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

Abstract

The invention provides a working condition identification method, a vehicle controller and a fuel cell commercial vehicle, and is applied to the vehicle controller of the fuel cell commercial vehicle, and the method comprises the steps: obtaining the feature data of the fuel cell commercial vehicle under a preset working condition identification feature factor, the characteristic data under the preset working condition identification characteristic factor comprises first characteristic data under a vehicle parking frequency factor, and the first characteristic data comprises parking frequencies and driving distances of the fuel cell commercial vehicle in a plurality of operation stages; and the parking frequency of the fuel cell commercial vehicle in the multiple operation stages is determined, if the parking frequency exceeds a preset frequency threshold value, the working condition type of the fuel cell commercial vehicle is identified as a reverse short working condition, and if the parking frequency does not exceed the preset frequency threshold value, the working condition type of the fuel cell commercial vehicle is identified as a long-line working condition. And the vehicle controller identifies the working condition online to perform battery management by adopting the battery energy management strategy matched with the working condition type, so that the vehicle working condition adaptability is improved.

Description

Technical Field

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

[0002] A fuel cell vehicle is a new energy vehicle that uses the electric power generated by an in-vehicle fuel cell device as power. Among them, the load mass change range of a fuel cell commercial vehicle (such as a fuel cell heavy truck) is very large and the operating road conditions are variable. It is necessary to adaptively adjust the output power of the fuel cell system according to the vehicle's operating conditions and driving requirements to improve the vehicle's power performance while enhancing the overall vehicle economy.

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

[0004] However, the above method has limitations in the face of unknown and highly uncertain complex traffic environments and cannot identify the vehicle's operating conditions online. Summary of the Invention

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

[0006] In a first aspect, an embodiment of the present application provides a working condition identification method applied to a vehicle controller of a fuel cell commercial vehicle. The method includes: Obtain the 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 includes: first characteristic data under a vehicle parking frequency factor, and the first characteristic data includes: the parking frequency and driving distance of the fuel cell commercial vehicle in multiple operation stages; Determine the parking frequency of the fuel cell commercial vehicle in the multiple operation stages according to the parking frequency and the driving distance. The parking frequency is used 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, identify the working condition type of the fuel cell commercial vehicle as a short-distance operation condition, and adopt a battery energy management strategy matching the short-distance operation condition for battery management; If the parking frequency does not exceed the preset frequency threshold, identify the working condition type of the fuel cell commercial vehicle as a long-distance operation condition, and adopt a battery energy management strategy matching the long-distance operation condition for battery management.

[0007] In an alternative embodiment, determining the parking frequency of the fuel cell commercial vehicle during the multiple operation stages according to the parking frequency and the driving distance includes: Determining the total driving distance of the fuel cell commercial vehicle during the multiple operation stages according to the driving distance; If the total driving distance reaches a first distance threshold, determining the total parking frequency of the fuel cell commercial vehicle during the multiple operation stages according to the parking frequency; Determining the parking frequency according to the total driving distance and the total parking frequency.

[0008] In an alternative embodiment, the method further includes: If the total driving distance is greater than the first distance threshold and does not exceed a second distance threshold, keeping the working condition type of the fuel cell commercial vehicle unchanged.

[0009] In an alternative embodiment, the method further includes: If the total driving distance exceeds the second distance threshold, re-identifying the working condition type of the fuel cell commercial vehicle.

[0010] In an alternative embodiment, the characteristic data under the preset working 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, the actual motor torque, and the average motor power of the fuel cell commercial vehicle during each operation stage; The method further includes: Determining the motor power standard deviation of the fuel cell commercial vehicle during each operation stage according to the actual motor speed, the actual motor torque, and the average motor power, and the motor power standard deviation is used as the factor parameter of the fuel cell commercial vehicle under the vehicle motor power standard deviation factor; Determining the working condition type of the fuel cell commercial vehicle as a mountainous condition or a plain condition under the long-line condition according to the motor power standard deviation.

[0011] In an alternative embodiment, determining the working condition type of the fuel cell commercial vehicle as a mountainous condition or a plain condition under the long-line condition according to the motor power standard deviation includes: If the motor power standard deviation exceeds a preset power difference threshold, adding one to the power count value to obtain a target power count value; Determining the proportion of the motor power standard deviation of the fuel cell commercial vehicle during the multiple operation stages according to the target power count value and the total number of operations of the multiple operation stages; If the proportion of the standard deviation of the motor power exceeds a preset proportion threshold, determine that the operating condition type of the fuel cell commercial vehicle is the mountain condition under the long-haul condition; If the proportion of the standard deviation of the motor power does not exceed the preset proportion threshold, determine that the operating condition type of the fuel cell commercial vehicle is the plain condition under the long-haul condition.

[0012] In an alternative embodiment, the vehicle parking frequency factor is determined in advance by the following method: Construct an operation feature library and an operation database. The operation feature library includes: a plurality of candidate condition identification feature factors. The operation database includes: the operation data and historical condition types of the operated fuel cell commercial vehicles in multiple historical operation stages; the historical condition types are the short-haul condition or the long-haul condition; According to the plurality of candidate condition identification feature factors and the operation data, obtain a plurality of candidate condition identification parameters of the operated fuel cell commercial vehicle under the plurality of candidate condition identification feature factors in each historical operation stage; According to the plurality of candidate condition identification parameters of the operated fuel cell commercial vehicle under the plurality of candidate condition identification feature factors and the historical condition types, draw a scatter plot of each candidate condition identification feature factor in the multiple historical operation stages; According to the scatter plot of each candidate condition identification feature factor, determine the vehicle parking frequency factor from the plurality of candidate condition identification feature factors.

[0013] In an alternative embodiment, the preset frequency threshold is determined in advance by the following method: According to the scatter plot of the vehicle parking frequency factor, determine the preset frequency threshold.

[0014] In a second aspect, an embodiment of the present application further provides a vehicle controller, a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the vehicle controller runs, the processor communicates with the memory through the bus. The processor executes the machine-readable instructions to execute the method according to any one of the first aspect.

[0015] In a third aspect, an embodiment of the present application further provides a fuel cell commercial vehicle, including: a vehicle body, and a vehicle controller disposed on the vehicle body; the vehicle controller is used to execute the method according to any one of the first aspect.

[0016] The present application provides a working condition identification method, a vehicle controller, and a fuel cell commercial vehicle, which are applied to the vehicle controller of a fuel cell commercial vehicle, and include: obtaining characteristic data of the fuel cell commercial vehicle under preset working condition identification characteristic factors, where the characteristic data under the preset working condition identification characteristic factors 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 operation stages. According to the parking frequency and driving distance, determine the parking frequency of the fuel cell commercial vehicle in multiple operation stages. If the parking frequency exceeds the preset frequency threshold, identify the working condition type of the fuel cell commercial vehicle as the short-distance operation condition. If the parking frequency does not exceed the preset frequency threshold, identify the working condition type of the fuel cell commercial vehicle as the long-distance operation condition. The vehicle controller identifies the working condition online to perform battery management by adopting a battery energy management strategy matching the working condition type, so as to improve the vehicle's working condition adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 Flow schematic diagram of the working condition identification method provided by the embodiment of the present application Figure 1 ; Figure 2 Flow schematic diagram of the working condition identification method provided by the embodiment of the present application Figure 2 ; Figure 3 Flow schematic diagram of the working condition identification method provided by the embodiment of the present application Figure 3 ; Figure 4 Flow schematic diagram of the working condition identification method provided by the embodiment of the present application Figure 4 ; Figure 5 Flow schematic diagram of the working condition identification method provided by the embodiment of the present application Figure 5 ; Figure 6 Scatter plot distribution diagram of the vehicle parking frequency factor provided by the embodiment of the present application; Figure 7 Scatter plot distribution diagram of the motor power standard deviation factor provided by the embodiment of the present application; Figure 8 Structure schematic diagram of the vehicle controller provided by the embodiment of the present application; Figure 9 Structure schematic diagram of the fuel cell commercial vehicle provided by the embodiment of the present application; Figure 10 This is a schematic structural diagram of the working condition recognition device provided by the embodiments of the present application. Detailed implementation manners

[0019] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Components of the embodiments of the present application usually described and illustrated in the accompanying 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 accompanying drawings is not intended to limit the scope of the present application claimed, but merely represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the protection scope of the present application.

[0020] A fuel cell system is a complex power generation device with non-linear multi-physical quantity coupling, and the system needs to be effectively controlled. Currently, in practical applications, new energy fuel cell vehicles (such as hydrogen fuel cell vehicles) mostly adopt rule-based or optimized strategies to adjust the output power of the fuel cell system to ensure the power output requirements of the fuel cell vehicle. However, the load mass change range of new energy fuel cell commercial vehicles (such as hydrogen fuel cell heavy trucks) is very large and the operating road conditions are variable. This requires the energy management control strategy to adaptively adjust the output power of the fuel cell system according to the vehicle working conditions and driving demands, reduce the efficiency loss caused by frequent large-power charging and discharging of the battery, and improve the vehicle economy while ensuring the power performance.

[0021] For new energy fuel cell commercial vehicles, currently, it is necessary to design corresponding function allocation strategies under known fixed driving conditions, and only offline operation can be performed (that is, analyzing and identifying vehicle working conditions based on driving data offline). It has limitations in the face of unknown and highly uncertain complex traffic environments and cannot identify vehicle working conditions online.

[0022] Based on this, the method of the present application based on working condition recognition uses the vehicle controller of a fuel cell commercial vehicle to online identify vehicle working conditions, and then actively updates the battery energy management strategy, which can improve the vehicle working condition adaptability, achieve efficient energy allocation, extend the vehicle life, and improve the economy while ensuring the vehicle power performance.

[0023] Figure 1 This is a flowchart of the working condition recognition method provided by the embodiments of the present application Figure 1 The execution subject of this embodiment can be the vehicle controller of a fuel cell commercial vehicle.

[0024] Such as Figure 1As shown, the method may include: S101. Obtain the characteristic data of the fuel cell commercial vehicle under the preset working condition identification characteristic factors.

[0025] Among them, the preset working condition identification characteristic factors are the characteristic factors for identifying different vehicle working conditions, and the preset working condition identification characteristic factors may include the vehicle parking frequency factor, and the vehicle parking frequency factor is the characteristic factor for identifying the short-distance operation condition and the long-distance operation condition.

[0026] The short-distance operation condition refers to the frequent transportation operation of the vehicle within a short distance, usually the transportation distance is within several hundred kilometers, and generally the goods are transferred near places such as factories, ports, docks, railway stations, etc. The long-distance operation condition refers to the long-distance transportation operation of the vehicle, and the transportation distance is usually several hundred kilometers or even more than a thousand kilometers, and generally needs to cross different cities or regions.

[0027] The characteristic data under the preset working condition identification characteristic factors includes: the first characteristic data under the vehicle parking frequency factor, and the first characteristic data includes: the parking frequency and the driving distance of the fuel cell commercial vehicle in multiple operation stages.

[0028] Among them, the multiple operation stages include: the current operation stage and the operation stages before the current operation stage. The fuel cell commercial vehicle is in a high-voltage state in each operation stage. In the high-voltage state, the fuel cell commercial vehicle can drive normally and cannot drive in the voltage state. Among them, when the fuel cell commercial vehicle changes from the high-voltage state to the low-voltage state, it is counted as one operation stage.

[0029] After the fuel cell commercial vehicle is in the high-voltage state, calculate the parking frequency in each operation stage. Among them, the parking frequency is the number of parking times of the fuel cell commercial vehicle in each operation stage, and the driving distance of each operation stage is the distance traveled by the fuel cell commercial vehicle in each operation stage.

[0030] S102. Determine the parking frequency of the fuel cell commercial vehicle in multiple operation stages according to the parking frequency and the driving distance.

[0031] Among them, the parking frequency is used as the factor parameter of the fuel cell commercial vehicle under the vehicle parking frequency factor.

[0032] Add up the parking frequencies of multiple operation stages to obtain the total parking frequency of the fuel cell commercial vehicle in multiple operation stages. Add up the driving distances of multiple operation stages to obtain the total driving distance of the fuel cell commercial vehicle in multiple operation stages. Then, take the ratio of the total driving distance to the total parking frequency as the parking frequency of the fuel cell commercial vehicle in multiple operation stages.

[0033] See the following formula (1): (1) Among them, is the parking frequency of the fuel cell commercial vehicle in multiple operation stages, with the unit of times / km. is the parking frequency of the fuel cell commercial vehicle in each operation stage, with the unit of times. is the driving distance of the fuel cell commercial vehicle in each operation stage, with the unit of km. is the th operation stage, and the value range is a positive integer from 1 to N, where N is the total number of operations in multiple operation stages.

[0034] S103. If the parking frequency exceeds the preset frequency threshold, identify the working condition type of the fuel cell commercial vehicle as the short-distance operation condition, and adopt the battery energy management strategy matching the short-distance operation condition for battery management.

[0035] Identify the working condition type of the fuel cell commercial vehicle as the short-distance operation condition or the long-distance operation condition according to the parking frequency. If the parking frequency exceeds the preset frequency threshold, identify the working condition type of the fuel cell commercial vehicle as the short-distance operation condition, and obtain the battery energy management strategy matching the short-distance operation condition according to the matching relationship between the working condition type and the battery energy management strategy to conduct battery management on the fuel cell. Among them, based on the matching relationship between the working condition type and the battery energy management strategy, battery energy management can achieve efficient energy distribution, extend the life of key components, and reduce energy consumption. The preset frequency threshold is denoted as Freq1.

[0036] Among them, the battery energy management strategy matching the short-distance operation condition can be a rule-based control strategy. For example, when the battery power exceeds the threshold power, control the fuel cell power to the power corresponding to the threshold power, etc. Through simulation analysis and test verification results, adopting a rule-based control strategy, the vehicle has higher efficiency and stronger robustness.

[0037] S104. If the parking frequency does not exceed the preset frequency threshold, identify the working condition type of the fuel cell commercial vehicle as the long-distance operation condition, and adopt the battery energy management strategy matching the long-distance operation condition for battery management.

[0038] If the parking frequency exceeds the preset frequency threshold, identify the working condition type of the fuel cell commercial vehicle as the long-distance operation condition, and obtain the battery energy management strategy matching the long-distance operation condition according to the matching relationship between the working condition type and the battery energy management strategy to conduct battery management on the fuel cell.

[0039] Among them, if the long-haul working condition is a mountain long-haul working condition, the battery energy management strategy matched with the mountain long-haul working condition can be a rule-based control strategy. If the long-haul working condition is a plain long-haul working condition, the battery energy management strategy matched with the plain long-haul working condition can be an optimized dynamic programming control strategy. For example, in combination with the real-time request of the vehicle (such as the request to step on the accelerator), the fuel cell power is determined. If the real-time request requires a large power, the fuel cell power is increased. If the real-time request requires a small power, the fuel cell power is decreased.

[0040] It should be noted that in fuel cell commercial vehicles, the optimized 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.

[0041] In some embodiments, after each operation stage of the fuel cell commercial vehicle, the parking frequency and driving distance during the operation stage are saved to the vehicle controller. The vehicle controller calculates the parking frequency of the fuel cell commercial vehicle during multiple operation stages based on the parking frequency and driving distance during multiple operation stages, and identifies the working condition type of the fuel cell commercial vehicle during multiple operation stages as the working condition type at the current moment.

[0042] It can be understood that the preset frequency threshold can be a frequency value determined based on the parking frequencies of the already-operated fuel cell commercial vehicles in the short-distance working condition and the long-haul working condition for identifying the short-distance working condition and the long-haul working condition.

[0043] In this embodiment, the vehicle controller online identifies the working condition to perform battery management by using the battery energy management strategy matched with the working condition type, improves the vehicle's working condition adaptability, and collaboratively optimizes the power distribution of the power battery of the fuel cell system to achieve efficient energy distribution, extend the service life of key components, and reduce hydrogen consumption (taking hydrogen fuel cell commercial vehicles as an example).

[0044] Figure 2 Flow schematic of the working condition identification method provided by the embodiment of the present application Figure 2 , as Figure 2 shown, in an optional implementation manner, the above step S102 of determining the parking frequency of the fuel cell commercial vehicle during multiple operation stages according to the parking frequency and driving distance may include: S201. Determine the total driving distance of the fuel cell commercial vehicle during multiple operation stages according to the driving distance.

[0045] Add up the driving distances of multiple operation stages to obtain the total driving distance of the fuel cell commercial vehicle during multiple operation stages.

[0046] S202. If the total driving distance reaches the first distance threshold, determine the total parking frequency of the fuel cell commercial vehicle during multiple operation stages according to the parking frequency.

[0047] Judge whether the total driving distance reaches the first distance threshold. If the total driving distance is equal to or greater than the first distance threshold, that is, the total driving distance reaches the first distance threshold, then add up the parking frequencies of multiple operation stages to obtain the total parking frequency of the fuel cell commercial vehicle during multiple operation stages.

[0048] Among them, if the total driving distance reaches the first distance threshold, it means that the sample sizes of the parking frequency and the driving distance are sufficient for evaluating the actual working conditions. If the total driving distance does not reach the first distance threshold, it means that the sample sizes are not sufficient for evaluating the actual working conditions. In this case, the evaluation of the working conditions may not be very accurate. Among them, the first distance threshold can be denoted as Dis1. That is to say, When it is greater than or equal to Dis1, calculate the total parking frequency.

[0049] S203. Determine the parking frequency according to the total driving distance and the total parking frequency.

[0050] Take the ratio of the total driving distance and the total parking frequency as the parking frequency of the fuel cell commercial vehicle during multiple operation stages. Among them, in formula (1), can be the parking frequency when the total driving distance reaches Dis1.

[0051] In this embodiment, when the total driving distance reaches the first distance threshold, calculate the parking frequency according to the total driving distance and the total parking frequency, and avoid calculating the parking frequency according to the total driving distance and the total parking frequency when the total driving distance does not reach the first distance threshold, which may lead to insufficient sample sizes for calculating the parking frequency and thus inaccurate problems.

[0052] In an optional implementation manner, the method may further include: If the total driving distance is greater than the first distance threshold and does not exceed the second distance threshold, keep the working condition type of the fuel cell commercial vehicle unchanged.

[0053] Among them, the second distance threshold is greater than the first distance threshold, and the second distance threshold can be denoted as Dis2.

[0054] Since the operating distance of the fuel cell commercial vehicle is limited and the usage scenarios generally do not change in a short time under normal circumstances, it is not necessary to frequently perform working condition identification within a certain distance, reducing the computing power requirements for the vehicle controller.

[0055] After identifying the working condition type of the fuel cell commercial vehicle, during the driving process of the fuel cell commercial vehicle, re-obtain the total driving distance of the fuel cell commercial vehicle in multiple operation stages. If the total driving distance is greater than the first distance threshold and does not exceed the second distance threshold, keep the working condition type of the fuel cell commercial vehicle unchanged, that is, when the total driving distance does not exceed the second distance threshold, the working condition type of the fuel cell commercial vehicle remains the current working condition type unchanged, and during this process, adopt the battery energy management strategy matching the current working condition type for battery management. Thus, it is possible to avoid frequent working condition identification and reduce the computing power requirement for the vehicle controller.

[0056] In an alternative embodiment, the method may further include: If the total driving distance exceeds the second distance threshold, re-identify the working condition type of the fuel cell commercial vehicle.

[0057] During the driving process of the fuel cell commercial vehicle, re-obtain the total driving distance of the fuel cell commercial vehicle in multiple operation stages. If the total driving distance exceeds the second distance threshold, re-obtain the total parking frequency of the fuel cell commercial vehicle in multiple operation stages, and based on the total driving distance and the re-obtained total parking frequency, re-calculate the parking frequency of the fuel cell commercial vehicle in the most recent multiple operation stages at the current moment, and based on the re-calculated parking frequency, re-identify the working condition type of the fuel cell commercial vehicle. If the re-calculated parking frequency exceeds the preset frequency threshold, identify the working condition type of the fuel cell commercial vehicle as the short-distance operation condition; if the re-calculated parking frequency does not exceed the preset frequency threshold, identify the working condition type of the fuel cell commercial vehicle as the long-distance operation condition.

[0058] In an alternative embodiment, the characteristic data under the preset working condition identification characteristic factor includes: the second characteristic data under the vehicle motor power standard deviation factor, and the second characteristic data includes: the actual motor speed, the actual motor torque, and the average motor power of the fuel cell commercial vehicle in each operation stage.

[0059] Among them, the preset public identification characteristic factor may include the vehicle motor power standard deviation factor, and the vehicle motor power standard deviation factor is a characteristic factor for identifying the mountain condition and the plain condition in the long-distance operation condition.

[0060] The mountain condition refers to the working condition of the vehicle on mountainous terrain, and the plain condition refers to the working condition of the vehicle on plain terrain.

[0061] 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 operation stage. Among them, the actual motor speed can include multiple motor speeds collected at multiple acquisition moments in each operation stage, the actual motor torque can be multiple motor torques collected at multiple sampling moments in each operation stage, and the average motor power can be the average power of multiple motor powers collected at multiple sampling moments in each operation stage.

[0062] Figure 3 Schematic flow of the working condition identification method provided by the embodiment of the present application Figure 3 , such as Figure 3 shown. In an alternative embodiment, the method may further include: S301. Determine the standard deviation of the motor power of the fuel cell commercial vehicle in each operation stage according to the actual motor speed, actual motor torque, and average motor power.

[0063] The standard deviation of the motor power is used as a factor parameter of the fuel cell commercial vehicle under the factor of the standard deviation of the vehicle motor power.

[0064] According to the actual motor speed, actual motor torque, and average motor power, the standard deviation of the motor power of the fuel cell commercial vehicle in each operation stage can be calculated by using the following formula (2). Among them, the standard deviation of the motor power is used to describe the degree of deviation of the data from the mean value, and more intuitively reflects the fluctuation range of the working condition's demand for the vehicle. Among them, different working conditions have different power demands for the vehicle. The larger the standard deviation of the motor power, the greater the demand. The smaller the standard deviation of the motor power, the smaller the demand. For example, the demand in the plain working condition is small, and the demand in the mountain working condition is large.

[0065] (2) Among them, is the standard deviation of the motor power of the fuel cell commercial vehicle in each operation stage, with the unit of kW, is the motor speed of the fuel cell commercial vehicle at the sampling moment , with the unit of rpm, The motor torque of the fuel cell commercial vehicle at the sampling moment , with the unit of Nm, is the average motor power of the drive motor of the fuel cell commercial vehicle at M sampling moments. Among them, is the sampling moment, and the value range is a positive integer from 1 to M.

[0066] S302. Determine that the working condition type of the fuel cell commercial vehicle is the mountain working condition or the plain working condition under the long-line working condition according to the standard deviation of the motor power.

[0067] According to the standard deviation of the motor power, determine the driving condition type of the fuel cell commercial vehicle as the mountain driving condition or the plain driving condition under the long-distance driving condition, where the mountain driving condition under the long-distance driving condition refers to the mountain long-distance driving condition, and the plain driving condition under the long-distance driving condition refers to the plain long-distance driving condition.

[0068] It should be noted that if the total driving distance reaches the first distance threshold, the above steps S301 - S302 can still be executed. If the total driving distance is greater than the first distance threshold and does not exceed the second distance threshold, the driving 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 still be continued.

[0069] In this embodiment, the standard deviation factor of the vehicle motor power is used as the identification characteristic factor for the mountain driving condition and the plain driving condition. On the basis of the long-distance driving condition, it can further identify the mountain long-distance driving condition or the plain long-distance driving condition, improving the accuracy of driving condition identification.

[0070] Figure 4 Flow schematic of the driving condition identification method provided by the embodiment of the present application Figure 4 , such as Figure 4 shown, in an alternative embodiment, the above step S302, according to the standard deviation of the motor power, determining the driving condition type of the fuel cell commercial vehicle as the mountain driving condition or the plain driving condition under the long-distance driving condition, may include: S401. If the standard deviation of the motor power exceeds the preset power difference threshold, increment the power count value by one to obtain the target power count value.

[0071] If the standard deviation of the motor power exceeds the preset power difference threshold (such as ), increment the power count value by one, that is, , the count value , to obtain the target power count value, where the initial value of the power count value is 0.

[0072] For example, if the standard deviation of the motor power in two of the multiple operation stages exceeds the preset power difference threshold, the target power count value is 2.

[0073] S402. According to the target power count value and the total number of operations in the multiple operation stages, determine the proportion of the standard deviation of the motor power of the fuel cell commercial vehicle within the multiple operation stages.

[0074] Calculate the total number of operations in the multiple operation stages. For example, if there are 5 operation stages, the total number of operations is 5 times.

[0075] Calculate the ratio of the target power count value to the total number of operations as the proportion of the standard deviation of the motor power of the fuel cell commercial vehicle within the multiple operation stages.

[0076] See the following formula (3): (3) Wherein, is the proportion of the standard deviation of the motor power, is the number of operations where the standard deviation of the motor power exceeds the preset power difference threshold, with the unit of number of times, is the total number of operations in multiple operation stages, with the unit of times.

[0077] S403. If the proportion of the standard deviation of the motor power exceeds the preset proportion threshold, determine that the working condition type of the fuel cell commercial vehicle is the mountain condition under the long-line working condition.

[0078] S404. If the proportion of the standard deviation of the motor power does not exceed the preset proportion threshold, determine that the working condition type of the fuel cell commercial vehicle is the plain condition under the long-line working condition.

[0079] Wherein, the preset proportion threshold can be a calibrated value, defined according to the big data analysis conclusion, denoted as , if the proportion of the standard deviation of the motor power exceeds the preset proportion threshold, that is , then determine that the working condition type of the fuel cell commercial vehicle is the mountain condition under the long-line working condition, that is, the mountain long-line working condition.

[0080] If , then determine that the working condition type of the fuel cell commercial vehicle is the plain condition under the long-line working condition, that is, the plain long-line working condition.

[0081] In this embodiment, during some operation stages, the standard deviation of the motor power under the mountain condition may fluctuate and be less than the preset power difference threshold. Based on this, by further calculating the proportion of the standard deviation of the motor power, identifying the mountain long-line working condition or the plain long-line working condition based on whether the proportion of the standard deviation of the motor power exceeds the preset proportion threshold improves the accuracy of working condition identification.

[0082] Figure 5 is the flowchart of the working condition identification method provided by the embodiment of the present application Figure 5 , as Figure 5 shown, in an alternative embodiment, the vehicle parking frequency factor is determined in advance in the following manner: S501. Construct an operation feature library and an operation database.

[0083] The operation feature library includes: multiple candidate working condition identification feature factors. Among them, the multiple candidate working condition identification feature factors may include, for example: vehicle parking frequency factor, air pump enable frequency factor, vehicle gear distribution factor, vehicle motor power standard deviation factor, energy recovery power distribution factor, etc.

[0084] The operation database includes the operation data and historical operating condition types of the operated fuel cell commercial vehicles in multiple historical operation stages. Among them, the operation data may include the number of vehicle stops in each historical operation stage (i.e., the number of single-operation stops of the vehicle from high voltage on to high voltage off, in units of times), the driving distance (i.e., the single-trip driving mileage of the vehicle from high voltage on to high voltage off, in units of km), the actual torque, actual speed, average power, etc. of the drive motor.

[0085] Among them, the historical operating condition type is the operating condition type of the operated fuel cell commercial vehicle in each operation stage. The historical operating condition types of the operated fuel cell commercial vehicle in each historical operation stage are known, and the historical operating condition type is a short-distance operation condition or a long-distance operation condition.

[0086] In some embodiments, through the connected data platform, the actual operation data of the operated fuel cell commercial vehicle is collected and an operation database is constructed.

[0087] S502. According to multiple candidate operating condition identification characteristic factors and operation data, obtain multiple candidate operating condition identification parameters of the operated fuel cell commercial vehicle under multiple candidate operating condition identification characteristic factors in each historical operation stage.

[0088] Among them, in each operation stage, one candidate operating condition identification characteristic factor corresponds to one candidate operating condition identification parameter. For example, the candidate operating condition identification parameter under the vehicle stop frequency factor is the vehicle stop 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.

[0089] According to multiple candidate operating condition identification characteristic factors and the operation data in each historical operation stage, calculate the candidate operating condition identification parameters of the operated fuel cell commercial vehicle under each candidate operating condition identification characteristic factor in each operation stage.

[0090] For example, if the operation data includes: the number of vehicle stops in each historical operation stage (i.e., the number of single-operation stops of the vehicle from high voltage on to high voltage off), and the driving distance (i.e., the single-trip driving mileage of the vehicle from high voltage on to high voltage off), then the vehicle stop frequency under the vehicle stop frequency factor can be calculated and determined based on this operation data and formula (4).

[0091] See the following formula (4): (4) Among them, is the number of vehicle stops in each historical operation stage, in units of times / km, is the driving distance of the vehicle in each historical operation stage, with the unit of km.

[0092] For another example, the operation data includes the actual torque, actual speed, and average power of the drive motor. Then, the vehicle motor power standard deviation under the vehicle motor power standard deviation factor can be determined based on this operation data and the above formula (2).

[0093] S503: According to the multiple candidate condition identification parameter under multiple candidate condition identification characteristic factors of the operated fuel cell commercial vehicle, and the historical condition types, draw the scatter distribution diagrams of each candidate condition identification characteristic factor in multiple historical operation stages.

[0094] Among them, the scatter distribution diagram is the distribution of each candidate condition identification characteristic factor in multiple historical operation stages drawn based on the historical condition types in each historical operation stage. One scatter distribution diagram is drawn for one candidate condition identification characteristic factor.

[0095] The abscissa of the scatter distribution diagram can be the historical operation stage, and the ordinate can be the candidate condition identification parameter under each candidate condition identification characteristic factor. Among them, different colors are used to distinguish the candidate condition identification parameters for different historical condition types.

[0096] Figure 6 is the scatter distribution diagram of the vehicle parking frequency factor provided by the embodiment of the present application. As Figure 6 shown, the abscissa represents the historical operation stage, with the unit of "each", and the ordinate is the vehicle parking frequency under the vehicle parking frequency factor, with the unit of "times / km". The vehicle parking frequency under the long-line condition is represented in black, and the vehicle parking frequency under the short-turn condition is represented in red.

[0097] S504: According to the scatter distribution diagrams of each candidate condition identification characteristic factor, determine the vehicle parking frequency factor from multiple candidate condition identification characteristic factors.

[0098] According to the scatter distribution diagrams of each candidate condition identification characteristic factor, screen out the characteristic factors that can clearly distinguish the condition types from multiple candidate condition identification characteristic factors as the vehicle parking frequency factor. As Figure 6 can be seen, under the vehicle parking frequency factor, the short-turn condition and the long-line condition can be clearly distinguished. Screen out the vehicle parking frequency factor from multiple candidate condition identification characteristic factors as the identification factor for the short-turn condition and the long-line condition.

[0099] In an optional implementation manner, the preset frequency threshold (Freq1) is determined in advance through the following method: According to the scatter distribution diagram of the vehicle parking frequency factor, determine the preset frequency threshold.

[0100] According to the scatter plot of the vehicle parking frequency factor, determine the parking frequency in the scatter plot for distinguishing short-haul conditions and long-haul conditions as the preset frequency threshold. Refer to Figure 6 The value range of the preset frequency threshold can be from 1 time / km to 3 times / km. For example, it can be 2 times / km, 2.5 times / km. This embodiment does not make special limitations on this.

[0101] In some embodiments, the historical operating condition type can also be the mountain condition or the plain condition under the long-haul condition. Then, multiple target operating stages with the historical operating condition type of mountain long-haul condition or plain long-haul condition can be determined from multiple operating stages. And according to the multiple candidate condition identification parameter of the fuel cell commercial vehicle that has been operated under multiple candidate condition identification characteristic factors, and the historical operating condition type of the fuel cell commercial vehicle in each target historical operating stage, draw the target scatter plot of each candidate condition identification characteristic factor in multiple target operating stages.

[0102] Among them, the abscissa of the scatter plot can be the target operating stage, and the ordinate can be the candidate condition identification parameter under each candidate condition identification characteristic factor. Among them, different colors are used to distinguish the candidate condition identification parameters for different historical operating condition types.

[0103] Figure 7 This is the scatter plot of the motor power standard deviation factor provided by the embodiment of the present application. As Figure 7 shown, the abscissa represents the target operating stage, and the ordinate is the motor power standard deviation under the motor power standard deviation factor, with the unit of kW. Black and red are used to represent the motor power standard deviation under plain long-haul 1 and plain long-haul 2 respectively. Blue, green, and orange are used to represent the motor power standard deviation under mountain long-haul 1, mountain long-haul 2, and mountain long-haul 3 respectively.

[0104] Then, according to the target scatter plot of each candidate condition identification characteristic factor, screen out the characteristic factors that can distinguish the operating condition types significantly from multiple candidate condition identification characteristic factors as the motor power standard deviation factor. As Figure 7 can be seen, under the motor power standard deviation factor, the mountain long-haul condition and the plain long-haul condition can be distinguished more significantly (the motor power standard deviation corresponding to plain long-haul 2 fluctuates in some operating stages). Among them, under other candidate condition identification characteristic factors, the mountain long-haul condition and the plain long-haul condition cannot be distinguished.

[0105] In some embodiments, the preset power difference threshold can also be determined according to the target scatter plot of the motor power standard deviation factor.

[0106] According to the target scatter plot of the standard deviation factor of the motor power, determine the standard deviation of the motor power in the target scatter plot that can better distinguish between the mountain long-haul condition and the plain long-haul condition as the preset power difference threshold. Refer to Figure 7 , the value range of the preset power difference threshold (Var1) can be from 130 kW to 140 kW, for example, it can be 135 kW, 136 kW, etc., and this embodiment does not make special limitations on this.

[0107] In this embodiment, according to information such as the vehicle operation route and altitude slope, classify the operation data of the three conditions of short-distance transfer, mountain long-haul, and plain long-haul. By drawing the scatter plots of the candidate condition identification characteristic factors, screen and distinguish the condition identification characteristic factors with obvious differences. Among them, use the vehicle parking frequency factor as the identification characteristic factor for the short-distance transfer condition and the long-haul condition, with the threshold being Freq1, and use the standard deviation of the motor power as the identification characteristic factor for the mountain long-haul condition and the plain long-haul condition, with the threshold being Var1. Thus, based on the historical situation of the operated fuel cell commercial vehicle, determine the identification characteristic factors and corresponding thresholds for different conditions, improving the accuracy of condition identification.

[0108] Figure 8 The structural schematic diagram of the vehicle controller provided by the embodiment of the present application is as Figure 8 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 runs, the processor 601 communicates with the memory 602 through the bus 603, and the processor 601 executes the machine-readable instructions to execute the above method.

[0109] Figure 9 The structural schematic diagram of the fuel cell commercial vehicle provided by the embodiment of the present application is as Figure 9 shown. The fuel cell commercial vehicle includes: a vehicle body 701, and a vehicle controller 702 disposed on the vehicle body 701; the vehicle controller 702 is used to execute the above method.

[0110] Figure 10 The structural schematic diagram of the condition identification device provided by the embodiment of the present application, and this device can be integrated in the vehicle controller.

[0111] As Figure 10 shown, this device may include: An acquisition module 801, configured to acquire the characteristic data of the fuel cell commercial vehicle under the preset condition identification characteristic factors. The characteristic data under the preset condition identification characteristic factors includes: the 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 operation stages; A determination module 802, configured to determine the parking frequency of a fuel cell commercial vehicle during multiple operation stages according to the parking frequency and the driving distance, where the parking frequency is used as a factor parameter of the fuel cell commercial vehicle under the vehicle parking frequency factor; An identification module 803, configured to, if the parking frequency exceeds a preset frequency threshold, identify the operating condition type of the fuel cell commercial vehicle as a short-distance operation condition, so as to perform battery management by using a battery energy management strategy matching the short-distance operation condition; The identification module 803 is further configured to, if the parking frequency does not exceed the preset frequency threshold, identify the operating condition type of the fuel cell commercial vehicle as a long-distance operation condition, so as to perform battery management by using a battery energy management strategy matching the long-distance operation condition.

[0112] In an optional implementation manner, the determination module 802 is specifically configured to: Determine the total driving distance of the fuel cell commercial vehicle during multiple operation stages according to the driving distance; If the total driving distance reaches a first distance threshold, determine the total parking frequency of the fuel cell commercial vehicle during multiple operation stages according to the parking frequency; Determine the parking frequency according to the total driving distance and the total parking frequency.

[0113] In an optional implementation manner, the determination module 802 is further configured to: If the total driving distance is greater than the first distance threshold and does not exceed the second distance threshold, keep the operating condition type of the fuel cell commercial vehicle unchanged.

[0114] In an optional implementation manner, the identification module 803 is further configured to: If the total driving distance exceeds the second distance threshold, re-identify the operating condition type of the fuel cell commercial vehicle.

[0115] In an optional implementation manner, the characteristic data under a preset operating condition identification characteristic factor includes: second characteristic data under a vehicle motor power standard deviation factor, where the second characteristic data includes: the actual motor speed, the actual motor torque, and the average motor power of the fuel cell commercial vehicle during each operation stage; The determination module 802 is further configured to determine the motor power standard deviation of the fuel cell commercial vehicle during each operation stage according to the actual motor speed, the actual motor torque, and the average motor power, where the motor power standard deviation is used as a factor parameter of the fuel cell commercial vehicle under the vehicle motor power standard deviation factor; The determination module 802 is further configured to determine, according to the motor power standard deviation, that the operating condition type of the fuel cell commercial vehicle is a mountain condition or a plain condition under the long-distance operation condition.

[0116] In an optional implementation manner, the determination module 802 is further configured to: If the standard deviation of the motor power exceeds the preset power difference threshold, increment the power meter value by one to obtain the target power meter value; Based on the target power meter value and the total number of operations in multiple operation stages, determine the proportion of the standard deviation of the motor power of the fuel cell commercial vehicle in multiple operation stages; If the proportion of the standard deviation of the motor power exceeds the preset proportion threshold, determine that the working condition type of the fuel cell commercial vehicle is the mountain condition under the long-line working condition; If the proportion of the standard deviation of the motor power does not exceed the preset proportion threshold, determine that the working condition type of the fuel cell commercial vehicle is the plain condition under the long-line working condition.

[0117] In an optional implementation manner, the vehicle parking frequency factor is determined in advance through the following method: Construct an operation feature library and an operation database. The operation feature library includes: multiple candidate working condition identification feature factors. The operation database includes: the operation data and historical working condition types of the operated fuel cell commercial vehicle in multiple historical operation stages; the historical working condition types are the short-haul working condition or the long-line working condition; Based on the multiple candidate working condition identification feature factors and the operation data, obtain multiple candidate working condition identification parameters of the operated fuel cell commercial vehicle under the multiple candidate working condition identification feature factors in each historical operation stage; Based on the multiple candidate working condition identification parameters of the operated fuel cell commercial vehicle under the multiple candidate working condition identification feature factors and the historical working condition types, draw a scatter plot of each candidate working condition identification feature factor in multiple historical operation stages; Based on the scatter plot of each candidate working condition identification feature factor, determine the vehicle parking frequency factor from the multiple candidate working condition identification feature factors.

[0118] In an optional implementation manner, the preset frequency threshold is determined in advance through the following method: Based on the scatter plot of the vehicle parking frequency factor, determine the preset frequency threshold.

[0119] For the processing flow of each module in the device and the interaction flow between modules, reference can be made to the relevant descriptions in the method embodiments above, which will not be elaborated here.

[0120] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the above method.

[0121] In the embodiment of the present application, when the computer program is run by a processor, it can also execute other machine-readable instructions to execute other methods described in the embodiments. For the specific method steps and principles executed, reference can be made to the descriptions in the embodiments, which will not be elaborated in detail here.

[0122] 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 illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0123] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] In addition, each functional unit in the embodiments provided in this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0125] If the functions are implemented in the form of software function 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 this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0126] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0127] Finally, it should be noted that the above-described embodiments are only specific implementation manners of the present application, used to illustrate the technical solutions of the present application, rather than limiting it. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: Any person skilled in the art within the technical scope disclosed by the present application can still modify the technical solutions recorded in the foregoing embodiments or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application. All should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described herein.

Claims

1. A working condition recognition method, characterized in that, A vehicle controller applied to a fuel cell commercial vehicle, the method 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 a vehicle parking frequency factor, the first characteristic data including: the parking frequency and driving distance of the fuel cell commercial vehicle in multiple operation stages; Determining the parking frequency of the fuel cell commercial vehicle in the multiple operation stages according to the parking frequency and the driving distance, and 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 working condition type of the fuel cell commercial vehicle as a short-distance operation condition, so as to perform battery management by adopting a battery energy management strategy matched with the short-distance operation condition; If the parking frequency does not exceed the preset frequency threshold, identifying the working condition type of the fuel cell commercial vehicle as a long-distance operation condition, so as to perform battery management by adopting a battery energy management strategy matched with the long-distance operation condition.

2. The method according to claim 1, characterized in that, The 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 the total driving distance of the fuel cell commercial vehicle in the multiple operation stages according to the driving distance; If the total driving distance reaches a first distance threshold, determining the total parking frequency of the fuel cell commercial vehicle in the multiple operation stages according to the parking frequency; Determining the parking frequency according to the total driving distance and the total parking frequency.

3. The method according to claim 2, wherein The method further includes: If the total driving distance is greater than the first distance threshold and does not exceed a second distance threshold, keeping the working condition type of the fuel cell commercial vehicle unchanged.

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

5. The method according to claim 1, characterized in that The characteristic data under the preset working condition identification characteristic factor includes: second characteristic data under a 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 operation stage; The method further includes: Determining the motor power standard deviation of the fuel cell commercial vehicle in each operation stage according to the actual motor speed, the actual motor torque, and the average motor power, and the motor power standard deviation serves as a factor parameter of the fuel cell commercial vehicle under the vehicle motor power standard deviation factor; Determining the working condition type of the fuel cell commercial vehicle as a mountain condition or a plain condition under the long-distance operation condition according to the motor power standard deviation.

6. The method according to claim 5, wherein The determining the working condition type of the fuel cell commercial vehicle as a mountain condition or a plain condition under the long-distance operation condition according to the motor power standard deviation includes: If the motor power standard deviation exceeds a preset power difference threshold, adding one to the power count value to obtain a target power count value; Determine the proportion of the standard deviation of the motor power of the fuel cell commercial vehicle in the multiple operation stages according to the target power meter value and the total number of operations in the multiple operation stages; If the proportion of the standard deviation of the motor power exceeds a preset proportion threshold, determine that the working condition type of the fuel cell commercial vehicle is the mountainous working condition under the long-line working condition; If the proportion of the standard deviation of the motor power does not exceed the preset proportion threshold, determine that the working condition type of the fuel cell commercial vehicle is the plain working condition under the long-line working condition.

7. The method according to claim 1, wherein The vehicle parking frequency factor is determined in advance by the following method: Construct an operation feature library and an operation database. The operation feature library includes: a plurality of candidate working condition identification feature factors. The operation database includes: operation data and historical working condition types of the operated fuel cell commercial vehicle in multiple historical operation stages; the historical working condition types are the short-haul working condition or the long-line working condition; According to the plurality of candidate working condition identification feature factors and the operation data, obtain a plurality of candidate working condition identification parameters of the operated fuel cell commercial vehicle under the plurality of candidate working condition identification feature factors in each historical operation stage; According to the plurality of candidate working condition identification parameters of the operated fuel cell commercial vehicle under the plurality of candidate working condition identification feature factors and the historical working condition types, draw a scatter plot distribution diagram of each candidate working condition identification feature factor in the multiple historical operation stages; According to the scatter plot distribution diagram of each candidate working condition identification feature factor, determine the vehicle parking frequency factor from the plurality of candidate working condition identification feature factors.

8. The method according to claim 7, wherein The preset frequency threshold is determined in advance by the following method: Determine the preset frequency threshold according to the scatter plot distribution diagram of the vehicle parking frequency factor.

9. A vehicle controller, characterized in that, Comprising: A processor, a memory and a bus. The memory stores machine-readable instructions executable by the processor. When the vehicle controller runs, the processor communicates with the memory through the bus. The processor executes the machine-readable instructions to execute the method according to any one of claims 1 to 8.

10. A fuel cell commercial vehicle, characterized in that, Comprising: A vehicle body and a vehicle controller disposed on the vehicle body; the vehicle controller is used to execute the method according to any one of claims 1-8.

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