Fault identification method and device for battery pack under random working condition

Abstract

The invention relates to a fault identification method and device for a random working condition battery pack. The method comprises the following steps: constructing a battery pack state model and a battery monomer deviation model; respectively performing state prediction on the battery pack state model and the battery monomer deviation model by using a filter to obtain a first prediction result of the battery pack at the next moment and a first prediction result of the battery monomer at the next moment; inputting the obtained battery state parameters and the vehicle state parameters into a trained sequential network prediction model to obtain a second prediction result of the battery pack at the next moment; inputting the second prediction result of the battery pack and the first prediction result of the single battery into a trained physical enhancement encoder to obtain a second prediction matrix of the single battery; and calculating a residual value of the second prediction matrix of the single batteries, and comparing the residual value with a preset threshold value to perform fault identification on the battery pack. According to the scheme, the fault of the battery pack under the random working condition can be accurately identified.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of power battery safety monitoring, and in particular to a fault identification method and device for a random operating condition battery pack. Background Art

[0002] The power battery system composed of lithium-ion battery packs has the advantages of high specific energy, long life, low self-discharge and wide applicable temperature range, and is currently the mainstream choice for electric vehicles. As the market share of electric vehicles increases, the safety failure of battery modules has become a major bottleneck restricting the development of electric vehicles. In recent years, vehicle battery failures have occurred frequently, and the number of market recalls is countless, which has increased users' safety anxiety and the economic burden of the market.

[0003] Under normal circumstances, as the number of charge and discharge cycles increases, the microscopic electrochemical components of lithium-ion batteries undergo irreversible losses, resulting in an increase in internal resistance and a decrease in capacity. However, due to the influence of random operating conditions and abuse tolerance, there is a certain probability of abnormal failures such as leakage, abnormal aging, etc., or even extreme safety accidents such as battery short circuit, fire, explosion, etc. during the battery's charge and discharge process. The abnormal degradation of the battery is a process from microscopic reaction abnormalities to macroscopic characterization abnormalities, and then to uncontrolled systems. Due to the uncontrollable causes of actual operating conditions, there are huge differences in the length of battery degradation time and the strong randomness of critical trigger points. At the same time, the type of data that can be monitored and the data quality during the battery application process are very limited, which undoubtedly greatly increases the difficulty of fault diagnosis.

[0004] In the related technology, most of the lithium-ion battery fault identification methods are carried out under experimental simulation conditions. However, in actual battery status monitoring, it is necessary to consider the impact of multiple factors on the battery status, such as the number of sensors, data quality, user behavior and random working conditions, which makes the diagnostic methods under laboratory research impossible to apply; moreover, the existing battery fault diagnosis algorithms are difficult to take into account both the accuracy and transferability of fault identification.

[0005] Therefore, there is an urgent need to provide a fault identification method and device for a random operating condition battery pack. Summary of the invention

[0006] In order to solve the problem that the traditional lithium-ion battery fault identification method is difficult to accurately identify the faults of the battery pack under various random working conditions, the embodiment of the present invention provides a random working condition battery pack fault identification method and device.

[0007] In a first aspect, an embodiment of the present invention provides a method for identifying a fault of a battery pack under random working conditions, the method comprising:

[0008] For each current moment, execute:

[0009] Performing distribution optimization processing on the voltage parameters of the battery cell obtained at the current moment to obtain the battery pack voltage and the battery cell deviation voltage at the current moment;

[0010] Based on the battery pack voltage and the battery cell deviation voltage, construct a battery pack state model and a battery cell deviation model;

[0011] Using filters to perform state prediction on the battery pack state model and the battery cell deviation model respectively, and obtain a first prediction result of the battery pack at a next moment and a first prediction result of the battery cell at a next moment respectively;

[0012] Input the battery state parameters and vehicle state parameters obtained at the current moment into the trained time series network prediction model to obtain a second prediction result of the battery pack at the next moment; wherein, during the training process of the time series network prediction model, the first prediction result of the battery pack is used as a loss function to reversely adjust the second prediction result;

[0013] Inputting the second prediction result of the battery pack and the first prediction result of the battery cell into a trained physical enhancement encoder to obtain a second prediction matrix of the battery cell;

[0014] The residual value of the second prediction matrix of the battery cell is calculated and compared with a preset threshold value to perform fault identification on the battery pack.

[0015] In a second aspect, an embodiment of the present invention further provides a fault identification device for a random working condition battery pack, the device comprising:

[0016] The optimization unit is used to perform distribution optimization processing on the voltage parameters of the battery cell obtained at the current moment to obtain the battery pack voltage and the battery cell deviation voltage at the current moment;

[0017] A model building unit, used to build a battery pack state model and a battery cell deviation model based on the battery pack voltage and the battery cell deviation voltage;

[0018] A first prediction unit, configured to use a filter to perform state prediction on the battery pack state model and the battery cell deviation model, respectively, to obtain a first prediction result of the battery pack at a next moment and a first prediction result of the battery cell at a next moment, respectively;

[0019] A second prediction unit is used to input the battery state parameter and the vehicle state parameter obtained at the current moment into the trained time series network prediction model to obtain a second prediction result of the battery pack at the next moment; wherein, during the training process of the time series network prediction model, the first prediction result of the battery pack is used as a loss function to reversely adjust the second prediction result;

[0020] A third prediction unit, configured to input the second prediction result of the battery pack and the first prediction result of the battery cell into a trained physical enhancement encoder to obtain a second prediction matrix of the battery cell;

[0021] The fault identification unit is used to calculate the residual value of the second prediction matrix of the battery cell and compare it with a preset threshold value to perform fault identification on the battery pack.

[0022] In a third aspect, an embodiment of the present invention further provides a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.

[0023] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the method described in any embodiment of this specification.

[0024] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, which implements the steps of the method described above when executed by a processor.

[0025] The embodiment of the present invention provides a fault identification method and device for a random working condition battery pack. First, the voltage parameters of the battery cell obtained from the cloud data platform are distributed and optimized, and the battery pack state model and the battery cell deviation model are respectively constructed based on the voltage data of the battery pack and the battery cell obtained after optimization. Then, the filter is used to predict the state of the battery pack and the battery cell respectively, so as to obtain a prediction result with good stability. Then, the state of the battery pack is secondary predicted by using a pre-trained time series network prediction model, and the prediction result of the filter is used in the training time series network prediction model to reversely adjust the prediction result of the time series network model. Finally, the prediction result of the filter and the prediction result of the time series network model are further compressed and decompressed by using a trained physical enhancement encoder, so as to obtain a battery cell prediction result with good stability and accuracy. In this way, in the embodiment of the present invention, the filter, the time series network prediction model and the physical enhancement encoder are used in sequence to predict the state of the battery pack, and a large amount of cloud data is used as a training sample set, so that the prediction result takes into account both robustness and accuracy, thereby realizing accurate identification of the fault state of the random working condition battery pack. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] 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.

[0027] Figure 1 is a flow chart of a fault identification method for a random working condition battery pack provided by an embodiment of the present invention;

[0028] Figure 2 is a hardware architecture diagram of a computing device provided by an embodiment of the present invention;

[0029] Figure 3 It is a structural diagram of a fault identification device for a random operating condition battery pack provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] 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.

[0031] The specific implementation of the above concept is described below.

[0032] Please refer to Figure 1 , an embodiment of the present invention provides a method for identifying a fault of a battery pack under random working conditions, the method comprising:

[0033] For each current moment, execute:

[0034] Step 100, performing distribution optimization processing on the voltage parameters of the battery cell obtained at the current moment to obtain the battery pack voltage and the battery cell deviation voltage at the current moment;

[0035] Step 102, constructing a battery pack state model and a battery cell deviation model based on the battery pack voltage and the battery cell deviation voltage;

[0036] Step 104, using a filter to perform state prediction on the battery pack state model and the battery cell deviation model, respectively, to obtain a first prediction result of the battery pack at the next moment and a first prediction result of the battery cell at the next moment, respectively; wherein the first prediction result includes the voltage and charge state of the battery pack and the battery cell at the next moment;

[0037] Step 106, inputting the battery state parameters and vehicle state parameters obtained at the current moment into the trained time series network prediction model to obtain a second prediction result of the battery pack at the next moment; wherein, during the training process of the time series network prediction model, the first prediction result of the battery pack is used as a loss function to reversely adjust the second prediction result;

[0038] Step 108, inputting the second prediction result of the battery pack and the first prediction result of the battery cell into the trained physical enhancement encoder to obtain a second prediction matrix of the battery cell;

[0039] Step 110 , calculating the residual value of the second prediction matrix of the battery cell, and comparing it with a preset threshold value to perform fault identification on the battery pack.

[0040] In the embodiment of the present invention, the voltage parameters of the battery cells obtained from the cloud data platform are firstly distributed and optimized, and the battery pack state model and the battery cell deviation model are respectively constructed based on the voltage data of the battery pack and the battery cell obtained after optimization, and then the filter is used to predict the states of the battery pack and the battery cell respectively, so as to obtain a prediction result with good stability; then the state of the battery pack is secondary predicted by using the pre-trained time series network prediction model, and the prediction result of the time series network model is reversely adjusted by using the prediction result of the filter in the training time series network prediction model, so as to enhance the accuracy of the prediction result; finally, the prediction result of the filter and the prediction result of the time series network model are further compressed and decompressed by using the constructed trained physical enhancement encoder, so as to obtain a battery cell prediction result with good stability and accuracy. In this way, in the embodiment of the present invention, the filter, the time series network prediction model and the physical enhancement encoder are used in sequence to predict the state of the battery pack, and the massive cloud data is used as the training sample set, so that the prediction result takes into account both robustness and accuracy, thereby realizing the accurate identification of the fault state of the battery pack under random working conditions.

[0041] For step 100:

[0042] In the embodiment of the present invention, in order to analyze the overall state of the battery pack and each battery cell in the battery pack, it is necessary to perform distribution optimization processing on the voltage parameters of the battery cells obtained by the cloud data platform.

[0043] In some embodiments, step 100 includes:

[0044] Fitting the inverse Gaussian distribution as the probability distribution of the battery cell voltage parameter to calculate the voltage distribution probability of the battery cell;

[0045] The battery pack voltage and the battery cell deviation voltage at the current moment are obtained according to the voltage distribution probability of the battery cell.

[0046] Due to the different production batches of battery cells or the mutual influence between local cells in the circuit, the distribution of battery cell voltage is often skewed. Therefore, in the embodiment of the present invention, the inverse Gaussian distribution is selected as the probability distribution fitting of the battery cell voltage parameters. In this way, a detailed description of the distribution characteristics of the battery cells can be achieved without increasing the parameters to be solved.

[0047] In some preferred embodiments, the voltage distribution probability of the battery cell is calculated by the following formula:

[0048]

[0049] Where μ = U m ,λ=μ 3 / Var(U i ), U i is the voltage of the battery cell, U m is the average voltage of the battery cell;

[0050] The voltage of the battery pack is corrected based on the obtained voltage distribution probability of the battery cell, thereby obtaining the battery pack voltage and the battery cell deviation voltage. In some preferred embodiments, the battery pack voltage and the battery cell deviation voltage are respectively calculated by the following formulas:

[0051] U T，p =(f(U i )*U i ) / ∑f(U i )

[0052] ΔU i =U i -U T，p

[0053] Where U T，p The battery pack voltage, f(U i ) is the voltage distribution probability of the battery cell, ΔU i is the battery cell deviation voltage.

[0054] Regarding step 102:

[0055] In some embodiments, the battery pack state model and the battery cell deviation state model are respectively as follows:

[0056]

[0057] ΔU i =ΔU OCV，i (ΔSOCi )-ΔR i I

[0058] Where U T，p is the terminal voltage of the battery pack state model, R P，p is the polarization internal resistance of the battery pack, C P，p is the polarization capacitance of the battery pack, U P，p is the polarization voltage of the battery pack, R 0，p is the ohmic resistance of the battery pack, I is the instantaneous current, U OCV，p is the open circuit voltage of the battery pack, ΔU i is the terminal voltage of the battery cell deviation state model, ΔU OCV，i is the open circuit voltage of the battery cell, ΔSOC i is the state of charge of the battery cell, ΔR i is the ohmic internal resistance of the battery cell.

[0059] Since the working circuit of the battery pack is extremely complex and there are many factors affecting the battery pack state during actual vehicle operation, in this embodiment, the battery pack voltage and battery cell deviation obtained after distribution optimization processing are used as model output parameters to construct the above-mentioned battery pack and battery cell deviation state model. This model can be used as an equivalent model of a complex battery circuit to dynamically describe the dynamic process of the battery pack system. The filter is further combined with the above-mentioned state model to achieve online closed-loop prediction of the battery pack and battery cell state.

[0060] Regarding step 104:

[0061] In some implementations, step 104 includes:

[0062] Using the least square method to identify the coefficients of the battery pack state model and the battery cell deviation model at the current moment, to obtain the state parameters of the battery pack state model and the battery cell deviation model at the current moment;

[0063] The state parameters of the battery pack state model and the state parameters of the battery cell deviation model at the current moment are respectively input into the filter to obtain the first prediction result of the battery pack at the next moment and the first prediction result of the battery cell at the next moment respectively.

[0064] In the embodiment of the present invention, it can be seen from the battery pack state model and the battery cell deviation state model that the state parameters of the battery pack state model include the polarization internal resistance of the battery pack, the polarization capacitance of the battery pack and the ohmic resistance of the battery pack, and the state parameters of the battery cell deviation model include the ohmic internal resistance of the battery cell; in order to facilitate the identification of the state parameters of the battery pack state model and the state parameters of the battery cell deviation model, it is necessary to preprocess the battery pack state model and the battery cell deviation model.

[0065] Specifically, we first define E T =U T，p -U OCV，p , and discretize the battery pack state model to obtain:

[0066] E T，p，k+1 =exp(-Δt / τ)E T，p，k +(-R O，p )I k+1 +(exp(-Δt / τ)R O，p -(1-exp(-Δt / τ))R P，p )I k

[0067] In the formula, Δt is the i-th sampling interval, τ = R P C P is the time constant, E T，p，k is the equivalent electromotive force of the battery pack at time k, R O，p is the ohmic resistance of the battery pack, I k+1 is the instantaneous current at time k+1;

[0068] Furthermore, define y p，k is the output matrix, Φ p，k is the input matrix, θ p，k is the state parameter matrix, the above formula can be further written as:

[0069]

[0070] In the formula, β1, β2, and β3 are all equivalent coefficients;

[0071] Similarly, after discretizing the above formula, we can get Δy i，k =ΔΦ i，k Δθ i，k ;in,

[0072]

[0073] In the formula, Δy i，k , ΔΦ i，k , Δθ i，i are all equivalent matrices, ΔU OCV，i，kis the open circuit voltage of the ith battery cell at time k;

[0074] On this basis, the least squares algorithm can be used to identify the above coefficient matrix to obtain the state parameters of the battery pack state model and the state parameters of the battery cell deviation model; then the identified parameters are respectively input into the filter (for example, a Kalman filter) to obtain the voltage and state of charge of the battery pack at the next moment, and the voltage and state of charge of the battery cell at the next moment.

[0075] Regarding step 106:

[0076] The state prediction method of the battery pack and the battery cell using the filter combined with the state model in step 104 has good stability, but the accuracy of the prediction result is relatively poor, and in the state prediction results of the battery pack and the battery cell obtained in step 104, the state of the battery pack has a greater impact on the entire battery pack; in the embodiment of the present invention, the trained timing network model is used to further predict the battery pack state prediction result output in the filter, which can enhance the accuracy of the battery pack system prediction, so that the prediction result takes into account both robustness and accuracy.

[0077] In some implementations, in step 106, the temporal network prediction model is trained in the following manner:

[0078] Build a preset time series network learning model;

[0079] The hyperparameters and loss function of the temporal network learning model are set; wherein the loss function is as follows:

[0080]

[0081] In the formula, loss1 is the loss function, t is the sampling time, is the prediction result of the time series network learning model, is the first prediction result of the battery pack, U T，p，k+1 is the terminal voltage of the battery pack at time k+1, x T，p，k+1 is the state of charge of the battery pack at time k+1;

[0082] The known vehicle state parameters and battery state parameters are used as input sample sets, and the known battery pack voltage and state of charge parameters are used as output sample sets, and the Adam algorithm is used to train the time series network learning model; wherein the vehicle state parameters include vehicle speed, vehicle mileage and vehicle operating status, and the battery state parameters include battery pack total voltage, current, battery pack average temperature and battery pack state of charge;

[0083] During the training process, the loss and accuracy of the time series network learning model are calculated, and the network parameters of the time series network learning model are iterated until the model converges or reaches a preset number of iterations to obtain the time series network prediction model.

[0084] In an embodiment of the present invention, the known vehicle state parameters and battery state parameters are both parameter sets under the normal operating state of the vehicle collected by the cloud data platform. When training the time series network learning model, massive cloud data is used as a training sample set, and the first prediction result of the battery pack output by the filter is further used as the objective function to reversely adjust the prediction result of the time series network learning model. In this way, not only can the battery pack state prediction in random real-life scenarios be achieved, breaking through the limitations of random data quality, but also compared with pure model judgment and data-driven algorithms, it takes more robustness and accuracy into account. The battery pack prediction result output by the time series network model has obvious advantages.

[0085] Regarding step 108:

[0086] In an embodiment of the present invention, a physical enhancement encoder is first constructed and trained, so that the physical enhancement encoder can be used to compress and decompress the second prediction result of the battery pack and the first prediction result of the battery cell, and realize the prediction of the battery cell state, thereby realizing accurate identification of battery pack system faults using the battery cell state prediction result.

[0087] In some preferred embodiments, the physical enhancement encoder comprises an encoding module, a latent space sampling module and a decoding module connected in sequence; wherein:

[0088] The encoding module is used to perform feature fusion and dimensionality reduction operations on the input first prediction result of the battery cell and the second prediction result of the battery pack to obtain latent variables;

[0089] The latent space sampling module is used to sum the latent variables output by the encoding module and the first prediction results of the battery cells to obtain sampling points corresponding to the number of battery cells;

[0090] The decoding module is used to map the sampling points corresponding to the number of battery cells output by the latent space sampling module to obtain a second prediction matrix of the battery cells.

[0091] In an embodiment of the present invention, first, a coding module is designed to fuse the first prediction result of the battery cell output by the filter and the second prediction result of the battery pack output by the timing network (described as layer 1 later), so as to effectively avoid the problems of low precision of the filter and poor robustness of the timing network prediction model, thereby achieving higher accuracy; at the same time, the current battery status data and vehicle status data are compressed (described as layer 2 later) as correction items of the latent space, playing a correction role of weakly correlated physical quantities.

[0092] The calculation process of the encoding module is as follows:

[0093]

[0094] In the formula, is the output of layer 1, is the layer 1 weight, For layer 1 input, is the bias for layer 1, is the output of layer 2, σ e is the activation function, Input weights for layer 2, x k For layer 2 input, Bias for layer 2.

[0095] Next, a latent space sampling module is designed to sum the latent variables output by the encoding module with the first prediction result of the battery cell predicted in step 104, so as to obtain sampling points corresponding to the number of battery cells (described as layer 3 later). The calculation process is as follows:

[0096]

[0097] In the formula, is the layer 3 output, is the deviation state matrix, is the output of layer 1, is the output of layer 2, I T is the identity matrix, is the state of charge deviation matrix, is the voltage state deviation matrix;

[0098] Finally, the decoding module is used to map the sampling points output by the latent space sampling module to the data space of the future state of the battery pack system, thereby realizing the prediction of the battery cell state (described later as layer 4). The designed decompression network is as follows:

[0099]

[0100] In the formula, is the second prediction matrix of the battery cell output by the physical enhancement encoder, σ dThe activation function is designed in combination with the battery operating conditions. After correction, the function is added with a specific coefficient to ensure that the output range is within the reasonable representation range of the voltage, which can effectively avoid gradient explosion during training. is the layer 4 weight, is the sampling point corresponding to the number of battery cells output by the latent space sampling module, Bias for layer 4.

[0101] In some preferred embodiments, the physical enhancement encoder is trained using known vehicle state parameters and battery state parameters as input sample sets and known battery pack voltage and state of charge parameters as output sample sets.

[0102] It is understandable that the training of the physical enhancement encoder and the temporal network prediction model are both trained with a training set consisting of normal vehicles, but the loss functions used by the two are different.

[0103] Specifically, during the training process of the physical enhancement encoder, the MSE function is used as the loss function to reversely adjust the prediction result of the physical enhancement encoder; wherein the loss function is:

[0104]

[0105] Where, loss2 is the MSE function, is the second prediction result of the battery cell output by the physical enhancement encoder, k is the sampling time, is the actual observation matrix of the battery cell, x n，k is the state of charge of the nth battery cell, U n，k is the terminal voltage of the nth battery cell.

[0106] It should be noted that, in the embodiment of the present invention, the prediction results obtained by the filter, the timing network prediction model and the physical enhancement encoder all include the voltage and the state of charge of the battery pack and / or the battery cell at the next moment.

[0107] Regarding step 110:

[0108] In an embodiment of the present invention, a residual matrix is ​​obtained by further subtracting the second prediction matrix of the battery cell output by the physical enhancement encoder from the actual observation matrix of the battery cell observed during the actual operation of the vehicle, and the residual matrix is ​​calculated to obtain a residual value. The calculated residual value is compared with a preset threshold value. If the preset threshold value is exceeded, it can be determined that an abnormal point exists in the battery pack, thereby realizing fault identification of the battery pack. At the same time, multiple preset thresholds can be set to further realize quantification and classification of battery pack faults.

[0109] like Figure 2 , Figure 3 As shown, an embodiment of the present invention provides a fault identification device for a random working condition battery pack. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 2 As shown, it is a hardware architecture diagram of a computing device where a random working condition battery pack fault identification device provided by an embodiment of the present invention is located, except Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown in the figure, the computing device in which the device is located in the embodiment may also generally include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 3 As shown, as a device in a logical sense, the CPU of the computing device in which it is located reads the corresponding computer program in the non-volatile memory into the memory and runs it. This embodiment provides a fault identification device for a random working condition battery pack, the device comprising:

[0110] The optimization unit 301 is used to perform distribution optimization processing on the voltage parameters of the battery cell obtained at the current moment to obtain the battery pack voltage and the battery cell deviation voltage at the current moment;

[0111] A model building unit 302, configured to build a battery pack state model and a battery cell deviation model based on the battery pack voltage and the battery cell deviation voltage;

[0112] A first prediction unit 303 is used to use a filter to perform state prediction on the battery pack state model and the battery cell deviation model, respectively, to obtain a first prediction result of the battery pack at the next moment and a first prediction result of the battery cell at the next moment, respectively; wherein the first prediction result includes the voltage and charge state of the battery pack and the battery cell at the next moment;

[0113] The second prediction unit 304 is used to input the battery state parameter and the vehicle state parameter obtained at the current moment into the trained time series network prediction model to obtain a second prediction result of the battery pack at the next moment; wherein, during the training process of the time series network prediction model, the first prediction result of the battery pack is used as a loss function to reversely adjust the second prediction result;

[0114] A third prediction unit 305 is used to input the second prediction result of the battery pack and the first prediction result of the battery cell into a trained physical enhancement encoder to obtain a second prediction matrix of the battery cell;

[0115] The fault identification unit 306 is used to calculate the residual value of the second prediction matrix of the battery cell and compare it with a preset threshold value to perform fault identification on the battery pack.

[0116] In one embodiment of the present invention, when the optimization unit 301 performs distribution optimization processing on the voltage parameters of the battery cells obtained at the current moment, it is used to perform the following operations:

[0117] Fitting the inverse Gaussian distribution as the probability distribution of the battery cell parameters to calculate the voltage distribution probability of the battery cell;

[0118] The battery pack voltage and the battery cell deviation voltage at the current moment are obtained according to the voltage distribution probability of the battery cell.

[0119] In one embodiment of the present invention, in the model building unit 302, the battery pack state model and the battery cell deviation state model are respectively as follows:

[0120]

[0121] ΔU i =ΔU OCV，i (ΔSOC i )-ΔR i I

[0122] Where U T，p is the terminal voltage of the battery pack state model, R P，p is the polarization internal resistance of the battery pack, C P，p is the polarization capacitance of the battery pack, U P，p is the polarization voltage of the battery pack, R 0，p is the ohmic resistance of the battery pack, I is the instantaneous current, U OCV，p is the open circuit voltage of the battery pack, ΔU i is the terminal voltage of the battery cell deviation state model, ΔU OCV，i is the open circuit voltage of the battery cell, ΔSOC i is the state of charge of the battery cell, ΔR i is the ohmic internal resistance of the battery cell.

[0123] In one embodiment of the present invention, when the first prediction unit 303 uses the filter to perform state prediction on the battery pack state model and the battery cell deviation model at the current moment, the first prediction unit 303 is used to perform the following operations:

[0124] Using the least square method to identify the coefficients of the battery pack state model and the battery cell deviation model at the current moment, to obtain the state parameters of the battery pack state model and the battery cell deviation model at the current moment;

[0125] The state parameters of the battery pack state model and the state parameters of the battery cell deviation model at the current moment are respectively input into the filter to obtain the first prediction result of the battery pack at the next moment and the first prediction result of the battery cell at the next moment respectively.

[0126] In one embodiment of the present invention, in the second prediction unit 304, the time series network prediction model is trained in the following manner:

[0127] Build a preset time series network learning model;

[0128] The hyperparameters and loss function of the temporal network learning model are set; wherein the loss function is as follows:

[0129]

[0130] In the formula, loss1 is the loss function, t is the sampling time, is the prediction result of the time series network learning model, is the first prediction result of the battery pack, U T，p，k+1 is the terminal voltage of the battery pack at time k+1, x T，p，k+1 is the state of charge of the battery pack at time k+1;

[0131] The known vehicle state parameters and battery state parameters are used as input sample sets, and the known battery pack voltage and state of charge parameters are used as output sample sets, and the Adam algorithm is used to train the time series network learning model; wherein the vehicle state parameters include vehicle speed, vehicle mileage and vehicle operating status, and the battery state parameters include battery pack total voltage, current, battery pack average temperature and battery pack state of charge;

[0132] During the training process, the loss and accuracy of the time series network learning model are calculated, and the network parameters of the time series network learning model are iterated until the model converges or reaches a preset number of iterations to obtain the time series network prediction model.

[0133] In one embodiment of the present invention, in the third prediction unit 305, the physical enhancement encoder includes an encoding module, a latent space sampling module and a decoding module connected in sequence; wherein:

[0134] The encoding module is used to perform feature fusion and dimensionality reduction operations on the input first prediction result of the battery cell and the second prediction result of the battery pack to obtain latent variables;

[0135] The latent space sampling module is used to sum the latent variables output by the encoding module and the first prediction results of the battery cells to obtain sampling points corresponding to the number of battery cells;

[0136] The decoding module is used to map the sampling points corresponding to the number of battery cells output by the latent space sampling module to obtain a second prediction matrix of the battery cells.

[0137] In one embodiment of the present invention, in the third prediction unit 305, the physical enhancement encoder is trained using known vehicle state parameters and battery state parameters as input sample sets and known battery pack voltage and state of charge parameters as output sample sets;

[0138] During the training process, the MSE function is used as the loss function to reversely adjust the prediction results of the physical enhancement encoder; wherein the loss function is:

[0139]

[0140] Where, loss2 is the MSE function, is the prediction result of the physical enhancement encoder, k is the sampling time, is the actual observation matrix of the battery cell, x n，k is the state of charge of the nth battery cell, U n，k is the terminal voltage of the nth battery cell.

[0141] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on a fault identification device for a random working condition battery pack. In other embodiments of the present invention, a fault identification device for a random working condition battery pack may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0142] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For the specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0143] An embodiment of the present invention further provides a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a fault identification method for a random operating condition battery pack in any embodiment of the present invention is implemented.

[0144] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes a fault identification method for a random operating condition battery pack in any embodiment of the present invention.

[0145] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0146] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0147] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0148] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0149] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0150] It should be noted that, in this article, relational terms such as first and second 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.

[0151] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying faults of a battery pack under random working conditions, characterized in that: include: For each current moment, execute: Performing distribution optimization processing on the voltage parameters of the battery cell obtained at the current moment to obtain the battery pack voltage and the battery cell deviation voltage at the current moment; Based on the battery pack voltage and the battery cell deviation voltage, construct a battery pack state model and a battery cell deviation model; Using filters to perform state prediction on the battery pack state model and the battery cell deviation model respectively, and obtain a first prediction result of the battery pack at a next moment and a first prediction result of the battery cell at a next moment respectively; Input the battery state parameters and vehicle state parameters obtained at the current moment into the trained time series network prediction model to obtain a second prediction result of the battery pack at the next moment; wherein, during the training process of the time series network prediction model, the first prediction result of the battery pack is used as a loss function to reversely adjust the second prediction result; Inputting the second prediction result of the battery pack and the first prediction result of the battery cell into a trained physical enhancement encoder to obtain a second prediction matrix of the battery cell; The residual value of the second prediction matrix of the battery cell is calculated and compared with a preset threshold value to perform fault identification on the battery pack.

2. The method according to claim 1, characterized in that The method of performing distribution optimization processing on the voltage parameters of the battery cell obtained at the current moment to obtain the battery pack voltage and the battery cell deviation voltage at the current moment includes: Fitting the inverse Gaussian distribution as the probability distribution of the battery cell parameters to calculate the voltage distribution probability of the battery cell; The battery pack voltage and the battery cell deviation voltage at the current moment are obtained according to the voltage distribution probability of the battery cell.

3. The method according to claim 1, characterized in that The battery pack state model and the battery cell deviation state model are as follows: D.U. i =ΔU OCV，i (ΔSOC i )-ΔR i I Where U T，p is the terminal voltage of the battery pack state model, R P，p is the polarization internal resistance of the battery pack, C P，p is the polarization capacitance of the battery pack, U P，p is the polarization voltage of the battery pack, R 0，p is the ohmic resistance of the battery pack, I is the instantaneous current, U OCV，p is the open circuit voltage of the battery pack, ΔU i is the terminal voltage of the battery cell deviation state model, ΔU OCV，i is the open circuit voltage of the battery cell, ΔSOC i is the state of charge of the battery cell, ΔR i is the ohmic internal resistance of the battery cell.

4. The method according to claim 1, characterized in that: The using of the filter to respectively perform state prediction on the battery pack state model and the battery cell deviation model at the current moment, and respectively obtaining a first prediction result of the battery pack at the next moment and a first prediction result of the battery cell at the next moment, comprises: Using the least square method to identify the coefficients of the battery pack state model and the battery cell deviation model at the current moment, to obtain the state parameters of the battery pack state model and the battery cell deviation model at the current moment; The state parameters of the battery pack state model and the state parameters of the battery cell deviation model at the current moment are respectively input into the filter to obtain the first prediction result of the battery pack at the next moment and the first prediction result of the battery cell at the next moment respectively.

5. The method according to claim 1, characterized in that The temporal network prediction model is trained in the following way: Build a preset time series network learning model; The hyperparameters and loss function of the temporal network learning model are set; wherein the loss function is as follows: In the formula, loss1 is the loss function, t is the sampling time, is the prediction result of the time series network learning model, is the first prediction result of the battery pack, U T，p，k+1 is the terminal voltage of the battery pack at time k+1, x T，p，k+1 is the state of charge of the battery pack at time k+1; The known vehicle state parameters and battery state parameters are used as input sample sets, and the known battery pack voltage and state of charge parameters are used as output sample sets, and the Adam algorithm is used to train the time series network learning model; wherein the vehicle state parameters include vehicle speed, vehicle mileage and vehicle operating status, and the battery state parameters include battery pack total voltage, current, battery pack average temperature and battery pack state of charge; During the training process, the loss and accuracy of the time series network learning model are calculated, and the network parameters of the time series network learning model are iterated until the model converges or reaches a preset number of iterations to obtain the time series network prediction model.

6. The method according to claim 1, characterized in that The physical enhancement encoder comprises an encoding module, a latent space sampling module and a decoding module connected in sequence; wherein: The encoding module is used to perform feature fusion and dimensionality reduction operations on the input first prediction result of the battery cell and the second prediction result of the battery pack to obtain latent variables; The latent space sampling module is used to sum the latent variables output by the encoding module and the first prediction results of the battery cells to obtain sampling points corresponding to the number of battery cells; The decoding module is used to map the sampling points corresponding to the number of battery cells output by the latent space sampling module to obtain a second prediction matrix of the battery cells.

7. The method according to claim 1 or 6, characterized in that: The physical enhancement encoder is trained by using known vehicle state parameters and battery state parameters as input sample sets and known battery pack voltage and state of charge parameters as output sample sets; During the training process, the MSE function is used as the loss function to reversely adjust the prediction results of the physical enhancement encoder; wherein the loss function is: Where loss2 is the MSE function, is the prediction result of the physical enhancement encoder, k is the sampling time, is the actual observation matrix of the battery cell, x n，k is the state of charge of the nth battery cell, U n，k is the terminal voltage of the nth battery cell.

8. A fault detection device for a random working condition battery pack, characterized in that: include: The optimization unit is used to perform distribution optimization processing on the voltage parameters of the battery cell obtained at the current moment to obtain the battery pack voltage and the battery cell deviation voltage at the current moment; A model building unit, used to build a battery pack state model and a battery cell deviation model based on the battery pack voltage and the battery cell deviation voltage; A first prediction unit, configured to use a filter to perform state prediction on the battery pack state model and the battery cell deviation model, respectively, to obtain a first prediction result of the battery pack at a next moment and a first prediction result of the battery cell at a next moment, respectively; A second prediction unit is used to input the battery state parameter and the vehicle state parameter obtained at the current moment into the trained time series network prediction model to obtain a second prediction result of the battery pack at the next moment; wherein, during the training process of the time series network prediction model, the first prediction result of the battery pack is used as a loss function to reversely adjust the second prediction result; A third prediction unit, configured to input the second prediction result of the battery pack and the first prediction result of the battery cell into a trained physical enhancement encoder to obtain a second prediction matrix of the battery cell; The fault identification unit is used to calculate the residual value of the second prediction matrix of the battery cell and compare it with a preset threshold value to perform fault identification on the battery pack.

9. A computing device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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