Energy management method and system for automobile hybrid power system

Abstract

An embodiment of the present invention provides an energy management method and system for a hybrid vehicle system, belonging to the field of new energy vehicle technology. The energy management method includes: establishing a parameter model for the hybrid vehicle system; establishing a fuel cost objective function based on the parameter model; setting a power source behavior boundary based on the parameter model; obtaining the required power of the motor; and allocating power based on the fuel cost objective function, the power source behavior boundary, and the required power of the motor. This energy management method and system can effectively improve the durability of the vehicle and has good adaptability, capable of reducing the power and voltage fluctuations of the fuel cell while maintaining a stable lithium battery SOC.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to an energy management method and system for a hybrid power system of an automobile. Background Art

[0002] A fuel cell is a device that generates electricity through an electrochemical reaction between hydrogen and oxygen. It is a clean energy technology that efficiently converts the chemical energy of hydrogen into electricity, producing water and heat as byproducts. It is widely used in transportation, stationary power supplies, portable power supplies, aerospace, and other fields. Fuel cell energy management strategies refer to a series of measures within a fuel cell system that achieve optimal performance and efficiency by rationally controlling the operating state of the fuel cell and other energy storage components (such as batteries and supercapacitors). These strategies aim to maximize the utilization of the electrical energy provided by the fuel cell system and ensure that the system can balance energy supply and demand under different operating conditions.

[0003] Existing fuel cell energy management strategies can be primarily categorized into rule-based and optimization-based approaches. Rule-based energy management strategies primarily include switching mode and power-following mode, with rules primarily formulated based on the vehicle's operating conditions. Rule-based strategies are unsuitable for strategy verification due to the complexity of the fuel cell system model, and the lithium-ion battery model cannot reflect the high-frequency current phenomena of the battery during operation. While optimization-based strategies address the issue of balancing fuel economy and durability in fuel cell hybrid systems, these approaches still lack effective power source constraints. Summary of the Invention

[0004] An object of the embodiments of the present invention is to provide an energy management method and system for a hybrid power system of an automobile, which can effectively improve the durability of the vehicle.

[0005] To achieve the above objectives, an embodiment of the present invention provides an energy management method for a hybrid vehicle system, comprising:

[0006] Establish a parametric model of the hybrid power system;

[0007] establishing a fuel cost objective function based on the parameter model;

[0008] setting a power source behavior boundary according to the parameter model;

[0009] Get the motor's required power;

[0010] Power is allocated based on the fuel cost objective function, the power source behavior boundary and the motor demand power.

[0011] Optionally, the hybrid power system includes:

[0012] fuel cells;

[0013] a unidirectional DC / DC converter connected to the fuel cell;

[0014] lithium batteries;

[0015] a bidirectional DC / DC converter connected to the lithium battery;

[0016] A DC / AC converter connected to the unidirectional DC / DC converter and the bidirectional DC / DC converter;

[0017] The motor is connected to the DC / AC converter.

[0018] Optionally, a parameter model of the hybrid power system is established, including:

[0019] Establishing a parameter model of the fuel cell, including:

[0020] Determine the output voltage of a single fuel cell according to formula (1):

[0021] V cell =E Nernst +η act +η omic +η conc , (1)

[0022] Among them, V cell is the output voltage of the single fuel cell, E Nernst is the thermodynamically predicted voltage of the fuel cell obtained from the Nernst equation, η act is the activation loss, η omic is the ohmic loss, η conc is the concentration loss;

[0023] According to formula (2), the differential equation of the dynamic characteristics of the single fuel cell is established.

[0024]

[0025] Among them, V act is the voltage across the activation loss equivalent resistance, i is the main circuit current, C dl is the polarized capacitance, is the kinetic voltage of the electrochemical reaction process, is the kinetic resistance of the electrochemical reaction process;

[0026] According to the output voltage of the single fuel cell, the fuel cell voltage is determined according to formula (3):

[0027] V stock =N·V cell, (3)

[0028] Among them, V stock is the fuel cell voltage, N is the number of single fuel cells, V cell is the output voltage of the single fuel cell.

[0029] Optionally, a parameter model of the hybrid power system is established, including:

[0030] Establishing a parameter model of the lithium battery, including:

[0031] Determine the lithium battery SOC according to formula (4):

[0032]

[0033] Wherein, z(t) is the SOC of the lithium battery at time t, z(t0) is the SOC of the lithium battery at time t0, η is the Coulomb coefficient, t and t0 are time, i(t) is the main circuit current at time t, and Q is the total capacity of the lithium battery;

[0034] According to the lithium battery SOC, the lithium battery output voltage is determined according to formula (5):

[0035]

[0036] Wherein, v(t) is the output voltage of the lithium battery at time t, z(t) is the SOC of the lithium battery at time t, OCV(z(t)) is the theoretical voltage of the lithium battery when the SOC of the lithium battery is z(t), R1 is the first polarization internal resistance, is the current passing through the first polarization internal resistance at time t, R2 is the second polarization internal resistance, is the current passing through the second polarization internal resistance at time t, and R0 is the ohmic internal resistance of the lithium battery;

[0037] According to formula (6) and formula (7), the dynamic characteristic differential equation of lithium battery is established.

[0038]

[0039]

[0040] Among them, C1 is the first polarized capacitor, and C2 is the second polarized capacitor.

[0041] Optionally, establishing a fuel cost objective function based on the parameter model includes:

[0042] Determine the hydrogen consumption according to formula (8):

[0043]

[0044] in, is the hydrogen consumption, P is the power of the fuel cell, F is the Faraday constant, V d is the voltage of a single fuel cell;

[0045] According to the hydrogen consumption, the fuel cost objective function is determined according to formula (9):

[0046]

[0047] Wherein, min f(x) is the fuel cost objective function, is the hydrogen consumption, I bat is the lithium battery current, U bat is the lithium battery voltage, I stack is the fuel cell current, U stack is the fuel cell voltage.

[0048] Optionally, setting a power source behavior boundary according to the parameter model includes:

[0049] The power source behavior boundary is set according to formula (10) to formula (16),

[0050] SOC min ≤SOC≤SOC max , (10)

[0051] Among them, SOC is the lithium battery SOC, SOC min is the minimum SOC value of lithium battery, SOC max is the maximum SOC of the lithium battery,

[0052] SOE min ≤SOE≤SOE max , (11)

[0053] Among them, SOE is the energy state of the fuel cell, SOE min is the minimum state of energy of the fuel cell, SOE max is the maximum energy state of the fuel cell,

[0054]

[0055] Among them, I bat is the lithium battery current, is the minimum current of the lithium battery, is the maximum current of the lithium battery, I fc is the fuel cell current, I fc,min is the minimum current of the fuel cell, I fc,maxis the maximum fuel cell current,

[0056] V b,min ≤V bat ≤V b,max V fc,min ≤V fc ≤V fc,max , (13)

[0057] Among them, V bat is the lithium battery voltage, V b,min is the minimum voltage of the lithium battery, V b,max is the maximum voltage of the lithium battery, V fc is the fuel cell voltage, V fc,min is the minimum voltage of the fuel cell, V fc,max is the maximum fuel cell voltage,

[0058]

[0059] Among them, P bat is the lithium battery power, is the minimum power of lithium battery, is the maximum power of lithium battery, P fc is the fuel cell power, P fc,min is the minimum power of the fuel cell, P fc,max is the maximum fuel cell power,

[0060] P fc η DC / DC,fc +P bat η DC / DC,bat =P req , (15)

[0061] Among them, η DC / DC,fc is the efficiency of the unidirectional DC / DC converter, η DC / DC,bat is the efficiency of the bidirectional DC / DC converter, P req is the motor power requirement,

[0062]

[0063] Among them, P′ fc_limit is the slope threshold of the fuel cell power as a function of time.

[0064] Optionally, performing power allocation according to the fuel cost objective function, the power source behavior boundary, and the motor required power includes:

[0065] The power source parameters calculated in the last optimization are input as the initial conditions of the optimization problem;

[0066] Determining a threshold value of a behavior boundary of the power source;

[0067] A nonlinear programming algorithm is used to solve the optimization problem based on the initial conditions, the motor power demand, and the threshold of the power source behavior boundary, and the power distribution of the fuel cell and the lithium battery under the optimal solution is calculated;

[0068] The fuel cell and the lithium battery are controlled to output respectively according to the distributed power, and the power source parameters at the end of the operation are stored.

[0069] Optionally, a nonlinear programming algorithm is used to solve the optimization problem based on the initial conditions, the motor power requirements, and the threshold of the power source behavior boundary, and the distributed power of the fuel cell and the lithium battery under the optimal solution is calculated, including:

[0070] Determine your current starting point;

[0071] Starting from the starting point, performing a one-dimensional search along the search direction, and calculating the fuel cost after each iteration according to the fuel cost objective function;

[0072] Determining whether an error between the fuel costs of two adjacent iterations is greater than a preset cost threshold;

[0073] If it is determined that the error is greater than the cost threshold, updating the starting point, and returning to the step of performing a one-dimensional search along the search direction starting from the starting point, and calculating the fuel cost after each iteration according to the fuel cost objective function;

[0074] When it is determined that the error is less than or equal to the cost threshold, the optimal solution in the feasible domain of the current nonlinear programming algorithm is selected as the optimal solution to be finally output.

[0075] On the other hand, the present invention further provides an energy management system for a hybrid power system of an automobile, wherein the energy management system includes a controller for executing any of the energy management methods described above.

[0076] Through the above-mentioned technical solutions, the energy management method and system for a hybrid vehicle system provided by the present invention establishes a model of an improved hybrid system comprising a fuel cell and a lithium battery. Secondly, addressing the lack of durability considerations in existing control strategies, a lifespan-guaranteed power source behavior boundary is established, effectively improving vehicle durability. Finally, the energy management problem is transformed into a nonlinear programming optimization problem, and an energy management method based on a nonlinear programming algorithm is proposed. This method has good adaptability and can reduce fuel cell power and voltage fluctuations while maintaining a stable lithium battery SOC.

[0077] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0079] Figure 1 is a flow chart of an energy management method for a hybrid vehicle system according to one embodiment of the present invention;

[0080] Figure 2 is a system architecture diagram of an energy management method for an automotive hybrid system according to one embodiment of the present invention;

[0081] Figure 3 is an equivalent circuit diagram of a single fuel cell of an energy management method for a hybrid vehicle system according to one embodiment of the present invention;

[0082] Figure 4 is a lithium battery equivalent circuit diagram of an energy management method for a hybrid vehicle system according to one embodiment of the present invention;

[0083] Figure 5 is a flow chart of power distribution of an energy management method of a hybrid vehicle system according to one embodiment of the present invention;

[0084] Figure 6 The figure is a flow chart of solving an optimization problem of an energy management method for a hybrid vehicle system according to an embodiment of the present invention.

[0085] Description of Reference Numerals

[0086] E Nernst , Thermodynamically predicted voltage R omic , ohmic resistance

[0087] R conc , concentration resistance R act , activation resistor

[0088] C dl , polarized capacitor R load , Main circuit resistance

[0089] I, main circuit current R0, ohmic internal resistance

[0090] R1, first polarization internal resistance R2, second polarization internal resistance

[0091] C1, first polarized capacitor C2, second polarized capacitor DETAILED DESCRIPTION

[0092] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention and is not used to limit the embodiment of the present invention.

[0093] Figure 1 The invention relates to an energy management method for a hybrid vehicle system according to an embodiment of the present invention. Figure 1 The specific steps of the energy management method may include:

[0094] In step S10, a parameter model of the hybrid power system is established;

[0095] In step S11, a fuel cost objective function is established based on the parameter model;

[0096] In step S12, the power source behavior boundary is set according to the parameter model;

[0097] In step S13, the motor required power is obtained;

[0098] In step S14 , power allocation is performed based on the fuel cost objective function, the power source behavior boundary, and the motor required power.

[0099] In this embodiment, the specific form of the hybrid system can be various known to those skilled in the art. In a preferred example of the present invention, the hybrid system may include a fuel cell, a unidirectional DC / DC converter, a lithium battery, a bidirectional DC / DC converter, a DC / AC converter, and a motor, such as Figure 2 As shown, the unidirectional DC / DC converter is connected to the fuel cell, the bidirectional DC / DC converter is connected to the lithium battery, the DC / AC converter is connected to the unidirectional DC / DC converter and the bidirectional DC / DC converter, and the motor is connected to the DC / AC converter.

[0100] In this embodiment, the specific steps for establishing the parameter model of the hybrid power system may be various steps known to those skilled in the art. In a preferred embodiment of the present invention, the method for establishing the parameter model of the hybrid power system may include:

[0101] Establish the parameter model of fuel cell, the equivalent circuit diagram of single fuel cell is as follows Figure 3 As shown, the parametric model of the fuel cell may include:

[0102] Determine the output voltage of a single fuel cell according to formula (1):

[0103] V cell =E Nernst +η act +η omic +η cpnc, (1)

[0104] Among them, V cell is the output voltage of the single fuel cell, E Nernst is the thermodynamically predicted voltage of the fuel cell obtained from the Nernst equation, η act is the activation loss, η omic is the ohmic loss, η conc is the concentration loss;

[0105] According to formula (2), the differential equation of the dynamic characteristics of the single fuel cell is established.

[0106]

[0107] Among them, V act is the voltage across the activation loss equivalent resistance, i is the main circuit current, C dl is the polarized capacitance, is the kinetic voltage of the electrochemical reaction process, is the kinetic resistance of the electrochemical reaction process;

[0108] According to the output voltage of the single fuel cell, the fuel cell voltage is determined according to formula (3):

[0109] V stock =N·V cell , (3)

[0110] Among them, V stock is the fuel cell voltage, N is the number of single fuel cells, V cell is the output voltage of a single fuel cell.

[0111] Furthermore, the method for establishing a parameter model of a hybrid power system may further include:

[0112] Establish the parameter model of lithium battery, the equivalent circuit diagram of lithium battery is as follows Figure 4 As shown, the parameter model of lithium battery can include:

[0113] Determine the lithium battery SOC according to formula (4):

[0114]

[0115] Wherein, z(t) is the SOC of the lithium battery at time t, z(t0) is the SOC of the lithium battery at time t0, η is the Coulomb coefficient, t and t0 are time, u(t) is the main circuit current at time t, and Q is the total capacity of the lithium battery;

[0116] According to the lithium battery SOC, the lithium battery output voltage is determined according to formula (5):

[0117]

[0118] Among them, v(t) is the output voltage of the lithium battery at time t, z(t) is the SOC of the lithium battery at time t, OCV(z(t)) is the theoretical voltage of the lithium battery when the SOC of the lithium battery is z(t), R1 is the first polarization internal resistance, is the current passing through the first polarization internal resistance at time t, R2 is the second polarization internal resistance, is the current passing through the second polarization internal resistance at time t, R0 is the ohmic internal resistance of the lithium battery;

[0119] According to formula (6) and formula (7), the dynamic characteristic differential equation of lithium battery is established.

[0120]

[0121]

[0122] Among them, C1 is the first polarized capacitor, and C2 is the second polarized capacitor.

[0123] In this embodiment, the specific steps for establishing the fuel cost objective function based on the parameter model can be various known to those skilled in the art. In a preferred embodiment of the present invention, the method for establishing the fuel cost objective function based on the parameter model can include:

[0124] Determine the hydrogen consumption according to formula (8):

[0125]

[0126] in, is the hydrogen consumption, P is the power of the fuel cell, F is the Faraday constant, V d is the voltage of a single fuel cell;

[0127] According to the hydrogen consumption, the fuel cost objective function is determined according to formula (9):

[0128]

[0129] Among them, min f(x) is the fuel cost objective function, is the hydrogen consumption, I bat is the lithium battery current, U bat is the lithium battery voltage, U stack is the fuel cell current, U stack is the fuel cell voltage.

[0130] In this embodiment, the specific form of setting the power source behavior boundary based on the parameter model can be various known to those skilled in the art. In a preferred example of the present invention, the method of setting the power source behavior boundary based on the parameter model may include:

[0131] According to formula (10) to formula (16), the power source behavior boundary is set.

[0132] SOC min ≤SOC≤SOC max , (10)

[0133] Among them, SOC is the lithium battery SOC, SOC min is the minimum SOC value of lithium battery, SOC max is the maximum SOC of the lithium battery,

[0134] SOE min ≤SOE≤SOE max , (11)

[0135] Among them, SOE is the energy state of the fuel cell, SOE min is the minimum state of energy of the fuel cell, SOE max is the maximum energy state of the fuel cell,

[0136]

[0137] Among them, I bat is the lithium battery current, is the minimum current of the lithium battery, is the maximum current of the lithium battery, I fc is the fuel cell current, I fc,min is the minimum current of the fuel cell, I fc,max is the maximum fuel cell current,

[0138] V b,min ≤V bat ≤V b,max V fc,min ≤V fc ≤V fc,max , (13)

[0139] Among them, V bat is the lithium battery voltage, V b,min is the minimum voltage of the lithium battery, V b,max is the maximum voltage of the lithium battery, V fc is the fuel cell voltage, V fc,min is the minimum voltage of the fuel cell, V fc,max is the maximum fuel cell voltage,

[0140]

[0141] Among them, P bat is the lithium battery power, is the minimum power of lithium battery, is the maximum power of lithium battery, P fc is the fuel cell power, P fc,min is the minimum power of the fuel cell, P fc,max is the maximum fuel cell power,

[0142] P fc η DC / DC,fc +P bat η DC / DC,bat =P req , (15)

[0143] Among them, η DC / DC,fc is the efficiency of the unidirectional DC / DC converter, η DC / DC,bat is the efficiency of the bidirectional DC / DC converter, P req is the motor power requirement,

[0144]

[0145] Among them, P′ fc_limit is the slope threshold of the fuel cell power as a function of time.

[0146] In this embodiment, the specific steps for distributing power based on the fuel cost objective function, the power source behavior boundary, and the motor power requirement can be various steps known to those skilled in the art. In a preferred embodiment of the present invention, the power distribution based on the fuel cost objective function, the power source behavior boundary, and the motor power requirement can be as follows: Figure 5 In FIG5 , the method for allocating power based on the fuel cost objective function, the power source behavior boundary, and the motor power requirement may include:

[0147] In step S140, the power source parameters calculated in the previous optimization are input as initial conditions of the optimization problem;

[0148] In step S141, a threshold value of a power source behavior boundary is determined;

[0149] In step S142, a nonlinear programming algorithm is used to solve the optimization problem based on the initial conditions, the motor power requirements, and the threshold of the power source behavior boundary, and the power distribution of the fuel cell and the lithium battery under the optimal solution is calculated;

[0150] In step S143, the fuel cell and the lithium battery are controlled to output according to the allocated power, and the power source parameters at the end of the operation are stored.

[0151] Furthermore, a nonlinear programming algorithm is used to solve the optimization problem based on the initial conditions, the motor power demand, and the threshold of the power source behavior boundary. The detailed steps for calculating the distribution power of the fuel cell and the lithium battery under the optimal solution can be as follows: Figure 6 The steps shown may specifically include:

[0152] In step S1420, the current starting point is determined;

[0153] In step S1421, starting from the starting point, a one-dimensional search is performed along the search direction, and the fuel cost after each iteration is calculated according to the fuel cost objective function;

[0154] In step S1422, it is determined whether the error between the fuel costs of two adjacent iterations is greater than a preset cost threshold;

[0155] If the error is greater than the cost threshold, the starting point is updated, and the process returns to the step of performing a one-dimensional search along the search direction from the starting point, and calculating the fuel cost after each iteration according to the fuel cost objective function, i.e., returning to step S1421;

[0156] In step S1423 , when the judgment error is less than or equal to the cost threshold, the optimal solution in the feasible region of the current nonlinear programming algorithm is selected as the optimal solution to be finally output.

[0157] On the other hand, the present invention further provides an energy management system for a hybrid power system of an automobile, wherein the energy management system includes a controller for executing any of the energy management methods described above.

[0158] Through the above-mentioned technical solutions, the energy management method and system for a hybrid vehicle system provided by the present invention establishes a model of an improved hybrid system comprising a fuel cell and a lithium battery. Secondly, addressing the lack of durability considerations in existing control strategies, a lifespan-guaranteed power source behavior boundary is established, effectively improving vehicle durability. Finally, the energy management problem is transformed into a nonlinear programming optimization problem, and an energy management method based on a nonlinear programming algorithm is proposed. This method has good adaptability and can reduce fuel cell power and voltage fluctuations while maintaining a stable lithium battery SOC.

[0159] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0160] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0161] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0162] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0163] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0164] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0165] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0166] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0167] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. An energy management method for a hybrid vehicle system, characterized in that: The energy management method comprises: Establish a parametric model of the hybrid power system; establishing a fuel cost objective function based on the parameter model; setting a power source behavior boundary according to the parameter model; Get the motor's required power; Allocating power based on the fuel cost objective function, the power source behavior boundary, and the motor required power; Power allocation is performed based on the fuel cost objective function, the power source behavior boundary, and the motor power requirement, including: The power source parameters calculated in the last optimization are input as the initial conditions of the optimization problem; Determining a threshold value of a behavior boundary of the power source; A nonlinear programming algorithm is used to solve the optimization problem based on the initial conditions, the motor power demand, and the threshold of the power source behavior boundary, and the power distribution of the fuel cell and the lithium battery under the optimal solution is calculated; Controlling the fuel cell and the lithium battery to output power respectively according to the distributed power, and storing the power source parameters at the end of the operation; A nonlinear programming algorithm is used to solve the optimization problem based on the initial conditions, the motor power requirements, and the threshold of the power source behavior boundary, and the distribution power of the fuel cell and the lithium battery under the optimal solution is calculated, including: Determine your current starting point; Starting from the starting point, performing a one-dimensional search along the search direction, and calculating the fuel cost after each iteration according to the fuel cost objective function; Determining whether an error between the fuel costs of two adjacent iterations is greater than a preset cost threshold; If it is determined that the error is greater than the cost threshold, updating the starting point, and returning to the step of performing a one-dimensional search along the search direction starting from the starting point, and calculating the fuel cost after each iteration according to the fuel cost objective function; When it is determined that the error is less than or equal to the cost threshold, the optimal solution in the feasible domain of the current nonlinear programming algorithm is selected as the optimal solution to be finally output.

2. The energy management method according to claim 1, characterized in that: The hybrid system comprises: fuel cells; a unidirectional DC / DC converter connected to the fuel cell; lithium batteries; a bidirectional DC / DC converter connected to the lithium battery; A DC / AC converter connected to the unidirectional DC / DC converter and the bidirectional DC / DC converter; The motor is connected to the DC / AC converter.

3. The energy management method according to claim 2, characterized in that: Establish a parametric model of the hybrid power system, including: Establishing a parameter model of the fuel cell, including: Determine the output voltage of a single fuel cell according to formula (1): ，（1） in, is the output voltage of the single fuel cell, is the thermodynamically predicted voltage of the fuel cell obtained from the Nernst equation, is the activation loss, is the ohmic loss, is the concentration loss; According to formula (2), the differential equation of the dynamic characteristics of the single fuel cell is established. ，（2） in, is the voltage across the activation loss equivalent resistance, is the main circuit current, is the polarized capacitance, is the kinetic voltage of the electrochemical reaction process, is the kinetic resistance of the electrochemical reaction process; According to the output voltage of the single fuel cell, the fuel cell voltage is determined according to formula (3): • ，（3） in, is the fuel cell voltage, is the number of single fuel cells, is the output voltage of the single fuel cell.

4. The energy management method according to claim 2, characterized in that: Establish a parametric model of the hybrid power system, including: Establishing a parameter model of the lithium battery, including: Determine the lithium battery SOC according to formula (4): ，（4） in, For the lithium battery The lithium battery SOC at the moment, For the lithium battery The lithium battery SOC at the moment, is the Coulomb coefficient, and For the moment, For Main circuit current at the moment, is the total capacity of the lithium battery; According to the lithium battery SOC, the lithium battery output voltage is determined according to formula (5): ，（5） in, For the lithium battery The lithium battery output voltage at the moment, For the lithium battery The lithium battery SOC at the moment, The lithium battery SOC is The theoretical voltage, is the first polarization internal resistance, For The moment of passing the The current of a polarized internal resistance, is the second polarization internal resistance, For The current through the second polarization internal resistance at the moment, is the ohmic internal resistance of the lithium battery; According to formula (6) and formula (7), the dynamic characteristic differential equation of lithium battery is established. ，（6） ，（7） in, is the first polarization capacitance, is the second polarization capacitance.

5. The energy management method according to claim 2, characterized in that: The fuel cost objective function is established based on the parameter model, including: Determine the hydrogen consumption according to formula (8): ，（8） in, is the hydrogen consumption, is the power of the fuel cell, is the Faraday constant, is the voltage of a single fuel cell; According to the hydrogen consumption, the fuel cost objective function is determined according to formula (9): ，（9） in, is the fuel cost objective function, is the hydrogen consumption, is the lithium battery current, is the lithium battery voltage, is the fuel cell current, is the fuel cell voltage.

6. The energy management method according to claim 2, characterized in that: Setting the power source behavior boundary according to the parameter model includes: The power source behavior boundary is set according to formula (10) to formula (16), ，（10） in, For lithium battery SOC, is the minimum SOC value of the lithium battery, is the maximum SOC of the lithium battery, ，（11） in, is the fuel cell energy state, is the minimum energy state of the fuel cell, is the maximum energy state of the fuel cell, ，（12） in, is the lithium battery current, is the minimum current of the lithium battery, is the maximum current of the lithium battery, is the fuel cell current, is the minimum current of the fuel cell, is the maximum fuel cell current, ，（13） in, is the lithium battery voltage, is the minimum voltage of the lithium battery, is the maximum voltage of the lithium battery, is the fuel cell voltage, is the minimum voltage of the fuel cell, is the maximum fuel cell voltage, ，（14） in, is the lithium battery power, is the minimum power of lithium battery, is the maximum power of lithium battery, is the fuel cell power, is the minimum fuel cell power, is the maximum fuel cell power, ，（15） in, is the efficiency of the unidirectional DC / DC converter, is the efficiency of the bidirectional DC / DC converter, is the motor power requirement, ，（16） in, is the slope threshold of the fuel cell power as a function of time.

7. An energy management system for a hybrid vehicle system, characterized in that: The energy management system includes a controller configured to execute the energy management method according to any one of claims 1 to 6.

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