Vehicle-mounted proton exchange membrane fuel cell high-robustness state estimation method

Abstract

The invention provides a high-robustness state estimation method for a vehicle-mounted proton exchange membrane fuel cell, and the method sequentially comprises the steps: building a fuel cell air supply system mechanism model, analyzing the sensitivity of system response to key parameters, achieving the parameter screening, constructing a multi-innovation volume-based Kalman filter and an augmented state Kalman filter based on the key parameters, and carrying out the high-robustness state estimation of the vehicle-mounted proton exchange membrane fuel cell. According to the method, the real-time verification of the effectiveness of the Kalman filter based on the standard multi-innovation volume and the dynamic fusion of two estimation results are realized based on the Kalman filter and the fusion estimation based on the effectiveness test and the fuzzy logic, so that an accurate observation mode suitable for the interior of the proton exchange membrane fuel cell system under various conditions is provided; compared with the prior art, the method can effectively improve the estimation performance of the unmeasurable variables under the condition of model mismatch, and is more beneficial to providing support for fuel cell system control.

Description

Technical Field

[0001] The invention belongs to the technical field of proton exchange membrane fuel cells, and in particular relates to a proton exchange membrane fuel cell. Background Art

[0002] As a complex system that integrates reactant supply system, water heat management system and power regulation system, designing and implementing a reasonable air supply control strategy is a necessary condition to ensure the overall efficiency and output performance of PEMFC. Since the oxygen excess ratio (OER) used to measure the air supply of PEMFC and the related oxygen partial pressure and nitrogen partial pressure in the cathode flow channel are still difficult to measure directly through sensors, currently only the measurement results of air mass flow sensors can be used for calculation as an alternative, but the external complex environment and noise and the response lag of the sensor itself will affect the accuracy of the results. In some existing technologies, fuel cell state estimation strategies based on high gain observers, sliding mode observers and Kalman filters are adopted. Although the accuracy is improved and the equipment cost is reduced compared with the above-mentioned sensor measurement-based methods, there are also shortcomings such as insufficient matching between the model and the actual situation, and the model parameters are often offset due to the influence of vehicle speed, temperature and external noise. There is still a lot of room for improvement in accuracy and robustness. Summary of the invention

[0003] In view of this, in view of the technical problems existing in the art, the present invention provides a highly robust state estimation method for a vehicle-mounted proton exchange membrane fuel cell, which specifically includes the following steps:

[0004] Step 1: Based on the physical structure and properties of the air compressor, intake manifold, cathode flow channel, exhaust manifold and the electrochemical reaction process mechanism in the proton exchange membrane fuel cell PEMFC air supply system, a PEMFC system lumped parameter model is established;

[0005] Step 2: determining the structural characteristic parameters, environmental parameters and static characteristic parameters involved in the PEMFC system lumped parameter model, determining the main system parameters to establish the system state variables, and constructing the corresponding system state equations on this basis;

[0006] Step 3: Select parameters other than the main system parameters that may affect the system state variables, and screen out the key parameters by performing variance-based global sensitivity analysis;

[0007] Step 4: Based on the third-order volumetric rule, the state prediction equation and measurement prediction equation of the standard volumetric Kalman filter are established to predict the system state; the innovation matrix is ​​introduced in the state update process, so as to realize the fusion of the prediction value and the sensor measurement value in the form of multi-innovation Kalman filtering to update the system state;

[0008] Step 5: Use the system state variables obtained in step 1 and the key parameters selected in step 3 to establish the system state variables of the augmented state and the corresponding system state equations, and predict and update the system state based on the standard Kalman filter;

[0009] Step 6: Calculate the normalized innovation square and its changes for the results of multi-innovation Kalman filtering and compare them with the corresponding threshold to perform a validity check. If it exceeds the threshold, it means that the test fails. At this time, the standard Kalman filtering result of the augmented state system is used as the final system state estimation result; if it does not exceed the threshold, the weighted fusion of the multi-innovation Kalman filtering and the standard Kalman filtering results of the augmented state system is used to obtain the final system state estimation result.

[0010] Furthermore, in step 2, the air compressor speed ω is obtained by using the nonlinear least squares method based on the trust region. cp , intake pressure in manifold p sm , Exhaust pressure in manifold p rm , oxygen partial pressure in cathode Nitrogen partial pressure in cathode mN 2 These main system parameters are used to establish the system state variables:

[0011] Furthermore, the global sensitivity analysis in step 3 is specifically implemented through Sobol principal index analysis, and the process includes: firstly, using Latin hypercube sampling method to select 2×10 4 sampling points, and then calculate the Sobol index S of each parameter i :

[0012]

[0013] In the formula, Var(·) represents variance, E(·) represents expectation, and X i represents the i-th input variable;

[0014] By sorting the Sobol index calculation results corresponding to each parameter, the cathode flow channel inlet flow coefficient k is screened out. sm , the flow coefficient k at the cathode flow channel outlet rm And the stack temperature T fc These three key parameters.

[0015] Furthermore, in step 4, the following state prediction equation and measurement prediction equation are specifically established:

[0016]

[0017] In the formula, is the predicted value of the system state at time k calculated based on the optimal state estimation result at the previous time, f(·) is the nonlinear system transfer equation, Xi,k|k-1 is the volume point set, is the measurement prediction value calculated based on the system state prediction value, H(·) is the measurement equation, Xi,k|k is the value calculated based on The new volume point set of

[0018] Using the calculated Kalman gain matrix K k，L =[K k , K k-1 , …, K k-L+1 ] and the introduced new information matrix E k，L =[e k , e k-1 , ..., e k-L+1 ]Get updated system status:

[0019]

[0020] In the formula, λ k,L =diag[λ k ,λ k-1 , ..., λ k-L+1 ] is the weight matrix, and L is the scale of the innovation matrix.

[0021] Furthermore, in step 5, the system state variables obtained in step 1 and the key parameters selected in step 3 are used to establish the system state variables of the augmented state: The specific form of the corresponding system state equation is as follows:

[0022]

[0023] In the formula, the superscript a represents the augmented state, w k is the system noise, u k is the system control variable, ucp,k is the air compressor input voltage at time k, which is set to be equal to u k Equivalent.

[0024] Furthermore, in step 6, the normalized innovation square NIS is calculated by the following formula: k，M :

[0025]

[0026] Where M is the moving window size, P zz，k is the new information covariance at time k;

[0027] When conducting a validity test, first calculate the change ΔNISk,M With threshold ΔNIS 0 If the threshold is exceeded, it indicates that a model mismatch has occurred. k，M No matter how it changes, the state estimation results all use the standard Kalman filtering results of the augmented state system; if ΔNIS k,M Always below ΔNIS 0 , then NIS k，M With the threshold χ 0 If the threshold is exceeded, it indicates that the result of multi-innovation Kalman filtering is invalid. At this time, the standard Kalman filtering result of the augmented state system is also used as the final state estimation result; if NIS k，M and ΔNIS k,M If both do not exceed the corresponding threshold, the final state estimation result is obtained through the following weighted fusion:

[0028]

[0029] Where α is the weight corresponding to the standard Kalman filtering result of the augmented state system, and They are the results of multi-innovation Kalman filtering and standard Kalman filtering for augmented state systems, respectively.

[0030] The highly robust state estimation method for the on-vehicle proton exchange membrane fuel cell provided by the present invention sequentially establishes a fuel cell air supply system mechanism model, analyzes the sensitivity of the system response to key parameters to achieve parameter screening, constructs a multi-innovation volumetric Kalman filter and an augmented state Kalman filter based on key parameters, and a fusion estimation based on validity check and fuzzy logic, thereby achieving real-time verification of the validity of the standard multi-innovation volumetric Kalman filter and dynamic fusion of the two estimation results, thereby providing accurate observation of the inside of the proton exchange membrane fuel cell system under various conditions. Compared with other existing technologies, the present invention can effectively improve the estimation performance of unmeasurable variables under model mismatch conditions, and is more conducive to providing support for fuel cell system control. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A flowchart of the method provided by the present invention;

[0032] Figure 2 This is a simulation comparison diagram of the method provided by the present invention and other prior arts. DETAILED DESCRIPTION

[0033] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] The present invention provides a highly robust state estimation method for a vehicle-mounted proton exchange membrane fuel cell, such as Figure 1 As shown, the specific steps include:

[0035] Step 1: Based on the physical structure and properties of the air compressor, intake manifold, cathode flow channel, exhaust manifold and the electrochemical reaction process mechanism in the proton exchange membrane fuel cell PEMFC air supply system, a PEMFC system lumped parameter model is established;

[0036] Step 2: determining the structural characteristic parameters, environmental parameters and static characteristic parameters involved in the PEMFC system lumped parameter model, determining the main system parameters to establish the system state variables, and constructing the corresponding system state equations on this basis;

[0037] Step 3: Select parameters other than the main system parameters that may affect the system state variables, and screen out the key parameters by performing variance-based global sensitivity analysis;

[0038] Step 4: Based on the third-order volumetric rule, the state prediction equation and measurement prediction equation of the standard volumetric Kalman filter are established to predict the system state; the innovation matrix is ​​introduced in the state update process, so as to realize the fusion of the prediction value and the sensor measurement value in the form of multi-innovation Kalman filtering to update the system state;

[0039] Step 5: Use the system state variables obtained in step 1 and the key parameters selected in step 3 to establish the system state variables of the augmented state and the corresponding system state equations, and predict and update the system state based on the standard Kalman filter;

[0040] Step 6: Calculate the normalized innovation square and its changes for the results of multi-innovation Kalman filtering and compare them with the corresponding threshold to perform a validity check. If it exceeds the threshold, it means that the test fails. At this time, the standard Kalman filtering result of the augmented state system is used as the final system state estimation result; if it does not exceed the threshold, the weighted fusion of the multi-innovation Kalman filtering and the standard Kalman filtering results of the augmented state system is used to obtain the final system state estimation result.

[0041] In a preferred embodiment of the present invention, in step 2, the air compressor speed ω is obtained by identifying the air compressor speed ω based on the nonlinear least squares method based on the trust region. cp, intake pressure in manifold p sm , Exhaust pressure in manifold p rm , oxygen partial pressure in cathode Nitrogen partial pressure in cathode These main system parameters are used to establish the system state variables:

[0042] In a preferred embodiment of the present invention, the global sensitivity analysis in step 3 is specifically implemented by Sobol principal index analysis, and the process includes: firstly, using Latin hypercube sampling method to select 2×10 4 sampling points, and then calculate the Sobol index S of each parameter i :

[0043]

[0044] In the formula, Var(·) represents variance, E(·) represents expectation, and X i represents the i-th input variable;

[0045] By sorting the Sobol index calculation results corresponding to each parameter, the cathode flow channel inlet flow coefficient k is screened out. sm , the flow coefficient k at the cathode flow channel outlet rm And the stack temperature T fc These three key parameters.

[0046] In a preferred embodiment of the present invention, the following state prediction equation and measurement prediction equation are specifically established in step 4:

[0047]

[0048] In the formula, is the predicted value of the system state at time k calculated based on the optimal state estimation result at the previous time, f(·) is the nonlinear system transfer equation, Xi,k|k-1 is the volume point set, is the measurement prediction value calculated based on the system state prediction value, H(·) is the measurement equation, Xi,k|k is the value calculated based on The new volume point set of

[0049] Using the calculated Kalman gain matrix K k，L =[K k , K k-1 , …, K k-L+1 ] and the introduced new information matrix E k，L =[e k , e k-1 , ..., e k-L+1 ]Get updated system status:

[0050]

[0051] In the formula, λ k,L =diag[λ k ,λ k-1 , ..., λ k-L+1 ] is the weight matrix, and L is the scale of the innovation matrix.

[0052] In a preferred embodiment of the present invention, in step 5, the system state variables obtained in step 1 and the key parameters selected in step 3 are used to establish the system state variables of the augmented state: The specific form of the corresponding system state equation is as follows:

[0053]

[0054] In the formula, the superscript a represents the augmented state, w k is the system noise, u k is the system control variable, u cp,k is the air compressor input voltage at time k, which is set to be equal to u k Equivalent.

[0055] In a preferred embodiment of the present invention, in step 6, the normalized innovation square NIS is calculated specifically by the following formula: k，M :

[0056]

[0057] Where M is the moving window size, P zz，k is the new information covariance at time k;

[0058] When conducting a validity test, first calculate the change ΔNIS k,M With threshold ΔNIS 0 If the threshold is exceeded, it indicates that a model mismatch has occurred. k，M No matter how it changes, the state estimation results all use the standard Kalman filtering results of the augmented state system; if ΔNIS k,M Always below ΔNIS 0 , then NIS k，M With the threshold χ 0 If the threshold is exceeded, it indicates that the result of multi-innovation Kalman filtering is invalid. At this time, the standard Kalman filtering result of the augmented state system is also used as the final state estimation result; if NIS k，M and ΔNIS k,M If both do not exceed the corresponding threshold, the final state estimation result is obtained through the following weighted fusion:

[0059]

[0060] Where α is the weight corresponding to the standard Kalman filtering result of the augmented state system, and They are the results of multi-innovation Kalman filtering and standard Kalman filtering for augmented state systems, respectively.

[0061] Figure 2 The comparison of the results of the present invention and other prior arts obtained by simulation using the MATLAB / SIMULINK platform is shown, and it can be seen that the present invention has obvious advantages in both accuracy and robustness of state estimation.

[0062] It should be understood that the size of the serial numbers of the steps in the embodiment of the present invention does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0063] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A highly robust state estimation method for a vehicle-mounted proton exchange membrane fuel cell, characterized in that: The specific steps include: Step 1: Based on the physical structure and properties of the air compressor, intake manifold, cathode flow channel, exhaust manifold and the electrochemical reaction process mechanism in the proton exchange membrane fuel cell PEMFC air supply system, a PEMFC system lumped parameter model is established; Step 2: determining the structural characteristic parameters, environmental parameters and static characteristic parameters involved in the PEMFC system lumped parameter model, determining the main system parameters to establish the system state variables, and constructing the corresponding system state equations on this basis; Step 3: Select parameters other than the main system parameters that may affect the system state variables, and screen out the key parameters by performing variance-based global sensitivity analysis; Step 4: Based on the third-order volumetric rule, a state prediction equation and a measurement prediction equation of a standard volumetric Kalman filter are established to predict the system state; In the process of state update, the new information matrix is ​​introduced to achieve the fusion of the predicted value and the sensor measurement value by multi-new information Kalman filtering, so that the system state is updated; Step 5: Use the system state variables obtained in step 1 and the key parameters selected in step 3 to establish the system state variables of the augmented state and the corresponding system state equations, and predict and update the system state based on the standard Kalman filter; Step 6: Calculate the normalized innovation square and its change for the result of multi-innovation Kalman filtering and compare it with the corresponding threshold to perform validity check. If it exceeds the threshold, it indicates that the test fails. At this time, the standard Kalman filtering result of the augmented state system is used as the final system state estimation result; If it does not exceed the threshold, the results of multi-innovation Kalman filtering and standard Kalman filtering of the augmented state system are weighted fused to obtain the final system state estimation result.

2. The method according to claim 1, characterized in that: In step 2, the air compressor speed ω is obtained by using the nonlinear least squares method based on the trust region. cp , intake pressure in manifold p sm , Exhaust pressure in manifold p rm , oxygen partial pressure in cathode Nitrogen partial pressure in cathode These main system parameters are used to establish the system state variables:

3. The method according to claim 1, characterized in that: The global sensitivity analysis in step 3 is implemented by Sobol principal index analysis, and the process includes: firstly, using Latin hypercube sampling method to select 2×10 4 sampling points, and then calculate the Sobol index S of each parameter i : In the formula, Var(·) represents variance, E(·) represents expectation, and X i represents the i-th input variable; By sorting the Sobol index calculation results corresponding to each parameter, the cathode flow channel inlet flow coefficient k is screened out. sm , the flow coefficient k at the cathode flow channel outlet rm And the stack temperature T fc These three key parameters.

4. The method according to claim 1, characterized in that: In step 4, the following state prediction equation and measurement prediction equation are specifically established: In the formula, is the predicted value of the system state at time k calculated based on the optimal state estimation result at the previous time, f(·) is the nonlinear system transfer equation, χ i,k|k-1 is the volume point set, is the measurement prediction value calculated based on the system state prediction value, H(·) is the measurement equation, χ i,k|k Based on The new volume point set of Using the calculated Kalman gain matrix And the new information matrix E introduced k,L =[e k ,e k-1 ,…,e k-L+1 ]Get updated system status: In the formula, λ k,L =diag[λ k ,λ k-1 ,…,λ k-L+1 ] is the weight matrix, and L is the scale of the innovation matrix.

5. The method according to claim 2, characterized in that: In step 5, the system state variables obtained in step 1 and the key parameters selected in step 3 are used to establish the system state variables of the augmented state: The specific form of the corresponding system state equation is as follows: In the formula, the superscript a represents the augmented state, w k is the system noise, u k is the system control variable, u cp,k is the air compressor input voltage at time k, which is set to be equal to u k Equivalent.

6. The method according to claim 1, characterized in that: In step 6, the normalized innovation square NIS is calculated by the following formula: k,M : Where M is the moving window size, P zz,k is the new information covariance at time k; When conducting a validity test, first calculate the change ΔNIS k,M Compared with the threshold ΔNIS0, if it exceeds the threshold, it indicates that a model mismatch occurs. k,M No matter how it changes, the state estimation results all use the standard Kalman filtering results of the augmented state system; if ΔNIS k,M If NIS is always lower than ΔNIS0, k,M Compared with the threshold χ0, if it exceeds the threshold, it indicates that the result of the multi-innovation Kalman filter is invalid. At this time, the standard Kalman filter result of the augmented state system is also used as the final state estimation result; if NIS k,M and ΔNIS k,M If both do not exceed the corresponding threshold, the final state estimation result is obtained through the following weighted fusion: Where α is the weight corresponding to the standard Kalman filtering result of the augmented state system, and They are the results of multi-innovation Kalman filtering and standard Kalman filtering for augmented state systems, respectively.

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