Knowledge-data fusion driven vehicle-mounted hydrogen system fault diagnosis method and device

Abstract

The invention discloses a knowledge-data fusion driven vehicle-mounted hydrogen system fault diagnosis method and device, and belongs to the technical field of fault diagnosis. The method comprises the steps that the actual hydrogen output flow of the vehicle-mounted hydrogen system is obtained, and the actual hydrogen concentration at the corresponding position is obtained through a sensor arranged in the vehicle environment; determining a current vehicle state; determining a current dynamic threshold value for fault diagnosis according to the current vehicle state; and detecting the actual hydrogen output flow and the actual hydrogen concentration according to the current dynamic threshold value so as to determine whether the vehicle-mounted hydrogen system has a hydrogen leakage fault or not. According to the calculation mode of the threshold value, knowledge and data are fused for joint driving, prediction of the hydrogen output flow and the hydrogen concentration is achieved, the threshold value in the corresponding vehicle state is determined by means of the predicted hydrogen output flow and the predicted hydrogen concentration, and therefore the threshold value can be dynamically adjusted in combination with the actual vehicle state, and the diagnosis result is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and in particular to a knowledge-data fusion driven on-board hydrogen system fault diagnosis method and device. Background Art

[0002] The on-board hydrogen system is the energy storage unit of the fuel cell, and the normal operation of the on-board hydrogen system is crucial.

[0003] In the related art, the fault diagnosis method of the on-board hydrogen system is to determine whether there is a fault based on whether the data collected by the sensor exceeds a fixed threshold. However, there is a problem of inaccurate diagnosis results. Summary of the invention

[0004] The present invention provides a knowledge-data fusion driven vehicle hydrogen system fault diagnosis method and device, which can solve the technical problem of inaccurate diagnosis results in related technologies. The technical solution is as follows:

[0005] On the one hand, a knowledge-data fusion driven vehicle hydrogen system fault diagnosis method is provided, the method comprising:

[0006] Obtain the actual hydrogen output flow of the onboard hydrogen system, and obtain the actual hydrogen concentration at the corresponding position using sensors deployed in the vehicle environment;

[0007] Determine a current vehicle state, wherein the vehicle state includes at least one of a power battery state, a vehicle pedal travel, a hydrogen bottle temperature, a hydrogen bottle pressure, and a hydrogen bottle flow rate;

[0008] determining a current dynamic threshold for fault diagnosis based on a current vehicle state;

[0009] detecting the actual hydrogen output flow rate and the actual hydrogen concentration according to the current dynamic threshold value to determine whether a hydrogen leakage fault occurs in the vehicle-mounted hydrogen system;

[0010] Among them, when the state of the target vehicle is determined, the calculation method of the corresponding target dynamic threshold includes:

[0011] The vehicle power model is constructed based on knowledge-driven, and the vehicle driving data under the current target vehicle state is calculated using the vehicle power model to obtain the ideal output power of the hydrogen system;

[0012] Using a pre-built mapping model, the ideal output power of the hydrogen system is mapped to a predicted hydrogen output flow rate;

[0013] Using a pre-built hydrogen leakage model, the predicted hydrogen output flow is substituted into the hydrogen leakage model for calculation to obtain a predicted hydrogen concentration at a specified position in the vehicle-mounted hydrogen system; the hydrogen leakage model is used to characterize the correlation between the hydrogen output flow and the hydrogen concentration at a specified position when hydrogen leakage occurs; the specified position is the layout position of the sensor;

[0014] Calculating a first threshold value related to the hydrogen output flow rate using the predicted hydrogen output flow rate;

[0015] calculating a second threshold value related to the hydrogen concentration using the predicted hydrogen concentration;

[0016] The first threshold and the second threshold are integrated to obtain a target dynamic threshold under the target vehicle state.

[0017] On the other hand, a knowledge-data fusion driven on-board hydrogen system fault diagnosis device is provided, the device comprising:

[0018] An acquisition unit, used to acquire an actual hydrogen output flow rate of the vehicle-mounted hydrogen system, and to acquire an actual hydrogen concentration at a corresponding position using a sensor arranged in the vehicle environment;

[0019] A first determination unit is used to determine a current vehicle state, wherein the vehicle state includes at least one of a power battery state, a vehicle pedal travel, a hydrogen bottle temperature, a hydrogen bottle pressure, and a hydrogen bottle flow rate;

[0020] a second determination unit, configured to determine a current dynamic threshold for fault diagnosis according to a current vehicle state;

[0021] a fault determination unit, configured to detect the actual hydrogen output flow rate and the actual hydrogen concentration according to the current dynamic threshold value, so as to determine whether a hydrogen leakage fault occurs in the vehicle-mounted hydrogen system;

[0022] Among them, when the state of the target vehicle is determined, the calculation method of the corresponding target dynamic threshold includes:

[0023] The vehicle power model is constructed based on knowledge-driven, and the vehicle driving data under the current target vehicle state is calculated using the vehicle power model to obtain the ideal output power of the hydrogen system;

[0024] Using a pre-built mapping model, the ideal output power of the hydrogen system is mapped to a predicted hydrogen output flow rate;

[0025] Using a pre-built hydrogen leakage model, the predicted hydrogen output flow is substituted into the hydrogen leakage model for calculation to obtain a predicted hydrogen concentration at a specified position in the vehicle-mounted hydrogen system; the hydrogen leakage model is used to characterize the correlation between the hydrogen output flow and the hydrogen concentration at a specified position when hydrogen leakage occurs; the specified position is the layout position of the sensor;

[0026] Calculating a first threshold value related to the hydrogen output flow rate using the predicted hydrogen output flow rate;

[0027] calculating a second threshold value related to the hydrogen concentration using the predicted hydrogen concentration;

[0028] The first threshold and the second threshold are integrated to obtain a target dynamic threshold under the target vehicle state.

[0029] On the other hand, a computer device is provided, which includes a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the steps of the above-mentioned knowledge-data fusion driven on-board hydrogen system fault diagnosis method.

[0030] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned knowledge-data fusion driven vehicle hydrogen system fault diagnosis method are implemented.

[0031] On the other hand, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned knowledge-data fusion driven vehicle hydrogen system fault diagnosis method.

[0032] The technical solution provided by the present invention can at least bring the following beneficial effects:

[0033] Taking into account that the vehicle itself has a certain degree of hydrogen leakage during actual driving, and the degree of hydrogen leakage is different under different vehicle states, the thresholds used for hydrogen leakage fault diagnosis under different vehicle states are also different. It is necessary to dynamically determine the thresholds under the corresponding vehicle state in order to use the thresholds under the corresponding vehicle state for fault diagnosis; the threshold calculation method is a fusion of knowledge and data to achieve the prediction of hydrogen output flow and hydrogen concentration, and use the predicted hydrogen output flow and predicted hydrogen concentration to determine the threshold under the corresponding vehicle state, so that the threshold can be dynamically adjusted in combination with the actual vehicle state, making the diagnosis result more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 It is a flow chart of a method for fault diagnosis of an on-board hydrogen system driven by knowledge-data fusion provided by an embodiment of the present invention;

[0036] Figure 2 This is a structural diagram of a knowledge-data fusion driven on-board hydrogen system fault diagnosis device provided by an embodiment of the present invention;

[0037] Figure 3 It is a hardware architecture diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] Please refer to Figure 1 , an embodiment of the present invention provides a knowledge-data fusion driven vehicle hydrogen system fault diagnosis method, the method comprising:

[0040] Step 100, obtaining the actual hydrogen output flow rate of the vehicle-mounted hydrogen system, and obtaining the actual hydrogen concentration at the corresponding position using sensors arranged in the vehicle environment;

[0041] Step 102, determining the current vehicle state, wherein the vehicle state includes at least one of a power battery state, a vehicle pedal travel, a hydrogen bottle temperature, a hydrogen bottle pressure, and a hydrogen bottle flow rate;

[0042] Step 104, determining a current dynamic threshold for fault diagnosis according to the current vehicle state; wherein, when the target vehicle state is determined, the calculation method of the corresponding target dynamic threshold includes: constructing a whole vehicle power model based on knowledge drive, and using the whole vehicle power model to calculate the vehicle driving data under the current target vehicle state to obtain the ideal output power of the hydrogen system; using a pre-constructed mapping model, mapping the ideal output power of the hydrogen system to a predicted hydrogen output flow; using a pre-constructed hydrogen leakage model, substituting the predicted hydrogen output flow into the hydrogen leakage model for calculation to obtain a predicted hydrogen concentration at a specified position in the vehicle-mounted hydrogen system; the hydrogen leakage model is used to characterize the correlation between the hydrogen output flow and the hydrogen concentration at the specified position when hydrogen leakage occurs; the specified position is the layout position of the sensor; using the predicted hydrogen output flow to calculate a first threshold related to the hydrogen output flow; using the predicted hydrogen concentration to calculate a second threshold related to the hydrogen concentration; integrating the first threshold and the second threshold to obtain the target dynamic threshold under the target vehicle state;

[0043] Step 106 , detecting the actual hydrogen output flow rate and the actual hydrogen concentration according to the current dynamic threshold value to determine whether a hydrogen leakage fault occurs in the vehicle-mounted hydrogen system.

[0044] In the embodiment of the present invention, considering that the vehicle itself has a certain degree of hydrogen leakage during actual driving, and the degree of hydrogen leakage is different in different vehicle states, the thresholds for hydrogen leakage fault diagnosis in different vehicle states are also different, and it is necessary to dynamically determine the thresholds under the corresponding vehicle state so as to use the thresholds under the corresponding vehicle state for fault diagnosis; the threshold calculation method is a fusion of knowledge and data to jointly drive, realize the prediction of hydrogen output flow and hydrogen concentration, and use the predicted hydrogen output flow and predicted hydrogen concentration to determine the threshold under the corresponding vehicle state, so that the threshold can be dynamically adjusted in combination with the actual vehicle state, so that the diagnosis result is more accurate.

[0045] When the target vehicle state is determined, the calculation method of the corresponding target dynamic threshold includes:

[0046] The calculation process of the dynamic threshold is described below.

[0047] Firstly, a vehicle power model is constructed based on knowledge-driven approach, and the vehicle driving data under the current target vehicle state is calculated using the vehicle power model to obtain the ideal output power of the hydrogen system.

[0048] In an embodiment of the present invention, a vehicle power model is constructed based on knowledge-driven method, including:

[0049] A1: Establish a vehicle dynamics model based on driving resistance, and use the vehicle dynamics model to establish a power demand model for calculating the vehicle's required power;

[0050] Wherein, the driving resistance F res Including vehicle rolling resistance F roll 、Vehicle air resistance F air , vehicle slope resistance F grad and vehicle acceleration resistance F acc Then, the vehicle dynamics model based on driving resistance is:

[0051] F res =F roll +F air +F grad +F acc

[0052] Vehicle rolling resistance F roll The calculation formula is as follows:

[0053] F roll =m·g·C r

[0054] Among them, C r represents the rolling resistance coefficient, m represents the mass of the vehicle, and g represents the acceleration due to gravity.

[0055] Vehicle air resistance F air The calculation formula is as follows:

[0056]

[0057] Where ρ represents the air density, C d Represents the air resistance coefficient, A f represents the frontal area of ​​the vehicle, and v represents the vehicle speed.

[0058] Vehicle slope resistance F grad The calculation formula is as follows:

[0059] F grad =m·g·sin(θ)

[0060] Here, θ represents the slope of the ramp.

[0061] Vehicle acceleration resistance F acc The calculation formula is as follows:

[0062] F acc =δ·m·a

[0063] Among them, δ represents the vehicle rotation mass conversion coefficient, and a represents the vehicle acceleration, which is related to the vehicle's accelerator pedal depth and the accelerator pedal change rate. The calculation equation is:

[0064]

[0065] Among them, G(s pedal ) is a function related to the accelerator pedal, which calculates the actual driving force given to the vehicle by the motor at different accelerator pedal depths and accelerator pedal change rates. pedal Represents the vehicle accelerator pedal depth, Represents the rate of change of the vehicle's accelerator pedal.

[0066] After establishing the vehicle dynamics model based on driving resistance, a power demand model for calculating the vehicle's required power can be established:

[0067] P theo_req =F res ·v

[0068] Among them, P theo_req The vehicle requires power.

[0069] A2: Based on the motor model and the power demand model, a motor power model is established to calculate the ideal output power of the motor;

[0070] The motor power model is as follows:

[0071]

[0072] Among them, P theo_ele_req is the ideal output power of the motor, η ele is the mechanical efficiency of the motor, which is related to temperature and motor speed; P FC is the power output of the battery.

[0073] A3: Use the motor power model to establish a vehicle power model for calculating the ideal output power of the hydrogen system.

[0074] The vehicle power model is as follows:

[0075] P FC =P theo_ele_req α FC

[0076] Among them, P FC is the ideal output power of the hydrogen system, α FC Represents the fuel cell power allocation factor, which is related to the vehicle control strategy and the power battery SOC.

[0077] It can be seen that in the embodiment of the present invention, the vehicle power model refers to a model that transmits power through a fuel cell system model, a power battery system model, a motor model and a vehicle dynamics model covering the hydrogen system. The vehicle dynamics model can calculate the ideal output power of the hydrogen system through vehicle driving data such as the vehicle speed, accelerator pedal travel, and accelerator pedal change rate.

[0078] The motor model is based on the power demand output by the vehicle dynamics model, taking into account factors such as motor speed, temperature, and operating efficiency, and calculates the actual power demand of the power system. Furthermore, the fuel cell system model and power battery system model of the hydrogen system allocate the actual power demand of the power system to the fuel cell system model and power battery system model of the hydrogen system by considering the energy management strategy of the entire vehicle, thereby calculating the ideal output power of the hydrogen system and the ideal output power of the motor.

[0079] A4: Based on the actual vehicle operating power, the unknown parameters in the vehicle power model are identified to obtain a vehicle power model that does not contain unknown parameters.

[0080] In the embodiment of the present invention, the calculation formulas involved in A1-A4 above constitute the vehicle power model in the embodiment of the present invention. Since the vehicle power model involves unknown parameters, it is necessary to use the actual vehicle operating power to identify the unknown parameters.

[0081] These unknowns are taken as parameters to be identified, and the parameters to be identified include: rolling resistance coefficient C r , air resistance coefficient C d , the vehicle rotation mass conversion factor δ and the motor's mechanical efficiency η ele .

[0082] In the embodiment of the present invention, these parameters to be identified can be identified by an integrated learning algorithm. The integrated learning algorithm can include basic integrated learning algorithm models such as random decision forest model, Adaboost algorithm model, XGBoost algorithm model, etc., and can also include deep integrated learning algorithm models based on shallow learners such as ELM, LSTM, GRU, etc., and can also include fusion algorithm models that optimize and adjust the parameters of the integrated learning algorithm by using particle swarm optimization algorithm, Osprey algorithm, etc.

[0083] Preferably, the integrated learning algorithm of the embodiment of the present invention includes: Fireworks Algorithm FWA, Harris Hawk Optimization Algorithm HHO and Black Hole Optimization Algorithm BHO. Specifically, the identification process of the parameters to be identified in the vehicle power model using the FWA-HHO-BHO integrated learning algorithm includes:

[0084] A41: Based on the upper and lower limits of the solution range of each parameter to be identified, multiple groups of potential feasible solutions are randomly generated; each group of potential feasible solutions includes solutions of four parameters to be identified;

[0085] A42: For each group of potential feasible solutions, the following steps are performed: substituting the potential feasible solutions into the vehicle power model to evaluate the degree of fit of the group of potential feasible solutions; if the degree of fit exceeds a set fitting threshold, the group of feasible solutions is determined to have good performance, and an explosion spark process is performed based on the degree of fit of the group of potential feasible solutions to generate a first number of sparks within a first explosion range; if the degree of fit does not exceed the set fitting threshold, the group of feasible solutions is determined to have poor performance, and an explosion spark process is performed based on the degree of fit of the group of potential feasible solutions to generate a second number of sparks within a second explosion range; wherein the first explosion range is smaller than the second explosion range, and the first number is larger than the second number; and each spark corresponds to a newly generated feasible solution;

[0086] A43: Substituting each newly generated feasible solution into the vehicle power model to evaluate the fitness of each newly generated feasible solution; classifying the newly generated feasible solutions based on the fitness average and variance of the newly generated feasible solutions to obtain feasible solutions with fitness higher than the average and feasible solutions with fitness lower than the average; executing step A44 for the feasible solutions with fitness higher than the average, and executing step A45 for the feasible solutions with fitness lower than the average;

[0087] A44: The feasible solutions with a degree of fit higher than the average value are input into the Harris Hawk optimization algorithm as the initial population of the Harris Hawk optimization algorithm to explore potential feasible solutions, output the optimal solution using the maximum number of iterations, and execute step A46.

[0088] Specifically, in the exploration phase, the Harris Hawk Optimization Algorithm achieves the initial population position update by formulating two different exploration strategies based on the positions of other hawks and the resting positions on random branches, and simultaneously balances the global and local explorations in the optimal solution solving process.

[0089] In the transition stage, the prey escape energy is calculated, and whether the Harris Hawk optimization algorithm should be switched from the exploration stage to the development stage is determined by defining whether the absolute value of the prey escape energy is less than 1; if the prey escape energy is less than 1, the Harris Hawk will switch from the exploration stage to the development stage, and adopt two attack methods, soft and hard attack, based on whether the prey successfully escapes.

[0090] Among them, the criterion for successful prey escape is that the absolute value of the prey escape energy is greater than 0.5;

[0091] If the prey successfully escapes, a soft attack is adopted to update the position of the Harris Hawk. The update equation is:

[0092] X(t+1)=Xbest (t)-E|JX best (t)-X(t)|

[0093] Among them, X best represents the best fit among all current eagles, and J represents the jump strength from 0 to 2;

[0094] If the prey fails to escape, a hard attack is adopted to update the position of the Harris hawk. The update equation is:

[0095] X(t+1)=X best (t)-E|X best (t)-X(t)|.

[0096] Determine whether the maximum number of iterations has been reached. If not, re-input the current positions of all eagles as the initial population into the Harris Hawk optimization algorithm. If the maximum number of iterations has been reached, output the current optimal eagle position as the optimal solution.

[0097] A45: inputting feasible solutions with a fitting degree lower than the average value into the black hole algorithm as the initial population of the black hole algorithm to output the optimal solution using the maximum number of iterations, and executing step A46;

[0098] Specifically, the initial population solution fit evaluation is to calculate the difference between the real and imaginary impedance of the model output at the acquisition frequency point of each initial population solution and the actual measured value to achieve the fit evaluation of the potential solution, and select the candidate solution with the best fit and set it as the black hole X BH , the other solutions are set as star X, the star moves around the gravitational field of the black hole;

[0099] Solution update: The star (i.e. other candidate solutions) will be attracted by the black hole's gravity and gradually approach the black hole. The update formula for each candidate solution is:

[0100] X i (t+1)=X i (t)+r(X BH (t)-X i (t))

[0101] Where X i (t) is the position of candidate solution i after t iterations, and r is a random number between 0 and 1, which is used to simulate the different gravitational effects on different stars.

[0102] Determination of the event horizon: Black holes have an important characteristic, namely the "event horizon", which refers to the gravitational boundary of the black hole. Any object that enters this boundary will be swallowed by the black hole. In the black hole algorithm, when a candidate solution (star) enters the event horizon (that is, too close to the black hole), the solution will be swallowed and a new solution will be randomly generated. The radius of the event horizon is defined as:

[0103]

[0104] Where X BH is the fitness value of the black hole, is the sum of the fitness values ​​of all candidate solutions. When the distance of a star satisfies the following conditions:

[0105] ||X i (t)-X BH (t)||≤r EH

[0106] The candidate solution will be considered to have entered the event horizon, the solution will be swallowed up, and new candidate solutions will be randomly generated to explore the entire search space.

[0107] Randomly generate new solutions: The swallowed candidate solutions will randomly generate new solutions in the search space to maintain the ability of global search. The new solutions are randomly initialized in the entire search space, and the formula is as follows:

[0108] X new =LB+r(UB-LB)

[0109] Where X new is the new solution, r is a random number between 0 and 1, LB is the lower bound of the feasible solution, and UB is the upper bound.

[0110] Determine whether the maximum number of iterations has been reached. If not, re-input all current star and black hole positions as the initial population into the black hole algorithm. If the maximum number of iterations has been reached, output the current black hole position as the optimal solution.

[0111] A46: The optimal solution calculated by the black hole algorithm and the Harris Eagle algorithm is re-input into the vehicle power model as a potential feasible solution.

[0112] After obtaining the vehicle power model without unknown parameters, the vehicle driving data is collected and input into the vehicle power model, so that the ideal output power of the hydrogen system can be accurately calculated, providing data support for the reverse prediction of the hydrogen system output flow and hydrogen concentration. The vehicle driving data includes vehicle speed, accelerator pedal travel, accelerator pedal change rate, battery SOC, ambient temperature and road slope.

[0113] Then, the ideal output power of the hydrogen system is mapped to the predicted hydrogen output flow rate using the pre-built mapping model.

[0114] In the embodiment of the present invention, after the ideal output power of the hydrogen system is calculated, in order to be able to reversely predict the hydrogen output flow rate of the hydrogen system, it is necessary to pre-build a mapping model that can map the ideal output power of the hydrogen system to the hydrogen output flow rate.

[0115] In one embodiment of the present invention, the mapping model may be constructed in the following manner:

[0116] B1: Identify the various driving conditions that the vehicle will face;

[0117] B2: For each driving condition, the following are performed: obtaining a data set that changes with time under the vehicle state under the driving condition based on experimental data; the data set includes the corresponding relationship between the output power of the hydrogen system and the hydrogen output flow rate; constructing a time domain convolutional network, and using the data set to train the time domain convolutional network so that the time domain convolutional network maps the input output power to the hydrogen output flow rate.

[0118] After determining the driving condition, the output power of the hydrogen system is continuously adjusted under the driving condition, and the hydrogen output flow rate under different output powers is measured, thereby obtaining a data set. Among them, the hydrogen output flow rate can be obtained through a hydrogen system bench leakage test.

[0119] Since the state of the vehicle at the current moment is correlated with the state at the previous moment during driving, and in order to use the data set to train the time domain convolutional network, in an embodiment of the present invention, the data set can be segmented using a time window to obtain multiple data segments of the same time length, and the multiple data segments are time-series, each data segment includes the hydrogen system output power and the hydrogen output flow rate, and then the hydrogen system output power of the multiple data segments is used as the input of the time domain convolutional network, and the hydrogen output flow rate of the multiple data segments is used as the output of the time domain convolutional network, so that a trained mapping model can be obtained.

[0120] Different driving conditions correspond to different mapping models. After the current driving condition, the ideal output power of the hydrogen system can be input into the mapping model corresponding to the current driving condition, and then the output hydrogen output flow rate can be obtained.

[0121] Next, using the pre-constructed hydrogen leakage model, the predicted hydrogen output flow rate is substituted into the hydrogen leakage model for calculation to obtain the predicted hydrogen concentration at a specified position in the vehicle-mounted hydrogen system; the hydrogen leakage model is used to characterize the correlation between the hydrogen output flow rate and the hydrogen concentration at a specified position when hydrogen leakage occurs; the specified position is the layout location of the sensor.

[0122] In the embodiment of the present invention, whether there is a hydrogen leakage in the vehicle-mounted hydrogen system can be determined based on the hydrogen concentration. Under normal circumstances, there is a nonlinear functional relationship between the hydrogen output flow rate and the hydrogen concentration at a specified position. If the hydrogen concentration changes relative to the hydrogen concentration under normal circumstances, it indicates that there is a possibility of hydrogen leakage.

[0123] Based on this, it is necessary to construct a hydrogen leakage model that can characterize the correlation between the hydrogen output flow rate and the hydrogen concentration at a specified location when hydrogen leakage occurs. In one implementation, this can be achieved through the following steps:

[0124] C1: Based on the hydrogen leakage test, the hydrogen concentration at different detection positions under different hydrogen output flow rates is obtained; the hydrogen concentration is collected by sensors arranged in the vehicle environment or density space environment. When conducting the hydrogen leakage test, the output of the hydrogen system flow valve is controlled to adjust the hydrogen output flow rate, and the sensor detects the hydrogen concentration at the corresponding detection position;

[0125] In the embodiment of the present invention, the detection positions may include various valves and pumps in the hydrogen system and positions near the hydrogen bottle and the hydrogen fuel cell.

[0126] C 2: Construct a hydrogen leakage model based on the gas diffusion equation; the hydrogen leakage model can characterize the correlation between the hydrogen output flow rate and the hydrogen concentration at each location when hydrogen leakage occurs;

[0127] In the embodiment of the present invention, the hydrogen leakage model based on the gas diffusion equation is:

[0128]

[0129] in, represents the hydrogen concentration at the ith spatial grid point at the t+1th time step, u i represents the hydrogen velocity field at the i-th spatial grid point, D represents the hydrogen diffusion coefficient, S i Represents the amount of hydrogen leaking from the leak source.

[0130] The conversion relationship between hydrogen concentration and hydrogen pressure is:

[0131]

[0132] Among them, P i t represents the hydrogen pressure at the i-th spatial grid point at the t-th time step, R represents the ideal gas constant, and T represents the absolute temperature.

[0133] The conversion relationship between hydrogen output flow and hydrogen velocity field is:

[0134]

[0135] Where A represents the cross-sectional area through which hydrogen flows, Represents the hydrogen output flow rate at the tth time step.

[0136] The conversion relationship between hydrogen concentration and hydrogen pressure and the conversion relationship between hydrogen output flow rate and hydrogen velocity field are substituted into the hydrogen leakage model to obtain a hydrogen leakage model that correlates the hydrogen output flow rate with the hydrogen concentration at each spatial grid point. At this time, the hydrogen leakage amount of the leakage source in the hydrogen leakage model is an unknown quantity.

[0137] C3: Using the hydrogen concentrations at different detection positions under different hydrogen output flow rates, parameter fitting is performed on the hydrogen leakage amount of the leakage source in the hydrogen leakage model to obtain a constructed hydrogen leakage model.

[0138] In the embodiment of the present invention, the hydrogen leakage amount S of the leakage source is i The fitting method can be achieved by combining the simulated annealing algorithm SA and the Harris Hawk optimization algorithm HHO.

[0139] Specifically, first set the initial temperature T of the simulated annealing algorithm initial , temperature cooling rate α, cut-off temperature T end , the maximum number of iterations T, and the upper and lower boundaries UB and LB of the feasible solution, and on this basis, a set of potential feasible solutions X is randomly generated based on the upper and lower boundaries of the feasible solution domain; then the Harris Eagle optimization algorithm is used to explore the feasible solutions to obtain the optimal solution.

[0140] In practical applications, if the location of the leakage source is determined, then when generating potential feasible solutions, the hydrogen leakage at other locations can be set to 0, and the hydrogen leakage at the leakage source location can be randomly selected within the boundary range, thereby obtaining a set of potential feasible solutions at different locations.

[0141] It should be noted that the Harris Hawk optimization algorithm in this step can refer to the Harris Hawk optimization algorithm described in step 100, and more siege strategies can be formulated when formulating the siege strategy, such as soft siege strategy, hard siege strategy, soft siege strategy with progressive rapid dive and soft siege strategy with progressive rapid dive, so as to obtain a more accurate optimal solution.

[0142] In this way, a hydrogen leakage model can be obtained for characterizing the correlation between the hydrogen output flow rate and the hydrogen concentration at each position when hydrogen leakage occurs.

[0143] Finally, the first threshold value related to the hydrogen output flow rate is calculated using the predicted hydrogen output flow rate; the second threshold value related to the hydrogen concentration is calculated using the predicted hydrogen concentration; the first threshold value and the second threshold value are integrated to obtain the target dynamic threshold value under the target vehicle state.

[0144] In the embodiment of the present invention, when the target vehicle state is determined, the power battery state in the target vehicle state is determined, and the hydrogen output flow corresponding to different power requirements under the power battery state can be predicted. The hydrogen tank temperature in the target vehicle state is determined, so that the hydrogen concentration corresponding to the hydrogen tank temperature can be predicted;

[0145] Based on this, the first threshold and the second threshold Calculated by the following formula:

[0146]

[0147] in, are the predicted hydrogen output flow rate and the predicted hydrogen concentration, respectively. α1 and α2 are adjustment factors. δ1 is the standard deviation of the error between the predicted hydrogen output flow rate and the measured hydrogen output flow rate. δ2 is the standard deviation of the error between the predicted hydrogen concentration and the measured hydrogen concentration. The adjustment factor can be an array of length 2. Hunger can generate the upper and lower boundaries of the first threshold and the upper and lower boundaries of the second threshold.

[0148] The upper boundary of the target dynamic threshold is the sum of the upper boundary of the first threshold and the upper boundary of the second threshold; the lower boundary of the target dynamic threshold is the sum of the lower boundary of the first threshold and the lower boundary of the second threshold.

[0149] The above calculation method can be used to obtain dynamic thresholds under different vehicle states, so a threshold table can be generated, which includes dynamic thresholds corresponding to different vehicle states.

[0150] Then, in step 104, determining the current dynamic threshold for fault diagnosis according to the current vehicle state may include:

[0151] Obtaining a threshold table; the threshold table includes dynamic thresholds corresponding to different vehicle states;

[0152] The threshold table is queried by a table lookup method to obtain a current dynamic threshold corresponding to the current vehicle state.

[0153] When the vehicle status includes the power battery, vehicle pedal travel, hydrogen bottle temperature, hydrogen bottle pressure and hydrogen bottle flow, the vehicle status is a combination of the values ​​of the above parameters. It can be seen that there are many combinations, so it is impossible to generate a threshold table covering the full range of combinations. Based on this, the required test data can be pre-stored on the server side. When the vehicle status is determined, the server side uses the vehicle status to calculate the dynamic threshold according to the above dynamic threshold calculation method, and then diagnoses the hydrogen system fault. That is:

[0154] In step 104, the current dynamic threshold for fault diagnosis is determined based on the current vehicle state. Another implementation method may include: taking the current vehicle state as the target vehicle state, and calculating the target dynamic threshold using the target dynamic threshold calculation method to obtain the current dynamic threshold for fault diagnosis.

[0155] Further, after obtaining the current dynamic threshold for fault diagnosis according to the current vehicle state, in step 106, detecting the actual hydrogen output flow rate and the actual hydrogen concentration according to the current dynamic threshold to determine whether a hydrogen leakage fault occurs in the vehicle-mounted hydrogen system may include:

[0156] Calculating the sum of the actual hydrogen output flow rate and the actual hydrogen concentration;

[0157] It is determined whether the sum value is between the lower boundary of the target dynamic threshold and the upper boundary of the target dynamic threshold. If not, it is determined that a hydrogen leakage fault has occurred.

[0158] The embodiments of the present invention can effectively identify leakage problems of the hydrogen system under large flow leakage conditions and improve the safety protection capability of the hydrogen system.

[0159] Please refer to Figure 2 The embodiment of the present invention provides a knowledge-data fusion driven vehicle hydrogen system fault diagnosis device, the device comprising:

[0160] The acquisition unit 200 is used to acquire the actual hydrogen output flow rate of the vehicle-mounted hydrogen system, and to acquire the actual hydrogen concentration at the corresponding position using sensors arranged in the vehicle environment;

[0161] A first determination unit 202 is used to determine a current vehicle state, where the vehicle state includes at least one of a power battery state, a vehicle pedal travel, a hydrogen bottle temperature, a hydrogen bottle pressure, and a hydrogen bottle flow rate;

[0162] A second determination unit 204, configured to determine a current dynamic threshold for fault diagnosis according to a current vehicle state;

[0163] A fault determination unit 206 is used to detect the actual hydrogen output flow rate and the actual hydrogen concentration according to the current dynamic threshold value to determine whether a hydrogen leakage fault occurs in the vehicle hydrogen system;

[0164] Among them, when the state of the target vehicle is determined, the calculation method of the corresponding target dynamic threshold includes:

[0165] The vehicle power model is constructed based on knowledge-driven, and the vehicle driving data under the current target vehicle state is calculated using the vehicle power model to obtain the ideal output power of the hydrogen system;

[0166] Using a pre-built mapping model, the ideal output power of the hydrogen system is mapped to a predicted hydrogen output flow rate;

[0167] Using a pre-built hydrogen leakage model, the predicted hydrogen output flow is substituted into the hydrogen leakage model for calculation to obtain a predicted hydrogen concentration at a specified position in the vehicle-mounted hydrogen system; the hydrogen leakage model is used to characterize the correlation between the hydrogen output flow and the hydrogen concentration at a specified position when hydrogen leakage occurs; the specified position is the layout position of the sensor;

[0168] Calculating a first threshold value related to the hydrogen output flow rate using the predicted hydrogen output flow rate;

[0169] calculating a second threshold value related to the hydrogen concentration using the predicted hydrogen concentration;

[0170] The first threshold and the second threshold are integrated to obtain a target dynamic threshold under the target vehicle state.

[0171] In one embodiment of the present invention, determining a current dynamic threshold for fault diagnosis according to the current vehicle state includes:

[0172] Obtaining a threshold table; the threshold table includes dynamic thresholds corresponding to different vehicle states;

[0173] The threshold value table is queried by a table lookup method to obtain a current dynamic threshold value corresponding to the current vehicle state;

[0174] or,

[0175] Determining a current dynamic threshold for fault diagnosis according to the current vehicle state includes:

[0176] The current vehicle state is used as the target vehicle state, and the target dynamic threshold is calculated using the target dynamic threshold calculation method to obtain the current dynamic threshold used for fault diagnosis.

[0177] In one embodiment of the present invention, the knowledge-driven construction of the vehicle power model includes:

[0178] Establishing a vehicle dynamics model based on driving resistance, and using the vehicle dynamics model to establish a power demand model for calculating the vehicle's required power; the driving resistance includes: vehicle rolling resistance, vehicle air resistance, vehicle ramp resistance and vehicle acceleration resistance;

[0179] Based on the motor model and the power demand model, a motor power model for calculating the ideal output power of the motor is established;

[0180] Using the motor power model, a vehicle power model is established for calculating the ideal output power of the hydrogen system;

[0181] Based on the actual vehicle operating power, the unknown parameters in the vehicle power model are identified to obtain a vehicle power model that does not contain unknown parameters.

[0182] In one embodiment of the present invention, the method for constructing the hydrogen leakage model includes:

[0183] Based on the hydrogen leakage test, the hydrogen concentration at different detection positions under different hydrogen output flow rates is obtained; the hydrogen concentration is collected by sensors arranged in the vehicle environment or density space environment. When conducting the hydrogen leakage test, the output of the hydrogen system flow valve is controlled to adjust the hydrogen output flow rate, and the sensor detects the hydrogen concentration at the corresponding detection position;

[0184] Construct a hydrogen leakage model based on the gas diffusion equation; the hydrogen leakage model can characterize the correlation between the hydrogen output flow rate and the hydrogen concentration at each location when hydrogen leakage occurs;

[0185] The hydrogen concentrations at different detection positions under different hydrogen output flow rates are used to perform parameter fitting on the hydrogen leakage amount of the leakage source in the hydrogen leakage model to obtain a constructed hydrogen leakage model.

[0186] In one embodiment of the present invention, the first threshold and the second threshold Calculated by the following formula:

[0187]

[0188] in, are the predicted hydrogen output flow rate and the predicted hydrogen concentration, respectively; α1 and α2 are adjustment factors; δ1 is the standard deviation of the error between the predicted hydrogen output flow rate and the measured hydrogen output flow rate; δ2 is the standard deviation of the error between the predicted hydrogen concentration and the measured hydrogen concentration;

[0189] The upper boundary of the target dynamic threshold is the sum of the upper boundary of the first threshold and the upper boundary of the second threshold; the lower boundary of the target dynamic threshold is the sum of the lower boundary of the first threshold and the lower boundary of the second threshold.

[0190] In one embodiment of the present invention, the actual hydrogen output flow rate and the actual hydrogen concentration are detected according to the current dynamic threshold to determine whether a hydrogen leakage fault occurs in the vehicle-mounted hydrogen system, including:

[0191] Calculating the sum of the actual hydrogen output flow rate and the actual hydrogen concentration;

[0192] It is determined whether the sum value is between the lower boundary of the target dynamic threshold and the upper boundary of the target dynamic threshold. If not, it is determined that a hydrogen leakage fault has occurred.

[0193] It should be noted that the knowledge-data fusion-driven on-board hydrogen system fault diagnosis device provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the knowledge-data fusion-driven on-board hydrogen system fault diagnosis device provided in the above embodiment and the knowledge-data fusion-driven on-board hydrogen system fault diagnosis method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0194] The embodiment of the present application also provides a computer device, please refer to Figure 3 The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the knowledge-data fusion driven on-board hydrogen system fault diagnosis method provided by the above-mentioned method embodiments.

[0195] An embodiment of the present application also provides a computer-readable storage medium, on which is stored at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the knowledge-data fusion driven on-board hydrogen system fault diagnosis method provided by the above-mentioned method embodiments.

[0196] An embodiment of the present application also provides a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the knowledge-data fusion driven on-board hydrogen system fault diagnosis method described in any of the above embodiments.

[0197] For the convenience of description, the above system or device is described by dividing it into various modules or units according to its functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0198] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0199] Finally, it should be noted that, in this article, relational terms such as first, second, third and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0200] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A knowledge-data fusion driven vehicle hydrogen system fault diagnosis method, characterized in that: The method comprises: Obtain the actual hydrogen output flow of the onboard hydrogen system, and obtain the actual hydrogen concentration at the corresponding position using sensors deployed in the vehicle environment; Determine a current vehicle state, wherein the vehicle state includes at least one of a power battery state, a vehicle pedal travel, a hydrogen bottle temperature, a hydrogen bottle pressure, and a hydrogen bottle flow rate; determining a current dynamic threshold for fault diagnosis based on a current vehicle state; detecting the actual hydrogen output flow rate and the actual hydrogen concentration according to the current dynamic threshold value to determine whether a hydrogen leakage fault occurs in the vehicle-mounted hydrogen system; Among them, when the state of the target vehicle is determined, the calculation method of the corresponding target dynamic threshold includes: The vehicle power model is constructed based on knowledge-driven, and the vehicle driving data under the current target vehicle state is calculated using the vehicle power model to obtain the ideal output power of the hydrogen system; Using a pre-built mapping model, the ideal output power of the hydrogen system is mapped to a predicted hydrogen output flow rate; Using a pre-built hydrogen leakage model, the predicted hydrogen output flow is substituted into the hydrogen leakage model for calculation to obtain a predicted hydrogen concentration at a specified position in the vehicle-mounted hydrogen system; the hydrogen leakage model is used to characterize the correlation between the hydrogen output flow and the hydrogen concentration at a specified position when hydrogen leakage occurs; the specified position is the layout position of the sensor; Calculating a first threshold value related to the hydrogen output flow rate using the predicted hydrogen output flow rate; calculating a second threshold value related to the hydrogen concentration using the predicted hydrogen concentration; The first threshold and the second threshold are integrated to obtain a target dynamic threshold under the target vehicle state.

2. The method according to claim 1, characterized in that Determining a current dynamic threshold for fault diagnosis according to the current vehicle state includes: Obtaining a threshold table; the threshold table includes dynamic thresholds corresponding to different vehicle states; The threshold value table is queried by a table lookup method to obtain a current dynamic threshold value corresponding to the current vehicle state; or, Determining a current dynamic threshold for fault diagnosis according to the current vehicle state includes: The current vehicle state is used as the target vehicle state, and the target dynamic threshold is calculated using the target dynamic threshold calculation method to obtain the current dynamic threshold used for fault diagnosis.

3. The method according to claim 1, characterized in that The knowledge-driven construction of the vehicle power model includes: Establishing a vehicle dynamics model based on driving resistance, and using the vehicle dynamics model to establish a power demand model for calculating the vehicle's required power; the driving resistance includes: vehicle rolling resistance, vehicle air resistance, vehicle ramp resistance and vehicle acceleration resistance; Based on the motor model and the power demand model, a motor power model for calculating the ideal output power of the motor is established; Using the motor power model, a vehicle power model is established for calculating the ideal output power of the hydrogen system; Based on the actual vehicle operating power, the unknown parameters in the vehicle power model are identified to obtain a vehicle power model that does not contain unknown parameters.

4. The method according to claim 1, characterized in that: The construction method of the hydrogen leakage model includes: Based on the hydrogen leakage test, the hydrogen concentration at different detection positions under different hydrogen output flow rates is obtained; the hydrogen concentration is collected by sensors arranged in the vehicle environment or density space environment. When conducting the hydrogen leakage test, the output of the hydrogen system flow valve is controlled to adjust the hydrogen output flow rate, and the sensor detects the hydrogen concentration at the corresponding detection position; Construct a hydrogen leakage model based on the gas diffusion equation; the hydrogen leakage model can characterize the correlation between the hydrogen output flow rate and the hydrogen concentration at each location when hydrogen leakage occurs; The hydrogen concentrations at different detection positions under different hydrogen output flow rates are used to perform parameter fitting on the hydrogen leakage amount of the leakage source in the hydrogen leakage model to obtain a constructed hydrogen leakage model.

5. The method according to any one of claims 1 to 4, characterized in that: The first threshold and the second threshold Calculated by the following formula: in, are the predicted hydrogen output flow rate and the predicted hydrogen concentration, respectively; α1 and α2 are adjustment factors; δ1 is the standard deviation of the error between the predicted hydrogen output flow rate and the measured hydrogen output flow rate; δ2 is the standard deviation of the error between the predicted hydrogen concentration and the measured hydrogen concentration; The upper boundary of the target dynamic threshold is the sum of the upper boundary of the first threshold and the upper boundary of the second threshold; the lower boundary of the target dynamic threshold is the sum of the lower boundary of the first threshold and the lower boundary of the second threshold.

6. The method according to claim 5, characterized in that The detecting the actual hydrogen output flow rate and the actual hydrogen concentration according to the current dynamic threshold value to determine whether a hydrogen leakage fault occurs in the vehicle-mounted hydrogen system includes: Calculating the sum of the actual hydrogen output flow rate and the actual hydrogen concentration; It is determined whether the sum value is between the lower boundary of the target dynamic threshold and the upper boundary of the target dynamic threshold. If not, it is determined that a hydrogen leakage fault has occurred.

7. A knowledge-data fusion driven vehicle hydrogen system fault diagnosis device, characterized in that: The device comprises: An acquisition unit, used to acquire an actual hydrogen output flow rate of the vehicle-mounted hydrogen system, and to acquire an actual hydrogen concentration at a corresponding position using a sensor arranged in the vehicle environment; A first determination unit is used to determine a current vehicle state, wherein the vehicle state includes at least one of a power battery state, a vehicle pedal travel, a hydrogen bottle temperature, a hydrogen bottle pressure, and a hydrogen bottle flow rate; a second determination unit, configured to determine a current dynamic threshold for fault diagnosis according to a current vehicle state; a fault determination unit, configured to detect the actual hydrogen output flow rate and the actual hydrogen concentration according to the current dynamic threshold value, so as to determine whether a hydrogen leakage fault occurs in the vehicle-mounted hydrogen system; Among them, when the state of the target vehicle is determined, the calculation method of the corresponding target dynamic threshold includes: The vehicle power model is constructed based on knowledge-driven, and the vehicle driving data under the current target vehicle state is calculated using the vehicle power model to obtain the ideal output power of the hydrogen system; Using a pre-built mapping model, the ideal output power of the hydrogen system is mapped to a predicted hydrogen output flow rate; Using a pre-built hydrogen leakage model, the predicted hydrogen output flow is substituted into the hydrogen leakage model for calculation to obtain a predicted hydrogen concentration at a specified position in the vehicle-mounted hydrogen system; the hydrogen leakage model is used to characterize the correlation between the hydrogen output flow and the hydrogen concentration at a specified position when hydrogen leakage occurs; the specified position is the layout position of the sensor; Calculating a first threshold value related to the hydrogen output flow rate using the predicted hydrogen output flow rate; calculating a second threshold value related to the hydrogen concentration using the predicted hydrogen concentration; The first threshold and the second threshold are integrated to obtain a target dynamic threshold under the target vehicle state.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the steps of any one of the methods described in claims 1-6.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The method comprises a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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