Hydrogen consumption abnormity diagnosis method and system of hydrogen fuel cell vehicle

Abstract

The invention discloses a hydrogen consumption abnormity diagnosis method and system for a hydrogen fuel cell vehicle, and relates to the technical field of vehicle diagnosis. The method comprises the following steps: acquiring remote telemetry data of each vehicle in a vehicle group in a preset time period, wherein the vehicles in the vehicle group share at least one common operation characteristic; calculating an energy consumption efficiency indicator for each vehicle based on the remote telemetry data; determining a first target vehicle with an outlier energy consumption index and a reference vehicle with a reference energy consumption index according to the index; and performing a hierarchical diagnosis strategy on the first target vehicle by comparing detailed operation parameters of the first target vehicle and the reference vehicle so as to locate a root cause causing the outlier energy consumption index. According to the method, transverse comparison and hierarchical diagnosis are carried out in the homogenized motorcade, so that external working condition interference is effectively eliminated, the root cause of hydrogen consumption abnormity can be accurately and efficiently positioned, and the operation cost of the motorcade is remarkably reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle diagnosis, and particularly relates to a hydrogen consumption abnormality diagnosis method and system for a hydrogen fuel cell vehicle. BACKGROUND

[0002] As an important development direction of new energy vehicles, hydrogen fuel cell vehicles have been widely used in urban construction, fixed-route logistics and other scenarios. In commercial operations with a fleet of vehicles, energy consumption efficiency, especially hydrogen consumption per 100 kilometers, is a core indicator for measuring economy. However, in practice, there is often a significant difference in hydrogen consumption per 100 kilometers among vehicles in the same fleet, even though the vehicle models and operating routes are the same, which brings great challenges to cost control and maintenance management of the operator.

[0003] In order to solve the energy consumption problem of hydrogen fuel cell vehicles, some explorations have been made in the prior art.

[0004] One type of technical solution focuses on accurate measurement of hydrogen consumption. For example, Chinese patent application CN202211322538.5 discloses a fuel cell hydrogen consumption calculation method, which calculates the reduction of hydrogen mass by monitoring the pressure and temperature in the hydrogen storage system bottle, combined with the volume of the hydrogen bottle, and identifies hydrogen refueling operations, aiming to solve the problem of inaccurate hydrogen consumption statistics. However, the essence of this type of solution is a "post-measurement" tool, which can accurately answer the question "how much hydrogen has been consumed", but it cannot provide any technical guidance for the deeper diagnostic question "why so much hydrogen has been consumed". It can find the phenomenon of hydrogen consumption anomaly, but it cannot locate the root cause of the anomaly.

[0005] Another type of technical solution attempts to make longitudinal self-comparison diagnosis. For example, Chinese patent application CN202411763218.2 discloses a management method, which obtains the hydrogen consumption, electricity consumption and other parameters of the vehicle in the current operating cycle, and compares them with the historical data of the previous operating cycle. If the parameters deteriorate, it is determined that the vehicle operating state has changed, and the vehicle operating state is judged and confirmed according to different changes. The fatal flaw of this longitudinal comparison method is that it cannot effectively exclude the interference of external operating condition variables. For example, an increase in vehicle load in the current cycle, encountering adverse weather, or changes in environmental temperature causing increased air conditioning power consumption, will all cause normal fluctuations in hydrogen consumption. This method is prone to misjudging these normal fluctuations caused by external factors as vehicle faults, resulting in incorrect diagnostic conclusions. Conversely, for a persistent "sub-health" problem (such as a low efficiency of a certain component) that exists from the factory, the longitudinal comparison method will completely fail to identify it.

[0006] Another type of technical solution focuses on specific, single failure modes. For example, Chinese patent application CN202210236820.5 discloses an analysis method that analyzes the pressure change slope by linearly fitting the hydrogen maximum pressure value of the vehicle's continuous driving segment to determine whether there is a hydrogen leakage or other abnormalities. The limitation of this method is that its diagnostic range is too narrow. It is effective for hydrogen consumption increase caused by hydrogen leakage, but it is completely powerless for hydrogen consumption abnormalities caused by other numerous reasons, such as DCDC conversion efficiency decline, stack attenuation, accessory power consumption abnormalities, improper software control strategy, non-standard driving behavior, etc. It only solves a very small subset of hydrogen consumption abnormality problems.

[0007] In summary, the existing technology either stays at the metering level and cannot perform root cause diagnosis, or uses a longitudinal comparison method that cannot exclude the interference of working conditions, making the diagnostic conclusion unreliable, or only targets a single failure mode, making the diagnostic range too limited. Therefore, there is an urgent need in the field for a diagnostic method that can effectively exclude external working condition interference and perform systematic, multi-dimensional root cause positioning to accurately and efficiently solve the widespread hydrogen consumption abnormality problem in commercial operation fleets. SUMMARY

[0008] The present application aims to solve the problem of the existing technology that cannot effectively locate the root cause of non-fault hydrogen consumption abnormalities in fuel cell vehicles, and provides a hydrogen consumption abnormality diagnosis method and system for hydrogen fuel cell vehicles that can accurately and efficiently diagnose the root cause of hydrogen consumption abnormalities.

[0009] To solve the above technical problems, in a first aspect, the present application provides a hydrogen consumption abnormality diagnosis method for hydrogen fuel cell vehicles, applied to a vehicle group, the hydrogen fuel cell vehicles in the vehicle group share at least one common operating feature, the method comprising: obtaining remote telemetry data of each vehicle in the vehicle group within a preset time period; based on the remote telemetry data, calculating the energy consumption efficiency index of each vehicle in the vehicle group; according to the energy consumption efficiency index, determining at least one first target vehicle with an outlier energy consumption index and at least one reference vehicle with a reference energy consumption index from the vehicle group; by comparing the detailed operating parameters of the first target vehicle and the reference vehicle, performing a layered diagnosis strategy on the first target vehicle to locate the root cause of the outlier energy consumption index.

[0010] The application adopts a technical means of hierarchical diagnosis by comparing the operation parameters of an outlier vehicle and a reference vehicle in a vehicle group sharing common operating characteristics, which is equivalent to using the reference vehicle as a "mobile reference", effectively eliminating the interference of external variables such as route, load, traffic conditions, environment, etc., so that the diagnosis can directly focus on the internal differences of the vehicle itself, thereby quickly and accurately locating the root cause of abnormal hydrogen consumption.

[0011] As a preferred scheme of the application, the hierarchical diagnosis strategy first includes: judging whether the first target vehicle has a whole vehicle electricity consumption abnormality; if so, performing an electricity consumption abnormality analysis process; if not, performing a fuel cell system abnormality analysis process. This scheme establishes a key top-level diagnosis logic: first distinguishing whether the reason for high hydrogen consumption is "high electricity consumption" or "low power generation efficiency", indicating the correct direction for subsequent diagnosis, and avoiding wasting time and computing resources on the wrong analysis branch.

[0012] As a preferred scheme of the application, the electricity consumption abnormality analysis process includes analyzing whole vehicle accessory power consumption and / or drive motor electricity consumption. By further subdividing electricity consumption abnormalities into two main sources of accessories and motors, the problem range can be more accurately narrowed down.

[0013] As a preferred scheme of the application, after determining that there is an accessory power consumption abnormality, driving behavior data can also be analyzed. This takes into account the direct impact of driver habits (such as long-term use of air conditioning) on accessory energy consumption, making the diagnosis more comprehensive.

[0014] As a preferred scheme of the application, after determining that there is a motor electricity consumption abnormality, the accelerator pedal, motor software version or motor hardware parameters can also be analyzed. This scheme provides an in-depth troubleshooting path for motor electricity consumption abnormalities, covering the complete chain from signal input, software control to hardware execution.

[0015] As a preferred scheme of the application, the fuel cell system abnormality analysis process includes analyzing the DC boost converter, stack performance, stack operating parameters, hydrogen management, auxiliary system power consumption and hydrogen leakage. This scheme provides a systematic and multi-dimensional troubleshooting list for potential problem points within the fuel cell system, ensuring the depth and breadth of diagnosis, and can locate various complex internal root causes.

[0016] As a preferred scheme of the application, after determining that the stack performance has an abnormality, the hydrogen quality can be verified or the stack online activation can be performed. This provides a specific and non-replacement solution for stack performance degradation, which helps to restore stack performance at low cost and reduce maintenance costs.

[0017] As a preferred scheme of the present application, the energy consumption efficiency indicator is hydrogen consumption per 100 kilometers; the first target vehicle with the outlier energy consumption indicator is the vehicle with the highest hydrogen consumption per 100 kilometers; and the reference vehicle with the reference energy consumption indicator is the vehicle with the lowest or median hydrogen consumption per 100 kilometers. By adopting the specific and industry-wide hydrogen consumption per 100 kilometers indicator and selecting the vehicle with the highest value, the lowest value or the median value for comparison, the diagnosis target is clear, easy to implement in engineering, and has strong practicality.

[0018] To solve the above technical problems, in a second aspect, the present application provides a hydrogen consumption anomaly diagnosis system for hydrogen fuel cell vehicles, which comprises a data acquisition module, an energy consumption calculation module, a vehicle identification module and a hierarchical diagnosis module. Since the present system is provided with modules with clear functions, the aforementioned diagnosis method can be automatically executed, continuous monitoring and intelligent diagnosis of hydrogen consumption of a vehicle fleet are realized, human resources are liberated, and management efficiency is improved.

[0019] As a preferred scheme of the present application, the hierarchical diagnosis module is configured to first determine whether the first target vehicle has a whole-vehicle energy consumption anomaly based on the detailed operation parameters, and selectively activate an energy consumption anomaly analysis submodule to analyze accessory power consumption or drive motor power consumption, or activate a fuel cell system anomaly analysis submodule to analyze the internal operation state of the fuel cell system, according to the determination result. Through this configuration, the system has the core intelligent diagnosis logic, can automatically execute the top-level branch judgment and enter the corresponding deep analysis process, and realizes high automation and intelligence of the diagnosis process.

[0020] Compared with the prior art, the present application has the following beneficial effects:

[0021] 1. Clear diagnosis logic and high precision: The present application innovatively decomposes the hydrogen consumption anomaly problem into two branches of "energy consumption anomaly" and "fuel cell system anomaly", the diagnosis path is clear, and conforms to the physical logic of vehicle energy flow. By performing horizontal comparison in a vehicle group sharing operation characteristics, the interference of external working condition variables is effectively eliminated, so that the root cause of hydrogen consumption anomaly can be accurately identified.

[0022] 2. High diagnosis efficiency: The structured hierarchical diagnosis strategy is adopted, starting from the top-level branch judgment, layer by layer, the logic is rigorous, and blind troubleshooting is avoided, which greatly shortens the diagnosis time and helps the operation team to quickly respond and solve problems.

[0023] 3. Wide diagnosis range: The diagnosis logic of the present application covers multiple dimensions from whole-vehicle energy consumption (accessories, motor, driving behavior) to fuel cell system internal (DCDC, stack, BOP, software strategy, hydrogen leakage), forming a comprehensive diagnosis system, which can cope with various complex hydrogen consumption anomaly scenarios.

[0024] 4. Significant economic benefits: By quickly and accurately locating the root cause of the problem, targeted maintenance or adjustment can be guided to avoid unnecessary replacement and maintenance costs, while solving the problem of excessive hydrogen consumption, directly reducing the operating cost of the vehicle and improving the overall economic benefit of the vehicle fleet. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the disclosed embodiments, the drawings of the embodiments will be briefly introduced below, which are merely used for illustrative purposes and are not intended to limit the protection scope of the present application.

[0026] Figure 1 is a high-level flowchart of a hydrogen consumption anomaly diagnosis method provided by an embodiment of the present application.

[0027] Figure 2 is a structural diagram of a hydrogen consumption anomaly diagnosis system provided by an embodiment of the present application.

[0028] Figure 3 is a detailed diagnosis logic flowchart of a specific embodiment of the present application. DETAILED DESCRIPTION

[0029] The technical solutions of the present application (including the preferred technical solutions) will be described in further detail below by means of the drawings and by listing some optional embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0030] Embodiment 1

[0031] The present embodiment provides a hydrogen consumption anomaly diagnosis method for a hydrogen fuel cell vehicle, as shown in the figure, which can be executed by a software program deployed in a cloud server or a local vehicle fleet management system. The present embodiment takes a fixed-route transport vehicle fleet of a certain logistics company as an example for illustration. Figure 1

[0032] The vehicle fleet (i.e. the "vehicle group" described in the present application) includes 10 hydrogen fuel tractors of model 49T, which share the same operating characteristics: same vehicle configuration, consistent software baseline version, daily fixed logistics route between Wuhan and a certain place.

[0033] After a predetermined time period (e.g. one week), the diagnosis method is executed according to the following steps:

[0034] S101: Obtain remote telemetry data of the vehicle group.

[0035] ​The diagnostic system automatically collects and aggregates all the operation data of the 10 vehicles in the past week through the on-board T-Box and wireless network, and stores them into the database. These remote telemetry data are collected at high frequency (e.g. once per second), including but not limited to: vehicle location, vehicle speed, driving distance, hydrogen tank pressure and temperature, fuel cell stack voltage and current, DCDC converter input and output parameters, working status and power consumption of each accessory (e.g. air conditioning PTC, air compressor), accelerator pedal opening, brake signal, etc.

[0036] S102: Calculate the energy consumption efficiency index of each vehicle.

[0037] The system calls the energy consumption calculation program to process the data of each vehicle. Specifically, through the hydrogen state equation (e.g. ideal gas state equation or more accurate van der Waals equation), the hydrogen consumption mass is accurately calculated according to the pressure, temperature and volume change of the hydrogen tank. Then, combined with the driving distance data of the same period, the hydrogen consumption per 100 km (kg / 100km) of each vehicle is calculated as its energy consumption efficiency index.

[0038] The calculation results show that the hydrogen consumption per 100 km of 8 vehicles in the vehicle fleet is about 10 kg, but the hydrogen consumption per 100 km of 2 vehicles (marked as V1 and V2) is as high as 11.5 kg.

[0039] S103: Identify the first target vehicle and the reference vehicle.

[0040] According to the calculation results, the system determines the vehicles V1 and V2 with the highest hydrogen consumption as the "first target vehicles" with the outlier energy consumption index. At the same time, the system selects a vehicle with hydrogen consumption at the median (e.g. a vehicle with hydrogen consumption per 100 km of 10.1 kg, marked as V_ref) in the vehicle fleet as the "reference vehicle" with the reference energy consumption index.

[0041] S104: Execute the hierarchical diagnosis strategy on the first target vehicle.

[0042] The system takes vehicle V1 as the diagnosis object and starts hierarchical diagnosis by comparing the detailed operation parameters of V1 and V_ref.

[0043] First level diagnosis: analysis of vehicle accessory power consumption.

[0044] The system first compares the power consumption of the main vehicle accessories of the two vehicles under similar ambient temperature and operating conditions. Analysis finds that the cumulative power consumption of the air conditioning system PTC (positive temperature coefficient heater) of vehicle V1 is significantly higher than that of the reference vehicle V_ref. Further data retrieval finds that the PTC of V1 still has abnormal power consumption under non-heating request. The system generates a diagnosis conclusion accordingly: the abnormal hydrogen consumption of vehicle V1 is caused by abnormal power consumption of the air conditioning PTC accessory. The operation team repairs the air conditioning system of V1 according to this conclusion, and after replacing the controller, the hydrogen consumption returns to the normal level of about 10 kg.

[0045] Now, the system takes vehicle V2 as the diagnosis object and repeats the diagnosis process of S104.

[0046] First-level diagnosis: vehicle accessory power consumption analysis.

[0047] After comparison, the system finds that the accessory power consumption of V2 has no obvious difference compared with the reference vehicle V_ref. The first-level diagnosis finds no abnormalities.

[0048] Second-level diagnosis: fuel cell system analysis.

[0049] The diagnosis process enters the second level. The system starts comparing the overall operating parameters of the fuel cell systems of V2 and V_ref. Analysis finds that under the same output power, the hydrogen consumption rate of V2 is systematically higher than that of V_ref. This indicates that the problem is in the fuel cell system.

[0050] Third-level diagnosis: sub-component / strategy analysis.

[0051] The diagnosis process further deepens to the third level. The system compares and analyzes the key sub-components of the fuel cell system one by one:

[0052] Stack performance analysis: comparing the I-V (current-voltage) curves of the stacks of the two vehicles under the same operating conditions, no obvious performance degradation of the stack of V2 is found.

[0053] DCDC efficiency analysis: the system calculates and compares the conversion efficiencies of the DCDCs of the two vehicles at different power points. The analysis result shows that the average conversion efficiency of the DCDC of vehicle V2 is about 2-3 percentage points lower than that of the reference vehicle V_ref. The system generates a diagnosis conclusion accordingly: the abnormal hydrogen consumption of vehicle V2 is caused by abnormally low DCDC conversion efficiency. The operation team replaces the DCDC module of V2, and its hydrogen consumption also returns to normal.

[0054] Examples of analysis in other diagnosis dimensions:

[0055] Suppose another vehicle V3 has abnormal hydrogen consumption, and the aforementioned hardware diagnosis finds no problems. The system will start diagnosis in other dimensions:

[0056] Driving behavior analysis: The system compares the driving behavior data of V3 and V_ref, such as average acceleration, number of sudden acceleration / sudden deceleration, high-speed driving proportion, and acceleration pedal depression depth distribution. If it is found that the driving style of V3 is much more aggressive than V_ref, the system determines that the hydrogen consumption anomaly is related to the driver's habit, and suggests that the driver be trained for energy-saving driving.

[0057] Software version check: The system automatically compares the software version numbers of the vehicle controllers (VCU), fuel cell controllers (FCU), and other key controllers of V3 and V_ref. If it is found that V3 uses an old software version that has not been optimized or has vulnerabilities, the system determines that the hydrogen consumption anomaly is related to the software version, and suggests OTA upgrade.

[0058] Through the above embodiments, the method of the present application can accurately and progressively locate hydrogen consumption anomalies caused by different reasons, providing strong and efficient technical support for solving actual operation problems.

[0059] Example 2

[0060] This embodiment provides a hydrogen consumption anomaly diagnosis system 100 for a hydrogen fuel cell vehicle, as shown in Figure 2 The system can be a server cluster deployed in the cloud, or a local server integrated in the fleet management center. The system 100 includes a data acquisition module 110, an energy consumption calculation module 120, a vehicle identification module 130, and a layered diagnosis module 140.

[0061] Data acquisition module 110: responsible for establishing remote communication connection with all vehicles in the vehicle group. It receives CAN bus messages and other sensor data reported by each vehicle T-Box through 4G / 5G network in real time or periodically, and parses, cleans and structures these massive remote telemetry data for storage, forming a data set for subsequent analysis.

[0062] Energy consumption calculation module 120: this module is built-in with a hydrogen physical state equation model and an energy consumption calculation algorithm. It will call the latest data set stored in the data acquisition module 110 at regular intervals (for example, daily or weekly) to accurately calculate the hydrogen consumption and driving distance of each vehicle, and finally output the hydrogen consumption per 100 kilometers of each vehicle in a specific time period, and update the vehicle energy consumption profile.

[0063] Vehicle identification module 130: This module performs statistical analysis on the energy consumption data of the entire vehicle fleet output by the energy consumption calculation module 120. It sorts the hydrogen consumption values per 100 kilometers and automatically identifies the first target vehicle that needs in-depth diagnosis and the benchmark vehicle for comparison according to preset rules (for example, selecting the top 5% of vehicles with the highest hydrogen consumption values as target vehicles and selecting vehicles with hydrogen consumption values in the 45%-55% range as benchmark vehicles).

[0064] Hierarchical diagnosis module 140: This is the core intelligent analysis unit of the system. After receiving the list of target vehicles and benchmark vehicles determined by the vehicle identification module 130, it starts the automated diagnosis process. This module contains multiple sub-modules:

[0065] First-level diagnosis module 142: Responsible for analyzing the power consumption of vehicle accessories. It extracts the running data of accessories such as air conditioners and air compressors of target vehicles and benchmark vehicles from the data set, compares the power consumption under similar working conditions, and determines whether there are significant differences.

[0066] Second-level diagnosis module 144: Responsible for fuel cell system analysis. If no problems are found in the first level, this module will be activated. It evaluates the overall efficiency of the fuel cell system by comparing the system-level input (hydrogen flow rate) and output (net output power).

[0067] Third-level diagnosis module 146: Responsible for deeper component-level analysis. When the second level confirms that the problem is within the fuel cell system, this module further compares the state of health (SOH) of the stack, the DCDC conversion efficiency, the hydrogen circulation pump power consumption, and the hydrogen exhaust valve action strategy to pinpoint the specific faulty components or strategy deviations.

[0068] The system 100 can also include a report generation and display module (not shown). When the hierarchical diagnosis module 140 locates the root cause, this module automatically generates a graphic and text-rich diagnosis report that clearly indicates the problem vehicle, the root cause, data comparison evidence, and maintenance recommendations (such as "recommend checking XX component" and "recommend XX training for the driver") based on the built-in knowledge base. The report can be pushed to the fleet manager through a web interface, a mobile app, or an email, realizing a closed loop of the diagnosis process.

[0069] The hardware carrier of the system can be one or more servers, which include a processor, memory, storage (such as a hard disk), and a network interface. The functions of the above-mentioned modules can be realized by the processor executing computer program instructions stored in the memory.

[0070] Example 3

[0071] The embodiment provides a more specific implementation of a hydrogen consumption abnormality diagnosis method, and detailed diagnosis logic is shown in Figure 3 The embodiment is based on the foregoing embodiment 1, and the step S104 "executing a layered diagnosis strategy" is described in detail.

[0072] When the system identifies the first target vehicle (such as V1) and the reference vehicle (V_ref) of the hydrogen consumption abnormality, the layered diagnosis strategy is started, and the core logic is to first judge whether the root cause of high hydrogen consumption is that the vehicle needs to consume more electric energy (electric consumption side problem) or the fuel cell system itself needs to consume more hydrogen to generate the same electric energy (hydrogen consumption side problem).

[0073] Step S104a: top branch judgment

[0074] The system first compares the vehicle electric consumption of V1 and V_ref. The vehicle electric consumption can be obtained by integrating the net output electric power of the fuel cell system with respect to time. If it is found that the total electric consumption of V1 is significantly higher than that of V_ref in completing the same transportation task (such as a round trip), the system determines that there is a vehicle electric consumption abnormality, and enters the electric consumption abnormality analysis process. On the contrary, if the total electric consumption is equivalent, but the total hydrogen consumption of V1 is higher, the system determines that there is a fuel cell system hydrogen consumption abnormality, and enters the fuel cell system abnormality analysis process.

[0075] Electric consumption abnormality analysis process:

[0076] It is assumed that V1 is determined to have a vehicle electric consumption abnormality.

[0077] 1. Vehicle accessory power consumption abnormality judgment: The system compares the average power and cumulative power consumption of each main accessory (air conditioner, steering assist pump, air compressor, etc.) of V1 and V_ref. If it is found that the power consumption of a certain accessory of V1 is significantly higher, it is judged that the accessory power consumption is abnormal.

[0078] 2. Driving habit analysis: After judging that the accessory power consumption is abnormal, the system further analyzes the driver behavior data. For example, if it is found that the driver of V1 frequently or for a long time turns on the high-power equipment (such as air conditioning heating) when it is not necessary, the diagnosis conclusion is that the driver's driving habit is not suitable, and energy-saving training is suggested. If the influence of the driving habit is excluded, the diagnosis conclusion is that the accessory itself is faulty, and the abnormal power consumption accessory is suggested to be replaced.

[0079] 3. Vehicle motor electric consumption abnormality judgment: If the accessory power consumption is normal, the system compares the electric consumption of the drive motor of V1 and V_ref. If the electric consumption of the motor of V1 is higher under similar working conditions (vehicle speed, load), it is judged that the motor electric consumption is abnormal.

[0080] 4. Motor system subdivision diagnosis: After judging that the motor electric consumption is abnormal, in-depth investigation is performed:

[0081] Compare the accelerator pedal sensor signal. If there is a drift or noise in V1's signal, resulting in an unexpected power request, diagnose the accelerator pedal travel data anomaly and recommend maintenance.

[0082] Compare the motor controller parameter calibration. If V1's parameter settings are not appropriate, diagnose the motor parameter anomaly.

[0083] Compare the motor controller software version. If V1 uses an old version known to have energy consumption problems, diagnose the motor software version anomaly and recommend software update.

[0084] If all the above are normal, analyze the motor temperature, three-phase current balance, and other data to determine if there are hardware problems such as bearing resistance, slight short circuit of winding, etc. Diagnose the motor hardware anomaly and recommend hardware repair.

[0085] Fuel cell system anomaly analysis process:

[0086] Suppose V1 is determined to have a fuel cell system hydrogen consumption anomaly.

[0087] 1. DCDC power consumption anomaly judgment: The system compares the conversion efficiency of V1 and V_ref's DCDC converter at different load points. If V1's efficiency is significantly lower than V_ref, diagnose the DCDC power consumption anomaly and recommend replacing the DCDC module.

[0088] 2. Stack power anomaly / decay anomaly judgment: If the DCDC is normal, the system compares the performance of the stacks of the two vehicles. By comparing the voltage and current data under the same working conditions, the polarization curve is drawn. If V1's stack has significantly lower voltage when outputting the same current, it is determined to have a stack power anomaly or stack decay anomaly. At this time, it is recommended to verify the hydrogen quality and try to restore performance through the stack online activation program. If it cannot be restored, the stack may need to be replaced.

[0089] 3. Stack operating parameter / software anomaly judgment: The system compares the operating parameters of the stacks of the two vehicles, such as working temperature, humidifier state, etc. If V1's operating parameters deviate from the optimal interval, diagnose the stack operating parameter anomaly. Further check the software version of the FCU (fuel cell controller), if the versions are inconsistent, it may be a stack software version anomaly, and software update is recommended.

[0090] 4. Hydrogen and water discharge anomaly judgment: The system compares the action frequency and duty cycle of the hydrogen discharge valve and water discharge valve of the two vehicles. If V1's hydrogen discharge is too frequent, resulting in hydrogen waste, diagnose the hydrogen and water discharge anomaly and check the related control strategy and sensors.

[0091] 5. Auxiliary system (BOP) power consumption anomaly judgment: Compare the power consumption of BOP components such as air compressor, hydrogen circulation pump, etc. If the BOP power consumption of V1 is abnormally high, diagnose as BOP power consumption anomaly, and suggest replacing the abnormal BOP component.

[0092] 6. Hydrogen leakage judgment: By analyzing the decline rate of hydrogen cylinder pressure when the vehicle is stationary, it is judged whether there is hydrogen leakage. If there is, it needs to be checked offline.

[0093] Through the detailed diagnosis logic described in Example 3, the present application can systematically and structurally locate a vague "high hydrogen consumption" problem to specific hardware, software or human factors, thereby achieving efficient and accurate diagnosis and maintenance guidance.

[0094] Those skilled in the art will readily understand that the above is only a preferred embodiment of the present application, and is not intended to limit the present application. Any modification, combination, replacement, improvement, etc. made within the spirit and principles of the present application is included in the protection scope of the present application.

Claims

1. A method for diagnosing abnormal hydrogen consumption in hydrogen fuel cell vehicles, applied to a vehicle group, wherein the hydrogen fuel cell vehicles in the vehicle group share at least one common operating characteristic, characterized in that, The method includes: Acquire remote telemetry data of each vehicle in the vehicle group within a preset time period, wherein the remote telemetry data includes operating parameters used to calculate energy consumption efficiency indicators; Based on the remote telemetry data, calculate the energy consumption efficiency index of each vehicle in the vehicle group; Based on the energy consumption efficiency index, at least one first target vehicle with an outlier energy consumption index and at least one benchmark vehicle with a benchmark energy consumption index are determined from the vehicle group. By comparing the detailed operating parameters of the first target vehicle with those of the benchmark vehicle, a hierarchical diagnostic strategy is performed on the first target vehicle to locate the root cause of the outlier energy consumption index.

2. The method according to claim 1, characterized in that, The stratified diagnostic strategy first includes: Based on the detailed operating parameters, it is determined whether the first target vehicle has abnormal overall vehicle power consumption; If the vehicle power consumption anomaly is found, the power consumption anomaly analysis process will be executed. If no abnormality in vehicle power consumption is found, the fuel cell system anomaly analysis process will be executed.

3. The method according to claim 2, characterized in that, The power consumption anomaly analysis process includes: Analyze the overall power consumption of the first target vehicle's accessories to determine if there are any abnormal accessory power consumption; and / or, Analyze the power consumption of the drive motor of the first target vehicle to determine if there is any abnormal power consumption of the motor.

4. The method according to claim 3, characterized in that, After confirming the presence of abnormal power consumption in the accessories, the power consumption anomaly analysis process further includes: Analyze the driving behavior data of the first target vehicle to determine if there are any abnormal driving habits that lead to increased energy consumption.

5. The method according to claim 3, characterized in that, After confirming the existence of abnormal motor power consumption, the power consumption anomaly analysis process also includes at least one of the following: Analyze accelerator pedal travel data to determine if there are any abnormalities in the accelerator pedal; Check the drive motor software version to determine if there are any anomalies. Analyze the motor's operating parameters to determine if there are any abnormalities in the motor hardware.

6. The method according to claim 2, characterized in that, The fuel cell system anomaly analysis process includes analyzing the fuel cell system of the first target vehicle, and the analysis includes at least one of the following: Analyze the power consumption of the DC-DC boost converter to determine if there are any abnormalities in its operating efficiency; Analyze the power characteristics of the fuel cell stack to determine if there are any abnormalities in its performance. Analyze the fuel cell stack's operating parameters to determine if there are any abnormalities in the stack's operating parameters or software version. Analyze the implementation data of hydrogen emission and wastewater discharge strategies to determine if there are any anomalies in hydrogen management; Analyze the power consumption of the auxiliary system to determine if there are any power consumption anomalies in the auxiliary system; Analyze the hydrogen system pressure data to determine if there is a hydrogen leak.

7. The method according to claim 6, characterized in that, After determining that there is an anomaly in the stack performance, the fuel cell system anomaly analysis process also includes: Verify the quality of hydrogen; and / or, Execute the online activation procedure for the fuel cell stack.

8. The method according to claim 1, characterized in that, The energy consumption efficiency index is hydrogen consumption per 100 kilometers; the first target vehicle with the outlier energy consumption index is the vehicle with the highest hydrogen consumption per 100 kilometers; the benchmark vehicle with the benchmark energy consumption index is the vehicle with the lowest or median hydrogen consumption per 100 kilometers.

9. A hydrogen consumption anomaly diagnosis system for hydrogen fuel cell vehicles, characterized in that, The system includes: The data acquisition module is used to acquire remote telemetry data of each vehicle in a vehicle group within a preset time period. The hydrogen fuel cell vehicles in the vehicle group share at least one common operating characteristic. The remote telemetry data includes operating parameters used to calculate energy consumption efficiency indicators. An energy consumption calculation module is used to calculate the energy consumption efficiency index of each vehicle in the vehicle group based on the remote telemetry data. The vehicle identification module is used to determine, based on the energy consumption efficiency index, at least one first target vehicle with an outlier energy consumption index and at least one benchmark vehicle with a benchmark energy consumption index from the vehicle group. The hierarchical diagnostic module is used to perform a hierarchical diagnostic strategy on the first target vehicle by comparing the detailed operating parameters of the first target vehicle with those of the benchmark vehicle, in order to locate the root cause of the outlier energy consumption index.

10. The system according to claim 9, characterized in that, The hierarchical diagnostic module is configured to: firstly, based on the detailed operating parameters, determine whether the first target vehicle has abnormal overall vehicle power consumption; Based on the judgment result, a power consumption anomaly analysis submodule is selectively activated to analyze the power consumption of vehicle accessories or drive motor, or a fuel cell system anomaly analysis submodule is activated to analyze the internal operating status of the fuel cell system.

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