expressed in the form:

[0098] P bat =V oc I bat -I bat 2 R o

[0099] Battery current I bat :

[0100]

[0101] The battery state of charge SOC is defined as follows:

[0102]

[0103] In the formula, SOC0 represents the initial value of the battery state of charge; η represents the coulomb efficiency, which is 1; Δt represents the sampling time, which is 1s; Q batRepresents the actual maximum charge of the battery.

[0104] Step 2: Obtain the current load power required and the current battery charge state. mot It represents the required power of the system, and SOC represents the state of charge of the battery.

[0105] By judging the current battery SOC state and the required power of the system, the switching strategies under different situations are formulated, specifically:

[0106] 1) Charging switch is turned on (SOC<SOCmin):

[0107] If charging is required, mot <P eff max When the fuel cell operates at the maximum efficiency power of the fuel cell, the excess power is used to charge the battery; when the required power P mot Greater than the maximum efficiency power P eff max But it is less than the maximum power P of the fuel cell max When the fuel cell is used alone to supply power, the power requirement P mot Greater than the maximum power of the fuel cell P max When the battery is powered, the fuel cell and battery are used together to provide power.

[0108] 2) Charging switch is turned off (SOC>SOC) max ):

[0109] When charging is not required, the required power P mot Lower than the minimum power P of the fuel cell low When the fuel cell does not work, the battery is used to supply power. mot Greater than the maximum power P of the fuel cell max When P low and P max The battery is powered solely by the fuel cell.

[0110] Figure 8 In SOC min The battery SOC low critical point is specified. If the lithium battery SOC is lower than SOCmin, the charging switch will be turned on; SOC max The battery SOC high critical point is specified. If the battery SOC is higher than SOC max , the charging switch will be turned off. The blue arrow indicates the motor power P mot That is, the required power of the system. The red arrow indicates the output power P of the fuel cell. FC .P low , P max Represent the minimum working output power and maximum output power of the fuel cell, Peff max Indicates the maximum efficiency power of the fuel cell.

[0111] Step 3: Collect the load power demand and battery state of charge parameters, and set the fuzzy controller. Fuzzy control is a method of controlling the system using fuzzy logic. It does not rely on precise mathematical models, but uses fuzzy sets and language rules to deal with uncertainty and nonlinear problems to achieve efficient control of complex systems. The specific steps are as follows:

[0112] Step 3-1: Dimensional change of fuzzy controller input parameters; In the present invention, the input parameters of the fuzzy controller are not the two constant parameters of the load demand power and the battery charge state, but are variable, i.e., dimensionally changeable, so as to improve the response rate of the fuel cell system and the response rate of the fuzzy controller to the fuel cell switching. By changing the dimension of the input parameters, the dynamic response rate of the fuel cell control is improved, which specifically includes:

[0113] Different energy supply methods include:

[0114] 1: The fuel cell supplies power to the load and the lithium battery at the same time.

[0115] 2: Only fuel cells are used for power supply.

[0116] 3: Only use lithium batteries for power supply.

[0117] 4: Use fuel cells and lithium batteries to power the load at the same time

[0118] The input parameters of the fuzzy controller are as follows:

[0119] Table 1

[0120]

[0121] When the fuel cell supplies power to the load and the lithium battery at the same time, the fuzzy controller input parameters are the battery state of charge SOC and the load demand power P mot .

[0122] When only fuel cells are used for power supply, the input parameter of the fuzzy controller is the load demand power P mot .

[0123] When only lithium batteries are used for power supply, the fuzzy controller has no input parameters and does not work.

[0124] When the fuel cell and lithium battery are used to supply power to the battery at the same time, the input parameters of the fuzzy controller are the battery state of charge SOC and the load demand power P mot .

[0125] Step 3-2: Set the load power requirement Pmot The fuzzy subsets are divided into five {VL, L, M, H, VH}, representing very low, low, medium, high, and very high;

[0126] The battery state of charge SOC fuzzy subset is divided into five parts: {VL1, L1, M1, H1, VH1}, representing very low SOC area, low SOC area, medium SOC area, high SOC area, and very high SOC area.

[0127] Fuel cell output power P FC The fuzzy subsets are divided into {NB, NM, NS, Z, PS, PM, PB}, which are respectively represented by NB (Negative Big) very low, NM (Negative Medium) medium low, NS (Negative Small) low, Z (Zero) medium, PS (Positive Small) slightly high, PM (Positive Medium) medium high, and PB (Positive Big) very high.

[0128] Step 3-3: According to the required power of the load and the state of charge of the battery, the membership function is set. Fuzzification is to convert the input data into the membership function of the fuzzy variable. This can be achieved by using fuzzy sets to map the actual values ​​to different membership functions. Optionally, a triangular or Gaussian membership function is used.

[0129] Step 3-4: Get the input value through the membership function, set the fuzzy rules about the load demand power and the battery charge state, and obtain the fuzzy parameters of the fuel cell output power through the fuzzy rules. The rules are shown in Table 2:

[0130] Table 2

[0131]

[0132] The principles for setting rules are:

[0133] 1. When the battery SOC is in a lower range, the fuel cell output power will charge the battery while meeting the required power, so that its SOC is increased to a reasonable range;

[0134] 2. When the battery SOC is in the medium range and the required power is also medium, the fuel cell outputs power as the main power source, following the change of the required power, so that the fuel cell works in the high efficiency range as much as possible, and the battery SOC is within a reasonable range;

[0135] 3 When the battery SOC is high, the battery is used as the main output. Under the premise of ensuring that the required power is met, the output power of the fuel cell is reduced as much as possible to reduce the battery SOC to a reasonable range as soon as possible; when the required power is too large, the fuel cell and lithium battery jointly output power.

[0136] Step 3-5: Use the variable weight method to clarify the fuzzy parameters of the fuel cell output power obtained in step 3-3, specifically:

[0137]

[0138] u i (x i )=F(a m a(x i ), b m b(x i ))

[0139] Output is the output power value of the fuel cell in this cycle, x i represents the output power of the fuel cell, μ i (x i ) represents the fuzzy parameter corresponding to the output power of the fuel cell, n represents the number of output power fuzzy parameters obtained after the fuzzy subset of the controller is mapped through the fuzzy rule. Since the elements in the fuzzy subset are not unique, the fuzzy parameters of its output power are also not unique; a m , b m Indicates the battery state of charge SOC input and the load power requirement P mot The weight in the energy supply mode, a(x i ) represents the fuzzy subset element of the battery state of charge SOC, b(x i ) represents the load power requirement P mot ; F(a, b) represents the fuzzy parameter output value corresponding to the fuzzy rule with a and b as input. Different parameters are weighted accordingly to obtain the fuel cell output power value.

[0140] Under different energy supply mode weights:

[0141] 1: The fuel cell supplies power to the load and the battery at the same time.

[0142] At this time, the volatility of the lithium battery will be greater than that of the load. In order to ensure the battery power, the battery state of charge SOC can be used as the dominant factor, so a m =0.3, b m =0.7.

[0143] 2: Only fuel cells are used for power supply.

[0144] At this time, the battery is not required for any operation, so a m =1, b m =0.

[0145] At this time, the battery is in M ​​state by default.

[0146] 3: Only use batteries for power supply.

[0147] At this time, the controller does not need to power the fuel cell, so no control is performed.

[0148] 4: Use fuel cells and batteries to power the load at the same time

[0149] This is because the power of the fuel cell can vary in a small range, and most of the fluctuations are solved by the battery, so the load power is the dominant factor. m =0.6, b m =0.4. Controller input see Table 3:

[0150] Table 3

[0151] Power supply mode\controller input <![CDATA[a m ]]> <![CDATA[b m ]]> Fuel cell to power load and lithium battery 0.3 0.7 Powered only by fuel cells Do not enter 1 Only powered by lithium batteries Do not enter Do not enter Power supply at the same time 0.6 0.4

[0152] Finally, the fuel cell output power value of this cycle is output, the required power of the load and the charge state of the battery in the next time are obtained, and the next cycle control is performed.

[0153] Figure 5 and Figure 6 In the first, under the whole load operation condition, the fuel cell mainly provides output power, while the battery is used as an auxiliary to provide additional output power. When the power demand is relatively low, the energy management strategy is more likely to match the power demand with the battery to reduce the number of starts and stops of the fuel cell, and avoid the fuel cell working under low load conditions and frequent start and stop conditions. The energy management strategy is effective and improves the life and safety of the fuel cell. Second, a buffer zone needs to be reserved for the lithium battery SOC value to ensure that the battery always works in a safe area. The battery SOC falls within the stable and relatively efficient range of [79,81]. It can be seen that when the power demand is small, the improved fuzzy control strategy controls the fuel cell output and the battery output is large, so the battery SOC continues to decline, but it has been relatively stable and maintained within a reasonable range.

Claims

1. A method for energy management of a hydrogen-lithium hybrid power generation system based on power following and fuzzy control, comprising a fuel cell and a battery, wherein the fuel cell is connected to one end of a unidirectional DC / DC converter; the other end of the unidirectional DC / DC converter is connected to a load; the battery and a supercapacitor are connected to one end of a bidirectional DC / DC converter, and the other end of the bidirectional DC / DC converter is connected to the other end of the unidirectional DC / DC converter; characterized in that: The method comprises the following steps: Step 1, modeling the fuel cell and battery; Step 2, obtaining the current load power required and the battery charge state; Step 3: output the fuel cell output power value according to the fuzzy control.

2. The method according to claim 1, characterized in that Step 1 includes: Fuel Cell Modeling: Voltage V of a single fuel cell electrochemical model fc for: V fc =E Nernst -V act -V ohm -V con Among them, E Nernst Represents the ideal thermodynamic voltage under non-standard conditions; V act Represents the voltage of activation polarization loss; V ohm The voltage representing the ohmic polarization loss; V con The voltage representing the concentration polarization losses; The voltage E0 of a single fuel cell under standard conditions: Where ΔG represents the change in Gibbs free energy; n represents the number of electrons transferred; F represents the Faraday constant 96485; Ideal thermodynamic voltage of a single fuel cell: Where R represents the gas constant; T represents the thermodynamic temperature; p H2 and p O2 represent the partial pressures of hydrogen and oxygen respectively.

3. The method according to claim 1, characterized in that Step 1 also includes: Battery Modeling: Battery terminal voltage V bat Expressed in the form: V bat =V oc -I bat R o Among them, V oc Represents the open circuit voltage of the battery; R o Represents the ohmic internal resistance of the battery; I bat Represents the output current of the battery; The required power of the battery P bat Expressed in the form: P bat =V oc I bat -I bat 2 R o Battery current I bat : The battery state of charge SOC is defined as follows: Where SOC0 represents the initial value of the battery state of charge; η represents the coulomb efficiency; Δt represents the sampling time; Q bat Represents the actual maximum charge of the battery.

4. The method according to claim 1, characterized in that: Step 2 also includes: By judging the current battery SOC state and the required power of the load, the switching strategies under different situations are formulated, specifically: 1) SOC <SOC min When the charging switch is turned on, If charging is required, mot <P eff max When the fuel cell operates at the maximum efficiency power of the fuel cell, the excess power is used to charge the battery; when the required power P mot Greater than the maximum efficiency power P eff max But it is less than the maximum power P of the fuel cell max When the fuel cell is used alone to supply power, the power requirement P mot Greater than the maximum power of the fuel cell P max When the battery is powered, the fuel cell and battery are used together to supply power; 2) SOC>SOC max When the charging switch is turned off, When charging is not required, the required power P mot Lower than the minimum power P of the fuel cell low When the fuel cell does not work, the battery is used to supply power. mot Greater than the maximum power P of the fuel cell max When the battery and fuel cell jointly provide power, the demand is P low and P max In between, it is powered solely by the fuel cell; Among them, SOC min The battery SOC low critical point is specified. If the lithium battery SOC is lower than SOCmin, the charging switch will be turned on; SOC max The battery SOC high critical point is specified. If the battery SOC is higher than SOC max , the charging switch will be turned off; P mot is the load power requirement, P FC is the output power of the fuel cell, P low With P max Represent the minimum working output power and maximum output power of the fuel cell, Peff max Indicates the maximum efficiency power of the fuel cell.

5. The method according to claim 4, characterized in that The storage battery is a lithium battery.

6. The method according to claim 4, characterized in that The fuel cell is a hydrogen proton exchange membrane fuel cell.

7. The method according to claim 4, characterized in that The step 3 comprises: Step 3-1: Dimensional transformation of fuzzy controller input parameters, including: When the fuel cell supplies power to the load and the lithium battery at the same time, the fuzzy controller input parameters are the battery state of charge SOC and the load demand power P mot ; When only fuel cells are used for power supply, the input parameter of the fuzzy controller is the load demand power P mot ; When only lithium batteries are used for power supply, the fuzzy controller has no input parameters and does not work; When the fuel cell and lithium battery are used to supply power to the battery at the same time, the input parameters of the fuzzy controller are the battery state of charge SOC and the load demand power P mot .

8. The method according to claim 7, characterized in that The step 3 also includes: Step 3-2: Set the load power requirement P mot The fuzzy subsets are divided into five {VL, L, M, H, VH}, representing very low, low, medium, high, and very high; The battery state of charge SOC fuzzy subset is divided into five parts: {VL1, L1, M1, H1, VH1}, representing very low SOC area, low SOC area, medium SOC area, high SOC area and very high SOC area; Fuel cell output power P FC The fuzzy subsets are divided into {NB, NM, NS, Z, PS, PM, PB}, which are represented by NB (Negative Big) very low, NM (Negative Medium) medium low, NS (Negative Small) low, Z (Zero) medium, PS (Positive Small) slightly high, PM (Positive Medium) medium high and PB (Positive Big) very high respectively.

9. The method according to claim 8, characterized in that The step 3 also includes: Step 3-3: Set the membership function according to the required power of the load and the state of charge of the battery; Step 3-4: Obtain the input value through the membership function, set the fuzzy rules about the load demand power and battery charge state, and obtain the fuzzy parameters of the fuel cell output power through the fuzzy rules.

10. The method according to claim 9, characterized in that The step 3 also includes: Step 3-5: Use the variable weight method to clarify the fuzzy parameters of the fuel cell output power obtained in step 3-3, specifically: u i (x i )=F(a m a(x i ),b m b(x i )) Output is the output power value of the fuel cell in this cycle, x i represents the output power of the fuel cell, μ i (x i ) represents the fuzzy parameter corresponding to the output power of the fuel cell, and n represents the number of output power fuzzy parameters obtained after the fuzzy subset of the controller is mapped through the fuzzy rule; a m and b m Indicates the battery state of charge SOC input and the load power requirement P mot The weight in the energy supply mode, a(x i ) represents the fuzzy subset element of the battery state of charge SOC, b(x i ) represents the load power requirement P mot ; F(a, b) represents the fuzzy parameter output value corresponding to the fuzzy rule with parameters a and b as input; The weights are adjusted to: 1) When the fuel cell supplies power to the load and the battery at the same time, a m =0.3, b m =0.7; 2) When only the fuel cell is used for power supply, a m =1, b m =0; 3) When using fuel cells and batteries to power the load at the same time, a m =0.6, b m =0.4; Finally, the fuel cell output power value of this cycle is output, the required power of the load and the charge state of the battery in the next time are obtained, and the next cycle control is performed.

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