Fuel cell waste heat recycling device

Abstract

The invention relates to the technical field of heat utilization, and discloses a fuel cell waste heat recycling device which comprises a fuel cell, a storage battery, a heat exchanger, a temperature sensing optical cable, a high-temperature cable and a temperature processor, the heat exchanger winds the fuel cell and is fixedly connected with the fuel cell, one end of the high-temperature cable is fixedly connected with the heat exchanger, and the other end of the high-temperature cable is fixedly connected with the temperature processor. One end of the high-temperature cable is fixedly connected with the fuel cell, the other end of the high-temperature cable is fixedly connected with the storage battery, the high-temperature cable is used for transmitting electric energy of the heat exchanger, the temperature sensing optical cable is embedded in the fuel cell, the temperature sensing optical cable is arranged in a U shape in the fuel cell, the temperature sensing optical cable is fixedly connected with the temperature processor, and the temperature processor is used for obtaining temperature data of the temperature sensing optical cable. The heat exchanger is wound around the fuel cell, the temperature processor is used for collecting temperature data, and the device has the advantages of good waste heat utilization and temperature monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of heat utilization, and in particular to a fuel cell waste heat recycling device. Background Art

[0002] With the rapid development of the global economy, energy consumption has increased dramatically, traditional fossil energy such as oil and coal are becoming increasingly depleted, and the energy crisis is becoming more severe. With the advancement of science and technology, fuel cells, as an efficient and environmentally friendly energy conversion device, are gradually becoming the key to solving the energy crisis and reducing carbon emissions. Fuel cells are a chemical device that directly converts the chemical energy of fuel into electrical energy, also known as electrochemical generators. It is the fourth power generation technology after hydropower, thermal power generation and atomic power generation. Fuel cells are a device that directly converts the chemical energy of fuel into electrical energy. It can continuously output electrical energy by simply introducing fuel and oxidant. However, every time a fuel cell generates electrical energy, it also generates a large amount of heat energy.

[0003] Traditional devices cannot reasonably utilize the waste heat of fuel cells, resulting in a waste of resources. If this heat is not effectively managed and utilized, it will not only reduce the efficiency of the fuel cell, but also have a negative impact on the stability of the fuel cell. In addition, the temperature distribution inside the fuel cell is crucial to its performance and life. Traditional temperature acquisition faces inaccurate temperature acquisition and cannot obtain the accurate temperature value inside the fuel cell.

[0004] Therefore, how to provide a fuel cell waste heat recycling device is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In view of this, the present invention proposes a fuel cell waste heat recycling device, which aims to solve the problem of how to efficiently recover and utilize the heat generated by the fuel cell and accurately monitor the internal temperature of the fuel cell.

[0006] The present invention proposes a fuel cell waste heat recycling device, comprising: Fuel cells, batteries, heat exchangers, temperature sensing optical cables, high temperature cables and temperature processors; The heat exchanger is wound around the fuel cell and fixedly connected to the fuel cell, one end of the high-temperature cable is fixedly connected to the heat exchanger, and the other end of the high-temperature cable is fixedly connected to the battery, and the high-temperature cable is used to transmit electrical energy from the heat exchanger; The temperature sensing optical cable is embedded in the fuel cell, the temperature sensing optical cable is arranged in a U shape in the fuel cell, and the temperature sensing optical cable is fixedly connected to the temperature processor; The temperature sensing optical cable comprises a PE sheath, a carbon fiber bundle and a temperature sensing optical fiber; The PE sheath is sheathed with the carbon fiber bundle, and the carbon fiber bundle is sheathed with the temperature sensing optical fiber; The temperature processor is used to obtain temperature data of the temperature sensing optical fiber.

[0007] Furthermore, the temperature processor is provided with a temperature acquisition system, and the temperature acquisition system comprises: Data acquisition unit, temperature judgment unit, temperature prediction unit and temperature processing unit; The data acquisition unit is configured to acquire temperature data of the temperature sensing optical fiber and pre-process and obtain an initial temperature value; The temperature judgment unit is configured to compare the initial temperature value with the historical data, and judge whether to correct the initial temperature value based on the comparison result, and when it is judged that the initial temperature value is to be corrected, determine the temperature data set by a clustering algorithm, and correct the initial temperature value according to the temperature data set to obtain a corrected temperature value; The temperature prediction unit is configured to establish a random forest model according to the historical temperature value data and obtain a temperature prediction value according to the initial temperature value; The temperature processing unit is configured to compare the corrected temperature value with the temperature prediction value, establish a temperature compensation model according to the comparison result, obtain a temperature compensation value according to the temperature compensation model, determine a temperature optimization coefficient according to the temperature compensation value, optimize the corrected temperature value according to the temperature optimization coefficient to obtain a target temperature value, and the target temperature value is the product of the corrected temperature value and the temperature optimization coefficient.

[0008] Furthermore, when comparing the initial temperature value with the historical data and determining whether to correct the initial temperature value based on the comparison result, the method includes: The historical data includes a minimum value of historical initial temperature data, an average value of historical initial temperature data, historical initial temperature correction data and a historical correction factor, and the historical initial temperature correction data and the historical correction factor correspond to each other one by one; When the initial temperature value is greater than or equal to the minimum value of the historical initial temperature data and greater than or equal to the average value of the historical initial temperature data, it is determined that the initial temperature value is not to be corrected, and the initial temperature value is determined as the target temperature value; When the initial temperature value is less than the minimum value of the historical initial temperature data, it is determined that the initial temperature value is to be corrected.

[0009] Furthermore, when the temperature data set is determined by the clustering algorithm, it includes: The initial temperature value to be corrected and the historical data are used as the data set to be aggregated, and the historical correction factor corresponding to each data in the data set to be aggregated is extracted, the expected number of clusters k is determined to be 3, and the parameters of the Gaussian distribution are initialized; The probability that each data in the data set to be aggregated belongs to each Gaussian distribution is calculated to obtain a responsibility value, and a corrected data set corresponding to the initial temperature value to be corrected is obtained according to the responsibility value, and the corrected data set is used as the temperature data set.

[0010] Further, when the initial temperature value is corrected according to the temperature data set to obtain a corrected temperature value, it includes: Determining, according to the average of the historical correction factors in the temperature data set, a correction factor for the initial temperature value to be corrected; The corrected temperature value is the product of the initial temperature value to be corrected and the correction factor.

[0011] Further, when a random forest model is established according to historical temperature value data and a temperature prediction value is obtained according to the initial temperature value, it includes: The historical temperature value data is divided into a temperature training set and a temperature test set, and a model establishment parameter of a random forest model is found by cross validation combined with a grid search to establish a random forest model; Substituting the temperature training set into the random forest model to fit the random forest model, substituting the temperature test set into the fitted random forest model to obtain the prediction accuracy of the temperature prediction value; When the prediction accuracy reaches a preset accuracy threshold, a temperature prediction value at the current moment is obtained according to the initial temperature value.

[0012] Furthermore, when comparing the corrected temperature value with the predicted temperature value and establishing a temperature compensation model according to the comparison result, it includes: When the corrected temperature value is less than or equal to 1.5 times of the predicted temperature value, it is determined that the corrected temperature value is not to be optimized, and the corrected temperature value is determined as the target temperature value; When the corrected temperature value is greater than 1.5 times the predicted temperature value, it is determined that the corrected temperature value is to be optimized and a temperature compensation model is to be established.

[0013] Furthermore, when determining to optimize the corrected temperature value and establish a temperature compensation model, it includes: Counting historical corrected temperature value data and historical temperature prediction value data optimized for all historical corrected temperature values, wherein the historical corrected temperature value data and the historical temperature prediction value data correspond to each other one by one; The temperature compensation model adopts a linear regression model, which is established by the following formula: ; in, Indicates any historical corrected temperature value data, Represents historical temperature forecast data, Indicates the temperature compensation value, Represents the intercept of the linear regression model.

[0014] Furthermore, when the temperature compensation value is obtained according to the temperature compensation model, it includes: All historical corrected temperature value data and all historical temperature prediction value data are substituted into the linear regression model one by one and difference calculation is performed to eliminate the intercept of the linear regression model and obtain the temperature compensation value T.

[0015] Further, when determining the temperature optimization coefficient according to the temperature compensation value, it includes: Presetting a first preset optimization coefficient, a second preset optimization coefficient, and a third preset optimization coefficient; When T≤2, the first preset optimization coefficient is used as the temperature optimization coefficient of the corrected temperature value; When 2<T≤4, the second preset optimization coefficient is used as the temperature optimization coefficient of the corrected temperature value; When 4<T, the third preset optimization coefficient is used as the temperature optimization coefficient of the corrected temperature value.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: by winding the fuel cell with a heat exchanger and converting the waste heat into electrical energy and transmitting it to the battery, the utilization rate of the waste heat energy of the fuel cell is improved, and efficient energy utilization is achieved. The temperature sensing optical cable is arranged in a U-shape inside the fuel cell, and then the distributed temperature sensing optical cable and the temperature processor are connected to form a comprehensive optical fiber monitoring network, which improves the efficiency of the temperature sensing optical cable in monitoring temperature and monitors the internal temperature of the fuel cell in real time. It has the characteristics of distributed and comprehensive coverage, realizes all-round monitoring of the inside of the fuel cell, and improves the accuracy and reliability of obtaining the internal temperature of the fuel cell in a high temperature environment. The temperature processor is used to analyze the collected temperature data to accurately obtain the actual temperature value inside the fuel cell, thereby realizing automation and intelligence of fuel cell temperature monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1A schematic structural diagram of a fuel cell waste heat recycling device provided by an embodiment of the present invention; Figure 2 A schematic diagram of the internal structure of a fuel cell provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a temperature sensing optical cable provided by an embodiment of the present invention; Figure 4 A schematic diagram of the structure of a temperature processor provided by an embodiment of the present invention; Figure 5 This is a functional block diagram of a temperature acquisition system provided in an embodiment of the present invention.

[0018] In the figure, 1. fuel cell; 2. heat exchanger; 3. high-temperature cable; 4. battery; 5. temperature processor; 6. temperature sensing optical cable; 7. PE sheath; 8. temperature sensing optical fiber; 9. carbon fiber bundle. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] See also Figure 1-3 As shown, in some embodiments of the present application, a fuel cell waste heat recycling device includes: a fuel cell 1, a battery 4, a heat exchanger 2, a temperature sensing optical cable 6, a high-temperature cable 3 and a temperature processor 5; the heat exchanger 2 is wrapped around the fuel cell 1 and fixedly connected to the fuel cell 1, one end of the high-temperature cable 3 is fixedly connected to the heat exchanger 2, and the other end of the high-temperature cable 3 is fixedly connected to the battery 4, and the high-temperature cable 3 is used to transmit the electric energy of the heat exchanger 2; the temperature sensing optical cable 6 is embedded in the interior of the fuel cell 1, the temperature sensing optical cable 6 is U-shapedly arranged inside the fuel cell 1, and the temperature sensing optical cable 6 is fixedly connected to the temperature processor 5; the temperature sensing optical cable 6 includes a PE sheath 7, a carbon fiber bundle 9 and a temperature sensing optical fiber 8; the PE sheath 7 is sheathed with the carbon fiber bundle 9, and the carbon fiber bundle 9 is sheathed with the temperature sensing optical fiber 8; the temperature processor 5 is used to obtain the temperature data of the temperature sensing optical fiber 8.

[0021] Specifically, the fuel cell 1 generates electrical energy through chemical reactions and releases a large amount of waste heat in the chemical process. The heat exchanger 2 is fixedly connected to the fuel cell 1 in a winding connection mode, which not only enhances the compactness of the device, but also improves the efficiency of the heat exchanger 2 in recovering the excess heat energy of the fuel cell 1. The waste heat generated by the fuel cell 1 is recovered by the heat exchanger 2, and the heat is transferred to the thermoelectric material inside the heat exchanger 2. The Seebeck effect occurs by utilizing the temperature difference between the thermoelectric materials to generate electrical energy, and the electrical energy is transmitted to the battery 4 through the high-temperature cable 3. The battery 4 is used to store the electrical energy converted by the heat exchanger 2, thereby realizing the recycling of waste heat and energy saving and emission reduction. The high-temperature cable 3 has high temperature resistance and good electrical conductivity, which enables it to operate stably in the high-temperature environment generated by the fuel cell 1, ensuring the transmission of electrical energy. The temperature sensing optical cable 6 is embedded in the fuel cell 1 and arranged in a U shape inside the fuel cell 1. The diameter of the temperature sensing optical cable 6 is 10 mm and the weight is 90 kg / km. The temperature sensing optical cable 6 includes a PE sheath 7, a carbon fiber bundle 9 and a temperature sensing optical fiber 8. The temperature sensing optical cable 6 has excellent high temperature resistance and anti-interference capabilities. The PE sheath 7 plays an external protection role to prevent the high temperature environment inside the fuel cell 1 from damaging the temperature sensing optical fiber 8. The carbon fiber bundle 9 enhances the structural strength of the temperature sensing optical cable 6. The temperature sensing optical fiber 8 is responsible for converting the internal temperature of the fuel cell 1 into temperature data. Through the U-shaped arrangement, the temperature sensing optical fiber 8 in the temperature sensing optical cable 6 can not only cover various areas of the fuel cell 1, but also monitor the temperature changes inside the fuel cell 1 in real time and accurately, providing reliable data support for the health management of the fuel cell 1. At the same time, through the U-shaped arrangement, the temperature sensing optical fiber 8 can also accurately locate the position of the abnormal temperature inside the fuel cell 1, which helps to discover potential safety hazards in time. The temperature processor 5 and the temperature sensing optical cable 6 are fixedly connected to obtain the temperature data collected by the temperature sensing optical fiber 8 , thereby analyzing and processing the temperature data, and providing data support for the operation of the fuel cell 1 .

[0022] It can be understood that by monitoring the temperature changes inside the fuel cell 1 in real time through the temperature sensing optical fiber 8 and transmitting the data to the temperature processor 5 for data processing, the specific temperature value inside the fuel cell 1 can be accurately obtained, providing accurate temperature data for the health management of the fuel cell 1, ensuring that the fuel cell 1 operates within a safe and efficient working range, so that the device has good energy efficiency improvement effects, excellent temperature control performance and stability, can effectively recover and utilize the waste heat of the fuel cell 1 in a variety of application scenarios, and provide accurate temperature data inside the fuel cell 1.

[0023] See also Figure 4-5As shown, in some embodiments of the present application, the temperature processor 5 is provided with a temperature acquisition system, and the temperature acquisition system includes: a data acquisition unit, a temperature judgment unit, a temperature prediction unit and a temperature processing unit; the data acquisition unit is configured to collect temperature data of the temperature sensing optical fiber 8 and pre-process to obtain an initial temperature value; the temperature judgment unit is configured to compare the initial temperature value with the historical data, and judge whether to correct the initial temperature value based on the comparison result, when it is judged that the initial temperature value is to be corrected, determine the temperature data set through a clustering algorithm, and correct the initial temperature value according to the temperature data set to obtain a corrected temperature value; the temperature prediction unit is configured to establish a random forest model according to the historical temperature value data and obtain a temperature prediction value according to the initial temperature value; the temperature processing unit is configured to compare the corrected temperature value with the temperature prediction value, establish a temperature compensation model according to the comparison result, obtain a temperature compensation value according to the temperature compensation model, determine a temperature optimization coefficient according to the temperature compensation value, optimize the corrected temperature value according to the temperature optimization coefficient to obtain a target temperature value, and the target temperature value is the product value of the corrected temperature value and the temperature optimization coefficient.

[0024] Specifically, the initial temperature value is obtained by preprocessing the temperature data, which improves the accuracy of obtaining the initial temperature value. The preprocessing includes data cleaning and standardization. Data cleaning removes the noise of the temperature data and eliminates data redundancy, and standardization unifies the temperature data format, so that the format of the initial temperature value remains unified, laying a data foundation for subsequent analysis. The temperature judgment unit compares the initial temperature value with the historical data, and uses a clustering algorithm to determine the temperature data set and correct it, effectively solving the problem of deviation in obtaining the internal temperature due to accidental errors or environmental fluctuations in the temperature sensing optical fiber 8. The temperature prediction unit establishes a random forest model and predicts the temperature prediction value in combination with the historical temperature value data, providing a reference benchmark for obtaining an accurate temperature value. The temperature processing unit establishes a temperature compensation model and calculates the temperature compensation value based on the comparison results of the corrected temperature value and the temperature prediction value, thereby determining the temperature optimization coefficient. Through the dynamic temperature compensation mechanism, the system can adjust the deviation of the corrected temperature value in real time, ensuring the accuracy of the target temperature value.

[0025] It can be understood that the introduction of the temperature optimization coefficient improves the accuracy of the target temperature value. Through the clustering algorithm and random forest model in the temperature acquisition system, the system can flexibly adjust the temperature correction strategy according to different working environments and historical data. It has good adaptability and can cope with temperature changes inside the fuel cell 1, thereby obtaining an accurate target temperature value.

[0026] In some embodiments of the present application, when comparing the initial temperature value with historical data and judging whether to correct the initial temperature value based on the comparison result, it includes: the historical data includes the minimum value of the historical initial temperature data, the average value of the historical initial temperature data, the historical initial temperature correction data and the historical correction factor, and the historical initial temperature correction data and the historical correction factor correspond to each other one by one; when the initial temperature value is greater than or equal to the minimum value of the historical initial temperature data and greater than or equal to the average value of the historical initial temperature data, it is judged that the initial temperature value is not corrected, and the initial temperature value is determined as the target temperature value; when the initial temperature value is less than the minimum value of the historical initial temperature data, it is judged that the initial temperature value is corrected.

[0027] It is understandable that using the mean value of historical initial temperature data and the minimum value of historical initial temperature data as reference standards can reflect the deviation between the initial temperature value and historical data. When the initial temperature value is less than the minimum value of historical initial temperature data, it means that it has deviated from the normal range of history and an extreme situation has occurred. Dynamic correction reduces the interference and error of human judgment, enhances the system's adaptability to different initial temperature values, and improves the system's adaptability and accuracy.

[0028] In some embodiments of the present application, when determining a temperature data set through a clustering algorithm, it includes: taking the initial temperature value to be corrected and the historical data as the data set to be aggregated, and extracting the historical correction factor corresponding to each data in the data set to be aggregated, determining the expected number of clusters k to be 3, and initializing the parameters of the Gaussian distribution; calculating the probability that each data in the data set to be aggregated belongs to each Gaussian distribution, obtaining the responsibility value, obtaining the corrected data set corresponding to the initial temperature value to be corrected according to the responsibility value, and using the corrected data set as the temperature data set.

[0029] In some embodiments of the present application, when the initial temperature value is corrected according to the temperature data set to obtain the corrected temperature value, it includes: determining the correction factor of the initial temperature value to be corrected according to the average of the historical correction factors in the temperature data set; and the corrected temperature value is the product of the initial temperature value to be corrected and the correction factor.

[0030] It can be understood that by analyzing historical data through clustering algorithms, the correction data set closest to the initial temperature value to be corrected is found, the accuracy of determining the correction factor is improved, and the mean of the historical correction factors is determined as the correction factor of the initial temperature value to be corrected, ensuring that the correction process is consistent with the historical range, and combining historical multivariate data to obtain the correction factor, reflecting the comprehensiveness of the obtained correction factor. The dynamic correction mechanism ensures that under different initial temperature values ​​to be corrected, the correction factor can be adaptively found and the initial temperature value to be corrected can be corrected, reducing the reliance on human experience and judgment, reducing human judgment errors and accidental errors of the temperature sensing optical fiber 8, and improving the automation level and reliability of the system.

[0031] In some embodiments of the present application, when a random forest model is established based on historical temperature value data and a temperature prediction value is obtained based on an initial temperature value, it includes: dividing the historical temperature value data into a temperature training set and a temperature test set, using cross-validation and combining grid search to find the model establishment parameters of the random forest model, and establishing a random forest model; substituting the temperature training set into the random forest model to fit the random forest model, substituting the temperature test set into the fitted random forest model and obtaining the prediction accuracy of the temperature prediction value; when the prediction accuracy reaches a preset accuracy threshold, obtaining the temperature prediction value at the current moment based on the initial temperature value.

[0032] It is understandable that the historical temperature value data is divided into a temperature training set and a temperature test set, and 60% to 90% of the data is used as the temperature training set, and the rest is used as the temperature test set. It is ensured that the temperature training set and the temperature test set contain a variety of historical temperature values, thereby improving the generalization ability of the random forest model. Cross-validation training and grid search ensure the appropriate parameters of the random forest model and the balance in the prediction process. The temperature training set is used to fit the random forest model, which improves the accuracy and stability of the random forest model prediction. The temperature test set is substituted into the trained random forest model and the prediction accuracy of the random forest model is calculated. The prediction accuracy reflects the performance of the random forest model on unknown data and is a reference benchmark for evaluating the performance of the random forest model. After the random forest model reaches the preset accuracy threshold, the initial temperature value is substituted into the random forest model to obtain the temperature prediction value at the current moment.

[0033] In some embodiments of the present application, when the corrected temperature value and the temperature prediction value are compared and a temperature compensation model is established based on the comparison result, it includes: when the corrected temperature value is less than or equal to 1.5 times the temperature prediction value, it is determined not to optimize the corrected temperature value, and the target temperature value is determined based on the corrected temperature value; when the corrected temperature value is greater than 1.5 times the temperature prediction value, it is determined to optimize the corrected temperature value and establish a temperature compensation model.

[0034] It can be understood that the temperature prediction value at the current moment is obtained through the random forest model and compared with the corrected temperature value, which avoids the deviation of the corrected temperature value. When the corrected temperature value is greater than 1.5 times the temperature prediction value, it indicates that the correction degree of the initial temperature value is too large, and there is a deviation from the temperature prediction value. The corrected temperature value needs to be optimized. After correcting and optimizing the initial temperature value, the accuracy of the corrected temperature value is verified, and the accuracy of the target temperature value is guaranteed.

[0035] In some embodiments of the present application, when determining to optimize the corrected temperature value and establish a temperature compensation model, it includes: statistically optimizing the historical corrected temperature value data and the historical temperature prediction value data of all historical corrected temperature values, and the historical corrected temperature value data and the historical temperature prediction value data correspond to each other one by one; the temperature compensation model adopts a linear regression model, and the linear regression model is established by the following formula: ; in, Indicates any historical corrected temperature value data, Represents historical temperature forecast data, Indicates the temperature compensation value, Represents the intercept of the linear regression model.

[0036] In some embodiments of the present application, when obtaining the temperature compensation value according to the temperature compensation model, it includes: substituting all historical corrected temperature value data and all historical temperature prediction value data into the linear regression model one by one and performing difference calculation, eliminating the intercept of the linear regression model and obtaining the temperature compensation value T.

[0037] It can be understood that statistics of all historical corrected temperature value data and corresponding historical temperature prediction value data for optimizing the historical corrected temperature values ​​are used to establish a linear regression model, which reflects the comprehensiveness of the optimization process. The linear regression model can accurately reflect the relationship between the historical corrected temperature value data and the historical temperature prediction value data. By substituting all historical corrected temperature values ​​and historical temperature prediction value data for calculation one by one, the temperature compensation value is obtained, thereby eliminating the deviation of the corrected temperature value and improving the accuracy of the target temperature value.

[0038] In some embodiments of the present application, when determining the temperature optimization coefficient according to the temperature compensation value, it includes: presetting a first preset optimization coefficient, a second preset optimization coefficient and a third preset optimization coefficient; when T≤2, the first preset optimization coefficient is used as the temperature optimization coefficient for correcting the temperature value; when 2<T≤4, the second preset optimization coefficient is used as the temperature optimization coefficient for correcting the temperature value; when 4<T, the third preset optimization coefficient is used as the temperature optimization coefficient for correcting the temperature value.

[0039] It can be understood that the first preset optimization coefficient is preferably 0.9, the first preset optimization coefficient is preferably 0.5, and the first preset optimization coefficient is preferably 0.3. The corresponding preset optimization coefficient is selected according to the temperature compensation value, and then the dynamic optimization of the corrected temperature value can be achieved, thereby ensuring the accuracy of the optimization and further ensuring the accuracy of the target temperature value.

[0040] In summary, the beneficial effects of the present invention are as follows: compared with the prior art, the beneficial effects of the present invention are as follows: by winding the fuel cell with a heat exchanger and converting the waste heat into electrical energy and transmitting it to the battery, the utilization rate of the waste heat energy of the fuel cell is improved, and efficient energy utilization is achieved. The temperature sensing optical cable is arranged in a U-shape inside the fuel cell, and then the distributed temperature sensing optical cable and the temperature processor are connected to form a comprehensive optical fiber monitoring network, which improves the efficiency of the temperature sensing optical cable in monitoring temperature and monitors the internal temperature of the fuel cell in real time. It has the characteristics of distributed and comprehensive coverage, realizes all-round monitoring of the inside of the fuel cell, and improves the accuracy and reliability of obtaining the internal temperature of the fuel cell in a high temperature environment. The temperature processor is used to parse the collected temperature data, and the actual temperature value inside the fuel cell is accurately obtained, thereby realizing the automation and intelligence of the fuel cell temperature monitoring.

[0041] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0042] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0043] These computer program instructions may also be stored in a computer-readable storage device that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable storage device produce a product including an instruction device that implements the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A fuel cell waste heat recycling device, characterized in that: include: Fuel cells, batteries, heat exchangers, temperature sensing optical cables, high temperature cables and temperature processors; The heat exchanger is wound around the fuel cell and fixedly connected to the fuel cell, one end of the high-temperature cable is fixedly connected to the heat exchanger, and the other end of the high-temperature cable is fixedly connected to the battery, and the high-temperature cable is used to transmit electrical energy from the heat exchanger; The temperature sensing optical cable is embedded in the fuel cell, the temperature sensing optical cable is arranged in a U shape in the fuel cell, and the temperature sensing optical cable is fixedly connected to the temperature processor; The temperature sensing optical cable comprises a PE sheath, a carbon fiber bundle and a temperature sensing optical fiber; The PE sheath is sheathed with the carbon fiber bundle, and the carbon fiber bundle is sheathed with the temperature sensing optical fiber; The temperature processor is used to obtain temperature data of the temperature sensing optical fiber.

2. The fuel cell waste heat recycling device according to claim 1, characterized in that: The temperature processor is provided with a temperature acquisition system, and the temperature acquisition system comprises: Data acquisition unit, temperature judgment unit, temperature prediction unit and temperature processing unit; The data acquisition unit is configured to acquire temperature data of the temperature sensing optical fiber and pre-process and obtain an initial temperature value; The temperature judgment unit is configured to compare the initial temperature value with the historical data, and judge whether to correct the initial temperature value based on the comparison result, and when it is judged that the initial temperature value is to be corrected, determine the temperature data set by a clustering algorithm, and correct the initial temperature value according to the temperature data set to obtain a corrected temperature value; The temperature prediction unit is configured to establish a random forest model according to the historical temperature value data and obtain a temperature prediction value according to the initial temperature value; The temperature processing unit is configured to compare the corrected temperature value with the temperature prediction value, establish a temperature compensation model according to the comparison result, obtain a temperature compensation value according to the temperature compensation model, determine a temperature optimization coefficient according to the temperature compensation value, optimize the corrected temperature value according to the temperature optimization coefficient to obtain a target temperature value, and the target temperature value is the product of the corrected temperature value and the temperature optimization coefficient.

3. The fuel cell waste heat recycling device according to claim 2, characterized in that: When comparing the initial temperature value with the historical data and determining whether to correct the initial temperature value based on the comparison result, the method includes: The historical data includes a minimum value of historical initial temperature data, an average value of historical initial temperature data, historical initial temperature correction data and a historical correction factor, and the historical initial temperature correction data and the historical correction factor correspond to each other one by one; When the initial temperature value is greater than or equal to the minimum value of the historical initial temperature data and greater than or equal to the average value of the historical initial temperature data, it is determined that the initial temperature value is not to be corrected, and the initial temperature value is determined as the target temperature value; When the initial temperature value is less than the minimum value of the historical initial temperature data, it is determined that the initial temperature value is to be corrected.

4. The fuel cell waste heat recycling device according to claim 3, characterized in that: When determining the temperature data set through the clustering algorithm, including: The initial temperature value to be corrected and the historical data are used as the data set to be aggregated, and the historical correction factor corresponding to each data in the data set to be aggregated is extracted, the expected number of clusters k is determined to be 3, and the parameters of the Gaussian distribution are initialized; The probability that each data in the data set to be aggregated belongs to each Gaussian distribution is calculated to obtain a responsibility value, and a corrected data set corresponding to the initial temperature value to be corrected is obtained according to the responsibility value, and the corrected data set is used as the temperature data set.

5. The fuel cell waste heat recycling device according to claim 4, characterized in that: When the initial temperature value is corrected according to the temperature data set to obtain a corrected temperature value, the method includes: Determining, according to the average of the historical correction factors in the temperature data set, a correction factor for the initial temperature value to be corrected; The corrected temperature value is the product of the initial temperature value to be corrected and the correction factor.

6. The fuel cell waste heat recycling device according to claim 5, characterized in that: When a random forest model is established according to historical temperature value data and a temperature prediction value is obtained according to the initial temperature value, it includes: The historical temperature value data is divided into a temperature training set and a temperature test set, and a model establishment parameter of a random forest model is found by cross validation combined with a grid search to establish a random forest model; Substituting the temperature training set into the random forest model to fit the random forest model, substituting the temperature test set into the fitted random forest model to obtain the prediction accuracy of the temperature prediction value; When the prediction accuracy reaches a preset accuracy threshold, a temperature prediction value at the current moment is obtained according to the initial temperature value.

7. The fuel cell waste heat recycling device according to claim 6, characterized in that: When the corrected temperature value is compared with the predicted temperature value and a temperature compensation model is established according to the comparison result, the method includes: When the corrected temperature value is less than or equal to 1.5 times of the predicted temperature value, it is determined that the corrected temperature value is not to be optimized, and the corrected temperature value is determined as the target temperature value; When the corrected temperature value is greater than 1.5 times the predicted temperature value, it is determined that the corrected temperature value is to be optimized and a temperature compensation model is to be established.

8. The fuel cell waste heat recycling device according to claim 7, characterized in that: When determining to optimize the corrected temperature value and establish a temperature compensation model, it includes: Counting historical corrected temperature value data and historical temperature prediction value data optimized for all historical corrected temperature values, wherein the historical corrected temperature value data and the historical temperature prediction value data correspond to each other one by one; The temperature compensation model adopts a linear regression model, which is established by the following formula: ； in, Indicates any historical corrected temperature value data, Represents historical temperature forecast data, Indicates the temperature compensation value, Represents the intercept of the linear regression model.

9. The fuel cell waste heat recycling device according to claim 8, characterized in that: When the temperature compensation value is obtained according to the temperature compensation model, it includes: All historical corrected temperature value data and all historical temperature prediction value data are substituted into the linear regression model one by one and difference calculation is performed to eliminate the intercept of the linear regression model and obtain the temperature compensation value T.

10. The fuel cell waste heat recycling device according to claim 9, characterized in that: When determining the temperature optimization coefficient according to the temperature compensation value, it includes: Presetting a first preset optimization coefficient, a second preset optimization coefficient, and a third preset optimization coefficient; When T≤2, the first preset optimization coefficient is used as the temperature optimization coefficient of the corrected temperature value; When 2<T≤4, the second preset optimization coefficient is used as the temperature optimization coefficient of the corrected temperature value; When 4<T, the third preset optimization coefficient is used as the temperature optimization coefficient of the corrected temperature value.

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