Method, Device, Equipment and Storage Medium for Controlling Water Temperature of Fuel Cell

Abstract

The present application discloses a method, device, equipment and storage medium for controlling the water temperature of a fuel cell. The method includes: collecting the inlet water temperature and the outlet water temperature of the fuel cell in real time, wherein the fuel cell includes an outlet; when the water temperature of the fuel cell rises to a preset target temperature range, performing Kalman filter iterative calculation on the inlet water temperature and the outlet water temperature to obtain a calculated heat value at the outlet of the fuel cell; performing PID control on the inlet water temperature and the outlet water temperature to obtain an error heat; using the calculated heat value and the error heat as references to determine the duty cycle for controlling the water temperature of the fuel cell, so as to perform temperature cycle control on the water temperature of the fuel cell. The present application reduces the fluctuation of temperature control, thereby improving the stability of the fuel cell.

Description

Technical Field

[0001] The present application relates to the field of fuel cells, and particularly to a method, device, equipment and storage medium for controlling the water temperature of a fuel cell. Background Art

[0002] As the most energy-saving and environmentally friendly new energy, hydrogen has gradually begun to be applied to vehicles. Since the product after hydrogen combustion is water, using hydrogen as the energy of the vehicle with a hydrogen fuel cell as the carrier can make the carbon emissions in the vehicle life cycle not only lower than those of fuel vehicles, but even lower than those of pure electric vehicles.

[0003] In order to ensure the stable output power of the hydrogen fuel cell, it is necessary to stably control the hydrogen fuel cell. Since the continuous operation of the hydrogen fuel cell will generate a large amount of heat, a suitable temperature can improve the activity of the catalyst in the hydrogen fuel cell, thereby increasing the chemical reaction rate. Therefore, the key to controlling the hydrogen fuel cell lies in controlling the water temperature of the hydrogen fuel cell. At present, when the power of the hydrogen fuel cell changes, there is a large uncertainty in controlling the water temperature of the hydrogen fuel cell. Parameters are adjusted through experience, and finally the control quantity is obtained to gradually approach the target by controlling the water temperature. However, for a fuel cell such as a hydrogen fuel cell with a long pipeline and large delay, the control of the water temperature of the hydrogen fuel cell fluctuates greatly, thereby reducing the stability of the fuel cell. Summary of the Invention

[0004] The main purpose of the present application is to provide a method, device, equipment and storage medium for controlling the water temperature of a fuel cell, aiming to solve the technical problem of large fluctuations in the control of the water temperature of a fuel cell such as a hydrogen fuel cell with a long pipeline and large delay in the prior art.

[0005] To achieve the above object, the present application provides a method for controlling the water temperature of a fuel cell, and the method for controlling the water temperature of the fuel cell includes:

[0006] Real-time collect the inlet water temperature and outlet water temperature of the fuel cell, wherein the fuel cell includes an outlet.

[0007] When the water temperature of the fuel cell rises to a preset target temperature range, perform Kalman filter iterative calculation on the inlet water temperature and the outlet water temperature to obtain the calculated heat value at the outlet of the fuel cell.

[0008] Perform PID control on the inlet water temperature and the outlet water temperature to obtain an error heat.

[0009] Use the calculated heat value and the error heat as references to determine the duty cycle for controlling the water temperature of the fuel cell, so as to perform temperature cycle control on the water temperature of the fuel cell.

[0010] Optionally, the step of performing Kalman filtering iterative calculation on the inlet water temperature and the outlet water temperature to obtain the calculated heat value of the fuel cell at the outlet includes:

[0011] Performing iterative state prediction calculation on the inlet water temperature and the outlet water temperature to obtain the estimated heat value of the fuel cell at present;

[0012] Based on the estimated heat value, matching the observed heat value of the current fuel cell from the preset experimental data;

[0013] Based on the budgeted Kalman coefficient, performing real-time iterative optimization calculation on the estimated heat value and the observed heat value to obtain the calculated heat value of the fuel cell at the outlet.

[0014] Optionally, the step of matching the observed heat value of the current fuel cell from the preset experimental data based on the estimated heat value includes:

[0015] Performing scalar calculation on the estimated heat value to obtain the predicted power heat of the current fuel cell;

[0016] Determining the power data corresponding to the predicted power heat;

[0017] Based on the experimental data, after performing quantization processing on the power data, determining the observed heat value corresponding to the power data.

[0018] Optionally, before the step of performing real-time iterative update and optimization calculation on the estimated heat value and the observed heat value to obtain the calculated heat value of the fuel cell at the outlet, the method includes:

[0019] Performing error analysis on the estimated heat to obtain the prediction error when performing state prediction on the inlet water temperature;

[0020] Based on the budgeted updated covariance, performing iterative covariance calculation on the prediction error to obtain the transfer covariance;

[0021] Performing error analysis on the observed heat value to obtain the operation error of the experimental data;

[0022] Performing iterative coefficient calculation on the transfer covariance and the operation error to obtain the Kalman coefficient.

[0023] Optionally, the step of performing iterative covariance calculation on the prediction error based on the budgeted updated covariance to obtain the transfer covariance includes:

[0024] Obtaining the historical covariance of the previous iterative covariance calculation and the historical Kalman coefficient of the previous iterative coefficient calculation;

[0025] Perform covariance calculation on the historical covariance and the historical Kalman coefficient to obtain the updated covariance;

[0026] Perform variance calculation on the updated covariance and the prediction error to obtain the transfer covariance.

[0027] Optionally, the step of using the calculated heat value and the error heat as references to determine the duty cycle for controlling the water temperature of the fuel cell to perform temperature cycle control on the water temperature of the fuel cell includes:

[0028] Add the calculated heat value and the error heat to obtain the control energy for controlling the water temperature of the fuel cell;

[0029] Based on the control energy, determine the duty cycle for controlling the water temperature in the fuel cell during the water circulation process;

[0030] Based on the duty cycle, determine the output power of the temperature control module for controlling the water temperature of the fuel cell to control the stability of the water temperature of the fuel cell.

[0031] Optionally, the step of performing iterative state prediction calculation on the inlet water temperature and the outlet water temperature to obtain the estimated heat value of the current fuel cell includes:

[0032] Calculate the change rate of the inlet water temperature and the outlet water temperature to obtain the change rate of the water temperature of the fuel cell;

[0033] Based on the change rate and the historical inlet water temperature during the previous iterative calculation, obtain the current temperature of the current fuel cell;

[0034] Calculate the heat of the water temperature of the fuel cell based on the change rate and the historical inlet water temperature;

[0035] Perform matrix calculation on the current temperature and the water temperature heat to obtain the estimated heat value of the current fuel cell.

[0036] This application also provides a water temperature control device for a fuel cell. The water temperature control device for the fuel cell includes:

[0037] An acquisition module for real-time acquisition of the inlet water temperature and the outlet water temperature of the fuel cell, where the fuel cell includes an outlet;

[0038] A Kalman calculation module for performing Kalman filter iterative calculation on the inlet water temperature and the outlet water temperature when the water temperature of the fuel cell rises to a preset target temperature range to obtain the calculated heat value of the fuel cell at the outlet;

[0039] The first control module is used to perform PID control on the inlet water temperature and the outlet water temperature to obtain the error heat quantity;

[0040] The second control module is used to determine the duty cycle for controlling the water temperature of the fuel cell with reference to the calculated heat quantity value and the error heat quantity, so as to perform temperature cycle control on the water temperature of the fuel cell.

[0041] The present application also provides a water temperature control device for a fuel cell. The water temperature control device for the fuel cell is an entity node device. The water temperature control device for the fuel cell includes: a memory, a processor, and a program of the water temperature control method for the fuel cell stored on the memory and executable on the processor. When the program of the water temperature control method for the fuel cell is executed by the processor, the steps of the water temperature control method for the fuel cell as described above can be implemented.

[0042] The present application also provides a storage medium. A program for implementing the water temperature control method for the fuel cell as described above is stored on the storage medium. When the program of the water temperature control method for the fuel cell is executed by the processor, the steps of the water temperature control method for the fuel cell as described above are implemented.

[0043] The present application provides a method, device, equipment and storage medium for controlling the water temperature of a fuel cell. Compared with the prior art in which for a fuel cell such as a hydrogen fuel cell with a long pipeline and large delay, the water temperature control of the hydrogen fuel cell fluctuates greatly, thereby reducing the stability of the fuel cell. In the present application, the water temperature at the inlet and outlet of the fuel cell is collected in real time, where the fuel cell includes an outlet. When the water temperature of the fuel cell rises to a preset target temperature range, the water temperature at the inlet and the water temperature at the outlet are subjected to Kalman filter iterative calculation to obtain a calculated heat value at the outlet of the fuel cell. PID control is performed on the water temperature at the inlet and the water temperature at the outlet to obtain an error heat. The calculated heat value and the error heat are used as references to determine the duty cycle for controlling the water temperature of the fuel cell, so as to perform temperature cycle control on the water temperature of the fuel cell. In the present application, after the water temperature of the fuel cell rises to the preset target temperature range, the collected water temperatures at the inlet and outlet of the fuel cell and the water temperature at the outlet are subjected to Kalman iterative calculation to obtain the calculated heat value closest to the true value at the outlet of the fuel cell. Then, by performing PID control on the water temperature at the outlet and the water temperature at the inlet, an error heat is obtained. The calculated heat value and the error heat are used as feedforward to control the water temperature of the fuel cell. That is, in the present application, Kalman iterative calculation is performed on the water temperature at the inlet and the water temperature at the outlet to reduce the sudden change amount, eliminate the delay and retention of the water temperature change caused by the long pipeline water path, and the obtained calculated heat value is more in line with the actual situation. Then, the calculated heat value and the error heat obtained by PID control are used as feedforward to control the water temperature of the fuel cell. Therefore, the temperature control fluctuation is reduced, and the stability of the fuel cell is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application and used together with the specification to explain the principles of the present application.

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a schematic flowchart of the first embodiment of the method for controlling the water temperature of the fuel cell in the present application;

[0047] Figure 2 It is a schematic flowchart of the second embodiment of the method for controlling the water temperature of the fuel cell in the present application;

[0048] Figure 3 It is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present application;

[0049] Figure 4 This is the algorithm schematic diagram in the method for controlling the water temperature of the fuel cell of the present application;

[0050] Figure 5 This is the structural schematic diagram of the device for controlling the water temperature of the fuel cell of the present application;

[0051] Figure 6 This is the schematic diagram of the algorithm flow in the method for controlling the water temperature of the fuel cell of the present application.

[0052] The realization of the purpose, functional characteristics and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0053] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] In the following description, suffixes such as "module", "component" or "unit" used to represent elements are only for the convenience of describing the present application, and they have no specific meaning in themselves. Therefore, "module", "component" or "unit" can be used interchangeably.

[0056] The embodiment of the present application provides a method for controlling the water temperature of a fuel cell. In the first embodiment of the method for controlling the water temperature of the fuel cell of the present application, refer to Figure 1 , the method for controlling the water temperature of the fuel cell includes:

[0057] Step S10, collect the inlet water temperature and outlet water temperature of the fuel cell in real time, wherein the fuel cell includes an outlet;

[0058] Step S20, when the water temperature of the fuel cell rises to a preset target temperature range, perform Kalman filter iterative calculation on the inlet water temperature and the outlet water temperature to obtain the calculated heat value at the outlet of the fuel cell;

[0059] Step S30, perform PID control on the inlet water temperature and the outlet water temperature to obtain an error heat;

[0060] Step S40, use the calculated heat value and the error heat as references to determine the duty cycle for controlling the water temperature of the fuel cell, so as to perform temperature cycle control on the water temperature of the fuel cell.

[0061] The purpose of this embodiment is to eliminate the delay in water temperature change caused by too long pipeline of the fuel cell, thereby improving the stability of the fuel cell.

[0062] In this embodiment, it should be noted that the water temperature control method of the fuel cell can be applied to the water temperature control device of the fuel cell. The water temperature control device of the fuel cell belongs to the water temperature control equipment of the fuel cell, and the water temperature control equipment of the fuel cell belongs to the water temperature control system of the fuel cell.

[0063] In this embodiment, as Figure 5 shown, the water temperature control device of the fuel cell includes a domain controller and a temperature regulator.

[0064] Among them, the domain controller can manage the start and stop of the fuel cell and can also manage the temperature of the fuel.

[0065] As Figure 5 shown, among them, the domain controller includes at least, but is not limited to, a temperature management algorithm interval and a PID control interval.

[0066] It should be noted that the temperature management algorithm interval includes a Kalman filter algorithm, which can perform iterative calculations on the inlet water temperature and outlet water temperature of the fuel cell to obtain the most realistic fuel cell heat value; the PID algorithm interval can eliminate the error of temperature change and can also reduce the error of temperature fluctuation; through the Kalman filter, the inaccuracy of the PID input caused by the large water temperature delay can be effectively eliminated, so that the PID regulation is distorted, especially it can well solve the problem of large water temperature regulation fluctuation when the fuel cell power changes frequently.

[0067] Specifically, PID is the general term for P (Proportion), I (Integration), and D (Differentiation).

[0068] It should be noted that the temperature regulator can automatically adjust according to the PID control interval and the heat feedback from the temperature management algorithm interval, so that the water temperature of the fuel cell always remains within the preset target temperature range, and the fuel cell is always at the most efficient operating temperature.

[0069] Specifically, the temperature regulator can be a fan, a cooler, a refrigerator, etc., and there is no specific limitation.

[0070] In this embodiment, the specific application scenario can be:

[0071] During the operation of a hydrogen fuel cell, a large amount of heat is generated. A suitable temperature can improve the activity of the catalyst, increase the proton transfer rate of the proton exchange membrane, enhance the chemical reaction, raise the reaction current, and improve the performance of the electric propulsion. Therefore, during the operation of a hydrogen fuel cell, it is necessary to control the water temperature of the hydrogen fuel cell to avoid overheating. Due to the characteristics of a long pipe water circuit and large delay in a hydrogen fuel cell, it is easy to have distortion when controlling the water temperature of the hydrogen fuel cell, and the water temperature of the hydrogen fuel cell cannot be controlled in a timely manner.

[0072] In this embodiment, by collecting the inlet water temperature and outlet water temperature in real time, and performing Kalman filter iterative calculation on the inlet water temperature and outlet water temperature, the sudden change amount of the temperature change is reduced, the delay of the water temperature is eliminated, and the most realistic calculated heat value is obtained. Then, the calculated heat value is used as the feedforward of the PID control to reduce the parameter adjustment of the PID at different water temperatures, thereby quickly and accurately controlling the water temperature and improving the stability of the fuel cell.

[0073] In this embodiment, the water temperature control of a hydrogen fuel cell is taken as an example for illustration.

[0074] The specific steps are as follows:

[0075] Step S10, collect the inlet water temperature and outlet water temperature of the fuel cell in real time, where the fuel cell includes an outlet.

[0076] Among them, a hydrogen fuel cell at least includes a proton exchange membrane, a catalyst, etc.

[0077] In this embodiment, since heat is continuously released during the operation of the fuel cell, causing the water temperature of the fuel cell to rise, after the domain controller starts the fuel cell, it is necessary to detect the inlet water temperature and outlet water temperature of the fuel cell in real time through the CAN (Controller Area Network) bus.

[0078] It should be noted that before the actual operation of the fuel cell, experiments on the water temperature and power data of the fuel cell need to be carried out in the laboratory to determine the target temperature range when the fuel cell operates most efficiently and the change relationship between the water temperature and power data of the fuel cell, which is convenient for more accurately controlling the water temperature of the operating fuel cell.

[0079] Among them, the power data can be voltage, current, resistance, output power, etc., and there is no specific limitation.

[0080] Step S20, when the water temperature of the fuel cell rises to the preset target temperature range, perform Kalman filter iterative calculation on the inlet water temperature and the outlet water temperature to obtain the calculated heat value of the fuel cell at the outlet.

[0081] It should be noted that after multiple iterations of Kalman filtering, the heat required by the fuel cell will increasingly approach the actual heat dissipation required. That is, the calculated heat value is the closest to the actual heat to be dissipated, which is a reference value. Using this reference value as a feedforward makes the PID control more accurate and the water temperature control more accurate.

[0082] It should be noted that as the temperature of the proton exchange membrane increases, the rate of protons passing through the proton exchange membrane also increases, and the conductivity also increases. When the temperature rises to a certain range, the passing rate of the proton exchange membrane will reach the highest, that is, the conductivity reaches the highest. If the temperature continues to rise, the proton exchange membrane will experience dehydration, resulting in a decrease in conductivity and a sharp decline in the life of the fuel cell.

[0083] In this embodiment, when the fuel cell is just started, the water temperature of the fuel cell is at room temperature. As the temperature of the fuel cell increases, the conductivity of the fuel cell also increases, increasing the combustion efficiency of the fuel cell for hydrogen. To keep the operating efficiency of the fuel cell stable at the maximum, when the water temperature of the fuel cell rises to the target temperature range, through Kalman filter iterative calculation of the inlet water temperature and the outlet water temperature, the heat required by the fuel cell to decrease will increasingly approach the actual heat dissipation required. Then, based on the heat dissipation, the temperature regulator is accurately and quickly controlled by PID, and further the water temperature of the fuel cell is accurately and quickly controlled to prevent the fuel cell temperature from being too high and damaging the proton exchange membrane in the fuel cell.

[0084] Specifically, the step of performing Kalman filter iterative calculation on the inlet water temperature and the outlet water temperature to obtain the calculated heat value of the fuel cell at the outlet includes:

[0085] Step S21, perform iterative state prediction calculation on the inlet water temperature and the outlet water temperature to obtain the estimated heat value of the fuel cell at present;

[0086] In this embodiment, to perform iterative state prediction calculation on the inlet temperature and the outlet temperature, a state prediction formula is required, and the state prediction formula is obtained by transforming the state prediction matrix expression.

[0087] In this embodiment, the estimated heat value is represented in the form of a matrix , which simplifies the calculation of the estimated heat value, can quickly and simply calculate the estimated heat value, and thus speeds up the process of calculating the estimated heat value.

[0088] Step S22, based on the estimated heat value, match the observed heat value of the fuel cell at present from the preset experimental data;

[0089] In this embodiment, since the data obtained in the laboratory are all scalars, when determining the observed heat of the fuel cell by estimating the heat value, it is necessary to convert the estimated heat value in matrix form into a scalar. Since the experimental data are obtained in an ideal environment, there are many uncertain factors during the actual operation of the fuel cell. Therefore, in order to obtain the observed heat value based on the estimated heat value calculated through actual operation, the error between the experiment and the real environment needs to be considered.

[0090] It should be noted that the error is determined through error analysis of the experimental data in the laboratory and the experimental data of off-site experiments, or through the experimental data in the laboratory and the experimental data simulating the actual working conditions.

[0091] In this embodiment, by using the preset observed quantity calculation formula to calculate the estimated heat value, the predicted power heat is obtained. By matching the predicted power heat with the observed heat of the current fuel cell from the preset experimental data, a more accurate observed heat of the fuel cell can be obtained.

[0092] Among them, the observed quantity calculation unit formula is:

[0093]

[0094] Among them, R is the error between the laboratory data and the actual operation data, and R is a scalar. is the predicted power heat, which is also a scalar.

[0095] It should be noted that since the estimated heat value is a matrix with two rows and one column, in order to scalarize , it is necessary to multiply by a matrix with one row and two columns and all elements being 1. In this embodiment, let H be the matrix [1 0], and scalarize the heat value .

[0096] Step S23: Based on the budgeted Kalman coefficient, perform real-time iterative optimization calculation on the estimated heat value and the observed heat value to obtain the calculated heat value of the fuel cell at the water outlet.

[0097] In this embodiment, the estimated heat value and the observed heat value are optimized and calculated through the optimal heat calculation formula to obtain the calculated heat value at the outlet of the fuel cell; it should be noted that since scalars are more convenient for recording and observation, when optimizing and calculating the estimated heat value and the observed heat value, the estimated heat value needs to be scalarized, and the finally obtained calculated heat value is a scalar.

[0098] Specifically, the optimal heat calculation formula:

[0099] + ( )

[0100] Among them, is the Kalman coefficient at time k, is the observed heat at time k, is the calculated heat value.

[0101] In this embodiment, the water temperature heat of the fuel cell at time k calculated from the water inlet temperature at time k - 1 and the water inlet temperature of the fuel cell at time k calculated from the water outlet temperature at time k - 1 are iteratively calculated in this way to obtain the current estimated heat value. Then, the estimated heat value is scalarized, and the error between the laboratory data and the actual working condition is determined. Based on the error and the scalarized estimated heat value, the electric heat for conveniently querying the experimental data is calculated. The corresponding observed heat is queried from the experimental data through the electric heat, and based on the Kalman coefficient at time k calculated in advance, the observed heat and the estimated heat value are optimized to obtain the calculated heat value of the fuel cell at the water outlet.

[0102] Specifically, the step of performing iterative state prediction calculation on the water inlet temperature and the water outlet temperature to obtain the current estimated heat value of the fuel cell includes:

[0103] Step S211, calculate the change rate of the water inlet temperature and the water outlet temperature to obtain the change rate of the water temperature of the fuel cell;

[0104] Step S212, based on the change rate and the historical water inlet temperature during the previous iterative calculation, obtain the current temperature of the current fuel cell;

[0105] Step S213, calculate the heat of the water temperature of the current fuel cell by calculating the change rate and the historical water inlet temperature;

[0106] Step S214, perform matrix calculation on the current temperature and the water temperature heat to obtain the current estimated heat value of the fuel cell.

[0107] It should be noted that since the fuel cell is unstable during actual operation, the changes in the water inlet temperature and the water outlet temperature are non - linear within a certain period of time. When obtaining the change rate calculation of the water inlet temperature, it is necessary to determine that the change rate can reflect the average change rate of the water inlet temperature. Specifically, it can be determined according to the variance, or a temperature time table can be generated for the water inlet temperature. Due to the instability of the fuel cell, the temperatures in the table are distributed in temperature change bands. Analyze the line in the middle of the temperature change bands in the temperature time table and determine the slope of this line to determine the change rate that can best reflect the average change of the water inlet temperature.

[0108] In this embodiment, the rate of change of the water temperature of the fuel cell is determined by the rates of change of the inlet water temperature and the outlet water temperature. Based on the rate of change and the inlet water temperature at the (k - 1)th moment, the temperature value of the fuel cell at the kth moment and the water temperature heat at the kth moment are obtained. The temperature value and the water temperature heat value are subjected to matrix calculation to obtain the estimated heat of the fuel cell.

[0109] Specifically, the step of matching the observed heat value of the current fuel cell from the preset experimental data based on the estimated heat value includes:

[0110] Step S221: Perform a scalar calculation on the estimated heat value to obtain the predicted power heat of the current fuel cell.

[0111] Step S222: Determine the power data corresponding to the predicted power heat.

[0112] Step S223: Based on the experimental data, after quantifying the power data, determine the observed heat value corresponding to the power data.

[0113] It should be noted that the data obtained from experiments in the laboratory are all pre - set sample point data for convenient analysis. There will be a deviation between the power data predicted and estimated from the actual operation data of the fuel cell and the sample point data. Therefore, when determining the observed heat value from the experimental data through the power data, the power data needs to be approximated, and the experimental data closest to the power data is selected from the experimental data, and the observed heat value is determined according to the experimental data.

[0114] In this embodiment, since the data recorded in the laboratory are all scalars, and the power data also corresponds to scalars, when determining the observed heat value through the estimated heat value, the estimated heat value needs to be tabulated and processed to obtain the scalar predicted power heat of the estimated heat value. Based on the predicted power heat, the corresponding power data is determined, and then based on the laboratory data, the observed heat value corresponding to the power data is determined.

[0115] Step S30: Perform PID control on the inlet water temperature and the outlet water temperature to obtain the error heat.

[0116] In this embodiment, during the operation of the hydrogen fuel cell, it may operate stably or unstably. During the stable operation of the hydrogen fuel cell, through PI control (proportional - integral control), the hydrogen fuel cell has almost no steady - state error after entering the steady state; during the unstable operation of the hydrogen fuel cell, through PD control (proportional - derivative control), the dynamic characteristics of the system during the adjustment process can be improved.

[0117] In this embodiment, since errors cannot be completely eliminated, in order to reduce errors, PID control is performed on the inlet water temperature and the outlet water temperature based on the calculated heat to obtain the error heat, and the error heat and the calculated heat are combined to determine the heat that most closely matches the actual heat of the fuel cell water temperature to be reduced.

[0118] Step S40: Using the calculated heat value and the error heat as references, determine the duty cycle for controlling the water temperature of the fuel cell to perform temperature cycle control on the water temperature of the fuel cell.

[0119] Among them, the duty cycle refers to the proportion of the energization time to the total time within a pulse cycle.

[0120] For example, if it takes 10 minutes to walk and 20 minutes to take the bus from home to the company, the duty cycle of walking is one-third.

[0121] Refer to Figure 4 , in this embodiment, the adjustment amount of PWM (Pulse Width Modulation) is determined through the calculated heat value and the error heat, and the temperature regulator is adjusted through PWM to keep the temperature of the hydrogen fuel cell stable within the target temperature range.

[0122] Among them, Figure 4 The "+" on the side away from the PWM in represents the inlet water temperature, and the "-" represents the outlet water temperature. It can also be understood as the temperature after Kalman adjustment. The "+" in the circle represents the node, and the "+" sign near the PWM represents the addition of the error heat and the calculated heat.

[0123] In this embodiment, the water temperature of the fuel cell is controlled in a cycle through the Kalman filter interval, PID control, and PWM to keep the temperature of the fuel cell stable.

[0124] In this embodiment, Kalman filter iteration calculations are performed on the inlet water temperature and the outlet water temperature of the fuel cell collected in real time to reduce the sudden change amount of the collected fuel cell temperature, eliminate the characteristic of the water temperature change delay caused by the long pipeline, and finally obtain the calculated heat value that most closely matches the actual situation, that is, the heat that needs to be dissipated by the fuel cell. Then, this heat is used as the feedforward of the PID control to reduce the error of the temperature change, and the error heat is determined. The calculated heat value and the error heat are combined to determine the adjustment amount required by the PWM, and the temperature regulator is adjusted through the PWM.

[0125] The present application provides a method, device, equipment and storage medium for controlling the water temperature of a fuel cell. Compared with the prior art, for a fuel cell such as a hydrogen fuel cell with a long pipeline and large delay, the water temperature control of the hydrogen fuel cell fluctuates greatly, thereby reducing the stability of the fuel cell. In the present application, the inlet water temperature and the outlet water temperature of the fuel cell are collected in real time, wherein the fuel cell includes an outlet. When the water temperature of the fuel cell rises to a preset target temperature range, the inlet water temperature and the outlet water temperature are subjected to Kalman filter iterative calculation to obtain the calculated heat value of the fuel cell at the outlet. PID control is performed on the inlet water temperature and the outlet water temperature to obtain an error heat. The calculated heat value and the error heat are used as references to determine the duty cycle for controlling the water temperature of the fuel cell, so as to perform temperature cycle control on the water temperature of the fuel cell. In the present application, after the water temperature of the fuel cell rises to the preset target temperature range, the collected inlet and outlet water temperatures and the outlet water temperature of the fuel cell are subjected to Kalman iterative calculation to obtain the calculated heat value closest to the true value at the outlet of the fuel cell. Then, by performing PID control on the outlet water temperature and the inlet water temperature, an error heat is obtained. The calculated heat value and the error heat are used as feedforward to control the water temperature of the fuel cell. That is, in the present application, Kalman iterative calculation is performed on the inlet water temperature and the outlet water temperature to reduce the sudden change amount, eliminate the delay and retention of the water temperature change caused by the long pipeline water path, and the obtained calculated heat value is more in line with the actual situation. Then, the calculated heat value and the error heat obtained by PID control are used as feedforward to control the water temperature of the fuel cell. Therefore, the temperature control fluctuation is reduced, thereby improving the stability of the fuel cell.

[0126] Further, based on the first embodiment of the present application, another embodiment of the present application is provided. In this embodiment, the method for controlling the water temperature of a fuel cell further includes:

[0127] Step S01, perform error analysis on the estimated heat to obtain a prediction error when performing state prediction on the inlet water temperature;

[0128] In this embodiment, since the estimated heat is obtained through state prediction, there will be a prediction error, that is, a prediction error, in the prediction process. The covariance transfer formula is determined by calculating the covariance through the inlet water temperature to determine the estimated prediction error.

[0129] Specifically, the covariance transfer formula is:

[0130]

[0131] Wherein, is the transfer covariance at time k, is the updated covariance at time k-1, is the transpose of the F matrix. For example, the transpose matrix of the matrix [1, 0] is .

[0132] Step S02, based on the budget to update the covariance, perform iterative covariance calculation on the prediction error to obtain the transfer covariance;

[0133] Among them, the updated covariance needs to be calculated through the covariance update formula by the Kalman coefficient and the transfer covariance.

[0134] Specifically, the covariance update formula is:

[0135]

[0136] Among them, is the Kalman coefficient at time k.

[0137] In this embodiment, I is a two-row and two-column matrix with a scalar value of 1. The iterative calculation of the covariance can be achieved by updating the covariance.

[0138] Step S03, perform error analysis on the observed heat value to obtain the running error of the experimental data;

[0139] In this embodiment, the experimental data obtained through laboratory experiments is compared and analyzed with the experimental data in the real environment obtained through off-site experiments to obtain the running error. It should be noted that this running error is the same error as the error R in Embodiment 1.

[0140] Step S04, perform iterative coefficient calculation on the transfer covariance and the running error to obtain the Kalman coefficient.

[0141] In this embodiment, after quantifying the transfer covariance at time k, the Kalman coefficient at time k is calculated by combining the running error through the Kalman coefficient calculation formula.

[0142] Specifically, the Kalman coefficient calculation formula is:

[0143] *

[0144] Or *

[0145] In this embodiment, through the inlet water temperature, error analysis and calculation are performed to determine the covariance transfer formula, and the basis for iterative calculation of the transfer covariance is calculated through the covariance update formula. Finally, in combination with the running error, the Kalman coefficient at each moment is iteratively calculated, and the Kalman coefficient is applied to the heat optimal formula in Embodiment 1.

[0146] Specifically, the step of performing iterative covariance calculation on the prediction error based on the budget-based updated covariance to obtain the transfer covariance includes:

[0147] Step S021: Obtain the historical covariance of the previous iterative covariance calculation and the historical Kalman coefficient of the previous iterative coefficient calculation;

[0148] Step S022: Perform covariance calculation on the historical covariance and the historical Kalman coefficient to obtain the updated covariance;

[0149] Step S023: Perform variance calculation on the updated covariance and the prediction error to obtain the transfer covariance.

[0150] In this embodiment, the transfer covariance at the k-th moment is obtained through the updated covariance calculation at the k-1 moment, completing the iterative calculation of the covariance, making the calculated calorific value more accurate.

[0151] In this embodiment, the transfer covariance is iteratively calculated through the inlet water temperature, and the Kalman coefficient is calculated through the transfer covariance and the running error. In this embodiment, the iterative Kalman coefficient is calculated through the transfer covariance obtained by iterative calculation, enabling the real-time update of the Kalman coefficient and ensuring the accuracy of the calculated calorific value.

[0152] Furthermore, based on the first embodiment of the present application, another embodiment of the present application is provided. In this embodiment, through five formulas including the state prediction formula, the covariance transfer formula, the Kalman coefficient calculation formula, the optimal calorific value calculation formula, and the covariance update formula, iterative calculation is performed on the fuel cell, and the optimal calculated calorific value for calorific value calculation can be obtained.

[0153] Refer to Figure 6 , in this embodiment, the fuel cell inlet temperature, the fuel cell outlet temperature, and the fuel cell voltage and current are collected by the domain controller. The first state equation and the second state equation are constructed through the fuel cell inlet temperature and the fuel cell outlet temperature, and the first state equation and the second state equation are expressed in matrix form to obtain the state prediction matrix expression formula. To reduce the recognition difficulty, the state prediction matrix expression formula is transformed into the state prediction formula. Since there will be prediction errors during the prediction process and the values of the prediction errors are regularly scattered, statistical processing is performed on the prediction errors to obtain the covariance transfer formula that can accurately represent the prediction errors. Finally, based on the state prediction formula combined with the Kalman filter, five formulas including the Kalman coefficient calculation formula, the optimal calorific value calculation formula, and the covariance update formula are obtained.

[0154] It should be noted that the above five formulas are all derived through the Kalman filtering theory. Since Kalman filtering is an algorithm that can perform optimal estimation of the system state, applying Kalman filtering to fuel cells can estimate the most realistic heat dissipation of fuel cells. Moreover, the algorithm is simple, does not require building a data model, and does not need to change the PID parameters according to the data model.

[0155] Referring to Figure 3 , Figure 3 is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present application.

[0156] As Figure 3 shown, the water temperature control device of the fuel cell may include: a processor 1001, such as a CPU, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0157] Optionally, the water temperature control device of the fuel cell may further include a rectangular user interface, a network interface, a camera, an RF (Radio Frequency) circuit, sensors, an audio circuit, a WiFi module, and so on. The rectangular user interface may include a display screen (Display) and an input sub-module such as a keyboard (Keyboard). Optionally, the rectangular user interface may further include a standard wired interface and a wireless interface. The network interface may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0158] Those skilled in the art can understand that Figure 3 the structure of the water temperature control device of the fuel cell shown in

[0159] As Figure 3 shown, in the memory 1005 as a storage medium, there may be included an operating system, a network communication module, and a water temperature control program for the fuel cell. The operating system is a program for managing and controlling the hardware and software resources of the water temperature control device of the fuel cell, and supports the operation of the water temperature control program for the fuel cell and other software and / or programs. The network communication module is used to realize the communication between the components inside the memory 1005 and the communication between the memory 1005 and other hardware and software in the water temperature control system of the fuel cell.

[0160] In Figure 3In the water temperature control device of the fuel cell shown, the processor 1001 is used to execute the water temperature control program of the fuel cell stored in the memory 1005 to implement the steps of the water temperature control method of the fuel cell described in any one of the above.

[0161] The specific implementation manner of the water temperature control device of the fuel cell in this application is basically the same as each embodiment of the above water temperature control method of the fuel cell, and will not be elaborated here.

[0162] This application also provides a water temperature control device for a fuel cell. The water temperature control device for the fuel cell includes:

[0163] An acquisition module, configured to acquire the inlet water temperature and the outlet water temperature of the fuel cell in real time, where the fuel cell includes an outlet;

[0164] A Kalman calculation module, configured to perform Kalman filter iterative calculation on the inlet water temperature and the outlet water temperature when the water temperature of the fuel cell rises to a preset target temperature range to obtain a calculated heat value of the fuel cell at the outlet;

[0165] A first control module, configured to perform PID control on the inlet water temperature and the outlet water temperature to obtain an error heat;

[0166] A second control module, configured to determine a duty ratio for controlling the water temperature of the fuel cell with reference to the calculated heat value and the error heat, so as to perform temperature cycle control on the water temperature of the fuel cell.

[0167] Optionally, the calculation module includes:

[0168] A prediction calculation module, configured to perform iterative state prediction calculation on the inlet water temperature and the outlet water temperature to obtain an estimated heat value of the current fuel cell;

[0169] A matching module, configured to match an observed heat value of the current fuel cell from preset experimental data based on the estimated heat value;

[0170] An optimization calculation module, configured to perform real-time iterative optimization calculation on the estimated heat value and the observed heat value based on a budgeted Kalman coefficient to obtain a calculated heat value of the fuel cell at the outlet.

[0171] Optionally, the matching module includes:

[0172] A scalar calculation, configured to perform scalar calculation on the estimated heat value to obtain a predicted power heat of the current fuel cell;

[0173] A first determination module, configured to determine power data corresponding to the predicted power heat;

[0174] A quantization module, configured to perform quantization processing on the power data based on the experimental data, and then determine an observed heat value corresponding to the power data.

[0175] Optionally, the water temperature control device of the fuel cell further includes:

[0176] A first analysis module, configured to perform error analysis on the estimated heat to obtain a prediction error when predicting the state of the inlet water temperature.

[0177] A covariance calculation module, configured to perform iterative covariance calculation on the prediction error based on a budgeted updated covariance to obtain a transfer covariance.

[0178] A second analysis module, configured to perform error analysis on the observed heat value to obtain an operation error of the experimental data.

[0179] A coefficient calculation module, configured to perform iterative coefficient calculation on the transfer covariance and the operation error to obtain a Kalman coefficient.

[0180] Optionally, the covariance calculation module includes:

[0181] An acquisition module, configured to acquire a historical covariance of a previous iterative covariance calculation and a historical Kalman coefficient of a previous iterative coefficient calculation.

[0182] A first calculation sub-module, configured to perform covariance calculation on the historical covariance and the historical Kalman coefficient to obtain the updated covariance.

[0183] A variance calculation module, configured to perform variance calculation on the updated covariance and the prediction error to obtain a transfer covariance.

[0184] Optionally, the second control module includes:

[0185] A second calculation sub-module, configured to add the calculated heat value and the error heat to obtain a control energy for controlling the water temperature of the fuel cell.

[0186] A second determination module, configured to determine, based on the control energy, a duty cycle for controlling the water temperature in the fuel cell during a single water circulation process.

[0187] A control sub-module, configured to determine, based on the duty cycle, an output power of a temperature control module for controlling the water temperature of the fuel cell to control the stability of the water temperature of the fuel cell.

[0188] Optionally, the prediction calculation module includes:

[0189] Rate of change calculation is used to calculate the rate of change of the temperature at the water inlet and the temperature at the water outlet to obtain the rate of change of the water temperature of the fuel cell;

[0190] The first determination sub-module is used to obtain the current temperature of the current fuel cell based on the rate of change and the historical water inlet temperature during the previous iterative calculation;

[0191] The heat calculation module is used to calculate the heat of the water temperature of the fuel cell by calculating the rate of change and the historical water inlet temperature;

[0192] Matrix calculation is used to perform matrix calculation on the current temperature and the heat of the water temperature to obtain the estimated heat value of the current fuel cell.

[0193] The specific implementation manner of the water temperature control device of the fuel cell in this application is basically the same as that of each embodiment of the above-mentioned water temperature control method of the fuel cell, and will not be elaborated here.

[0194] The embodiments of this application provide a storage medium, and the storage medium stores one or more programs, and the one or more programs can also be executed by one or more processors to be used to implement the steps of the water temperature control method of the fuel cell described in any one of the above.

[0195] The specific implementation manner of the storage medium of this application is basically the same as that of each embodiment of the above-mentioned water temperature control method of the fuel cell, and will not be elaborated here.

[0196] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including that element.

[0197] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0198] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0199] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for controlling the water temperature of a fuel cell, characterized in that, The method for controlling the water temperature of the fuel cell includes: Collecting the inlet water temperature and the outlet water temperature of the fuel cell in real time, where the fuel cell includes an outlet; When the water temperature of the fuel cell rises to a preset target temperature range, performing Kalman filter iterative calculation on the inlet water temperature and the outlet water temperature to obtain the calculated heat value of the fuel cell at the outlet; Performing PID control on the inlet water temperature and the outlet water temperature to obtain an error heat; Using the calculated heat value and the error heat as references to determine the duty cycle for controlling the water temperature of the fuel cell, so as to perform temperature cycle control on the water temperature of the fuel cell.

2. The method for controlling the water temperature of a fuel cell according to claim 1, characterized in that, The step of performing Kalman filter iterative calculation on the inlet water temperature and the outlet water temperature to obtain the calculated heat value of the fuel cell at the outlet includes: Performing iterative state prediction calculation on the inlet water temperature and the outlet water temperature to obtain the estimated heat value of the fuel cell at present; Based on the estimated heat value, matching the observed heat value of the current fuel cell from the preset experimental data; Performing real-time iterative optimization calculation on the estimated heat value and the observed heat value based on the budgeted Kalman coefficient to obtain the calculated heat value of the fuel cell at the outlet.

3. The method for controlling the water temperature of a fuel cell according to claim 2, characterized in that, The step of matching the observed heat value of the current fuel cell from the preset experimental data based on the estimated heat value includes: Performing scalar calculation on the estimated heat value to obtain the predicted power heat of the current fuel cell; Determining the power data corresponding to the predicted power heat; Based on the experimental data, after quantifying the power data, determining the observed heat value corresponding to the power data.

4. The method for controlling the water temperature of a fuel cell according to claim 2, characterized in that, Before the step of performing real-time iterative update optimization calculation on the estimated heat value and the observed heat value to obtain the calculated heat value of the fuel cell at the outlet, the method includes: Performing error analysis on the estimated heat to obtain the prediction error when performing state prediction on the inlet water temperature; Performing iterative covariance calculation on the prediction error based on the budgeted updated covariance to obtain the transfer covariance; Performing error analysis on the observed heat value to obtain the operation error of the experimental data; Performing iterative coefficient calculation on the transfer covariance and the operation error to obtain the Kalman coefficient.

5. The method for controlling the water temperature of a fuel cell according to claim 4, characterized in that, The step of performing iterative covariance calculation on the prediction error based on the budgeted updated covariance to obtain the transfer covariance includes: Obtaining the historical covariance of the previous iterative covariance calculation and the historical Kalman coefficient of the previous iterative coefficient calculation; Performing covariance calculation on the historical covariance and the historical Kalman coefficient to obtain the updated covariance; Performing variance calculation on the updated covariance and the prediction error to obtain the transfer covariance.

6. The method for controlling the water temperature of a fuel cell according to claim 1, characterized in that, The step of using the calculated heat value and the error heat as references to determine the duty cycle for controlling the water temperature of the fuel cell, so as to perform temperature cycle control on the water temperature of the fuel cell includes: Add the calculated heat value and the error heat value to obtain the control energy for controlling the water temperature of the fuel cell. Based on the control energy, determine the duty cycle for controlling the water temperature in the fuel cell during the water circulation process; Based on the duty cycle, determine the output power of the temperature control module for controlling the water temperature of the fuel cell to control the stability of the water temperature of the fuel cell.

7. The method for controlling the water temperature of a fuel cell according to claim 2, characterized in that, The step of performing iterative state prediction calculation on the inlet water temperature and the outlet water temperature to obtain the estimated heat value of the current fuel cell includes: Calculate the change rate of the inlet water temperature and the outlet water temperature to obtain the change rate of the water temperature of the fuel cell; Based on the change rate and the historical inlet water temperature during the previous iterative calculation, obtain the current temperature of the current fuel cell; Calculate the heat of the water temperature of the fuel cell by calculating the change rate and the historical inlet water temperature; Perform matrix calculation on the current temperature and the water temperature heat to obtain the estimated heat value of the current fuel cell.

8. A device for controlling the water temperature of a fuel cell, characterized in that,The water temperature control device of the fuel cell includes: An acquisition module for real-time acquisition of the inlet water temperature and the outlet water temperature of the fuel cell, wherein the fuel cell includes an outlet; A Kalman calculation module for performing Kalman filter iterative calculation on the inlet water temperature and the outlet water temperature when the water temperature of the fuel cell rises to a preset target temperature range to obtain the calculated heat value of the fuel cell at the outlet; A first control module for performing PID control on the inlet water temperature and the outlet water temperature to obtain an error heat; A second control module for determining the duty cycle for controlling the water temperature of the fuel cell with the calculated heat value and the error heat as references to perform temperature cycle control on the water temperature of the fuel cell.

9. A water temperature control device for a fuel cell, characterized in that, The water temperature control device of the fuel cell includes: a memory, a processor, and a program stored on the memory for implementing the water temperature control method of the fuel cell, The memory is used for storing the program for implementing the water temperature control method of the fuel cell; The processor is used for executing the program for implementing the water temperature control method of the fuel cell to implement the steps of the water temperature control method of the fuel cell as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, A program for implementing the water temperature control method of the fuel cell is stored on the storage medium, and the program for implementing the water temperature control method of the fuel cell is executed by the processor to implement the steps of the water temperature control method of the fuel cell as described in any one of claims 1 to 7.

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