Working condition identification method, vehicle controller and fuel cell commercial vehicle

Abstract

The present application provides a working condition identification method, a vehicle controller, and a fuel cell commercial vehicle. The vehicle controller is applied to a fuel cell commercial vehicle, comprising: obtaining characteristic data of the fuel cell commercial vehicle under a preset working condition identification characteristic factor, the characteristic data under the preset working condition identification characteristic factor including: first characteristic data under the vehicle parking frequency factor, the first characteristic data including: the parking frequency and driving distance of the fuel cell commercial vehicle in multiple operating stages, determining the parking frequency of the fuel cell commercial vehicle in multiple operating stages based on the parking frequency and driving distance, identifying the working condition type of the fuel cell commercial vehicle as a short-distance working condition if the parking frequency exceeds a preset frequency threshold, and identifying the working condition type of the fuel cell commercial vehicle as a long-distance working condition if the parking frequency does not exceed the preset frequency threshold. The vehicle controller identifies the working condition online to perform battery management using a battery energy management strategy that matches the working condition type, thereby improving the vehicle's adaptability to working conditions.

Description

Technical Field

[0001] The present application relates to the field of new energy vehicle technology, and more specifically, to a working condition identification method, a vehicle controller, and a fuel cell commercial vehicle. Background Art

[0002] Fuel cell vehicles are new energy vehicles that use electricity generated by on-board fuel cell devices as their power. Among them, fuel cell commercial vehicles (such as fuel cell heavy trucks) have a wide range of variations in cargo mass and operate in changeable road conditions. The output power of the fuel cell system needs to be adaptively adjusted according to the vehicle's operating conditions and driving requirements to improve the vehicle's power while also improving the vehicle's economy.

[0003] In related technologies, for fuel cell commercial vehicles, it is usually necessary to design a corresponding battery power allocation strategy under known fixed driving conditions, and the vehicle operating conditions can only be identified based on offline analysis of driving data.

[0004] However, the above methods have limitations when facing unknown and highly uncertain complex traffic environments and are unable to identify vehicle operating conditions online. Summary of the Invention

[0005] In view of this, embodiments of the present application provide an operating condition identification method, a vehicle controller, and a fuel cell commercial vehicle to online identify the entire vehicle operating condition of a fuel cell commercial vehicle.

[0006] In a first aspect, an embodiment of the present application provides a method for identifying an operating condition, which is applied to a vehicle controller of a fuel cell commercial vehicle. The method includes:

[0007] Acquiring characteristic data of the fuel cell commercial vehicle under a preset operating condition identification characteristic factor, the characteristic data under the preset operating condition identification characteristic factor comprising: first characteristic data under a vehicle stop frequency factor, the first characteristic data comprising: stop frequency and travel distance of the fuel cell commercial vehicle in multiple operating stages;

[0008] Determining, based on the parking frequency and the driving distance, a parking frequency of the fuel cell commercial vehicle in the multiple operation stages, wherein the parking frequency serves as a factor parameter of the fuel cell commercial vehicle under the vehicle parking frequency factor;

[0009] If the parking frequency exceeds a preset frequency threshold, identifying the operating condition of the fuel cell commercial vehicle as a reverse short-circuit operating condition, and adopting a battery energy management strategy matching the reverse short-circuit operating condition for battery management;

[0010] If the parking frequency does not exceed the preset frequency threshold, the operating condition type of the fuel cell commercial vehicle is identified as a long-distance operating condition, so that battery management is performed using a battery energy management strategy that matches the long-distance operating condition.

[0011] In an optional embodiment, determining the parking frequency of the fuel cell commercial vehicle in the multiple operation stages according to the parking frequency and the driving distance includes:

[0012] determining, based on the driving distance, a total driving distance of the fuel cell commercial vehicle in the plurality of operation stages;

[0013] If the total driving distance reaches a first distance threshold, determining a total stop frequency of the fuel cell commercial vehicle in the multiple operation stages according to the stop frequency;

[0014] The parking frequency is determined according to the total driving distance and the total parking frequency.

[0015] In an optional embodiment, the method further includes:

[0016] If the total driving distance is greater than the first distance threshold and does not exceed the second distance threshold, the operating mode type of the fuel cell commercial vehicle remains unchanged.

[0017] In an optional embodiment, the method further includes:

[0018] If the total driving distance exceeds the second distance threshold, the operating condition type of the fuel cell commercial vehicle is re-identified.

[0019] In an optional embodiment, the characteristic data under the preset operating condition identification characteristic factor includes: second characteristic data under the vehicle motor power standard deviation factor, the second characteristic data including: the actual motor speed, actual motor torque and average motor power of the fuel cell commercial vehicle in each operating stage;

[0020] The method further comprises:

[0021] Determining a motor power standard deviation of the fuel cell commercial vehicle in each operation phase according to the actual motor speed, the actual motor torque, and the average motor power, wherein the motor power standard deviation serves as a factor parameter of the fuel cell commercial vehicle under the vehicle motor power standard deviation factor;

[0022] According to the motor power standard deviation, it is determined that the operating condition type of the fuel cell commercial vehicle is a mountainous operating condition or a plain operating condition under the long-distance operating condition.

[0023] In an optional embodiment, determining, based on the motor power standard deviation, whether the operating condition type of the fuel cell commercial vehicle is a mountainous operating condition or a plain operating condition under the long-distance operating condition includes:

[0024] If the motor power standard deviation exceeds a preset power difference threshold, the power count value is increased by one to obtain a target power count value;

[0025] Determining a standard deviation ratio of motor power of the fuel cell commercial vehicle in the multiple operating stages according to the target power count value and the total number of operations in the multiple operating stages;

[0026] If the motor power standard deviation ratio exceeds the preset ratio threshold, it is determined that the operating condition type of the fuel cell commercial vehicle is the mountainous operating condition under the long-distance operating condition;

[0027] If the motor power standard deviation ratio does not exceed the preset ratio threshold, it is determined that the operating condition type of the fuel cell commercial vehicle is the plain operating condition under the long-distance operating condition.

[0028] In an optional embodiment, the vehicle parking frequency factor is pre-determined in the following manner:

[0029] Constructing an operation feature library and an operation database, wherein the operation feature library includes: a plurality of candidate operating condition identification feature factors; the operation database includes: operating data and historical operating condition types of fuel cell commercial vehicles in multiple historical operating stages; the historical operating condition types are short-distance operating conditions or long-distance operating conditions;

[0030] According to the multiple candidate operating condition identification characteristic factors and the operating data, a plurality of candidate operating condition identification parameters of the fuel cell commercial vehicle in operation in each historical operating stage under the multiple candidate operating condition identification characteristic factors are obtained;

[0031] Drawing a scatter plot of the candidate operating condition identification characteristic factors in the multiple historical operating stages based on the multiple candidate operating condition identification characteristic factors of the operated fuel cell commercial vehicle and the historical operating condition type;

[0032] The vehicle parking frequency factor is determined from the plurality of candidate operating condition identification feature factors according to a scatter plot of the candidate operating condition identification feature factors.

[0033] In an optional embodiment, the preset frequency threshold is determined in advance by:

[0034] The preset frequency threshold is determined according to the scatter plot of the vehicle parking frequency factor.

[0035] In the second aspect, an embodiment of the present application also provides a vehicle controller, a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the vehicle controller is running, the processor and the memory communicate through the bus, and the processor executes the machine-readable instructions to execute any one of the methods described in the first aspect.

[0036] In a third aspect, an embodiment of the present application further provides a fuel cell commercial vehicle, comprising: a vehicle body, and a vehicle controller arranged on the vehicle body; the vehicle controller is used to execute any of the methods described in the first aspect.

[0037] The present application provides a working condition identification method, a vehicle controller, and a fuel cell commercial vehicle. The vehicle controller is applied to a fuel cell commercial vehicle, comprising: obtaining characteristic data of the fuel cell commercial vehicle under a preset working condition identification characteristic factor, the characteristic data under the preset working condition identification characteristic factor including: first characteristic data under the vehicle parking frequency factor, the first characteristic data including: the parking frequency and driving distance of the fuel cell commercial vehicle in multiple operating stages, determining the parking frequency of the fuel cell commercial vehicle in multiple operating stages based on the parking frequency and driving distance, identifying the working condition type of the fuel cell commercial vehicle as a short-distance working condition if the parking frequency exceeds a preset frequency threshold, and identifying the working condition type of the fuel cell commercial vehicle as a long-distance working condition if the parking frequency does not exceed the preset frequency threshold. The vehicle controller identifies the working condition online to perform battery management using a battery energy management strategy that matches the working condition type, thereby improving the vehicle's adaptability to working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 Schematic diagram of the process of the working condition identification method provided in the embodiment of the present application Figure 1 ;

[0040] Figure 2 Schematic diagram of the process of the working condition identification method provided in the embodiment of the present application Figure 2 ;

[0041] Figure 3 Schematic diagram of the process of the working condition identification method provided in the embodiment of the present application Figure 3 ;

[0042] Figure 4Schematic diagram of the process of the working condition identification method provided in the embodiment of the present application Figure 4 ;

[0043] Figure 5 Schematic diagram of the process of the working condition identification method provided in the embodiment of the present application Figure 5 ;

[0044] Figure 6 A scatter plot of the vehicle parking frequency factor provided in an embodiment of the present application;

[0045] Figure 7 A scatter plot of the motor power standard deviation factor provided in an embodiment of the present application;

[0046] Figure 8 A schematic diagram of the structure of a vehicle controller provided in an embodiment of the present application;

[0047] Figure 9 A schematic diagram of the structure of a fuel cell commercial vehicle provided in an embodiment of the present application;

[0048] Figure 10 A schematic diagram of the structure of the operating condition identification device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0050] The fuel cell system is a complex, nonlinear, multi-physics coupled power generation device that requires effective system control. Currently, in practical applications, new energy fuel cell vehicles (such as hydrogen fuel cell vehicles) often use rule-based or optimization-based strategies to regulate the fuel cell system's output power to ensure the power output requirements of the fuel cell vehicle. However, new energy fuel cell commercial vehicles (such as hydrogen fuel cell heavy-duty trucks) have a wide range of cargo weight variations and operate in highly variable road conditions. This requires energy management control strategies that can adaptively adjust the fuel cell system's output power based on the vehicle's operating conditions and driving requirements, minimizing efficiency losses caused by frequent high-power charging and discharging of the battery, thereby ensuring power while improving vehicle economy.

[0051] For new energy fuel cell commercial vehicles, it is currently necessary to design corresponding function allocation strategies under known fixed driving conditions, and these strategies can only be calculated offline (i.e., identifying vehicle operating conditions based on offline analysis of driving data). This has limitations when facing unknown, highly uncertain and complex traffic environments, and it is impossible to identify vehicle operating conditions online.

[0052] Based on this, this application uses a method based on operating condition identification to use the vehicle controller of a fuel cell commercial vehicle to online identify the vehicle operating condition, and then actively updates the battery energy management strategy, which can improve the vehicle's adaptability to operating conditions, achieve efficient energy distribution, extend vehicle life, and improve economy while ensuring the power of the entire vehicle.

[0053] Figure 1 Schematic diagram of the process of the working condition identification method provided in the embodiment of the present application Figure 1 The execution subject of this embodiment may be a vehicle controller of a fuel cell commercial vehicle.

[0054] like Figure 1 As shown, the method may include:

[0055] S101. Acquire characteristic data of a fuel cell commercial vehicle under a preset operating condition identification characteristic factor.

[0056] Among them, the preset operating condition identification characteristic factor is a characteristic factor for identifying different vehicle operating conditions. The preset operating condition identification characteristic factor may include a vehicle parking frequency factor. The vehicle parking frequency factor is a characteristic factor for identifying short-distance operating conditions and long-line operating conditions.

[0057] Short-distance operation refers to frequent transportation operations carried out by vehicles within a short distance, usually within a few hundred kilometers, and generally involves the transshipment of goods near factories, ports, docks, railway stations and other places. Long-line operation refers to long-distance transportation operations carried out by vehicles, and the transportation distance is usually hundreds of kilometers or even thousands of kilometers, and generally needs to cross different cities or regions.

[0058] The characteristic data under the preset operating condition identification characteristic factor includes: first characteristic data under the vehicle parking frequency factor, and the first characteristic data includes: the parking frequency and driving distance of the fuel cell commercial vehicle in multiple operating stages.

[0059] Among them, multiple operation stages include: the current operation stage and the operation stage before the current operation stage. Fuel cell commercial vehicles are in a high-voltage state in each operation stage. In the high-voltage state, fuel cell commercial vehicles can drive normally, and cannot drive in the low-voltage state. Among them, when a fuel cell commercial vehicle changes from a high-voltage state to a low-voltage state, it is counted as one operation stage.

[0060] After the fuel cell commercial vehicle is in a high-voltage state, the parking frequency in each operating stage is calculated, where the parking frequency is the number of times the fuel cell commercial vehicle stops in each operating stage, and the driving distance in each operating stage is the distance traveled by the fuel cell commercial vehicle in each operating stage.

[0061] S102: Determine the parking frequency of the fuel cell commercial vehicle in multiple operation stages based on the parking frequency and the driving distance.

[0062] Among them, the parking frequency is used as a factor parameter of fuel cell commercial vehicles under the vehicle parking frequency factor.

[0063] The parking frequencies of multiple operating stages are added together to obtain the total parking frequencies of the fuel cell commercial vehicle in multiple operating stages. The driving distances of multiple operating stages are added together to obtain the total driving distances of the fuel cell commercial vehicle in multiple operating stages. The ratio of the total driving distance to the total parking frequencies is then used as the parking frequency of the fuel cell commercial vehicle in multiple operating stages.

[0064] See the following formula (1):

[0065] (1)

[0066] in, The parking frequency of fuel cell commercial vehicles in multiple operation stages, in times / km, The number of stops of fuel cell commercial vehicles in each operation phase is times. is the driving distance of fuel cell commercial vehicles in each operating stage, in km, For the operation phases, and the value range is a positive integer from 1 to N, where N is the total number of operations in multiple operation phases.

[0067] S103: If the parking frequency exceeds a preset frequency threshold, the operating condition of the fuel cell commercial vehicle is identified as a reverse short-circuit condition, and a battery energy management strategy matching the reverse short-circuit condition is adopted for battery management.

[0068] Based on the stopping frequency, the operating condition of a fuel cell commercial vehicle is identified as either a short-distance or long-distance condition. If the stopping frequency exceeds a preset frequency threshold, the fuel cell commercial vehicle is identified as a short-distance condition. Based on the matching relationship between the operating condition type and the battery energy management strategy, a battery energy management strategy that matches the short-distance condition is derived to manage the fuel cell. Battery energy management based on the matching relationship between the operating condition type and the battery energy management strategy can achieve efficient energy distribution, extend the life of key components, and reduce energy consumption. The preset frequency threshold is denoted as Freq1.

[0069] The battery energy management strategy for reverse short-cycle matching can be a rule-based control strategy. For example, when the battery charge exceeds a threshold, the fuel cell power is controlled to the power corresponding to the threshold. Simulation analysis and experimental verification results show that the rule-based control strategy achieves higher efficiency and stronger robustness in the vehicle.

[0070] S104: If the parking frequency does not exceed the preset frequency threshold, the operating condition type of the fuel cell commercial vehicle is identified as a long-distance operating condition, and battery management is performed using a battery energy management strategy that matches the long-distance operating condition.

[0071] If the parking frequency exceeds the preset frequency threshold, the operating condition type of the fuel cell commercial vehicle is identified as a long-distance operating condition, and based on the matching relationship between the operating condition type and the battery energy management strategy, a battery energy management strategy matching the long-distance operating condition is obtained to manage the fuel cell.

[0072] Among them, if the long-line operating condition is a long-line operating condition in a mountainous area, the battery energy management strategy matching the long-line operating condition in the mountainous area can be a rule-based control strategy; if the long-line operating condition is a long-line operating condition in a plain area, the battery energy management strategy matching the long-line operating condition in the plain area can be an optimization-based dynamic programming control strategy. For example, the fuel cell power is determined in combination with the vehicle's real-time request (such as an accelerator request). If the real-time request requires a larger power, the fuel cell power is increased; if the real-time request requires a smaller power, the fuel cell power is reduced.

[0073] It is worth noting that in fuel cell commercial vehicles, the optimization-based dynamic programming control strategy can optimally allocate the power of the fuel cell according to the real-time request of the vehicle to achieve the best fuel economy and emission performance.

[0074] In some embodiments, after the end of each operating stage, the fuel cell commercial vehicle saves the parking frequency and driving distance in the operating stage to the vehicle controller. Based on the parking frequency and driving distance in multiple operating stages, the vehicle controller calculates the parking frequency of the fuel cell commercial vehicle in multiple operating stages, and identifies the operating condition type of the fuel cell commercial vehicle in multiple operating stages as the operating condition type at the current moment.

[0075] It can be understood that the preset frequency threshold can be a frequency value for identifying the short-distance operating condition and the long-distance operating condition determined based on the parking frequency of the operating fuel cell commercial vehicle in the short-distance operating condition and the long-distance operating condition.

[0076] In this embodiment, the vehicle controller identifies the operating conditions online and adopts a battery energy management strategy that matches the operating condition type to perform battery management, thereby improving the vehicle's adaptability to operating conditions and collaboratively optimizing the power distribution of the fuel cell system's power batteries to achieve efficient energy distribution, extend the life of key components, and reduce hydrogen consumption (taking hydrogen fuel cell commercial vehicles as an example).

[0077] Figure 2 Schematic diagram of the process of the working condition identification method provided in the embodiment of the present application Figure 2 ,like Figure 2 As shown, in an optional embodiment, the above step S102, determining the parking frequency of the fuel cell commercial vehicle in multiple operation stages according to the parking frequency and the driving distance, may include:

[0078] S201. Determine the total driving distance of the fuel cell commercial vehicle in multiple operation stages based on the driving distance.

[0079] The driving distances of multiple operating stages are added together to obtain the total driving distance of the fuel cell commercial vehicle in multiple operating stages.

[0080] S202: If the total driving distance reaches a first distance threshold, determine the total stop frequency of the fuel cell commercial vehicle in multiple operation stages according to the stop frequency.

[0081] It is determined whether the total driving distance reaches a first distance threshold. If the total driving distance is equal to or greater than the first distance threshold, the total driving distance reaches the first distance threshold. The stop frequencies of multiple operating stages are added together to obtain the total stop frequencies of the fuel cell commercial vehicle in multiple operating stages.

[0082] If the total driving distance reaches the first distance threshold, it means that the sample size of the stop frequency and driving distance is sufficient to evaluate the actual working condition. If the total driving distance does not reach the first distance threshold, it means that the sample size is insufficient to evaluate the actual working condition. In this case, the working condition evaluation may not be accurate. The first distance threshold can be recorded as Dis1. That is, When it is greater than or equal to Dis1, the total parking frequency is calculated.

[0083] S203: Determine the parking frequency according to the total driving distance and the total parking frequency.

[0084] The ratio of total driving distance to total parking frequency is used as the parking frequency of fuel cell commercial vehicles in multiple operation stages. The total driving distance can reach the stopping frequency of Dis1.

[0085] In this embodiment, when the total driving distance reaches the first distance threshold, the parking frequency is calculated based on the total driving distance and the total stop frequency. This avoids the problem of insufficient sample size and thus inaccurate parking frequency calculation caused by calculating the parking frequency based on the total driving distance and the total stop frequency when the total driving distance does not reach the first distance threshold.

[0086] In an optional embodiment, the method may further include:

[0087] If the total driving distance is greater than the first distance threshold and does not exceed the second distance threshold, the operating mode type of the fuel cell commercial vehicle remains unchanged.

[0088] The second distance threshold is greater than the first distance threshold, and the second distance threshold can be recorded as Dis2.

[0089] Since the operating distance of fuel cell commercial vehicles is limited, the usage scenarios will not change in a short period of time under normal circumstances. Therefore, there is no need for frequent operating condition identification within a certain distance, which reduces the computing power requirements of the vehicle controller.

[0090] After identifying the operating condition type of the fuel cell commercial vehicle, the total driving distance of the fuel cell commercial vehicle in multiple operating stages is re-acquired during the driving process of the fuel cell commercial vehicle. If the total driving distance is greater than the first distance threshold and does not exceed the second distance threshold, the operating condition type of the fuel cell commercial vehicle remains unchanged. That is, when the total driving distance does not exceed the second distance threshold, the operating condition type of the fuel cell commercial vehicle remains unchanged, and during this process, the battery energy management strategy that matches the current operating condition type is adopted for battery management. This avoids frequent operating condition identification and reduces the computing power required by the vehicle controller.

[0091] In an optional embodiment, the method may further include:

[0092] If the total driving distance exceeds the second distance threshold, the operating mode type of the fuel cell commercial vehicle is re-identified.

[0093] During the driving process of the fuel cell commercial vehicle, the total driving distance of the fuel cell commercial vehicle in multiple operating stages is reacquired. If the total driving distance exceeds the second distance threshold, the total stop frequency of the fuel cell commercial vehicle in multiple operating stages is reacquired, and based on the total driving distance and the reacquired total stop frequency, the stop frequency of the fuel cell commercial vehicle in the multiple operating stages most recently at the current moment is recalculated. Based on the recalculated stop frequency, the operating condition type of the fuel cell commercial vehicle is re-identified. If the recalculated stop frequency exceeds the preset frequency threshold, the operating condition type of the fuel cell commercial vehicle is identified as a short-distance operating condition. If the recalculated stop frequency does not exceed the preset frequency threshold, the operating condition type of the fuel cell commercial vehicle is identified as a long-distance operating condition.

[0094] In an optional embodiment, the characteristic data under the preset operating condition identification characteristic factor includes: second characteristic data under the vehicle motor power standard deviation factor, and the second characteristic data includes: the actual motor speed, actual motor torque and average motor power of the fuel cell commercial vehicle in each operating stage.

[0095] Among them, the preset public identification characteristic factors may include a vehicle motor power standard deviation factor, and the vehicle motor power standard deviation factor is a characteristic factor for identifying mountainous operating conditions and plain operating conditions under long-line operating conditions.

[0096] Mountain operating conditions refer to the operating conditions of the vehicle in mountainous terrain, and plain operating conditions refer to the operating conditions of the vehicle in plain terrain.

[0097] Among them, the vehicle controller can obtain the actual motor speed, actual motor torque and average motor power of the fuel cell commercial vehicle in each operating stage, among which the actual motor speed can include multiple motor speeds collected at multiple collection moments in each operating stage, the actual motor torque can be multiple motor torques collected at multiple sampling moments in each operating stage, and the average motor power can be the average power of multiple motor powers collected at multiple sampling moments in each operating stage.

[0098] Figure 3 Schematic diagram of the process of the working condition identification method provided in the embodiment of the present application Figure 3 ,like Figure 3 As shown, in an optional embodiment, the method may further include:

[0099] S301. Determine the standard deviation of the motor power of the fuel cell commercial vehicle in each operation stage based on the actual motor speed, the actual motor torque, and the average motor power.

[0100] The motor power standard deviation is used as a factor parameter of fuel cell commercial vehicles under the vehicle motor power standard deviation factor.

[0101] Based on the actual motor speed, actual motor torque, and average motor power, the following formula (2) can be used to calculate the motor power standard deviation of a fuel cell commercial vehicle in each operating phase. The motor power standard deviation is used to describe the degree to which the data deviates from the mean, and more intuitively reflects the fluctuation range of the operating conditions on the vehicle demand. Different operating conditions have different vehicle power demands. The larger the motor power standard deviation, the greater the demand, and the smaller the motor power standard deviation, the smaller the demand. For example, the demand is small under plain conditions, and large under mountainous conditions.

[0102] (2)

[0103] in, is the standard deviation of motor power of fuel cell commercial vehicles in each operating stage, in kW. For fuel cell commercial vehicles at the sampling time The motor speed in rpm, Fuel cell commercial vehicles at the sampling time The motor torque, in Nm, is the average motor power of the fuel cell commercial vehicle's drive motor at M sampling moments, where is the sampling time, and its value range is a positive integer from 1 to M.

[0104] S302: Determine, based on the motor power standard deviation, whether the operating condition of the fuel cell commercial vehicle is a mountainous operating condition or a plain operating condition under a long-distance operating condition.

[0105] According to the standard deviation of motor power, the operating condition type of the fuel cell commercial vehicle is determined to be a mountainous operating condition or a plain operating condition under a long-line operating condition. Among them, the mountainous operating condition under the long-line operating condition refers to the mountainous long-line operating condition, and the plain operating condition under the long-line operating condition refers to the plain long-line operating condition.

[0106] It is worth noting that if the total driving distance reaches the first distance threshold, the above steps S301-S302 can also be executed. If the total driving distance is greater than the first distance threshold and does not exceed the second distance threshold, the operating condition type of the fuel cell commercial vehicle remains unchanged. If the total driving distance exceeds the second distance threshold, after executing the above steps S101-S104, the above steps S301-S302 can be continued.

[0107] In this embodiment, the vehicle motor power standard deviation factor is used as the identification characteristic factor for mountainous working conditions and plain working conditions. On the basis of long-line working conditions, it can further identify long-line working conditions in mountainous areas or long-line working conditions in plains, thereby improving the accuracy of working condition identification.

[0108] Figure 4 Schematic diagram of the process of the working condition identification method provided in the embodiment of the present application Figure 4 ,like Figure 4 As shown, in an optional embodiment, the above step S302, determining the operating condition type of the fuel cell commercial vehicle as a mountainous operating condition or a plain operating condition under a long-distance operating condition based on the motor power standard deviation, may include:

[0109] S401: If the motor power standard deviation exceeds a preset power difference threshold, the power count value is increased by one to obtain a target power count value.

[0110] If the motor power standard deviation exceeds the preset power difference threshold (e.g. ), then the power count value is increased by one, that is, , count value , and obtain the target power count value, where the initial value of the power count value is 0.

[0111] For example, if the motor power standard deviations in two operating stages in the multiple operating stages both exceed the preset power difference threshold, the target power count value is 2.

[0112] S402: Determine a standard deviation ratio of motor power of the fuel cell commercial vehicle in the multiple operation phases based on the target power count value and the total number of operations in the multiple operation phases.

[0113] Calculate the total number of operations for multiple operation stages. For example, if there are 5 operation stages, the total number of operations is 5.

[0114] The ratio of the target power count value to the total number of operations is calculated as the standard deviation of the motor power of the fuel cell commercial vehicle in multiple operation stages.

[0115] See the following formula (3):

[0116] (3)

[0117] in, is the standard deviation of motor power, The number of times the motor power standard deviation exceeds the preset power difference threshold, in units of times. It is the total number of operations in multiple operation stages, in times.

[0118] S403: If the motor power standard deviation ratio exceeds a preset ratio threshold, it is determined that the operating condition type of the fuel cell commercial vehicle is a mountainous operating condition under a long-distance operating condition.

[0119] S404: If the motor power standard deviation ratio does not exceed the preset ratio threshold, the operating condition type of the fuel cell commercial vehicle is determined to be a plain operating condition under a long-distance operating condition.

[0120] Among them, the preset proportion threshold can be a calibration value, which is defined according to the conclusion of big data analysis and recorded as , if the motor power standard deviation ratio exceeds the preset ratio threshold, that is, , then the operating condition type of the fuel cell commercial vehicle is determined to be the mountainous operating condition under the long-distance operating condition, that is, the long-distance operating condition in the mountainous area.

[0121] like , then the operating condition type of the fuel cell commercial vehicle is determined to be the plain operating condition under the long-distance operating condition, that is, the plain long-distance operating condition.

[0122] In this embodiment, during some operation stages, the motor power standard deviation under mountainous conditions may fluctuate and be less than a preset power difference threshold. Based on this, the motor power standard deviation ratio is further calculated, and the long-line working condition in mountainous areas or long-line working condition in plains is identified based on whether the motor power standard deviation ratio exceeds the preset ratio threshold, thereby improving the working condition identification accuracy.

[0123] Figure 5 Schematic diagram of the process of the working condition identification method provided in the embodiment of the present application Figure 5 ,like Figure 5 As shown, in an optional embodiment, the vehicle parking frequency factor is pre-determined in the following manner:

[0124] S501: Build an operation feature library and an operation database.

[0125] The operating feature library includes multiple candidate operating condition identification feature factors, which may include, for example, a vehicle parking frequency factor, an air pump activation frequency factor, a vehicle gear distribution factor, a vehicle motor power standard deviation factor, and an energy recovery power distribution factor.

[0126] The operation database includes operational data and historical operating conditions for fuel cell commercial vehicles across multiple historical operating phases. This operational data includes the number of vehicle stops during each historical operating phase (i.e., the number of stops from high voltage to low voltage), distance traveled (i.e., the distance traveled from high voltage to low voltage, in kilometers), and the actual torque, actual speed, and average power of the drive motor.

[0127] Among them, the historical operating condition type is the operating condition type of the fuel cell commercial vehicle that has been in operation in each operating stage. The historical operating condition type of the fuel cell commercial vehicle that has been in operation in each historical operating stage is known, and the historical operating condition type is a short-term operating condition or a long-term operating condition.

[0128] In some embodiments, actual operating data of fuel cell commercial vehicles in operation are collected and gathered through an Internet-connected data platform, and an operating database is constructed.

[0129] S502 , obtaining multiple candidate operating condition identification parameters of fuel cell commercial vehicles in operation in each historical operation stage under the multiple candidate operating condition identification characteristic factors based on the multiple candidate operating condition identification characteristic factors and the operation data.

[0130] Among them, in each operation stage, a candidate operating condition identification characteristic factor corresponds to a candidate operating condition identification parameter. For example, the candidate operating condition identification parameter under the vehicle parking frequency factor is the vehicle parking frequency, the candidate operating condition identification parameter under the air pump enable frequency factor is the air pump enable frequency, the candidate operating condition identification parameter under the vehicle gear distribution factor is the vehicle gear distribution, the candidate operating condition identification parameter under the vehicle motor power standard deviation factor is the vehicle motor power standard deviation, and the candidate operating condition identification parameter under the energy recovery power distribution factor is the energy recovery power distribution.

[0131] Based on multiple candidate operating condition identification characteristic factors and operating data in each historical operating stage, the candidate operating condition identification parameters of the fuel cell commercial vehicles in operation in each operating stage are calculated under each candidate operating condition identification characteristic factor.

[0132] For example, the operating data includes: the number of vehicle stops in each historical operating stage (i.e., the number of single operation stops of the vehicle from upper high pressure to lower high pressure), and the driving distance (i.e., the single driving distance of the vehicle from upper high pressure to lower high pressure). Then, the vehicle stop frequency under the vehicle stop frequency factor can be calculated and determined based on the operating data and formula (4).

[0133] See the following formula (4):

[0134] (4)

[0135] in, The number of stops of the vehicle in each historical operation stage, in times / km, It is the distance traveled by the vehicle in each historical operation stage, in km.

[0136] For another example, the operating data includes the actual torque, actual speed, and average power of the drive motor. The vehicle motor power standard deviation under the vehicle motor power standard deviation factor can be calculated and determined based on the operating data and the above formula (2).

[0137] S503. Draw a scatter plot of the candidate operating condition identification characteristic factors in multiple historical operating stages based on multiple candidate operating condition identification characteristic factors of the fuel cell commercial vehicle in operation and the historical operating condition types.

[0138] The scatter distribution diagram is based on the historical operating condition types in each historical operating stage, and is a distribution diagram of the candidate operating condition identification characteristic factors in multiple historical operating stages. A scatter distribution diagram is drawn for each candidate operating condition identification characteristic factor.

[0139] The horizontal axis of the scatter distribution diagram can be the historical operation stage, and the vertical axis can be the candidate operating condition identification parameters under each candidate operating condition identification characteristic factor, wherein different colors are used to distinguish the candidate operating condition identification parameters for different historical operating condition types.

[0140] Figure 6 The scatter plot of the vehicle parking frequency factor provided in the embodiment of the present application is as follows: Figure 6 As shown, the horizontal axis represents the historical operation stage, the unit is individual, the vertical axis represents the vehicle stop frequency under the vehicle stop frequency factor, the unit is times / km, black is used to represent the vehicle stop frequency under long-line working conditions, and red is used to represent the vehicle stop frequency under short-line working conditions.

[0141] S504 : Determine a vehicle parking frequency factor from a plurality of candidate operating condition identification characteristic factors according to a scatter plot of each candidate operating condition identification characteristic factor.

[0142] According to the scatter plot of each candidate working condition identification characteristic factor, a characteristic factor that clearly distinguishes the working condition types is selected from multiple candidate working condition identification characteristic factors as the vehicle parking frequency factor, which is obtained by Figure 6 It can be seen that under the vehicle stop frequency factor, the reverse short-distance working condition and the long-line working condition can be clearly distinguished. The vehicle stop frequency factor is selected from multiple candidate working condition identification characteristic factors as the identification factor of the reverse short-distance working condition and the long-line working condition.

[0143] In an optional implementation manner, the preset frequency threshold (Freq1) is determined in advance in the following manner: the preset frequency threshold is determined according to a scatter plot of the vehicle parking frequency factor.

[0144] According to the scatter distribution diagram of the vehicle parking frequency factor, the parking frequency used to distinguish between the reverse short-distance working condition and the long-distance working condition in the scatter distribution diagram is determined as the preset frequency threshold, see Figure 6 The preset frequency threshold may range from 1 time / km to 3 times / km, for example, 2 times / km or 2.5 times / km, which is not particularly limited in this embodiment.

[0145] In some embodiments, the historical operating condition type can also be a mountainous operating condition or a plain operating condition under a long-line operating condition. In this case, multiple target operating stages in which the historical operating condition type is a mountainous long-line operating condition or a plain long-line operating condition can be determined from multiple operating stages, and a target scatter distribution diagram of each candidate operating condition identification characteristic factor in multiple target operating stages is drawn based on multiple candidate operating condition identification parameters of the operated fuel cell commercial vehicle under multiple candidate operating condition identification characteristic factors, as well as the historical operating condition type of the fuel cell commercial vehicle in each target historical operating stage.

[0146] The horizontal axis of the scatter plot may be the target operation stage, and the vertical axis may be the candidate operating condition identification parameters under each candidate operating condition identification characteristic factor. Different colors are used to distinguish the candidate operating condition identification parameters for different historical operating condition types.

[0147] Figure 7 The scatter plot of the motor power standard deviation factor provided in the embodiment of the present application is as follows: Figure 7 As shown, the horizontal axis represents the target operation stage, and the vertical axis is the motor power standard deviation under the motor power standard deviation factor, in kW. Black and red represent the motor power standard deviation under plain long line 1 and plain long line 2, respectively. Blue, green and orange represent the motor power standard deviation under mountain long line 1, mountain long line 2 and mountain long line 3, respectively.

[0148] Then, according to the target scatter distribution diagram of each candidate working condition identification characteristic factor, the characteristic factor that can clearly distinguish the working condition type is selected from multiple candidate working condition identification characteristic factors as the motor power standard deviation factor, which is obtained by Figure 7 It can be seen that under the motor power standard deviation factor, the mountainous long-distance operating condition and the plain long-distance operating condition can be clearly distinguished (the motor power standard deviation corresponding to the plain long-distance line 2 fluctuated in some operating stages). Among them, under other candidate operating condition identification characteristic factors, the mountainous long-distance operating condition and the plain long-distance operating condition cannot be distinguished.

[0149] In some embodiments, the preset power difference threshold may also be determined based on a target scatter plot of the motor power standard deviation factor.

[0150] According to the target scatter distribution diagram of the motor power standard deviation factor, the motor power standard deviation in the target scatter distribution diagram that can better distinguish between the long-distance working conditions in mountainous areas and the long-distance working conditions in plains is determined as the preset power difference threshold. Figure 7 The preset power difference threshold (Var1) may have a value range of 130 kW to 140 kW, for example, 135 kW, 136 kW, etc., which is not particularly limited in this embodiment.

[0151] In this embodiment, operational data for three operating conditions—short reverse, long mountain routes, and long plain routes—are categorized based on information such as vehicle routes and altitude gradients. By plotting a scatter plot of the identification characteristic factors for each candidate operating condition, distinct operating condition identification characteristic factors are screened and identified. The vehicle stop frequency factor is used as the identification characteristic factor for short reverse and long route conditions, with a threshold of Freq1. The motor power standard deviation is used as the identification characteristic factor for long mountain routes and long plain routes, with a threshold of Var1. This approach, based on the historical experience of existing fuel cell commercial vehicles, determines the identification characteristic factors and corresponding thresholds for different operating conditions, improving the accuracy of operating condition identification.

[0152] Figure 8 A schematic diagram of the structure of the vehicle controller provided in the embodiment of the present application is shown in FIG. Figure 8 As shown, the vehicle controller includes: a processor 601, a memory 602 and a bus 603. The memory 602 stores machine-readable instructions executable by the processor 601. When the vehicle controller is running, the processor 601 and the memory 602 communicate through the bus 603, and the processor 601 executes the machine-readable instructions to perform the above method.

[0153] Figure 9 A schematic diagram of the structure of a fuel cell commercial vehicle provided in an embodiment of the present application is shown in FIG. Figure 9 As shown, the fuel cell commercial vehicle includes: a vehicle body 701, and a vehicle controller 702 arranged on the vehicle body 701; the vehicle controller 702 is used to execute the above method.

[0154] Figure 10 This is a structural diagram of the operating condition identification device provided in an embodiment of the present application, which can be integrated into a vehicle controller.

[0155] like Figure 10 As shown, the device may include:

[0156] An acquisition module 801 is configured to acquire characteristic data of a fuel cell commercial vehicle under a preset operating condition identification characteristic factor, wherein the characteristic data under the preset operating condition identification characteristic factor includes first characteristic data under a vehicle stop frequency factor, wherein the first characteristic data includes the stop frequency and travel distance of the fuel cell commercial vehicle in multiple operating stages;

[0157] A determination module 802 is configured to determine the parking frequency of the fuel cell commercial vehicle in multiple operation phases based on the parking frequency and the driving distance, wherein the parking frequency serves as a factor parameter of the fuel cell commercial vehicle under the vehicle parking frequency factor;

[0158] Identification module 803, configured to identify the fuel cell commercial vehicle as a reverse short-circuit condition if the parking frequency exceeds a preset frequency threshold, and to employ a battery energy management strategy that matches the reverse short-circuit condition for battery management;

[0159] The identification module 803 is further configured to identify the operating condition of the fuel cell commercial vehicle as a long-distance operating condition if the parking frequency does not exceed a preset frequency threshold, so as to adopt a battery energy management strategy matching the long-distance operating condition for battery management.

[0160] In an optional implementation, the determination module 802 is specifically configured to:

[0161] Determine the total driving distance of fuel cell commercial vehicles in multiple operating stages based on the driving distance;

[0162] If the total driving distance reaches a first distance threshold, then determining the total stop frequency of the fuel cell commercial vehicle in multiple operation stages based on the stop frequency;

[0163] Determine the stopping frequency based on the total driving distance and the total stopping frequency.

[0164] In an optional implementation, the determining module 802 is further configured to:

[0165] If the total driving distance is greater than the first distance threshold and does not exceed the second distance threshold, the operating mode type of the fuel cell commercial vehicle remains unchanged.

[0166] In an optional implementation, the identification module 803 is further configured to:

[0167] If the total driving distance exceeds the second distance threshold, the operating mode type of the fuel cell commercial vehicle is re-identified.

[0168] In an optional embodiment, the characteristic data under the preset operating condition identification characteristic factor includes: second characteristic data under the vehicle motor power standard deviation factor, the second characteristic data including: the actual motor speed, actual motor torque and average motor power of the fuel cell commercial vehicle in each operating stage;

[0169] The determination module 802 is further configured to determine the motor power standard deviation of the fuel cell commercial vehicle in each operation phase based on the actual motor speed, the actual motor torque, and the average motor power, wherein the motor power standard deviation serves as a factor parameter of the fuel cell commercial vehicle under the vehicle motor power standard deviation factor;

[0170] The determination module 802 is further configured to determine, based on the motor power standard deviation, whether the operating condition type of the fuel cell commercial vehicle is a mountainous operating condition or a plain operating condition under a long-distance operating condition.

[0171] In an optional implementation, the determining module 802 is further configured to:

[0172] If the motor power standard deviation exceeds the preset power difference threshold, the power count value is increased by one to obtain the target power count value;

[0173] Determine the standard deviation of motor power for fuel cell commercial vehicles in multiple operating phases based on the target power count value and the total number of operations in multiple operating phases;

[0174] If the motor power standard deviation ratio exceeds the preset ratio threshold, the fuel cell commercial vehicle is determined to be in a mountainous area under a long-distance operating condition.

[0175] If the proportion of the motor power standard deviation does not exceed the preset proportion threshold, it is determined that the operating condition type of the fuel cell commercial vehicle is a plain operating condition under a long-distance operating condition.

[0176] In an optional embodiment, the vehicle parking frequency factor is pre-determined as follows:

[0177] Construct an operation feature library and an operation database. The operation feature library includes: multiple candidate operating condition identification feature factors. The operation database includes: operating data and historical operating condition types of fuel cell commercial vehicles in multiple historical operating stages; the historical operating condition types are short-distance operating conditions or long-distance operating conditions;

[0178] Based on multiple candidate operating condition identification characteristic factors and operating data, multiple candidate operating condition identification parameters of fuel cell commercial vehicles in operation during each historical operating stage are obtained under multiple candidate operating condition identification characteristic factors;

[0179] Based on multiple candidate operating condition identification parameters of fuel cell commercial vehicles in operation under multiple candidate operating condition identification characteristic factors and historical operating condition types, a scatter plot of the candidate operating condition identification characteristic factors in multiple historical operating stages is drawn;

[0180] According to the scatter distribution diagram of each candidate operating condition identification characteristic factor, a vehicle parking frequency factor is determined from a plurality of candidate operating condition identification characteristic factors.

[0181] In an optional embodiment, the preset frequency threshold is determined in advance by:

[0182] A preset frequency threshold is determined based on a scatter plot of the vehicle parking frequency factor.

[0183] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference can be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0184] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above method is executed.

[0185] In the embodiment of the present application, the computer program can also execute other machine-readable instructions when run by the processor to execute other methods described in the embodiment. For the specific execution method steps and principles, please refer to the description of the embodiment and will not be repeated here.

[0186] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0187] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0188] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0189] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0190] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0191] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed in the present application, or make equivalent replacements for some of the technical features thereof. However, these modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application. They should all be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for identifying an operating condition, characterized in that: A vehicle controller applied to a fuel cell commercial vehicle, the method comprising: Acquiring characteristic data of the fuel cell commercial vehicle under a preset operating condition identification characteristic factor, the characteristic data under the preset operating condition identification characteristic factor comprising: first characteristic data under a vehicle stop frequency factor, the first characteristic data comprising: stop frequency and travel distance of the fuel cell commercial vehicle in multiple operating stages; Determining, based on the parking frequency and the driving distance, a parking frequency of the fuel cell commercial vehicle in the multiple operation stages, wherein the parking frequency serves as a factor parameter of the fuel cell commercial vehicle under the vehicle parking frequency factor; If the parking frequency exceeds a preset frequency threshold, identifying the operating condition of the fuel cell commercial vehicle as a reverse short-circuit operating condition, and adopting a battery energy management strategy matching the reverse short-circuit operating condition for battery management; If the parking frequency does not exceed the preset frequency threshold, identifying the operating condition of the fuel cell commercial vehicle as a long-distance operating condition, and adopting a battery energy management strategy matching the long-distance operating condition for battery management; The characteristic data under the preset operating condition identification characteristic factor includes: second characteristic data under the vehicle motor power standard deviation factor, the second characteristic data including: the actual motor speed, actual motor torque and average motor power of the fuel cell commercial vehicle in each operating stage; The method further comprises: Determining a motor power standard deviation of the fuel cell commercial vehicle in each operation phase according to the actual motor speed, the actual motor torque, and the average motor power, wherein the motor power standard deviation serves as a factor parameter of the fuel cell commercial vehicle under the vehicle motor power standard deviation factor; According to the motor power standard deviation, it is determined that the operating condition type of the fuel cell commercial vehicle is a mountainous operating condition or a plain operating condition under the long-distance operating condition.

2. The method according to claim 1, characterized in that Determining the parking frequency of the fuel cell commercial vehicle in the multiple operation stages according to the parking frequency and the driving distance includes: determining, based on the driving distance, a total driving distance of the fuel cell commercial vehicle in the plurality of operation stages; If the total driving distance is greater than or equal to a first distance threshold, determining a total stop frequency of the fuel cell commercial vehicle in the plurality of operation stages according to the stop frequency; The parking frequency is determined according to the total driving distance and the total parking frequency.

3. The method according to claim 2, characterized in that The method further comprises: If the total driving distance is greater than the first distance threshold and does not exceed the second distance threshold, the operating mode type of the fuel cell commercial vehicle remains unchanged.

4. The method according to claim 3, characterized in that The method further comprises: If the total driving distance exceeds the second distance threshold, the operating condition type of the fuel cell commercial vehicle is re-identified.

5. The method according to claim 1, wherein The determining, based on the motor power standard deviation, that the operating condition type of the fuel cell commercial vehicle is a mountainous operating condition or a plain operating condition under the long-distance operating condition includes: If the motor power standard deviation exceeds a preset power difference threshold, the power count value is increased by one to obtain a target power count value; Determining a standard deviation ratio of motor power of the fuel cell commercial vehicle in the multiple operating stages according to the target power count value and the total number of operations in the multiple operating stages; If the motor power standard deviation ratio exceeds the preset ratio threshold, it is determined that the operating condition type of the fuel cell commercial vehicle is the mountainous operating condition under the long-distance operating condition; If the motor power standard deviation ratio does not exceed the preset ratio threshold, it is determined that the operating condition type of the fuel cell commercial vehicle is the plain operating condition under the long-distance operating condition.

6. The method according to claim 1, characterized in that The vehicle parking frequency factor is determined in advance by: Constructing an operation feature library and an operation database, wherein the operation feature library includes: a plurality of candidate operating condition identification feature factors; the operation database includes: operating data and historical operating condition types of fuel cell commercial vehicles in multiple historical operating stages; the historical operating condition types are short-distance operating conditions or long-distance operating conditions; According to the multiple candidate operating condition identification characteristic factors and the operating data, a plurality of candidate operating condition identification parameters of the fuel cell commercial vehicle in operation in each historical operating stage under the multiple candidate operating condition identification characteristic factors are obtained; Drawing a scatter plot of the candidate operating condition identification characteristic factors in the multiple historical operating stages based on the multiple candidate operating condition identification characteristic factors of the operated fuel cell commercial vehicle and the historical operating condition type; The vehicle parking frequency factor is determined from the plurality of candidate operating condition identification feature factors according to a scatter plot of the candidate operating condition identification feature factors.

7. The method according to claim 6, characterized in that The preset frequency threshold is determined in advance by: The preset frequency threshold is determined according to the scatter plot of the vehicle parking frequency factor.

8. A vehicle controller, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the vehicle controller is running, the processor and the memory communicate via the bus, and the processor executes the machine-readable instructions to perform the method according to any one of claims 1 to 7.

9. A fuel cell commercial vehicle, characterized in that: include: A vehicle body, and a vehicle controller arranged on the vehicle body; the vehicle controller is used to execute the method described in any one of claims 1-7.

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