Hydrogen-lithium energy management method based on multi-physical field dynamic threshold and CBF correction

Abstract

The application discloses a hydrogen-lithium energy management method based on multi-physical field dynamic threshold and CBF correction, which comprises the following steps: 1) collecting hydrogen-lithium hybrid power system operation parameters in real time; 2) based on the collected multi-physical field parameters, the dynamic power threshold of the fuel cell and the allowable power interval of the lithium battery are calculated on line through a multi-physical field coupling model to form a system power distribution hard constraint; 3) a control barrier function (CBF) is constructed with the dynamic threshold as a boundary, an adaptive feedforward feedback algorithm is adopted to generate a fuel cell initial power instruction, and the initial instruction is checked and corrected through the control barrier function safety constraint to obtain a final power instruction meeting the safety and threshold constraints. The application can adapt to complex working conditions such as low temperature, high altitude and load mutation, can ensure the safe and efficient operation of the system, has low algorithm power requirement, can be deployed on a low-cost embedded platform, and can improve the engineering practicability and reliability of the hydrogen-lithium hybrid power source in new energy vehicle, energy storage power station and portable power source and the like scenes.

Description

Technical Field

[0001] This invention relates to the field of new energy storage and power system technology, and in particular to a hydrogen-lithium energy management method based on multi-physics dynamic threshold and CBF correction. Background Technology

[0002] Hydrogen-lithium hybrid power supplies combine the advantages of lithium batteries (fast response and high power density) with hydrogen fuel cells (high energy density and long range), making them a core solution for new energy vehicles, energy storage power stations, and other fields. The power management method determines the system's operating efficiency, lifespan, and cost; a stable and timely energy management method that can operate in multiple scenarios is undoubtedly highly desirable.

[0003] Existing energy management methods for lithium-ion hybrid power supplies mainly fall into three categories: First, single-threshold strategies, which set fixed thresholds based solely on SOC or power requirements. These are difficult to adapt to complex operating conditions involving multi-physics coupling and are prone to problems such as equipment damage and low operating efficiency under extreme conditions. Second, multi-layer optimization strategies, such as dual-layer MPC and Bayesian optimization combined with MPC. While these can balance efficiency and economy, they have extremely high computational complexity, demanding stringent computing power from embedded controllers and making low-cost mass production deployment difficult. Third, intelligent learning strategies, which rely on large amounts of training data, have weak generalization ability, and are prone to computational latency in actual operation, affecting system stability. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a hydrogen-lithium energy management method based on multi-physics dynamic threshold and CBF correction. By combining "multi-physics dynamic constraints" with "control barrier function CBF safety correction", it can not only solve the limitations of traditional threshold strategies, but also reduce the computational complexity of the algorithm, providing support for the safe and real-time response of new energy vehicles, energy storage power stations, and portable hydrogen-lithium power supplies under extreme conditions (such as low temperature and high altitude).

[0005] The objective of this invention is achieved as follows: a hydrogen-lithium energy management method based on multi-physics dynamic threshold and CBF correction, comprising the following steps:

[0006] Step 1) Collect the operating parameters of the hydrogen-lithium hybrid power system in real time. The operating parameters include the state of charge (SOC) of the lithium battery, the operating temperature of the lithium battery, the output power of the fuel cell, the temperature of the fuel cell stack, the pressure of the hydrogen storage tank, the altitude, and the power demand of the load.

[0007] Step 2) Based on the collected multi-physics parameters, the dynamic power threshold of the fuel cell and the allowable power range of the lithium battery are calculated online through the multi-physics coupling model to form a hard constraint on the system power allocation;

[0008] Step 3) Construct a control barrier function (CBF) with a dynamic threshold as the boundary, use an adaptive feedforward feedback algorithm to generate the initial power command of the fuel cell, and verify and correct the initial command through the safety constraints of the control barrier function to obtain the final power command that meets the safety and threshold constraints, thus completing the hydrogen-lithium co-power allocation.

[0009] Furthermore, step 2) specifically includes calculating the fuel cell dynamic power threshold: fuel cell dynamic threshold. The result is calculated using the basic high-efficiency interval and the multiphysics correction factor, as follows:

[0010] The efficiency-power characteristic curve was calibrated through standard operating condition bench tests to determine the high-efficiency operating range under standard operating conditions. ;

[0011] Then, the temperature correction factor, pressure correction factor, and altitude correction factor are calculated based on the stack temperature, hydrogen storage pressure, and altitude, respectively. The minimum value of the three is taken as the comprehensive correction factor. The dynamic threshold upper limit is obtained by multiplying the fuel cell rated power by the comprehensive correction factor, and the minimum stable power hardware limit is superimposed.

[0012] Temperature correction factor calculated based on real-time reactor temperature. :

[0013]

[0014] Calculate the pressure correction factor λ based on the hydrogen storage tank pressure. p =0.1·P H2 +0.4, 0.5MPa≤P H2 ≤3MPa; P H2 When <0.5MPa, λ p =0.3, limiting the high power output of fuel cells;

[0015] Calculate the altitude correction factor based on altitude. , 0m≤h≤5000m;

[0016] Obtain the comprehensive correction factor The dynamic threshold is obtained by scaling the basic high-efficiency interval. And superimposed hardware security limiting constraints.

[0017] Furthermore, the calculation process for the allowable power range of the lithium battery mentioned in step 2) is as follows: the power range of the lithium battery is calculated by combining temperature linear correction and SOC range limiting; when the operating temperature of the lithium battery is higher than 45℃ or lower than 0℃, the charging and discharging power range is linearly reduced according to the degree of temperature deviation, and the correction rules are as follows:

[0018] When the real-time temperature T of the lithium battery is less than 0°C, the low-temperature linear correction formula is used:

[0019]

[0020] when At that time, the power is allowed to remain at the baseline value. ;

[0021] When T>45℃, the high-temperature linear correction formula is used:

[0022]

[0023] in This represents the basic allowable charge and discharge power of a lithium battery at room temperature (25℃).

[0024] Furthermore, step 3) specifically includes:

[0025] 3.1) Constructing the control barrier function CBF: using the dynamic threshold of the fuel cell For the safety boundary, construct a scalar-valued control barrier function:

[0026]

[0027] In the formula The output power of the fuel cell is given; a safety factor α > 0 is set to meet the requirements. The safety conditions ensure that the output power of the fuel cell is within the safe range defined by the dynamic threshold.

[0028] 3.2) Calculate the adaptive allocation coefficient k based on the dynamic threshold, and obtain the feedforward term by combining the real-time power demand of the load and the load power change rate; obtain the adaptive feedback coefficient by looking up a table based on the lithium battery SOC interval. ,

[0029] The feedback term is calculated; by superimposing the feedforward term and the feedback term, the initial power allocation command for the fuel cell is obtained. ;

[0030] Initial allocation of base power: Calculate the adaptive allocation coefficient k based on the dynamic threshold:

[0031]

[0032] In the formula This represents the maximum permissible output power of the lithium battery. This represents the maximum permissible output power of the fuel cell;

[0033] By introducing the load power change rate as feedforward compensation, we obtain the feedforward term:

[0034]

[0035] In the formula To meet the real-time power requirements of the load, This is the power change rate compensation coefficient. The load power change rate;

[0036] Simultaneously, an adaptive feedback coefficient K is set according to the segmented state of charge (SOC) of the lithium battery. p Calculate feedback items:

[0037]

[0038] In the formula This is the reference state of charge for lithium batteries;

[0039] Finally, by superimposing the feedforward and feedback terms, the initial command for basic power allocation of the fuel cell is obtained: .

[0040] 3.3) CBF Safety Constraint Correction: Modify the initial power allocation command Substitute the CBF security conditions for verification and correction, if In Within the safe zone, and satisfying ,Will Directly used as a power distribution command for fuel cells; if If the threshold is approaching or about to be exceeded, a linear correction method is used. Intervention and adjustment are performed to obtain a fuel cell power command that meets CBF safety constraints. .

[0041] Furthermore, the adaptive feedback coefficients described in step 3.2) Based on the state of charge (SOC) of the lithium battery, the following segments are defined: SOC < 30% is considered the low charge range. Take the maximum value to increase the fuel cell output to supplement the lithium battery charge; 30% ≤ SOC ≤ 70% is the appropriate charge range. A middle value is chosen to balance power distribution efficiency and lithium battery SOC stability; SOC > 70% is considered the high charge range. To minimize the output of the fuel cell, the minimum value is selected; each interval All values ​​were obtained offline by ZN tuning method.

[0042] Furthermore, the linear correction method described in step 3.3) is as follows:

[0043] First, calculate the span between the initial command for fuel cell base power allocation and the current dynamic threshold boundary. If the initial instruction exceeds the limit, then calculate... ,in The reduction factor, which takes values ​​in the range [0.5, 1.0], is used to mitigate the aggressiveness of power requests. This process is repeated until the CBF safety conditions and threshold constraints are met.

[0044] .

[0045] Compared with the prior art, the beneficial effects of the present invention are: (1) by calculating the threshold range in real time through multi-dimensional parameters such as stack temperature, hydrogen storage pressure, altitude, battery temperature, and SOC, the problem of poor adaptability of fixed threshold in traditional methods is solved, so that the system can still operate safely and efficiently under extreme conditions such as low temperature, high altitude, and sudden load changes, protecting the fuel cell and lithium battery and extending their service life.

[0046] (2) The dynamic threshold is transformed into a control barrier function constraint. The power command is guaranteed not to exceed the limit from a mathematical perspective through CBF safety correction. Compared with the traditional simple limit, it is more stable and smooth, and there is no shock or jitter.

[0047] (3) The adaptive feedforward-feedback loop only includes explicit analytical calculation, table lookup and segmentation judgment. It has low computing power requirements and can run on low-cost microcontrollers, avoiding the problems of high computing power requirements and high deployment costs of traditional algorithms.

[0048] (4) The dynamic threshold change directly drives the synchronous change of the allocation coefficient and the correction strategy, without the need for manual parameter adjustment. The adaptability and robustness of the working conditions are significantly better than the fixed parameter strategy. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0050] Figure 1 This is a block diagram of the overall system structure.

[0051] Figure 2 This is the algorithm flowchart.

[0052] Figure 3 The curve for the multiphysics correction factor of a fuel cell.

[0053] Figure 4 This is the efficiency-power characteristic curve of a fuel cell. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] like Figure 1-2 The method for hydrogen-lithium energy management based on multiphysics dynamic threshold and CBF correction, as shown, includes the following steps:

[0056] Step 1) Collect the operating parameters of the hydrogen-lithium hybrid power system in real time. The operating parameters include the state of charge (SOC) of the lithium battery, the operating temperature of the lithium battery, the output power of the fuel cell, the temperature of the fuel cell stack, the pressure of the hydrogen storage tank, the altitude, and the power demand of the load. The SOC of the lithium battery is output in real time by the battery management system (BMS) as the known state quantity of the system.

[0057] Step 2) Based on the collected multi-physics parameters, the dynamic power threshold of the fuel cell and the allowable power range of the lithium battery are calculated online through the multi-physics coupling model to form a hard constraint on the system power allocation;

[0058] Fuel cell dynamic threshold The result is calculated using the basic high-efficiency interval and the multiphysics correction factor, as follows:

[0059] The efficiency-power characteristic curve of the fuel cell, obtained by bench testing at 25°C, normal pressure, and rated operating conditions, was used to select a continuous power range with an efficiency higher than 70% of the rated efficiency to determine the basic high-efficiency operating range under standard operating conditions. ;

[0060] Then, the temperature correction factor, pressure correction factor, and altitude correction factor are calculated based on the stack temperature, hydrogen storage pressure, and altitude, respectively. The minimum value of the three is taken as the comprehensive correction factor. The dynamic threshold upper limit is obtained by multiplying the fuel cell rated power by the comprehensive correction factor, and the minimum stable power hardware limit is superimposed.

[0061] Temperature correction factor calculated based on real-time reactor temperature. :

[0062]

[0063] Temperature correction factor Based on bench test calibration, the maximum value of 1 is taken in the optimal operating range of the fuel cell stack (50~65℃); in the low temperature range (<50℃) due to increased activation polarization and in the high temperature range (>65℃) due to intensified membrane dry / side reactions, the power capacity decreases, so piecewise linear correction is adopted; in the extreme temperature range (<40℃ or >70℃), the power capacity is significantly reduced, so a fixed coefficient of 0.5 is used to limit the power capacity to ensure the safe and stable operation of the system.

[0064] Calculate the pressure correction factor λ based on the hydrogen storage tank pressure. p =0.1·P H2 +0.4, 0.5MPa≤P H2 ≤3MPa; P H2 When <0.5MPa, λ p =0.3, limiting the high power output of fuel cells;

[0065] Calculate the altitude correction factor based on altitude. , 0m≤h≤5000m;

[0066] Obtain the comprehensive correction factor The dynamic threshold is obtained by scaling the basic high-efficiency interval. Furthermore, hardware safety limits are imposed to ensure that the threshold is not lower than the minimum stable operating power of the fuel cell and not higher than the maximum allowable power of the hardware.

[0067] The lithium battery power range is calculated using a combination of temperature linear correction and SOC range limiting. When the lithium battery operating temperature is above 45℃ or below 0℃, the charge and discharge power range is linearly reduced according to the degree of temperature deviation, with the following correction rules:

[0068] When the real-time temperature T of the lithium battery is less than 0°C, the low-temperature linear correction formula is used:

[0069]

[0070] when At that time, the power is allowed to remain at the baseline value. ;

[0071] When T>45℃, the high-temperature linear correction formula is used:

[0072]

[0073]

[0074]

[0075] in This represents the basic allowable charge and discharge power of a lithium battery at room temperature (25℃), obtained through bench testing. This is the temperature correction factor. This is the SOC correction factor.

[0076] Step 3) Construct a control barrier function (CBF) with a dynamic threshold as the boundary, use an adaptive feedforward feedback algorithm to generate the initial power command of the fuel cell, and verify and correct the initial command through the safety constraints of the control barrier function to obtain the final power command that meets the safety and threshold constraints, thus completing the hydrogen-lithium co-power allocation.

[0077] 3.1) Constructing the control barrier function CBF: using the dynamic threshold of the fuel cell For the safety boundary, construct a scalar-valued control barrier function:

[0078]

[0079] In the formula The output power of the fuel cell is given; a safety factor α > 0 is set to meet the requirements. The safety conditions ensure that the output power of the fuel cell is within the safe range defined by the dynamic threshold.

[0080] The safety factor α is calculated from the system stability analysis:

[0081]

[0082] in This represents the maximum value of the time derivative of the barrier function. This represents the minimum safety margin within the threshold range.

[0083] 3.2) Calculate the adaptive allocation coefficient k based on the dynamic threshold, and obtain the feedforward term by combining the real-time power demand of the load and the load power change rate; obtain the adaptive feedback coefficient by looking up a table based on the lithium battery SOC interval. ,

[0084] The feedback term is calculated; by superimposing the feedforward term and the feedback term, the initial power allocation command for the fuel cell is obtained. ;

[0085] Initial allocation of base power: Calculate the adaptive allocation coefficient k based on the dynamic threshold:

[0086]

[0087] In the formula This represents the maximum permissible output power of the lithium battery. This represents the maximum permissible output power of the fuel cell;

[0088] By introducing the load power change rate as feedforward compensation, we obtain the feedforward term:

[0089]

[0090] In the formula To meet the real-time power requirements of the load, This is the power change rate compensation coefficient. The load power change rate;

[0091] Power change rate compensation coefficient Tuning through system response experiments: ,in The system time constant is obtained from actual measurements; The controller sampling period; This represents the optimal offline tuning result.

[0092] Simultaneously, an adaptive feedback coefficient K is set according to the segmented state of charge (SOC) of the lithium battery. p Calculate feedback items:

[0093]

[0094] In the formula This is the reference state of charge for lithium batteries;

[0095] Finally, by superimposing the feedforward and feedback terms, the initial command for basic power allocation of the fuel cell is obtained: .

[0096] Adaptive feedback coefficient Based on the state of charge (SOC) of the lithium battery, the following segments are defined: SOC < 30% is considered the low charge range. Take the maximum value to increase the fuel cell output to supplement the lithium battery charge; 30% ≤ SOC ≤ 70% is the appropriate charge range. A middle value is chosen to balance power distribution efficiency and lithium battery SOC stability; SOC > 70% is considered the high charge range. To minimize the output of the fuel cell, the minimum value is selected; each interval All values ​​were obtained offline by ZN tuning method.

[0097]

[0098] 3.3) CBF Safety Constraint Correction: Modify the initial power allocation command Substitute the CBF security conditions for verification and correction, if In Within the safe zone, and satisfying ,Will Directly used as a power distribution command for fuel cells; if If the threshold is approaching or about to be exceeded, a linear correction method is used. Intervention and adjustment are performed to obtain a fuel cell power command that meets CBF safety constraints. Calculate the lithium battery output power based on load requirements. Complete the collaborative allocation.

[0099] First, calculate the span between the initial command for fuel cell base power allocation and the current dynamic threshold boundary. If the initial instruction exceeds the limit, then calculate... ,in The reduction factor, which takes values ​​in the range [0.5, 1.0], is used to mitigate the aggressiveness of power requests. This process is repeated until the CBF safety conditions and threshold constraints are met.

[0100] .

[0101] In this embodiment, all correction factors, allocation coefficients, and feedback coefficients are obtained through bench tests, system identification, and offline tuning methods. The algorithm only includes explicit analytical operations, logical judgments, and table lookup operations, with low computational load and strong real-time performance. It can run stably on embedded controllers, making it suitable for various application scenarios such as new energy vehicles, energy storage power stations, and portable lithium hydrogen power supplies.

[0102] The following is combined Figure 3 and Figure 4 The invention will be further illustrated by specific examples.

[0103] Example 1: Controller sampling period in this example System time constant No sudden changes in operating conditions The system is set to a reference SOC of 50% for the lithium battery, ensuring it operates under suitable and safe conditions.

[0104] Table 1. Operating Parameters of Electric Vehicle Data Acquisition System (Safety and Environment)

[0105]

[0106] a. Referring to the fuel cell efficiency-power characteristic curve, 70% of the rated efficiency and above is considered the high-efficiency range (e.g., Figure 4 ), thus obtaining the standard high-efficiency range for fuel cells:

[0107]

[0108] b. A stack temperature of 60℃ is within the optimal range, refer to the fuel cell multiphysics correction factor curve (). Figure 3 The temperature correction factor is obtained.

[0109] c. Obtain the hydrogen storage pressure correction factor by referring to the multiphysics correction factor curve of the fuel cell:

[0110] d. Obtain the altitude correction factor by referring to the multiphysics correction factor curve of the fuel cell:

[0111] e. Comprehensive correction factor:

[0112] get

[0113] The lithium battery temperature is 25℃ and SOC is 55%, which is within the normal range. Therefore: = 50kW, together with the fuel cell threshold, constitutes a hard constraint on the system.

[0114] Next, construct the CBF function: Known ,

[0115] Now seeking : Differentiation yields ,thereby In engineering, the maximum rate of change of fuel cell output power is limited by system hardware and identified through bench tests. , .

[0116] ,Pick

[0117] Next, the adaptive feedforward-feedback is calculated:

[0118]

[0119]

[0120]

[0121] 10 kW < 13.44 kW < 60 kW, which is within the safe range and requires no correction. Therefore:

[0122]

[0123]

[0124] Finally, the CBF barrier function constraint test is performed.

[0125] ,

[0126] Operating under conditions without sudden changes

[0127] ,

[0128] The system is operating within an absolutely safe range and requires no correction.

[0129] Example 2: Controller sampling period in this example System time constant The system is set to a lithium battery reference SOC of 50% and a load power change rate. The fuel cell has a rate of change of 10 kW / s and operates in harsh environments at high altitudes.

[0130] Table 2. Operating Parameters of Electric Vehicle Data Acquisition System (Extreme Environment)

[0131]

[0132] a. Referring to the fuel cell efficiency-power characteristic curve, 70% of the rated efficiency and above is considered the high-efficiency range. Figure 4 ), thus obtaining the standard high-efficiency range for fuel cells:

[0133]

[0134] b. A stack temperature of 45℃ is within the optimal range, refer to the fuel cell multiphysics correction factor curve (). Figure 3 The temperature correction factor is obtained.

[0135] c. Obtain the hydrogen storage pressure correction factor by referring to the multiphysics correction factor curve of the fuel cell:

[0136] d. Obtain the altitude correction factor by referring to the multiphysics correction factor curve of the fuel cell:

[0137] e. Comprehensive correction factor:

[0138] Fuel cell dynamic threshold:

[0139]

[0140] Lithium battery temperature 25℃, SOC=25%, , ,

[0141] Together with the fuel cell threshold, they constitute the system's hard constraints.

[0142] Next, construct the CBF function: Known ,

[0143] Now seeking : Differentiation yields ,thereby In engineering, the maximum rate of change of fuel cell output power is limited by system hardware and identified through bench tests. , .

[0144] ,Pick

[0145] Next, the adaptive feedforward-feedback is calculated:

[0146]

[0147]

[0148]

[0149] 57.18 kW > 52 kW, exceeding the upper limit of the multiphysics dynamic threshold. Therefore, a CBF barrier function constraint check is performed.

[0150]

[0151] It is evident that the obstacle function operation constraints are not met, and CBF safety constraint correction is required:

[0152] , ,

[0153]

[0154] Satisfying the threshold constraint

[0155] Finally, the CBF barrier function constraint test is performed:

[0156]

[0157] The obstacle function execution constraints are satisfied.

[0158] This invention provides a hydrogen-lithium energy management method based on multiphysics dynamic thresholds and CBF correction. By real-time acquisition of multiphysics operating parameters such as lithium battery SOC, operating temperature, fuel cell stack temperature, hydrogen storage pressure, and altitude, a multiphysics coupling model is established to calculate the dynamic power thresholds of the fuel cell and lithium battery online, transforming equipment constraints under extreme operating conditions into hard boundaries for power allocation. Based on this, an adaptive feedforward-feedback algorithm is used to generate the initial power command for the fuel cell, and a control barrier function (CBF) is introduced to perform safety verification and smooth correction of the initial command, ensuring that the power output simultaneously meets the dynamic threshold constraints and CBF safety conditions. This invention is adaptable to complex operating conditions such as low temperature, high altitude, and sudden load changes. While ensuring the safe and efficient operation of the system, the algorithm has low computational requirements and can be deployed on low-cost embedded platforms, improving the engineering practicality and reliability of hydrogen-lithium hybrid power supplies in scenarios such as new energy vehicles, energy storage power stations, and portable power supplies.

[0159] The above description of the embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A method for managing lithium hydrogen energy based on multiphysics dynamic threshold and CBF correction, characterized in that, Includes the following steps: Step 1) Collect the operating parameters of the hydrogen-lithium hybrid power system in real time. The operating parameters include the state of charge (SOC) of the lithium battery, the operating temperature of the lithium battery, the output power of the fuel cell, the temperature of the fuel cell stack, the pressure of the hydrogen storage tank, the altitude, and the power demand of the load. Step 2) Based on the collected multi-physics parameters, the dynamic power threshold of the fuel cell and the allowable power range of the lithium battery are calculated online through the multi-physics coupling model to form a hard constraint on the system power allocation; Step 3) Construct a control barrier function (CBF) with a dynamic threshold as the boundary, use an adaptive feedforward feedback algorithm to generate the initial power command of the fuel cell, and verify and correct the initial command through the safety constraints of the control barrier function to obtain the final power command that meets the safety and threshold constraints, thus completing the hydrogen-lithium co-power allocation.

2. The hydrogen-lithium energy management method based on multiphysics dynamic threshold and CBF correction according to claim 1, characterized in that, Step 2) specifically includes calculating the dynamic power threshold of the fuel cell, which includes: fuel cell dynamic threshold. The result is calculated using the basic high-efficiency interval and the multiphysics correction factor, as follows: The efficiency-power characteristic curve was calibrated through standard operating condition bench tests to determine the high-efficiency operating range under standard operating conditions. ; Then, the temperature correction factor, pressure correction factor, and altitude correction factor are calculated based on the stack temperature, hydrogen storage pressure, and altitude, respectively. The minimum value of the three is taken as the comprehensive correction factor. The dynamic threshold upper limit is obtained by multiplying the fuel cell rated power by the comprehensive correction factor, and the minimum stable power hardware limit is superimposed. Temperature correction factor calculated based on real-time reactor temperature. : ； Calculate the pressure correction factor λ based on the hydrogen storage tank pressure. p =0.1·P H2 +0.4, 0.5MPa≤P H2 ≤3MPa; P H2 When <0.5MPa, λ p =0.3, limiting the high power output of fuel cells; Calculate the altitude correction factor based on altitude. , 0m≤h≤5000m; Obtain the comprehensive correction factor The dynamic threshold is obtained by scaling the basic high-efficiency interval. And superimposed hardware security limiting constraints.

3. The hydrogen-lithium energy management method based on multiphysics dynamic threshold and CBF correction according to claim 1, characterized in that, The calculation process for the allowable power range of the lithium battery mentioned in step 2) is as follows: The allowable power range of the lithium battery is corrected; when the lithium battery operating temperature is above 45℃ or below 0℃, the charging and discharging power range is linearly reduced according to the degree of temperature deviation. The correction rules are as follows: When the real-time temperature T of the lithium battery is less than 0°C, the low-temperature linear correction formula is used: ； when At that time, the power is allowed to remain at the baseline value. ; When T>45℃, the high-temperature linear correction formula is used: ； in This represents the basic allowable charge and discharge power of a lithium battery at room temperature (25℃).

4. The hydrogen-lithium energy management method based on multiphysics dynamic threshold and CBF correction according to claim 1, characterized in that, Step 3) specifically includes: 3-1) Constructing the control barrier function CBF: using the dynamic threshold of the fuel cell For the safety boundary, construct a scalar-valued control barrier function: ； In the formula The output power of the fuel cell is given; a safety factor α > 0 is set to meet the requirements. The safety conditions ensure that the output power of the fuel cell is within the safe range defined by the dynamic threshold. 3-2) Calculate the adaptive allocation coefficient k based on the dynamic threshold, and obtain the feedforward term by combining the real-time power demand of the load and the rate of change of the load power; obtain the adaptive feedback coefficient by looking up a table based on the range of lithium battery SOC. , The feedback term is calculated; by superimposing the feedforward term and the feedback term, the initial power allocation command for the fuel cell is obtained. ; Initial allocation of base power: Calculate the adaptive allocation coefficient k based on the dynamic threshold: ； In the formula This represents the maximum permissible output power of the lithium battery. This represents the maximum permissible output power of the fuel cell; By introducing the load power change rate as feedforward compensation, we obtain the feedforward term: ； In the formula To meet the real-time power requirements of the load, This is the power change rate compensation coefficient. The load power change rate; Simultaneously, an adaptive feedback coefficient K is set according to the segmented state of charge (SOC) of the lithium battery. p Calculate feedback items: ； In the formula This is the reference state of charge for lithium batteries; Finally, by superimposing the feedforward and feedback terms, the initial command for basic power allocation of the fuel cell is obtained: ; 3-3) CBF Safety Constraint Correction: Modify the initial power allocation command Substitute the CBF security conditions for verification and correction, if In Within the safe zone, and satisfying ,Will Directly used as a power distribution command for fuel cells; if If the threshold is approaching or about to be exceeded, a linear correction method is used. Intervention and adjustment are performed to obtain a fuel cell power command that meets CBF safety constraints. .

5. The hydrogen-lithium energy management method based on multiphysics dynamic threshold and CBF correction according to claim 4, characterized in that, The adaptive feedback coefficient mentioned in step 3.2) Based on the state of charge (SOC) of the lithium battery, the following segments are defined: SOC < 30% is considered the low charge range. Take the maximum value to increase the fuel cell output to supplement the lithium battery charge; 30% ≤ SOC ≤ 70% is the appropriate charge range. A middle value is chosen to balance power distribution efficiency and lithium battery SOC stability; SOC > 70% is considered the high charge range. To minimize the output of the fuel cell, the minimum value is selected; each interval All values ​​were obtained offline by ZN tuning method.

6. The hydrogen-lithium energy management method based on multiphysics dynamic threshold and CBF correction according to claim 4, characterized in that, The linear correction method described in step 3.3) is as follows: First, calculate the span between the initial command for fuel cell base power allocation and the current dynamic threshold boundary. If the initial instruction exceeds the limit, then calculate... ,in The reduction factor, which takes values ​​in the range [0.5, 1.0], is used to mitigate the aggressiveness of power requests. This process is repeated until the CBF safety conditions and threshold constraints are met. 。

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