Fuel cell vehicle energy management and control method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of new energy vehicles, and provides a fuel cell vehicle energy management and control method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a current SOC value of a power battery of the fuel cell vehicle; determining a target penalty factor according to the current SOC value; according to the target penalty factor, constructing a target function for equivalent hydrogen consumption calculation; and based on the target function, performing energy management and control on a power system of the fuel cell vehicle by adopting an equivalent hydrogen consumption minimum strategy. According to the method, on the basis of adopting the ECMS strategy, the accuracy of penalty factor selection can be improved, so that the energy management and control effect is improved.

Description

Technical Field

[0001] The present application relates to the technical field of new energy vehicles, and particularly relates to a method, device, electronic device, and storage medium for energy management and control of a fuel cell vehicle. Background Technique

[0002] The power system of a fuel cell vehicle generally includes a fuel cell system and a power battery used as a buffer. How to keep the SOC (State Of Charge) of the power battery within a reasonable range as much as possible, reduce the start-stop times of the fuel cell system, and thus improve the operating life of the power system is an issue that needs to be considered in the energy management and control strategy of fuel cell vehicles.

[0003] Currently, a common energy management and control strategy is the equivalent hydrogen consumption minimum strategy, that is, the ECMS strategy. The ECMS strategy converts the power consumed by the battery into equivalent hydrogen consumption through an equivalent factor, so as to obtain the optimal power distribution at the current moment, making the equivalent hydrogen consumption of the entire power system the smallest.

[0004] Since the efficiency of the fuel cell system gradually decreases as the current density point increases, the energy management and control effect of the ECMS strategy has a great relationship with the selection result of the penalty factor in the objective function. However, the penalty factor used in the objective function of the existing ECMS strategy is usually an empirical value selected manually, and there may be a problem of inaccurate selection of the penalty factor, resulting in a reduction in the energy management and control effect. Summary of the Invention

[0005] In view of this, the embodiments of the present application provide a method, device, electronic device, and storage medium for energy management and control of a fuel cell vehicle, which can improve the accuracy of selecting the penalty factor on the basis of adopting the ECMS strategy, thereby improving the energy management and control effect.

[0006] The first aspect of the embodiments of the present application provides a method for energy management and control of a fuel cell vehicle, including:

[0007] Obtain the current SOC value of the power battery of the fuel cell vehicle;

[0008] Determine the target penalty factor according to the current SOC value;

[0009] Construct an objective function for calculating the equivalent hydrogen consumption according to the target penalty factor;

[0010] Based on the objective function, adopt the equivalent hydrogen consumption minimum strategy to perform energy management and control on the power system of the fuel cell vehicle.

[0011] In an embodiment of the present application, first, obtain the current SOC value of the power battery of a fuel cell vehicle, and determine the corresponding target penalty factor according to the current SOC value; then, construct an objective function for calculating the equivalent hydrogen consumption based on the target penalty factor; finally, based on the objective function, adopt the strategy of minimizing the equivalent hydrogen consumption to perform energy management and control on the power system of the fuel cell vehicle. In the above process, the penalty factor used in the objective function is an adapted value reasonably selected according to the current SOC value of the power battery. Compared with the method of manually selecting empirical values, the accuracy of selecting the penalty factor is improved, thereby improving the energy management and control effect.

[0012] In an implementation manner of the embodiment of the present application, determining the target penalty factor according to the current SOC value includes:

[0013] Select the target penalty factor look-up table from each pre-constructed penalty factor look-up table; wherein, each penalty factor look-up table respectively records the corresponding relationship between the SOC value and the penalty factor under its corresponding working condition;

[0014] Search for the penalty factor corresponding to the current SOC value from the target penalty factor look-up table as the target penalty factor.

[0015] In an implementation manner of the embodiment of the present application, any one of the penalty factor look-up tables in each penalty factor look-up table is constructed by the following method:

[0016] Initialize each SOC value and its corresponding multiple initial penalty factors according to the working condition corresponding to the any one penalty factor look-up table;

[0017] Use each SOC value and its corresponding multiple initial penalty factors as particles of the particle swarm algorithm, and run the simulation calculation of the particle swarm algorithm;

[0018] Determine the optimal penalty factor corresponding to each SOC value according to the result of the simulation calculation;

[0019] Construct the any one penalty factor look-up table according to the optimal penalty factor corresponding to each SOC value.

[0020] In an implementation manner of the embodiment of the present application, the independent variables of the fitness function used in the particle swarm algorithm include the first integral value of the output power of the fuel cell system of the fuel cell vehicle and the second integral value of the hydrogen consumption of the fuel cell system; in the process of running the simulation calculation of the particle swarm algorithm, it includes:

[0021] If there is at least one simulated SOC value lower than the preset SOC limit value among the various simulated SOC values generated in the simulation calculation, or the simulated mileage of the simulation calculation is lower than the preset mileage limit value, then adjust the second integral value.

[0022] In an implementation manner of the embodiment of the present application, if at least one of the simulated SOC values generated in the simulation calculation is lower than a preset SOC limit value, or the simulated mileage of the simulation calculation is lower than a preset mileage limit value, then the second integral value is adjusted, including:

[0023] If at least one of the simulated SOC values generated in the simulation calculation is lower than a preset SOC limit value, then a first adjustment amount is determined according to the minimum value among the simulated SOC values and the SOC limit value, and the second integral value is adjusted according to the first adjustment amount;

[0024] Or, if the simulated mileage of the simulation calculation is lower than a preset mileage limit value, then a second adjustment amount is determined according to the simulated mileage and the mileage limit value, and the second integral value is adjusted according to the second adjustment amount.

[0025] In an implementation manner of the embodiment of the present application, after performing energy management and control on the power system of a fuel cell vehicle by adopting the minimum equivalent hydrogen consumption strategy based on the objective function, it further includes:

[0026] Determine the number of times that the SOC value of the power battery is higher than the first threshold within a set duration;

[0027] Determine a first penalty factor correction coefficient according to the number of times that the SOC value of the power battery is higher than the first threshold within a set duration;

[0028] According to the first penalty factor correction coefficient, correct the target penalty factor, and return to execute the steps of constructing the objective function for calculating the equivalent hydrogen consumption according to the target penalty factor and subsequent steps.

[0029] In an implementation manner of the embodiment of the present application, after performing energy management and control on the power system of a fuel cell vehicle by adopting the minimum equivalent hydrogen consumption strategy based on the objective function, it further includes:

[0030] Determine the number of times that the SOC value of the power battery is lower than the second threshold within a set duration;

[0031] Determine a second penalty factor correction coefficient according to the number of times that the SOC value of the power battery is lower than the second threshold within a set duration;

[0032] According to the second penalty factor correction coefficient, correct the target penalty factor, and return to execute the steps of constructing the objective function for calculating the equivalent hydrogen consumption according to the target penalty factor and subsequent steps.

[0033] The second aspect of the embodiment of the present application provides a fuel cell vehicle energy management and control device, including:

[0034] The current SOC value acquisition module is used to acquire the current SOC value of the power battery of a fuel cell vehicle;

[0035] The penalty factor determination module is used to determine the target penalty factor according to the current SOC value;

[0036] The objective function construction module is used to construct the objective function for calculating the equivalent hydrogen consumption according to the target penalty factor;

[0037] The energy management and control module is used to perform energy management and control on the power system of the fuel cell vehicle based on the objective function by adopting the strategy of minimizing the equivalent hydrogen consumption.

[0038] The third aspect of the embodiments of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the fuel cell vehicle energy management and control method provided in the first aspect of the embodiments of the present application.

[0039] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the fuel cell vehicle energy management and control method provided in the first aspect of the embodiments of the present application.

[0040] The fifth aspect of the embodiments of the present application provides a computer program product. When the computer program product runs on an electronic device, it causes the electronic device to execute the fuel cell vehicle energy management and control method provided in the first aspect of the embodiments of the present application.

[0041] It can be understood that the beneficial effects of the above second aspect to fifth aspect can refer to the relevant descriptions in the above first aspect, and will not be elaborated here. Description of the Drawings

[0042] Figure 1 is a flowchart of a fuel cell vehicle energy management and control method provided by an embodiment of the present application;

[0043] Figure 2 is an operation schematic diagram of obtaining a penalty factor by looking up a table provided by an embodiment of the present application;

[0044] Figure 3 is an operation process schematic diagram of optimizing the penalty factor using a particle swarm algorithm provided by an embodiment of the present application;

[0045] Figure 4 is a structural schematic diagram of a fuel cell vehicle energy management and control device provided by an embodiment of the present application;

[0046] Figure 5It is a schematic diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0047] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system architectures and technologies are presented to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application. Additionally, in the description of the specification of the present application and the appended claims, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0048] A fuel cell vehicle is a vehicle that uses the electric power generated by a fuel cell system and a power battery as power. Currently, the equivalent hydrogen consumption minimization strategy, that is, the ECMS strategy, is usually adopted to control the energy of the fuel cell vehicle. The ECMS strategy converts the electric energy consumed by the battery into equivalent hydrogen consumption through an equivalent factor, thereby obtaining the optimal power distribution at the current moment, making the equivalent hydrogen consumption of the entire power system the smallest. However, in the existing ECMS strategy, the penalty factor used in the objective function is usually an empirically selected value by humans, and there may be a problem of inaccurate selection of the penalty factor, resulting in a reduction in the energy control effect.

[0049] To address the above problems, the embodiments of the present application provide a fuel cell vehicle energy control method, device, electronic device, and storage medium, which can improve the accuracy of selecting the penalty factor on the basis of adopting the ECMS strategy, thereby improving the energy control effect. For more specific technical implementation details of the embodiments of the present application, please refer to the method embodiments described below.

[0050] It should be understood that the execution subject of each method embodiment of the present application is various types of electronic devices. For example, it can be a mobile phone, a tablet computer, a wearable device, a vehicle-mounted terminal, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), and so on. The embodiments of the present application do not impose any restrictions on the specific type of the electronic device.

[0051] Please refer to Figure 1 , which shows a fuel cell vehicle energy control method provided by an embodiment of the present application, including:

[0052] 101. Obtain the current SOC value of the power battery of a fuel cell vehicle;

[0053] It should be understood that the execution subject of the embodiments of this method is an electronic device for energy management and control of a fuel cell vehicle, which can usually be the on-vehicle terminal of the fuel cell vehicle.

[0054] First, obtain the current state of charge of the power battery of the fuel cell vehicle, denoted as the current SOC value. The power system of the fuel cell vehicle includes a fuel cell system and a power battery, where the power battery can be used as a buffer to play an energy regulation role. The ECMS strategy can be called the instantaneous optimization energy management control strategy or the equivalent hydrogen consumption minimum strategy. This strategy realizes local optimization by reasonably distributing the vehicle's demand power between the fuel cell system and the power battery, minimizing the energy consumption of the powertrain within the control cycle, that is, minimizing the sum of the hydrogen energy consumed by the fuel cell system and the electrical energy of the power battery consumed by the motor, so that the fuel cell vehicle can obtain better energy economy under the entire driving condition.

[0055] In the ECMS strategy, the objective function f for instantaneous energy consumption optimization can be expressed as:

[0056]

[0057] Among them, is the hydrogen consumption of the fuel cell system, is the equivalent hydrogen consumption of the power battery, k_ soc is the penalty factor.

[0058] Here, the concept of the equivalent hydrogen consumption of battery electrical energy is introduced. That is, to compensate for the consumption of battery electrical energy, this equivalent hydrogen consumption converts the electrical energy of the battery consumed by the drive motor and the hydrogen energy consumed by the fuel cell system into a unified hydrogen consumption index in an equivalent manner. The following describes the calculation method of the equivalent hydrogen consumption.

[0059] The equivalent hydrogen consumption when the power battery is charged at power P bat can be expressed as:

[0060]

[0061] Among them, is the equivalent hydrogen consumption when the power battery is charged at power P bat , f eq_chg is the equivalent hydrogen consumption conversion coefficient that defines the future consumption condition of battery electrical energy, η chg is the charging efficiency of the power battery.

[0062] The equivalent hydrogen consumption when the power battery discharges at power P bat can be expressed as:

[0063]

[0064] Among them, is the equivalent hydrogen consumption when the power battery discharges at power P bat , f eq_disg is the equivalent hydrogen consumption conversion coefficient that defines the future charging condition of the battery's electrical energy, and η disg is the discharge efficiency of the power battery.

[0065] The charging efficiency and discharge efficiency of the power battery can be calculated by the following formula:

[0066]

[0067] Among them, R dis is the discharge resistance of the power battery, and R chg is the charging resistance of the power battery, and U OCV is the open-circuit voltage of the power battery. The upper half of the formula is used to calculate the discharge efficiency, corresponding to the case where P bat > 0, and the lower half of the formula is used to calculate the charging efficiency, corresponding to the case where P bat < 0.

[0068] 102. Determine the target penalty factor according to the current SOC value;

[0069] In order to enable the energy management strategy to have the function of restoring the SOC of the power battery to a reasonable range, a penalty factor needs to be introduced into the objective function. After obtaining the current SOC value of the power battery, a corresponding penalty factor is determined according to the current SOC value, denoted as the target penalty factor. Compared with the method of selecting the penalty factor through manual experience in the prior art, the embodiment of the present application can reasonably select an appropriate penalty factor according to the current SOC value of the power battery, thereby improving the accuracy of selecting the penalty factor and ultimately achieving the purpose of improving the energy management effect.

[0070] In one implementation manner of the embodiment of the present application, determining the target penalty factor according to the current SOC value includes:

[0071] (1) Select the target penalty factor look-up table from each pre-constructed penalty factor look-up table; among them, each penalty factor look-up table respectively records the corresponding relationship between the SOC value and the penalty factor under their respective corresponding working conditions;

[0072] (2) Search for the penalty factor corresponding to the current SOC value from the target penalty factor look-up table as the target penalty factor.

[0073] In the embodiments of the present application, the penalty factor is strongly correlated with the SOC value of the power battery. After obtaining the current SOC value, the corresponding target penalty factor can be obtained by looking up a table. Specifically, multiple penalty factor look-up tables can be pre-constructed, and each penalty factor look-up table records the corresponding relationship between the SOC value and the penalty factor under its respective corresponding working conditions. As shown in Table 1 and Table 2 below, Table 1 is the penalty factor look-up table corresponding to the high-speed and full-load working conditions, which records the corresponding relationship between the SOC value and the penalty factor under the high-speed and full-load working conditions; Table 2 is the penalty factor look-up table corresponding to the short-distance and no-load working conditions, which records the corresponding relationship between the SOC value and the penalty factor under the short-distance and no-load working conditions.

[0074] Table 1

[0075]

[0076] Table 2

[0077]

[0078] The above Table 1 and Table 2 are only examples of the penalty factor look-up tables corresponding to two working conditions. In actual operation, more penalty factor look-up tables corresponding to different working conditions can be constructed, such as high-speed and no-load, short-distance and full-load, and so on.

[0079] When determining the target penalty factor according to the current SOC value, a table can be selected from these penalty factor look-up tables in a certain way, denoted as the target penalty factor look-up table. In theory, the penalty factor look-up table corresponding to the current working condition of the fuel cell vehicle should be selected here to obtain the most accurate penalty factor. However, since the current road spectrum is unknown, the current working condition cannot be accurately known. Based on this situation, methods such as random selection, selection according to a specified order, or selection according to the historical working condition record of the vehicle can be used to select the target penalty factor look-up table from each penalty factor look-up table. After that, the penalty factor corresponding to the current SOC value can be determined by looking up the table, that is, the penalty factor corresponding to the current SOC value is found from the target penalty factor look-up table as the target penalty factor.

[0080] As Figure 2 shown, it is a schematic diagram of the operation of obtaining the penalty factor by looking up the table. In Figure 2 , by inputting the current SOC value, the penalty factor corresponding to the current SOC value can be found from the target penalty factor look-up table and output as the target penalty factor.

[0081] In actual operation, a penalty factor comparison table corresponding to each different working condition can be constructed by means of manual calibration. In order to improve the efficiency and accuracy of constructing the penalty factor comparison table, the embodiments of the present application can also introduce a particle swarm algorithm to optimize the penalty factors, and create each penalty factor comparison table according to the optimization results. For the specific operation process, please refer to the following text.

[0082] In one implementation manner of the embodiments of the present application, any one of the penalty factor comparison tables in each penalty factor comparison table is constructed by the following method:

[0083] (1) According to the working condition corresponding to the any one of the penalty factor comparison tables, initialize each SOC value and its corresponding multiple initial penalty factors;

[0084] (2) Take each SOC value and its corresponding multiple initial penalty factors as the particles of the particle swarm algorithm, and run the simulation calculation of the particle swarm algorithm;

[0085] (3) According to the results of the simulation calculation, determine the optimal penalty factor corresponding to each SOC value;

[0086] (4) According to the optimal penalty factor corresponding to each SOC value, construct the any one of the penalty factor comparison tables.

[0087] Each penalty factor comparison table can be constructed in this way. Suppose it is currently necessary to construct a penalty factor comparison table corresponding to the high-speed and full-load working condition. Then, according to the high-speed and full-load working condition, initialize each SOC value and its corresponding multiple initial penalty factors, such as SOC1 (factor 1, factor 2, factor 3...), SOC2 (factor 1, factor 2, factor 3...), SOC3 (factor 1, factor 2, factor 3...).... Among them, the most suitable initial SOC values and initial penalty factors for each working condition can be determined by means of manual calibration. Then, take each SOC value and its corresponding multiple initial penalty factors as the particles of the particle swarm algorithm, and run the simulation calculation of the particle swarm algorithm. According to the results of the simulation calculation, the optimal penalty factor corresponding to each SOC value can be determined. After that, the corresponding penalty factor comparison table can be constructed according to the optimal penalty factor corresponding to each SOC value. For example, if the optimal penalty factors corresponding to each SOC value are: SOC1 (factor 1), SOC2 (factor 3), SOC3 (factor 2)..., the constructed penalty factor comparison table is shown in Table 3 below:

[0088] Table 3

[0089]

[0090] In actual operation, simulation can be carried out based on a typical road spectrum, and the penalty factors can be optimized by means of the particle swarm algorithm. AsFigure 3 As shown, it is a schematic diagram of the operation process for optimizing the penalty factor using the particle swarm optimization algorithm. First, parameters such as the particle swarm size and dimension of the particle swarm optimization algorithm are initialized, the position and velocity of each particle are initialized, the optimal position of the particle and the optimal position of the particle swarm are initialized. Among them, each particle is a different penalty factor corresponding to the SOC value. For example, SOC1 (Factor 1, Factor 2, Factor 3...) represents a particle. During the operation of the particle swarm optimization algorithm, the position and velocity of the particle swarm will be updated according to the search formulas for velocity and position. The specific search formulas are as follows:

[0091] v t+1 = wvt + c1r1(P t - x r ) + c2r2(G t - x t )

[0092] x t+1 = x t + v t+1

[0093] Among them, x represents the position of the particle, v represents the velocity of the particle, w represents the inertia factor, c1 and c2 represent acceleration constants, r1 and r2 are random numbers within the range of [0, 1], P t represents the optimal position searched by the particle so far, and G t represents the optimal position searched by the particle swarm so far.

[0094] Under the action of the search formula and the fitness function, the position and velocity of the particle swarm will be continuously iterated and updated, so as to obtain the final optimal position of the example and the final optimal position of the particle swarm, that is, to obtain the result of the simulation calculation. According to this result, the optimal penalty factor corresponding to each SOC value can be determined, and thus a penalty factor comparison table can be constructed.

[0095] In an implementation manner of the embodiment of the present application, the independent variable of the fitness function used by the particle swarm optimization algorithm includes the first integral value of the output power of the fuel cell system of the fuel cell vehicle and the second integral value of the hydrogen consumption of the fuel cell system; during the simulation calculation of running the particle swarm optimization algorithm, it includes:

[0096] If there is at least one simulated SOC value lower than the preset SOC limit value among the various simulated SOC values generated during the simulation calculation, or the simulated mileage of the simulation calculation is lower than the preset mileage limit value, then the second integral value is adjusted.

[0097] In the embodiment of the present application, the dynamic efficiency of the fuel cell system is used as the fitness function, which can be expressed by the following formula:

[0098] Psystem *1000 / (Fuel_Consump * 1.2 * 100000)

[0099] Among them, P system represents the integral value of the output power of the fuel cell system, denoted as the first integral value; Fuel_Consump represents the integral value of the hydrogen consumption of the fuel cell system, denoted as the second integral value.

[0100] To avoid the problem that the power battery SOC is lower than the limit value or the vehicle driving mileage is too low during the simulation process, the embodiments of the present application introduce a penalty function and use the penalty function to control and adjust the fitness function accordingly.

[0101] Specifically, if there is at least one simulated SOC value lower than the preset SOC limit value (such as 20% or 10%) among the various simulated SOC values generated during the simulation calculation, the second integral value Fuel_Consump in the fitness function will be adjusted. Or, if the simulated mileage of the simulation calculation is lower than the preset mileage limit value (this limit value can be set according to the actual simulation requirements), the second integral value Fuel_Consump in the fitness function will also be adjusted.

[0102] In an implementation manner of the embodiments of the present application, if there is at least one simulated SOC value lower than the preset SOC limit value among the various simulated SOC values generated during the simulation calculation, or the simulated mileage of the simulation calculation is lower than the preset mileage limit value, the adjustment of the second integral value includes:

[0103] (1) If there is at least one simulated SOC value lower than the preset SOC limit value among the various simulated SOC values generated during the simulation calculation, determine the first adjustment amount according to the minimum value among the various simulated SOC values and the SOC limit value, and adjust the second integral value according to the first adjustment amount;

[0104] (2) Or, if the simulated mileage of the simulation calculation is lower than the preset mileage limit value, determine the second adjustment amount according to the simulated mileage and the mileage limit value, and adjust the second integral value according to the second adjustment amount.

[0105] If there is at least one simulated SOC value lower than the preset SOC limit value among the various simulated SOC values generated during the simulation calculation, determine the first adjustment amount according to the minimum value among the various simulated SOC values and the SOC limit value, and then adjust the second integral value according to the first adjustment amount. For example, assuming that the SOC limit value is Bat_Soc_limit and the minimum value among the various simulated SOC values is Min(Bat_Soc), the second integral value Fuel_Consump can be adjusted according to the following formula:

[0106] Fuel_Consump = Fuel_Consump + (Bat_Soc_limit - Min(Bat_Soc)) * 500

[0107] If the simulated mileage calculated by the simulation is lower than the preset mileage limit, a second adjustment amount is determined according to the simulated mileage and the mileage limit, and the second integral value is adjusted according to the second adjustment amount. For example, assuming that the mileage limit is Distance_limit and the simulated mileage is Distance_sim, the second integral value Fuel_Consump can be adjusted according to the following formula:

[0108] Fuel_Consump = Fuel_Consump + Max(Distance_limit - Distance_sim, 1) * Distance_limit

[0109] As described above, by introducing the particle swarm optimization algorithm to optimize the penalty factor, the optimal penalty factor corresponding to each SOC value can be determined, thereby constructing a penalty factor comparison table with relatively high accuracy.

[0110] 103. Construct an objective function for calculating the equivalent hydrogen consumption according to the target penalty factor;

[0111] After determining the target penalty factor according to the current SOC value of the power battery, an objective function for calculating the equivalent hydrogen consumption can be constructed. According to the description above, the objective function f can be expressed as:

[0112]

[0113] Wherein, is the hydrogen consumption of the fuel cell system, is the equivalent hydrogen consumption of the power battery, k _Soc is the penalty factor. Since and The calculation methods of are known, so the target penalty factor is used as k _Soc Substituting into this formula, the corresponding objective function f can be obtained.

[0114] In an implementation manner of the embodiment of the present application, the method further includes:

[0115] (1) Obtain the vehicle request power of the fuel cell vehicle;

[0116] (2) Determine the equivalent factor according to the current SOC of the power battery and the vehicle request power;

[0117] (3) Determine the equivalent hydrogen consumption of the electric energy consumed by the power battery in the objective function according to the equivalent factor.

[0118] When the output power that can be provided by the power battery plus the current output power of the fuel cell system cannot meet the vehicle's requested power of the fuel cell vehicle, it can be solved by adjusting the equivalent factor, so as to avoid the situation where the SOC value of the power battery is within an appropriate range, the penalty factor is close to 1, and the fuel cell system cannot continuously output high power. Specifically, the vehicle's requested power of the fuel cell vehicle can be obtained, and according to the current SOC of the power battery and the vehicle's requested power, an appropriate equivalent factor can be determined. This equivalent factor is a conversion coefficient that converts the electrical energy consumed by the battery into hydrogen consumption, and based on this equivalent factor, the equivalent hydrogen consumption of the electrical energy consumed by the power battery in the objective function can be determined. In actual operation, the equivalent factor table corresponding to different SOCs and different vehicle's requested powers can be pre-constructed by means of manual calibration, as shown in Table 4 below:

[0119] Table 4

[0120]

[0121] The various values 0.077, 0.073, 0.071... recorded in this table represent equivalent factors. According to the current SOC of the power battery and the vehicle's requested power, an appropriate equivalent factor can be found from this table, so as to meet the vehicle's high-power request and improve the vehicle's power response.

[0122] 104. Based on the objective function, the equivalent hydrogen consumption minimum strategy is adopted to perform energy management and control on the power system of the fuel cell vehicle.

[0123] After determining the objective function, based on this objective function, the ECMS strategy can be adopted to perform energy management and control on the power system of the fuel cell vehicle, that is, to perform energy management and control on the fuel cell system and the power battery. The specific energy management and control principle can refer to the description of the ECMS strategy in the prior art, which will not be elaborated here.

[0124] According to the description above, since the current actual working condition cannot be accurately known, the selected target penalty factor comparison table may not be adapted to the current actual working condition, that is, the selected target penalty factor may not be accurate enough. In view of this situation, the present application embodiment introduces an adaptive fitting and correction mechanism for the penalty factor, which can continuously correct the value of the penalty factor during the energy management and control process until a better penalty factor is obtained, so as to ensure the effect of energy management and control.

[0125] In an implementation manner of the present application embodiment, after performing energy management and control on the power system of the fuel cell vehicle by adopting the equivalent hydrogen consumption minimum strategy based on the objective function, it further includes:

[0126] (1) Determine the number of times that the SOC value of the power battery is higher than the first threshold within a set time period;

[0127] (2) Determine the first penalty factor correction coefficient according to the number of times the SOC value of the power battery is higher than the first threshold within the set duration.

[0128] (3) Correct the target penalty factor according to the first penalty factor correction coefficient, and return to execute the steps of constructing the objective function for equivalent hydrogen consumption calculation based on the target penalty factor and subsequent steps.

[0129] If the penalty factors corresponding to the same SOC under different working conditions are the same and the output power of the fuel cell system is the same, there will be significant differences in the number of times the SOC of the power battery reaches the set threshold within a certain duration. Therefore, to improve the adaptability of the penalty factor to different working conditions, a penalty factor correction coefficient comparison table can be pre-constructed through manual calibration. According to the number of times the SOC value of the power battery is higher than the set threshold within a time period of the energy management process, the appropriate penalty factor correction coefficient can be found from this comparison table to correct the target penalty factor. After correcting the target penalty factor, it is possible to return to execute the steps of constructing the objective function for equivalent hydrogen consumption calculation based on the target penalty factor and subsequent steps, that is, update the objective function using the corrected target penalty factor, and then continue to adopt the ECMS strategy for energy management based on the updated objective function until the next time period to continue counting the number of times the SOC value of the power battery is higher than the set threshold, and continue to find the appropriate penalty factor correction coefficient to correct the target penalty factor. By continuously looping like this, an optimal target penalty factor can ultimately be obtained.

[0130] As an example, a certain penalty factor correction coefficient comparison table is shown in Table 5 below:

[0131] Table 5

[0132]

[0133]

[0134] In Table 5, the coefficient α represents the penalty factor correction coefficient. The set time period is 3 hours, and the set threshold is 80%. When the SOC value exceeds 80%, it means that the SOC is too high and not within the reasonable range (the reasonable range can generally be set to 25% - 80%). The more the number of times, the smaller the corresponding coefficient α.

[0135] In specific operations, the number of times x that the SOC value of the power battery is higher than 80% within 3 hours can be first counted, and then the corresponding penalty factor correction coefficient α can be found from Table 5 according to the number of times x to correct the target penalty factor.

[0136] In an implementation manner of the embodiment of the present application, after performing energy management and control on the power system of a fuel cell vehicle by adopting the minimum equivalent hydrogen consumption strategy based on the objective function, the following steps are further included:

[0137] (1) Determine the number of times that the SOC value of the power battery is lower than the second threshold within a set time period;

[0138] (2) Determine the second penalty factor correction coefficient according to the number of times that the SOC value of the power battery is lower than the second threshold within a set time period;

[0139] (3) Correct the target penalty factor according to the second penalty factor correction coefficient, and return to execute the step of constructing the objective function for calculating the equivalent hydrogen consumption according to the target penalty factor and subsequent steps.

[0140] To avoid the problem that too low a value of the penalty factor causes a low output power of the fuel cell system, resulting in a low SOC value of the power battery, and further affecting the medium and high power output of the whole vehicle, the number of times that the SOC value of the power battery is lower than the set threshold within a time period can also be statistically analyzed in a similar way. Similarly, a suitable penalty factor correction coefficient can be found by looking up a table to correct the target penalty factor. Similarly, a penalty factor correction coefficient comparison table can be pre-constructed by manual calibration. According to the number of times that the SOC value of the power battery is lower than the set threshold within a time period of the energy management and control process, a suitable penalty factor correction coefficient can be found from this comparison table to correct the target penalty factor. After correcting the target penalty factor, the step of constructing the objective function for calculating the equivalent hydrogen consumption according to the target penalty factor and subsequent steps can be returned to execute, that is, update the objective function with the corrected target penalty factor, and then continue to adopt the ECMS strategy for energy management and control based on the updated objective function until the number of times that the SOC value of the power battery is lower than the set threshold is statistically analyzed in the next time period, and continue to find a suitable penalty factor correction coefficient to correct the target penalty factor. By continuously looping like this, an optimal target penalty factor can finally be obtained.

[0141] As an example, a certain penalty factor correction coefficient comparison table is shown in Table 6 below:

[0142] Table 6

[0143] Within 3 hours, SOC <= 25%, > 1 time SOC value Coefficient α value Within 3 hours, SOC <= 25%, > 2 times … … Within 3 hours, SOC <= 25%, > 3 times … … Within 3 hours, SOC <= 25%, > 4 times … … Within 3 hours, SOC <= 25%, > 5 times … … … … …

[0144] In Table 6, the coefficient α represents the penalty factor correction coefficient. The set time period is 3 hours, and the set threshold is 25%. When the SOC value is lower than 25%, it means that the SOC is too low and not within a reasonable range. The more the number of times, the larger the corresponding coefficient α.

[0145] In specific operations, the number of times y that the SOC value of the power battery is lower than 25% within 3 hours can be first counted, and then the corresponding penalty factor correction coefficient α can be found from Table 6 according to the number of times y to correct the target penalty factor.

[0146] The time period and the number of times in Table 5 and Table 6 can be reasonably calibrated according to the differences in the matching of the power system and the power battery. After determining the penalty factor correction coefficient α, α is used to correct the target penalty factor, which is finally reflected in the following objective function:

[0147]

[0148] It can be seen from this objective function that by multiplying the penalty factor k_ Soc by the correction coefficient α, the correction is completed.

[0149] It can be predicted that when the penalty factor is corrected by the correction coefficient α each time, the number of times that the SOC value exceeds the range in the next time period will decrease. Through continuous cyclic correction, the SOC value can ultimately be maintained within a reasonable range, such as within the range of 25% - 80%, that is, the stability of the SOC value is achieved. This process is equivalent to adaptively fitting the penalty factor of the ECMS strategy, which can improve the adaptability of the ECMS strategy, maintain the SOC value of the power battery within a reasonable range, and thus reduce the variable load times and start-stop times of the fuel cell system.

[0150] In the embodiment of the present application, first, the current SOC value of the power battery of the fuel cell vehicle is obtained, and the corresponding target penalty factor is determined according to the current SOC value; then, based on the target penalty factor, an objective function for calculating the equivalent hydrogen consumption is constructed; finally, based on the objective function, the equivalent hydrogen consumption minimum strategy is used to perform energy management and control on the power system of the fuel cell vehicle. In the above process, the penalty factor used in the objective function is an adaptively selected value reasonably according to the current SOC value of the power battery. Compared with the method of manually selecting empirical values, the accuracy of selecting the penalty factor is improved, thereby improving the energy management and control effect.

[0151] It should be understood that the magnitudes of the sequence numbers of the steps in the above various embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0152] The above mainly describes a method for energy management and control of a fuel cell vehicle. Next, a device for energy management and control of a fuel cell vehicle will be described.

[0153] Please refer to Figure 4 , an embodiment of a device for energy management and control of a fuel cell vehicle in the embodiment of the present application includes:

[0154] The current SOC value acquisition module 401 is used to acquire the current SOC value of the power battery of the fuel cell vehicle;

[0155] The penalty factor determination module 402 is used to determine the target penalty factor according to the current SOC value;

[0156] The objective function construction module 403 is used to construct the objective function for calculating the equivalent hydrogen consumption according to the target penalty factor;

[0157] The energy management and control module 404 is used to perform energy management and control on the power system of the fuel cell vehicle based on the objective function by adopting the strategy of minimizing the equivalent hydrogen consumption.

[0158] In an implementation manner of the embodiment of the present application, the penalty factor determination module includes:

[0159] The look-up table selection unit is used to select the target penalty factor look-up table from the pre-constructed penalty factor look-up tables; wherein, each penalty factor look-up table respectively records the corresponding relationship between the SOC value and the penalty factor under the corresponding working condition;

[0160] The penalty factor search unit is used to search for the penalty factor corresponding to the current SOC value from the target penalty factor look-up table as the target penalty factor.

[0161] In an implementation manner of the embodiment of the present application, the fuel cell vehicle energy management and control device further includes:

[0162] The initialization module is used to initialize each SOC value and its corresponding multiple initial penalty factors according to the working condition corresponding to any penalty factor look-up table;

[0163] The particle swarm operation module is used to use each SOC value and its corresponding multiple initial penalty factors as the particles of the particle swarm algorithm to run the simulation calculation of the particle swarm algorithm;

[0164] The optimal penalty factor determination module is used to determine the optimal penalty factor corresponding to each SOC value according to the result of the simulation calculation;

[0165] The look-up table construction module is used to construct the any penalty factor look-up table according to the optimal penalty factor corresponding to each SOC value.

[0166] In an implementation manner of the embodiment of the present application, the independent variable of the fitness function used by the particle swarm algorithm includes the first integral value of the output power of the fuel cell system of the fuel cell vehicle and the second integral value of the hydrogen consumption of the fuel cell system; the fuel cell vehicle energy management and control device further includes:

[0167] A fitness function adjustment module, configured to adjust the second integral value if at least one of the simulated SOC values generated in the simulation calculation is lower than a preset SOC limit value, or if the simulated mileage of the simulation calculation is lower than a preset mileage limit value.

[0168] In an implementation manner of the embodiment of the present application, the fitness function adjustment module includes:

[0169] A first adjustment unit, configured to, if at least one of the simulated SOC values generated in the simulation calculation is lower than a preset SOC limit value, determine a first adjustment amount according to the minimum value among the simulated SOC values and the SOC limit value, and adjust the second integral value according to the first adjustment amount;

[0170] A second adjustment unit, configured to, if the simulated mileage of the simulation calculation is lower than a preset mileage limit value, determine a second adjustment amount according to the simulated mileage and the mileage limit value, and adjust the second integral value according to the second adjustment amount.

[0171] In an implementation manner of the embodiment of the present application, the fuel cell vehicle energy management and control device further includes:

[0172] A first number determination module, configured to determine the number of times that the SOC value of the power battery is higher than a first threshold within a set duration;

[0173] A first correction coefficient determination module, configured to determine a first penalty factor correction coefficient according to the number of times that the SOC value of the power battery is higher than the first threshold within the set duration;

[0174] A first factor correction module, configured to correct the target penalty factor according to the first penalty factor correction coefficient, and return to execute the steps of constructing the target function for calculating the equivalent hydrogen consumption according to the target penalty factor and subsequent steps.

[0175] In an implementation manner of the embodiment of the present application, the fuel cell vehicle energy management and control device further includes:

[0176] A second number determination module, configured to determine the number of times that the SOC value of the power battery is lower than a second threshold within a set duration;

[0177] A second correction coefficient determination module, configured to determine a second penalty factor correction coefficient according to the number of times that the SOC value of the power battery is lower than the second threshold within the set duration;

[0178] A second factor correction module, configured to correct the target penalty factor according to the second penalty factor correction coefficient, and return to execute the steps of constructing the target function for calculating the equivalent hydrogen consumption according to the target penalty factor and subsequent steps.

[0179] An embodiment of the present application further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the fuel cell vehicle energy management and control method represented by any of the above embodiments.

[0180] An embodiment of the present application further provides a computer program product. When the computer program product runs on an electronic device, the electronic device is enabled to execute the fuel cell vehicle energy management and control method represented by any of the above embodiments.

[0181] Figure 5 It is a schematic diagram of an electronic device provided by an embodiment of the present application. As Figure 5 shown, the electronic device 5 of this embodiment includes: a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and executable on the processor 50. When the processor 50 executes the computer program 52, it implements the steps in the embodiments of the above various fuel cell vehicle energy management and control methods, such as Figure 1 the steps 101 to 104 shown. Alternatively, when the processor 50 executes the computer program 52, it implements the functions of each module / unit in the above device embodiments, such as Figure 4 the functions of the modules 401 to 404 shown.

[0182] The computer program 52 can be divided into one or more modules / units, and the one or more modules / units are stored in the memory 51 and executed by the processor 50 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 52 in the electronic device 5.

[0183] The so-called processor 50 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0184] The memory 51 may be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 51 may also be an external storage device of the electronic device 5, such as a plug-in hard disk equipped on the electronic device 5, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 51 may also include both an internal storage unit and an external storage device of the electronic device 5. The memory 51 is used to store the computer program and other programs and data required by the electronic device. The memory 51 may also be used to temporarily store data that has been output or will be output.

[0185] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In practical applications, the above functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment and will not be elaborated herein.

[0186] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the system, device, and unit described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0187] In the above embodiments, the descriptions of each embodiment have their own emphases. For parts not described or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0188] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0189] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0190] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.

[0191] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0192] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned method embodiments of the present application, it can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0193] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A method for controlling the energy of a fuel cell vehicle, characterized in that, Including: Obtaining the current SOC value of the power battery of a fuel cell vehicle; Determining a target penalty factor according to the current SOC value; Constructing an objective function for equivalent hydrogen consumption calculation according to the target penalty factor; Based on the objective function, adopting an equivalent hydrogen consumption minimization strategy to perform energy management and control on the power system of the fuel cell vehicle.

2. The method according to claim 1, characterized in that, The determining the target penalty factor according to the current SOC value includes: Selecting a target penalty factor look-up table from each pre-constructed penalty factor look-up table; wherein, each penalty factor look-up table respectively records the corresponding relationship between the SOC value and the penalty factor under its corresponding working condition; Searching for the penalty factor corresponding to the current SOC value from the target penalty factor look-up table as the target penalty factor.

3. The method according to claim 2, wherein Any one of the penalty factor look-up tables in each of the penalty factor look-up tables is constructed by the following method: Initializing each SOC value and its corresponding multiple initial penalty factors according to the working condition corresponding to the any one of the penalty factor look-up tables; Taking each SOC value and its corresponding multiple initial penalty factors as particles of a particle swarm algorithm, and running the simulation calculation of the particle swarm algorithm; Determining the optimal penalty factor corresponding to each SOC value according to the result of the simulation calculation; Constructing the any one of the penalty factor look-up tables according to the optimal penalty factor corresponding to each SOC value.

4. The method according to claim 3, wherein The independent variables of the fitness function used in the particle swarm algorithm include the first integral value of the output power of the fuel cell system of the fuel cell vehicle and the second integral value of the hydrogen consumption of the fuel cell system; During the process of running the simulation calculation of the particle swarm algorithm, it includes: If there is at least one simulated SOC value lower than a preset SOC limit value among the various simulated SOC values generated in the simulation calculation, or the simulated mileage of the simulation calculation is lower than a preset mileage limit value, then adjust the second integral value.

5. The method according to claim 4, characterized in that The if there is at least one simulated SOC value lower than a preset SOC limit value among the various simulated SOC values generated in the simulation calculation, or the simulated mileage of the simulation calculation is lower than a preset mileage limit value, then adjusting the second integral value includes: If there is at least one simulated SOC value lower than a preset SOC limit value among the various simulated SOC values generated in the simulation calculation, then determining a first adjustment amount according to the minimum value of the various simulated SOC values and the SOC limit value, and adjusting the second integral value according to the first adjustment amount; Or, if the simulated mileage of the simulation calculation is lower than a preset mileage limit value, then determining a second adjustment amount according to the simulated mileage and the mileage limit value, and adjusting the second integral value according to the second adjustment amount.

6. The method according to any one of claims 1 to 5, characterized in that, After performing energy management and control on the power system of the fuel cell vehicle by adopting an equivalent hydrogen consumption minimization strategy based on the objective function, it further includes: Determining the number of times that the SOC value of the power battery is higher than a first threshold within a set time period; Determining a first penalty factor correction coefficient according to the number of times that the SOC value of the power battery is higher than the first threshold within the set time period; Modify the target penalty factor according to the first penalty factor correction coefficient, and return to execute the steps of constructing the objective function for calculating the equivalent hydrogen consumption according to the target penalty factor and subsequent steps.

7. The method according to any one of claims 1 to 5, characterized in that After the power system of the fuel cell vehicle is energy-controlled by adopting the equivalent hydrogen consumption minimum strategy based on the objective function, it further includes: Determine the number of times that the SOC value of the power battery is lower than the second threshold within a set time period; Determine the second penalty factor correction coefficient according to the number of times that the SOC value of the power battery is lower than the second threshold within a set time period; Modify the target penalty factor according to the second penalty factor correction coefficient, and return to execute the steps of constructing the objective function for calculating the equivalent hydrogen consumption according to the target penalty factor and subsequent steps.

8. An energy management and control device for a fuel cell vehicle, characterized in that, It includes: A current SOC value acquisition module for acquiring the current SOC value of the power battery of the fuel cell vehicle; A penalty factor determination module for determining a target penalty factor according to the current SOC value; An objective function construction module for constructing an objective function for calculating the equivalent hydrogen consumption according to the target penalty factor; An energy control module for energy-controlling the power system of the fuel cell vehicle by adopting the equivalent hydrogen consumption minimum strategy based on the objective function.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the fuel cell vehicle energy control method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the fuel cell vehicle energy control method according to any one of claims 1 to 7.

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