A typical working condition construction method and device and related products

Abstract

The application provides a typical working condition construction method and device and related products, and relates to the technical field of vehicles. First, historical vehicle parameter values are acquired, wherein the vehicle parameter values include vehicle latitude and longitude data. Then, the historical vehicle parameter values are processed to obtain historical vehicle working condition characteristic evaluation parameters. Next, the historical vehicle working condition characteristic evaluation parameters are divided by using a condensed layer clustering algorithm and the vehicle latitude and longitude data to obtain historical vehicle working condition characteristic evaluation parameters of different categories of vehicles in different scenarios. Finally, a plurality of typical working conditions are constructed by using a multi-level two-dimensional Markov chain Monte Carlo method and the historical vehicle working condition characteristic evaluation parameters of different categories of vehicles in different scenarios. In this way, the real driving mode and the dynamic characteristics of a fuel cell vehicle can be accurately captured, and the accuracy of the energy efficiency evaluation of the fuel cell vehicle is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, apparatus and related products for constructing a typical working condition. Background Technology

[0002] Real-world driving cycles and energy efficiency assessments of fuel cell vehicles are crucial for improving their technological maturity and market penetration. Currently, traditional driving cycle testing and simulation methods often fail to accurately reflect the complexity and variability of real-world driving conditions, resulting in low accuracy in energy efficiency assessments of fuel cell vehicles.

[0003] In conclusion, improving the accuracy of energy efficiency assessments for fuel cell vehicles is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, this application provides a typical operating condition construction method, apparatus and related products, which aim to improve the accuracy of energy efficiency assessment of fuel cell vehicles.

[0005] Firstly, this application provides a method for constructing typical working conditions, including:

[0006] Obtain historical vehicle parameter values; the vehicle parameter values ​​include vehicle latitude and longitude data;

[0007] The historical vehicle parameter values ​​are processed to obtain historical vehicle operating condition characteristic evaluation parameters;

[0008] Using agglomerative hierarchical clustering algorithm and the vehicle latitude and longitude data, the historical vehicle operating condition characteristic evaluation parameters are divided to obtain historical vehicle operating condition characteristic evaluation parameters for different categories of vehicles in different scenarios.

[0009] Using the multi-level two-dimensional Markov chain Monte Carlo method and historical vehicle operating condition feature evaluation parameters of different vehicle categories under different scenarios, multiple typical operating conditions are constructed.

[0010] Optionally, after constructing multiple typical operating conditions using the multi-level two-dimensional Markov chain Monte Carlo method and the historical vehicle operating condition feature evaluation parameters of different categories of vehicles in different scenarios, the method further includes:

[0011] Obtain the target vehicle parameter values;

[0012] The target vehicle parameter values ​​are processed to obtain target vehicle operating condition characteristic evaluation parameters;

[0013] The target vehicle operating condition characteristic evaluation parameters are matched with the multiple typical operating conditions to obtain the target typical operating condition; the target typical operating condition is the typical operating condition that is closest to the target vehicle operating condition.

[0014] Optionally, after matching the target vehicle operating condition characteristic evaluation parameters with the plurality of typical operating conditions to obtain the target typical operating condition, the method further includes:

[0015] An economic evaluation is performed on the target typical operating condition to obtain the economic evaluation results of the target typical operating condition.

[0016] Optionally, the step of matching the target vehicle operating condition characteristic evaluation parameters with the plurality of typical operating conditions to obtain the target typical operating condition includes:

[0017] Based on the mean absolute percentage error and Pearson correlation coefficient, the typical operating condition is matched with the target vehicle operating condition characteristic evaluation parameters to obtain the target typical operating condition.

[0018] Optionally, the step of performing an economic evaluation on the target typical operating condition to obtain the economic evaluation result of the target typical operating condition includes:

[0019] The economic feasibility of the target under typical operating conditions was evaluated using a Simulink simulation model and an environmental test bench, and the economic evaluation results for the target under typical operating conditions were obtained.

[0020] Optionally, the vehicle parameter values ​​may also include vehicle number, timestamp, vehicle speed, vehicle power demand, fuel cell power, battery status, hydrogen tank temperature, and hydrogen tank pressure.

[0021] Optionally, the historical vehicle operating condition characteristic evaluation parameters include maximum speed, maximum acceleration, maximum deceleration, average operating speed, average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, constant speed time percentage, and idling time percentage.

[0022] Secondly, this application provides a typical working condition construction device, including:

[0023] The first acquisition module is used to acquire historical vehicle parameter values; the vehicle parameter values ​​include vehicle latitude and longitude data.

[0024] The first processing module is used to process the historical vehicle parameter values ​​to obtain historical vehicle operating condition characteristic evaluation parameters.

[0025] The segmentation module is used to segment the historical vehicle operating condition feature evaluation parameters using the agglomerative hierarchical clustering algorithm and the vehicle latitude and longitude data, so as to obtain the historical vehicle operating condition feature evaluation parameters of different categories of vehicles in different scenarios.

[0026] The module is used to construct multiple typical operating conditions using the multi-level two-dimensional Markov chain Monte Carlo method and the historical vehicle operating condition feature evaluation parameters of the different categories of vehicles in different scenarios.

[0027] Optionally, the device further includes:

[0028] The second acquisition module is used to acquire the target vehicle parameter values;

[0029] The second processing module is used to process the target vehicle parameter values ​​to obtain target vehicle operating condition characteristic evaluation parameters.

[0030] The matching module is used to match the target vehicle operating condition feature evaluation parameters with the multiple typical operating conditions to obtain the target typical operating condition; the target typical operating condition is the typical operating condition that is closest to the target vehicle operating condition.

[0031] Optionally, the device further includes:

[0032] The evaluation module is used to perform an economic evaluation on the target typical working conditions and obtain the economic evaluation results of the target typical working conditions.

[0033] Optionally, the matching module includes:

[0034] The matching unit is used to match the typical operating condition with the target vehicle operating condition characteristic evaluation parameters based on the mean absolute percentage error and the Pearson correlation coefficient to obtain the target typical operating condition.

[0035] Optionally, the evaluation module includes:

[0036] The evaluation unit is used to perform an economic evaluation of the target's typical operating conditions using a Simulink simulation model and an environmental test bench, and to obtain the economic evaluation results of the target's typical operating conditions.

[0037] Optionally, the vehicle parameter values ​​may also include vehicle number, timestamp, vehicle speed, vehicle power demand, fuel cell power, battery status, hydrogen tank temperature, and hydrogen tank pressure.

[0038] Optionally, the historical vehicle operating condition characteristic evaluation parameters include maximum speed, maximum acceleration, maximum deceleration, average operating speed, average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, constant speed time percentage, and idling time percentage.

[0039] Thirdly, embodiments of this application provide a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a typical working condition construction method as described in any of the embodiments of the first aspect of this application.

[0040] Fourthly, embodiments of this application provide a computer-readable storage medium storing instructions that, when executed on a terminal device, cause the terminal device to perform a typical working condition construction method as described in any of the embodiments of the first aspect of this application.

[0041] This application provides a method for constructing typical operating conditions. When executing the method, historical vehicle parameter values ​​are first obtained, including vehicle latitude and longitude data. Then, these historical vehicle parameter values ​​are processed to obtain historical vehicle operating condition characteristic evaluation parameters. Next, the historical vehicle operating condition characteristic evaluation parameters are divided using a condensed layer clustering algorithm and the vehicle latitude and longitude data to obtain historical vehicle operating condition characteristic evaluation parameters for different categories of vehicles under different scenarios. Finally, multiple typical operating conditions are constructed using a multi-level two-dimensional Markov chain Monte Carlo method and the historical vehicle operating condition characteristic evaluation parameters for different categories of vehicles under different scenarios. In this way, by using a condensed layer clustering algorithm and vehicle latitude and longitude data to divide historical vehicle operating condition characteristic evaluation parameters for different categories of vehicles under different scenarios, and then using a multi-level two-dimensional Markov chain Monte Carlo method to construct typical operating conditions, the actual driving modes and dynamic characteristics of fuel cell vehicles can be accurately captured, thereby improving the accuracy of fuel cell vehicle energy efficiency evaluation. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings used in the description of the embodiment or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A flowchart illustrating a typical working condition construction method provided in this application embodiment;

[0044] Figure 2 A flowchart illustrating another typical working condition construction method provided in this application embodiment;

[0045] Figure 3 This is a schematic diagram of the two-bit encoding logic provided in the embodiments of this application;

[0046] Figure 4Radar image showing the speed characteristics of a fuel cell bus provided in this application embodiment;

[0047] Figure 5 Heat maps showing the correlation between 11 speed characteristics of 10 fuel cell buses provided in this embodiment of the application;

[0048] Figure 6 Speed ​​characteristic radar image of the first category of 10 fuel cell buses provided in this application embodiment;

[0049] Figure 7 Speed ​​characteristic radar image of 10 fuel cell buses classified into the second category of buses provided in this application embodiment;

[0050] Figure 8 A schematic diagram illustrating the speed characteristics of the first category of 10 fuel cell buses classified according to an embodiment of this application;

[0051] Figure 9 A schematic diagram illustrating the speed characteristics of the second category of 10 fuel cell buses as provided in this embodiment of the application;

[0052] Figure 10 The state transition probability matrix and a magnified view of the first category of 10 fuel cell buses after classification, as provided in the embodiments of this application;

[0053] Figure 11 The state transition probability matrix and a magnified view of the second category of 10 fuel cell buses after classification, as provided in the embodiments of this application;

[0054] Figure 12 The speed and fuel cell power spectrum of the first category of 10 fuel cell buses provided in this application embodiment;

[0055] Figure 13 The speed and fuel cell power spectrum of the second category of 10 fuel cell buses provided in this application embodiment;

[0056] Figure 14 The speed and fuel cell power spectrum of the heavy-duty truck self-learning system provided in the embodiments of this application;

[0057] Figure 15 A schematic diagram of a typical working condition construction device provided in this application embodiment;

[0058] Figure 16 A computer device provided in an embodiment of this application. Detailed Implementation

[0059] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. This application provides a typical working condition construction method, apparatus, and related products, applicable to the field of vehicle technology. The above are merely examples and do not limit the application areas of the methods and apparatus provided in this application.

[0060] Real-world driving cycles and energy efficiency assessments of fuel cell vehicles are crucial for improving their technological maturity and market penetration. Currently, traditional driving cycle testing and simulation methods often fail to accurately reflect the complexity and variability of real-world driving conditions, resulting in low accuracy in energy efficiency assessments of fuel cell vehicles.

[0061] The inventors, through research, proposed the technical solution of this application. First, historical vehicle parameter values ​​are obtained, including vehicle latitude and longitude data. Next, these historical vehicle parameter values ​​are processed to obtain historical vehicle operating condition characteristic evaluation parameters. Then, using a condensed layer clustering algorithm and the vehicle latitude and longitude data, the historical vehicle operating condition characteristic evaluation parameters are divided into different categories of vehicles under different scenarios. Finally, using a multi-level two-dimensional Markov chain Monte Carlo method and the historical vehicle operating condition characteristic evaluation parameters of different vehicle categories under different scenarios, multiple typical operating conditions are constructed. In this way, by using a condensed layer clustering algorithm and vehicle latitude and longitude data to divide historical vehicle operating condition characteristic evaluation parameters of different vehicle categories under different scenarios, and then using a multi-level two-dimensional Markov chain Monte Carlo method to construct typical operating conditions, the actual driving modes and dynamic characteristics of fuel cell vehicles can be accurately captured, thereby improving the accuracy of fuel cell vehicle energy efficiency evaluation.

[0062] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of them. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present application. It should be noted that, for ease of description, only the parts related to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of the present application can be combined with each other.

[0063] See Figure 1 , Figure 1 A flowchart of a typical working condition construction method provided in this application embodiment includes:

[0064] S101: Obtain historical vehicle parameter values.

[0065] The system acquires historical vehicle parameter values ​​transmitted from the vehicle-mounted T-Box to the cloud storage. These vehicle parameter values ​​include, but are not limited to: vehicle latitude and longitude data, vehicle number, timestamp, vehicle speed, vehicle power demand, fuel cell power, battery status, hydrogen tank temperature, and hydrogen tank pressure.

[0066] Specifically, before executing step S101, it is necessary to first create a fuel cell vehicle system parameter database project using the Clickhouse columnar storage database, collect the T-Box transmission signals of the fuel cell vehicle application module, and design database tables according to the signal type for cloud storage of data. The attributes of the database tables cover characteristic parameter values ​​such as vehicle number, vehicle latitude and longitude data, timestamp, vehicle speed, vehicle power demand, fuel cell power, battery status, hydrogen tank temperature, and hydrogen tank pressure.

[0067] In some implementations, since the total amount of data for a complete fuel cell vehicle is enormous, in order to optimize the query time each time, the required vehicle parameter values ​​for each vehicle can be obtained only within a certain time period for a portion of the vehicles, depending on actual needs.

[0068] S102: Process the historical vehicle parameter values ​​to obtain historical vehicle operating condition characteristic evaluation parameters.

[0069] The vehicle parameter values ​​obtained in step S101 are processed and extracted using the Python language to obtain historical vehicle operating condition characteristic evaluation parameters. These parameters include: maximum speed, maximum acceleration, maximum deceleration, average operating speed, average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, constant speed time percentage, and idling time percentage.

[0070] S103: Using agglomerative hierarchical clustering algorithm and vehicle latitude and longitude data, the historical vehicle operating condition characteristic evaluation parameters are divided to obtain the historical vehicle operating condition characteristic evaluation parameters of different categories of vehicles in different scenarios.

[0071] The AGNES (Agglomerative hierarchical clustering algorithm) and vehicle latitude and longitude data were used to divide vehicle operating condition feature evaluation parameters, obtaining historical vehicle operating condition feature evaluation parameters for different vehicle categories under different scenarios. Specifically, the AGNES algorithm was used to group samples in the dataset based on similarity. First, each sample was treated as a single cluster, and then the most similar clusters were iteratively merged until all samples belonged to the same cluster. The AGNES algorithm is advantageous for generating a complete hierarchical clustering structure without prior knowledge of the number of clusters, making it suitable for vehicle category classification.

[0072] The Ward method is a special case of the agglomerative hierarchical clustering algorithm AGNES. In this method, cluster merging is based on the minimum increment of the within-cluster sum of squared errors (WSS), which is the sum of the squared distances from all points within a cluster to the centroid of that cluster. During each merge, the Ward method selects the clusters whose minimum increment of the within-cluster sum of squared errors is minimized. The two smallest clusters are merged, using the following formula:

[0073] ;

[0074] ;

[0075] ;

[0076] ;

[0077] in, and These are different clusters, which are the multidimensional feature data of each vehicle; Indicates the merged cluster; This represents the sampling points in the cluster; , and Representing clusters , and The center of mass; and Representing clusters and cluster The number of elements in; Indicates sampling point With center of mass The square of the Euclidean distance between them.

[0078] S104: Using the multi-level two-dimensional Markov chain Monte Carlo method and historical vehicle operating condition feature evaluation parameters of different types of vehicles in different scenarios, several typical operating conditions are constructed.

[0079] Based on historical vehicle operating condition characteristic evaluation parameters for different vehicle categories under different scenarios, operating conditions are constructed for each category and each operating scenario using a multi-level two-dimensional Markov chain Monte Carlo model. The specific implementation process is as follows:

[0080] (1) Status Code:

[0081] Historical vehicle speed or acceleration sequences are encoded into discrete state sequences. state of time It includes the speed of the vehicle. and acceleration A two-dimensional group, denoted as The speed of the vehicle and acceleration Combination methods such as Figure 3 As shown, Figure 3 A schematic diagram of the two-bit encoding logic provided for embodiments of this application. Vehicle acceleration. The calculation method is as follows:

[0082] ;

[0083] in, Is the vehicle in The speed of time, Is the vehicle in The speed at that time.

[0084] (2) Calculation of the state transition matrix:

[0085] A Markov chain gives the relationship between the current state and the next state, that is, from the current state... To the next state The probability is The formula is as follows:

[0086] ;

[0087] The transition probabilities of a Markov chain can be calculated using the following formula:

[0088] ;

[0089] The formula for the state transition matrix TPM, which consists of state transition probabilities, is as follows:

[0090] ;

[0091] in, It is time; From the current state To the next state The transition probability; From state Transition to state The number of samples; It is the total number of encoded states.

[0092] (3) State transition:

[0093] The Metropolis Hastings (MH) Markov Chain Monte Carlo (MCMC) sampling method aims to sample from multidimensional distributions. MCMC uses Markov chains to generate a series of states that converge to a steady-state distribution, which is typically the target distribution from which sampling is performed, i.e., the distribution obtained from the state transition matrix TPM. Its core idea is to start from an initial state and, according to certain rules, accept or reject state transitions until a steady state is reached. This process generates samples that conform to the target distribution. The Markov chain starts from the state... Transition to the next state The formula for the acceptance probability is shown below; otherwise, the state remains unchanged. :

[0094] ;

[0095] in and It is the target distribution. and This is a proposed distribution; a normal distribution is used here.

[0096] (4) State decoding:

[0097] After obtaining the state distribution results at each time point, Decoding the state yields a continuous velocity spectrum and acceleration spectrum, thus establishing a typical operating condition.

[0098] In the embodiments provided in this application, historical vehicle parameter values, including vehicle latitude and longitude data, are first obtained. Then, these historical vehicle parameter values ​​are processed to obtain historical vehicle operating condition characteristic evaluation parameters. Next, the historical vehicle operating condition characteristic evaluation parameters are divided using a condensed layer clustering algorithm and the vehicle latitude and longitude data to obtain historical vehicle operating condition characteristic evaluation parameters for different categories of vehicles under different scenarios. Finally, multiple typical operating conditions are constructed using a multi-level two-dimensional Markov chain Monte Carlo method and the historical vehicle operating condition characteristic evaluation parameters for different categories of vehicles under different scenarios. In this way, by using a condensed layer clustering algorithm and vehicle latitude and longitude data to divide historical vehicle operating condition characteristic evaluation parameters for different categories of vehicles under different scenarios, and then using a multi-level two-dimensional Markov chain Monte Carlo method to construct typical operating conditions, the actual driving modes and dynamic characteristics of fuel cell vehicles can be accurately captured, thereby improving the accuracy of fuel cell vehicle energy efficiency evaluation.

[0099] In addition, after constructing the typical operating conditions, it is also necessary to evaluate them. The specific steps are as follows: Figure 2 As shown, Figure 2A flowchart of another typical working condition construction method provided in the embodiments of this application includes:

[0100] The implementation methods of steps S201-S204 are the same as those of steps S101-S104, and will not be repeated here.

[0101] S205: Obtain the target vehicle parameter values.

[0102] First, it is necessary to obtain the vehicle parameter values ​​of the target vehicle. These vehicle parameter values ​​include, but are not limited to, vehicle latitude and longitude data, vehicle number, timestamp, vehicle speed, vehicle power demand, fuel cell power, battery status, hydrogen tank temperature, and hydrogen tank pressure.

[0103] S206: Process the target vehicle parameter values ​​to obtain the target vehicle operating condition characteristic evaluation parameters.

[0104] The vehicle parameter values ​​of the actual vehicle obtained in step S205 are processed and extracted using the Python language to obtain the vehicle operating condition characteristic evaluation parameters of the actual vehicle. The vehicle operating condition characteristic evaluation parameters include: maximum speed, maximum acceleration, maximum deceleration, average operating speed, average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, constant speed time percentage, and idling time percentage.

[0105] S207: Match the target vehicle's operating condition characteristic evaluation parameters with multiple typical operating conditions to obtain the target typical operating conditions.

[0106] By using the mean absolute percentage error and Pearson correlation coefficient, the typical operating condition is compared with the vehicle operating condition characteristic evaluation parameters of the actual vehicle to obtain the target typical operating condition, which is the typical operating condition that is closest to the actual vehicle operating condition.

[0107] S208: Conduct an economic evaluation of the target typical working conditions to obtain the economic evaluation results of the target typical working conditions.

[0108] Economic evaluation of the target typical operating conditions is conducted through Simulink simulation (HiL) verification and environmental bench testing. Simulink simulation (HiL) is used to model system performance under real-world conditions, providing crucial data for the economic assessment. A higher-level computer with a Simulink model manages the energy spectrum, including the operation of the lithium-ion battery and PEMFC system. A test cabinet integrated with the fuel cell system is equipped with a hydrogen flow meter for accurate measurement of hydrogen consumption. The environmental bench test creates a controlled environment for comprehensive evaluation of the fuel cell system's performance. This area houses a PEMFC benchmark system and an axial fan for simulating vehicle cooling capacity, ensuring test conditions are highly similar to real-world application scenarios. Similarly, a hydrogen flow meter is used in this area to accurately record hydrogen consumption, further validating the system's economic performance.

[0109] The target typical operating condition and the optimal typical operating condition are input into the Simulink simulation (HiL) model to obtain the power spectrum and simulated hydrogen consumption of the fuel cell. Then, the obtained power spectrum is input into the environmental chamber bench test to obtain the bench test hydrogen consumption. It is determined whether the hydrogen consumption error is within the required range. If it is within the required range, a typical operating condition with economic evaluation results is obtained; otherwise, a new target typical operating condition is selected.

[0110] In some implementations, to ensure the consistency and comparability of the proposed method, the vehicle parameters for Simulink simulation (HiL) verification and environmental test bench testing are kept the same.

[0111] The typical working condition construction method provided by the embodiments of this application has been introduced above. The following is an exemplary description of the typical working condition construction method in combination with specific application scenarios.

[0112] (1) Taking 10 fuel cell 18t buses as an example:

[0113] First, a fuel cell vehicle system parameter database project was created using the Clickhouse columnar storage database. This project collects vehicle parameter values ​​in real time from the onboard T-Box of the fuel cell bus, such as vehicle latitude and longitude data, vehicle number, timestamp, vehicle speed, vehicle power demand, fuel cell power, battery status, hydrogen tank temperature, and hydrogen tank pressure. Next, the vehicle parameter values ​​were processed using the Python programming language to obtain operating condition characteristic evaluation parameters, such as… Figure 4 As shown, Figure 4The speed characteristic radar chart of the fuel cell bus provided in this application embodiment includes the following operating condition characteristic evaluation parameters: maximum speed, maximum acceleration, maximum deceleration, average operating speed, average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, constant speed time percentage, and idling time percentage.

[0114] Next, the AGNES algorithm is used to perform cluster analysis on the operating condition characteristic evaluation parameters, and combined with the vehicle's GPS signals, the vehicles are divided into different operating scenarios and categories. The AGNES algorithm classifies vehicles by continuously merging the most similar clusters until all samples belong to a certain cluster. Figure 5 As shown, Figure 5 This application provides a heatmap showing the correlation of 11 speed features of 10 fuel cell buses. The correlation of the speed features of the 10 buses is categorized into two classes using the AGNES algorithm: the first class (vehicles 4, 6, and 7) represented by the yellow line, and the second class (vehicles 1, 2, 3, 5, 8, 9, and 10) represented by the blue line. Speed ​​is divided into 60 segments, and acceleration is divided into 10 segments; therefore, the total number of two-dimensional codes is 600. Figure 6 , Figure 7 As shown, Figure 6 This is a radar image showing the speed characteristics of the first category of 10 fuel cell buses classified according to embodiments of this application. Figure 7 A radar image showing the speed characteristics of 10 fuel cell buses classified into the second category, as provided in this embodiment of the application. The accompanying two-dimensional coding is as follows. Figure 8 , Figure 9 As shown, Figure 8 This is a schematic diagram illustrating the speed characteristics of the first category of 10 fuel cell buses classified according to an embodiment of this application. Figure 9 The speed characteristic diagram of the second category of 10 fuel cell buses classified according to the embodiments of this application clearly shows... Figure 8 The acceleration fluctuation range of the first type of bus in the vehicle is greater than [a certain value] across all speed ranges. Figure 9 The acceleration fluctuation range of the second type of bus across all speed ranges will be considered, therefore, operating conditions will be constructed separately for these two types of vehicles.

[0115] Then, based on the results of agglomerative hierarchical clustering and partitioning, a multi-level two-dimensional Markov chain Monte Carlo method is used to construct typical operating conditions for each category and each operating scenario. This process includes four steps: state encoding, calculation of the state transition matrix, state transition, and state decoding. The specific steps have been explained in detail above and will not be repeated here. Using this method, typical operating conditions highly similar to real driving conditions can be constructed. Figure 10 , Figure 11 As shown, Figure 10 This document presents the state transition probability matrix and a magnified view of the first category of 10 fuel cell buses classified according to embodiments of this application. Figure 11 The state transition probability matrix and a magnified view of the second category of 10 fuel cell buses classified according to embodiments of this application are shown. For example, in... Figure 10 In this case, state 26 (i.e., speed range 0.83 to 1.66 km / h, acceleration range -1.18 to 0.13 m / s²) is used. 2 Transition to State 7 (speed 0 km / h, acceleration range -0.13 to 0.93 m / s²) 2 The probability of transitioning from state 26 to state 17 is 0.8, while the speed range is 0 to 0.83 km / h and the acceleration range is -0.13 to 0.93 m / s². 2 The probability of ( ) is 0.14. Figure 10 , Figure 11 The provided state transition probability matrix is ​​used to construct 1000 typical operating conditions with a 1-hour velocity spectrum based on a set duration (3600 seconds).

[0116] Then, using both Pearson correlation coefficient and MAPE methods, the generated 1000 typical operating conditions were evaluated. By comparing the characteristic parameters with those of actual vehicle driving conditions, the optimal typical operating conditions that best approximate the actual vehicle driving conditions were obtained. The optimal typical operating conditions for the two types of vehicles are as follows: Figure 12 , Figure 13 As shown, Figure 12 The speed and fuel cell power spectrum of the first category of 10 fuel cell buses provided in this application embodiment are shown. Figure 13 The speed and fuel cell power spectrum of the second category of 10 fuel cell buses provided in this application embodiment are shown. Figure 12 The blue line represents the typical speed spectrum operating conditions for the first type of bus. Figure 13 The blue line represents the typical speed spectrum operating condition of the second type of bus. Its eleven speed and acceleration characteristic indicators are close to the actual situation and conform to the actual speed time sequence change law.

[0117] Finally, the optimal typical operating condition is input into the Simulink simulation (HiL) model to simulate the power spectrum and hydrogen consumption of the fuel cell. The simulation results are then compared with the hydrogen consumption obtained from environmental bench testing to verify the economic efficiency of the constructed typical operating condition. If the hydrogen consumption error is within the required range, the verification is successful; otherwise, the typical operating condition is reconstructed. Figure 12 , Figure 13 As shown, Figure 12 The speed and fuel cell power spectrum of the first category of 10 fuel cell buses provided in this application embodiment are shown. Figure 13The speed and fuel cell power spectrum of the second category of 10 fuel cell buses provided in this application embodiment are shown. Figure 12 , Figure 13 Using the velocity spectrum of the blue line as input, the corresponding... Figure 12 , Figure 13 The power spectrum of the fuel cell in the image is represented by the green line.

[0118] (2) Taking a 49t fuel cell tractor as an example:

[0119] First, a fuel cell vehicle system parameter database project was created using the Clickhouse columnar storage database. This database collects vehicle parameter values ​​in real time from the onboard T-Box of the fuel cell bus, including vehicle latitude and longitude data, vehicle number, timestamp, vehicle speed, vehicle power demand, fuel cell power, battery status, hydrogen tank temperature, and hydrogen tank pressure. Next, the vehicle parameter values ​​were processed using the Python programming language to obtain operating condition characteristic evaluation parameters. These parameters include: maximum speed, maximum acceleration, maximum deceleration, average operating speed, average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, constant speed time percentage, and idling time percentage.

[0120] Next, the AGNES algorithm is used to perform cluster analysis on the operating condition characteristic evaluation parameters, and combined with the vehicle's GPS signals, the vehicles are divided into different operating scenarios and categories. The AGNES algorithm classifies vehicles by continuously merging the most similar clusters until all samples belong to a certain cluster. This embodiment focuses on the historical data of a single fully loaded vehicle, therefore, there is no need to perform the step of clustering multiple vehicles.

[0121] Based on the results of agglomerative hierarchical clustering and partitioning, a multi-level two-dimensional Markov chain Monte Carlo method is used to construct typical operating conditions for each category and each operating scenario. This process includes four steps: state encoding, calculation of the state transition matrix, state transition, and state decoding. The specific steps have been explained in detail above and will not be repeated here. Typical operating conditions that are highly similar to real driving conditions can be constructed. 1000 typical operating conditions with a speed spectrum of 1 hour are constructed according to the set duration (3600 seconds).

[0122] Then, using the Pearson correlation coefficient and MAPE methods, the 1000 typical operating conditions generated were evaluated. By comparing the characteristic parameters with the actual vehicle driving conditions, the optimal typical operating conditions that are closest to the actual vehicle driving conditions were obtained.

[0123] Finally, the optimal typical operating condition is input into the Simulink simulation (HiL) model to simulate the power spectrum and hydrogen consumption of the fuel cell. The simulation results are then compared with the hydrogen consumption obtained from environmental test bench measurements to verify the economic efficiency of the constructed typical operating condition. If the hydrogen consumption error is within the required range, the verification is successful; otherwise, the typical operating condition is reconstructed. Figure 14 The speed and fuel cell power spectrum of the heavy-duty truck self-learning constructed in the embodiments of this application are provided. Figure 14 Using the velocity spectrum of the blue line as input, the corresponding... Figure 14 The power spectrum of the fuel cell in the image is shown by the red line.

[0124] The above are some specific implementations of the typical working condition construction method provided in the embodiments of this application. Based on this, this application also provides a corresponding device. The device provided in the embodiments of this application will be described below from the perspective of functional modularization.

[0125] See Figure 15 , Figure 15 This is a schematic diagram of a typical working condition construction device provided in an embodiment of this application. The typical working condition construction device 1100 includes:

[0126] The first acquisition module 1110 is used to acquire historical vehicle parameter values; the vehicle parameter values ​​include vehicle latitude and longitude data.

[0127] The first processing module 1120 is used to process the historical vehicle parameter values ​​to obtain historical vehicle operating condition characteristic evaluation parameters.

[0128] The segmentation module 1130 is used to segment the historical vehicle operating condition feature evaluation parameters using the agglomerative hierarchical clustering algorithm and the vehicle latitude and longitude data, so as to obtain the historical vehicle operating condition feature evaluation parameters of different categories of vehicles in different scenarios.

[0129] Module 1140 is used to construct multiple typical operating conditions using the multi-level two-dimensional Markov chain Monte Carlo method and the historical vehicle operating condition feature evaluation parameters of the different categories of vehicles in different scenarios.

[0130] Optionally, the device 1100 further includes:

[0131] The second acquisition module is used to acquire the target vehicle parameter values;

[0132] The second processing module is used to process the target vehicle parameter values ​​to obtain target vehicle operating condition characteristic evaluation parameters.

[0133] The matching module is used to match the target vehicle operating condition feature evaluation parameters with the multiple typical operating conditions to obtain the target typical operating condition; the target typical operating condition is the typical operating condition that is closest to the target vehicle operating condition.

[0134] Optionally, the device 1100 further includes:

[0135] The evaluation module is used to perform an economic evaluation on the target typical working conditions and obtain the economic evaluation results of the target typical working conditions.

[0136] Optionally, the matching module includes:

[0137] The matching unit is used to match the typical operating condition with the target vehicle operating condition characteristic evaluation parameters based on the mean absolute percentage error and the Pearson correlation coefficient to obtain the target typical operating condition.

[0138] Optionally, the evaluation module includes:

[0139] The evaluation unit is used to perform an economic evaluation of the target's typical operating conditions using a Simulink simulation model and an environmental test bench, and to obtain the economic evaluation results of the target's typical operating conditions.

[0140] Optionally, the vehicle parameter values ​​may also include vehicle number, timestamp, vehicle speed, vehicle power demand, fuel cell power, battery status, hydrogen tank temperature, and hydrogen tank pressure.

[0141] Optionally, the historical vehicle operating condition characteristic evaluation parameters include maximum speed, maximum acceleration, maximum deceleration, average operating speed, average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, constant speed time percentage, and idling time percentage.

[0142] This application also provides corresponding devices and computer storage media for implementing the solutions provided in this application.

[0143] like Figure 16 As shown, computer device 01 is represented in the form of a general-purpose computing device. The components of computer device 01 may include, but are not limited to: one or more processors or processor units 03, system memory 08, and bus 04 connecting different system components (including system memory 08 and processor unit 03).

[0144] Bus 04 represents one or more of several bus architectures, including memory buses or memory controllers, peripheral buses, graphics acceleration ports, processors, or local buses using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0145] Computer device 01 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 01, including volatile and non-volatile media, removable and non-removable media.

[0146] System memory 08 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 09 and / or cache memory 10. Computer device 01 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 11 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 16 Not shown; usually referred to as a "hard drive"). Although Figure 16 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 04 via one or more data media interfaces. System memory 08 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0147] A program / utility 12 having a set (at least one) of program modules 13 may be stored, for example, in system memory 08. Such program modules 13 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 13 typically perform the functions and / or methods described in the embodiments of the present invention.

[0148] Computer device 01 can also communicate with one or more external devices 02 (e.g., keyboard, pointing device, display 07, etc.), and with one or more devices that enable a user to interact with the computer device 01, and / or with any device that enables the computer device 01 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 06. Furthermore, computer device 01 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 05. Figure 16 As shown, network adapter 05 communicates with other modules of computer device 01 via bus 04. It should be understood that, although... Figure 16 As not shown in the diagram, it can be used in conjunction with computer device 01 with other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0149] The processor unit 03 executes various functional applications and data processing by running programs stored in the system memory 08, such as implementing a typical working condition construction method provided in the embodiments of this application.

[0150] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0151] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the methods of the above embodiments can be implemented by means of software plus a general-purpose hardware platform. Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as a read-only memory (ROM) / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network communication device such as a router) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0152] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0153] The above description is merely an exemplary implementation of this application and is not intended to limit the scope of protection of this application.

Claims

1. A method for constructing a typical working condition, characterized in that, include: Obtain historical vehicle parameter values; the vehicle parameter values ​​include vehicle latitude and longitude data; The historical vehicle parameter values ​​are processed to obtain historical vehicle operating condition characteristic evaluation parameters; Using the agglomerative hierarchical clustering algorithm and the vehicle latitude and longitude data, the historical vehicle operating condition characteristic evaluation parameters are divided to obtain the historical vehicle operating condition characteristic evaluation parameters of different categories of vehicles in different scenarios. Using the multi-level two-dimensional Markov chain Monte Carlo method and the historical vehicle operating condition feature evaluation parameters of the different categories of vehicles in different scenarios, multiple typical operating conditions are constructed. Obtain the target vehicle parameter values; The target vehicle parameter values ​​are processed to obtain target vehicle operating condition characteristic evaluation parameters; The target vehicle operating condition characteristic evaluation parameters are matched with the multiple typical operating conditions to obtain the target typical operating condition; the target typical operating condition is the typical operating condition that is closest to the target vehicle operating condition. The step of matching the target vehicle operating condition characteristic evaluation parameters with the multiple typical operating conditions to obtain the target typical operating condition includes: matching the typical operating condition with the target vehicle operating condition characteristic evaluation parameters based on the mean absolute percentage error and Pearson correlation coefficient to obtain the target typical operating condition. An economic evaluation is performed on the target typical working condition to obtain the economic evaluation result of the target typical working condition; The step of conducting an economic evaluation of the target typical operating conditions to obtain the economic evaluation results of the target typical operating conditions includes: using a Simulink simulation model and an environmental test bench to conduct an economic evaluation of the target typical operating conditions to obtain the economic evaluation results of the target typical operating conditions.

2. The method according to claim 1, characterized in that, The vehicle parameter values ​​also include vehicle number, timestamp, vehicle speed, vehicle power demand, fuel cell power, battery status, hydrogen tank temperature, and hydrogen tank pressure.

3. The method according to claim 2, characterized in that, The historical vehicle operating condition characteristic evaluation parameters include maximum speed, maximum acceleration, maximum deceleration, average operating speed, average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, constant speed time percentage, and idling time percentage.

4. A typical working condition construction device, characterized in that, include: The first acquisition module is used to acquire historical vehicle parameter values; the vehicle parameter values ​​include vehicle latitude and longitude data. The first processing module is used to process the historical vehicle parameter values ​​to obtain historical vehicle operating condition characteristic evaluation parameters. The segmentation module is used to segment the historical vehicle operating condition feature evaluation parameters using the agglomerative hierarchical clustering algorithm and the vehicle latitude and longitude data, so as to obtain the historical vehicle operating condition feature evaluation parameters of different categories of vehicles in different scenarios. The module is used to construct multiple typical operating conditions by utilizing the multi-level two-dimensional Markov chain Monte Carlo method and the historical vehicle operating condition feature evaluation parameters of the different categories of vehicles in different scenarios. The second acquisition module is used to acquire the target vehicle parameter values; The second processing module is used to process the target vehicle parameter values ​​to obtain target vehicle operating condition characteristic evaluation parameters. The matching module is used to match the target vehicle operating condition feature evaluation parameters with the multiple typical operating conditions to obtain the target typical operating conditions; The target typical operating condition is the typical operating condition that is closest to the target vehicle's operating condition; The matching module includes: The matching unit is used to match the typical working condition with the target vehicle working condition characteristic evaluation parameters based on the mean absolute percentage error and Pearson correlation coefficient to obtain the target typical working condition. The device further includes: an evaluation module, used to perform an economic evaluation on the target typical working condition and obtain the economic evaluation result of the target typical working condition; The evaluation module includes an evaluation unit, used to perform an economic evaluation of the target typical operating conditions using a Simulink simulation model and an environmental test bench, and to obtain the economic evaluation results of the target typical operating conditions.

5. A computer device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the typical operating condition construction method as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a terminal device, cause the terminal device to perform the typical operating condition construction method as described in any one of claims 1-3.

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