Method and device for evaluating operation reliability of battery energy storage system based on data driving

Abstract

The invention relates to the technical field of battery energy storage systems, in particular to a battery energy storage system operation reliability evaluation method and device based on data driving, and the method comprises the steps: obtaining the junction temperature fluctuation information and cycle index of a target energy storage converter, so as to calculate the total fault probability and wear fault probability of components, constructing a converter reliability evaluation model; obtaining energy storage sample data of the target battery module and voltages and temperature measurement points of all single batteries to calculate a safety early warning probability and a consistency safety early warning probability based on a data reconstruction error, and further constructing a battery energy storage safety early warning model; and establishing an operation reliability evaluation model according to the converter reliability evaluation model and the battery energy storage safety early warning model. Therefore, the problem that the reliability of the battery energy storage system is difficult to accurately evaluate because the PCS operation cycle index, the battery monitoring data and the state imbalance are not considered in the existing battery energy storage system operation reliability evaluation is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery energy storage system operation, and in particular to a data-driven method and device for evaluating the operation reliability of a battery energy storage system. Background Art

[0002] A battery energy storage system (BESS) is a device used to store and release electrical energy. It usually includes an energy storage system, such as a battery pack or supercapacitor, and a corresponding power electronic converter. It is designed to provide efficient and reliable energy storage solutions to meet power demand in different scenarios. BESS has the advantages of fast response and flexibility. It has been widely used in balancing grid loads, fluctuations in new energy output, power compensation, and emergency power reserves, and has gradually become the mainstream trend of power systems.

[0003] The power conversion system (PCS) is one of the key components in BESS. The DC side of the PCS is connected to the battery, and the AC side is connected to the low-voltage side of the step-up transformer. Each PCS corresponds to an energy storage unit. The PCS can realize bidirectional charging and discharging functions. It can work in active inverter mode to realize DC to AC conversion and transmit electric energy to the grid; it can also work in active rectification mode to realize AC to DC conversion, absorb electric energy from the grid and store it in the battery energy storage. PCS plays a role in regulating the flow of electric energy and optimizing energy conversion efficiency in BESS. It is usually equipped with advanced control system components such as insulated gate bipolar transformer (IGBT), Mosfet and anti-parallel crystal diode (Diode), which can adapt to different power needs and provide stable power output. The main functions include short circuit protection, DC reverse connection protection, battery overcurrent protection, hardware fault protection, overload protection, etc. The reliability of PCS is related to the reliability of each component. PCS failure is largely attributed to the failure of the power devices IGBT and Diode used in it. Therefore, it is necessary to evaluate the reliability of the IGBT module in the PCS and then study the measures and methods to extend the reliability of the PCS.

[0004] Research shows that due to changes in junction temperature and fluctuation intensity, the aluminum bonding wires and solder layers of each power device in the PCS are subjected to long-term and frequent expansion and contraction, which will lead to thermal fatigue failure of the device after long-term accumulation. This is the root cause of thermal fatigue failure of IGBT. Since the operating power of the PCS is directly related to the junction temperature of the power device, the operating power of the PCS is related to the junction temperature of its components. Therefore, considering the junction temperature fluctuation can accurately reflect the impact of random changes in real-time power on PCS component failures. By combining the actual energy storage system PCS operation data, the energy storage system operation reliability considering the influence of junction temperature fluctuation is proposed, which is of great significance for formulating a reasonable energy storage system operation and maintenance strategy.

[0005] Considering that the impact of the number of PCS operating cycles on its reliability assessment has not been fully studied, this aspect is crucial for assessing its reliability. In addition, with the rapid development of BESS, the issue of its intrinsic safety has become increasingly prominent. However, the reliability of batteries often involves a time span of dozens of hours, so it is necessary to explore the potential laws of its data. The reliability state variable response characteristics mainly include voltage, current, temperature, state of charge, etc.

[0006] Therefore, there is an urgent need for an operation reliability evaluation scheme that can take into account the impact of complex operating conditions and component health levels on the operating power of the energy storage inverter and battery monitoring data. Summary of the invention

[0007] The present invention provides a data-driven method and device for evaluating the operation reliability of a battery energy storage system, so as to solve the problem that the existing battery energy storage system operation reliability evaluation does not take into account aspects such as the number of PCS operation cycles, battery monitoring data and state imbalance, making it difficult to accurately evaluate the reliability of the battery energy storage system.

[0008] The first aspect of the present invention provides a data-driven method for evaluating the operation reliability of a battery energy storage system, comprising the following steps:

[0009] The junction temperature fluctuation information and the number of cycles of the target energy storage converter are obtained, so as to calculate the total component failure probability and the wear failure probability of the target energy storage converter according to the junction temperature fluctuation information and the number of cycles; a converter reliability assessment model is constructed according to the total component failure probability and the wear failure probability; energy storage sample data of the target battery module are obtained, so as to train the bidirectional long short-term memory network using the energy storage sample data to obtain multiple reconstruction errors; the multiple reconstruction errors are input into a pre-constructed safety warning judgment model for judgment, so as to calculate the safety warning probability based on the data reconstruction error; the voltage and temperature measurement points of all single cells in the target battery module are obtained, so as to calculate the consistency safety warning probability according to the voltage and temperature measurement points of all single cells; a battery energy storage safety warning model is constructed according to the safety warning probability based on the data reconstruction error and the consistency safety warning probability; an operation reliability assessment model is established according to the converter reliability assessment model and the battery energy storage safety warning model, so as to use the operation reliability assessment model to conduct operation reliability assessment on the target battery energy storage system.

[0010] Optionally, the acquiring the junction temperature fluctuation information and the number of cycles of the target energy storage converter to calculate the total component failure probability and the wear failure probability of the target energy storage converter according to the junction temperature fluctuation information includes:

[0011] Calculate the junction temperature fluctuation information and the number of cycles of the target energy storage converter under different operating conditions; calculate the total failure probability of the components according to the junction temperature fluctuation information, and calculate the wear failure probability according to the number of cycles.

[0012] Optionally, calculating the total failure probability of the component according to the junction temperature fluctuation information, and calculating the wear failure probability according to the number of cycles includes:

[0013] The thermal stress factor and temperature cycle factor of each component in the target energy storage inverter are calculated according to the junction temperature fluctuation information; based on the FIDES reliability guidelines, the failure rate of each component is calculated according to the preset basic failure rate parameters, thermal stress factor and temperature cycle factor of each component; the failure rates of each component are added to obtain the total component failure probability of the target energy storage inverter; based on the Weibull distribution, the aluminum bonding line probability density and the solder layer probability density of the target energy storage inverter are calculated; the wear failure probability of the target energy storage inverter under the number of cycles is calculated according to the number of cycles, the aluminum bonding line probability density and the solder layer probability density.

[0014] Optionally, the acquiring of energy storage sample data of the target battery module to train a bidirectional long short-term memory network using the energy storage sample data to obtain a reconstruction error includes:

[0015] The energy storage sample data of the target battery module is obtained, and the energy storage sample data is planned and processed to obtain normalized energy storage sample data; a battery energy storage data feature mining model constructed based on a bidirectional long short-term memory network is trained using the normalized energy storage sample data to obtain a reconstructed data set, and the multiple reconstruction errors are calculated based on the reconstructed data set.

[0016] Optionally, the acquiring the voltages and temperature measurement points of all the single cells in the target battery module to calculate the consistency safety warning probability according to the voltages and temperature measurement points of all the single cells includes:

[0017] Obtain the voltages of all single cells in the target battery module to calculate the voltage average value of the target battery module at a preset time; screen the maximum voltage value and the minimum voltage value of the target battery module at the preset time; calculate the extreme voltage root mean square error according to the maximum voltage value and the minimum voltage value; use the ratio of the extreme voltage root mean square error to the voltage average value as the voltage consistency coefficient; obtain the temperature measurement points of all single cells in the target battery module to calculate the maximum temperature value and the minimum temperature value of the target battery module at the preset time; calculate the extreme temperature root mean square error according to the maximum temperature value and the minimum temperature value, and use the extreme temperature root mean square error as the temperature consistency standard deviation; calculate the consistency safety warning probability according to the voltage consistency coefficient and the temperature consistency standard deviation.

[0018] Optionally, the expression of the operation reliability evaluation model is:

[0019] P BESS =1-(1-P fault,PCS )(1-P TR&SOB )(1-P fault,AE )

[0020] Among them, P BESS is the integrated safety warning probability, P fault,PCS is the target energy storage converter failure rate, P TR&SOB is the battery failure probability based on data reconstruction error and consistency, P fault,AE is the failure probability of auxiliary equipment in the target battery energy storage system except the target energy storage converter and target battery module.

[0021] A second aspect of the present invention provides a data-driven battery energy storage system operation reliability assessment device, comprising:

[0022] The first acquisition module is used to obtain the junction temperature fluctuation information and the number of cycles of the target energy storage converter, so as to calculate the total failure probability and wear failure probability of the components of the target energy storage converter according to the junction temperature fluctuation information and the number of cycles; the first construction module is used to construct a converter reliability assessment model according to the total failure probability of the components and the wear failure probability; the training module is used to obtain the energy storage sample data of the target battery module, so as to train the bidirectional long short-term memory network with the energy storage sample data to obtain multiple reconstruction errors; the discrimination module is used to input the multiple reconstruction errors into a pre-constructed safety warning judgment model for discrimination, so as to obtain the energy storage sample data of the target battery module and the bidirectional long short-term memory network and obtain multiple reconstruction errors; the discrimination module is used to input the multiple reconstruction errors into a pre-constructed safety warning judgment model for discrimination, so as to obtain the energy storage sample data of the target battery module and the bi-directional long short-term memory network and obtain multiple reconstruction errors; the first construction module is used to construct a converter reliability assessment model according to the total failure probability of the components and the wear failure probability ... Calculate the safety warning probability based on the data reconstruction error; a second acquisition module is used to obtain the voltages and temperature measurement points of all single cells in the target battery module, so as to calculate the consistency safety warning probability according to the voltages and temperature measurement points of all single cells; a second construction module is used to construct a battery energy storage safety warning model according to the safety warning probability based on the data reconstruction error and the consistency safety warning probability; an evaluation module is used to establish an operation reliability evaluation model according to the converter reliability evaluation model and the battery energy storage safety warning model, so as to use the operation reliability evaluation model to perform operation reliability evaluation on the target battery energy storage system.

[0023] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the data-driven battery energy storage system operation reliability assessment method as described in the above embodiment.

[0024] A fourth aspect of the present invention provides a computer program product, which, when executed by a processor, implements the above data-driven method for evaluating the operation reliability of a battery energy storage system.

[0025] A fifth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above data-driven method for evaluating the operation reliability of a battery energy storage system.

[0026] The data-driven operation reliability evaluation method and device of the battery energy storage system proposed in the embodiment of the present invention establishes a thermal stress factor and junction temperature cycle factor model of the converter components based on the FIDES reliability guideline, then extracts the converter junction temperature cycle information by the rain flow counting method, and then introduces the influence of the number of cycles of the PCS based on the Weibull probability distribution on its failure probability, thereby constructing a converter reliability evaluation model; in view of the imbalance of battery monitoring data and state, a battery cluster reliability method based on reconstruction error calculation and balanced state SOB is proposed to mine the potential laws of battery operation data, thereby constructing a battery energy storage safety early warning model; finally, according to the converter reliability evaluation model and the battery energy storage safety early warning model, an evaluation model is established that considers the influence of the pressure, temperature, and number of cycles of the PCS on the operation reliability of the DESS, and also considers the influence of the potential laws of the battery operation data on the operation reliability of the DESS, so that a more accurate operation reliability evaluation result can be obtained, the resources of the DESS can be more reasonably configured, and the overall performance of the DESS can be maintained.

[0027] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0029] Figure 1 A flow chart of a data-driven method for evaluating the operation reliability of a battery energy storage system provided in an embodiment of the present invention;

[0030] Figure 2 A schematic diagram of a specific implementation of a data-driven battery energy storage system operation reliability assessment method provided by an embodiment of the present invention;

[0031] Figure 3 A schematic diagram of a battery module SOB safety warning framework provided by an embodiment of the present invention;

[0032] Figure 4 A block diagram of a data-driven battery energy storage system operation reliability assessment device provided by an embodiment of the present invention;

[0033] Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0035] The following describes a data-driven method and device for evaluating the operating reliability of a battery energy storage system according to an embodiment of the present invention with reference to the accompanying drawings.

[0036] Figure 1 A schematic flow chart of a data-driven method for evaluating the operation reliability of a battery energy storage system provided in an embodiment of the present invention.

[0037] like Figure 1 As shown, the data-driven battery energy storage system operation reliability assessment method includes the following steps:

[0038] In step S101, the junction temperature fluctuation information and the number of cycles of the target energy storage converter are obtained, so as to calculate the total component failure probability and the wear failure probability of the target energy storage converter according to the junction temperature fluctuation information and the number of cycles.

[0039] In some embodiments, the junction temperature fluctuation information and the number of cycles of the target energy storage converter are obtained to calculate the total component failure probability and the wear failure probability of the target energy storage converter according to the junction temperature fluctuation information, including:

[0040] Calculate the junction temperature fluctuation information and cycle times of the target energy storage converter under different operating conditions;

[0041] The total component failure probability is calculated based on the junction temperature fluctuation information, and the wear-out failure probability is calculated based on the number of cycles.

[0042] In some embodiments, calculating the total failure probability of components based on the junction temperature fluctuation information and calculating the wear failure probability based on the number of cycles includes:

[0043] Calculate the thermal stress factor and temperature cycle factor of each component in the target energy storage converter based on the junction temperature fluctuation information;

[0044] Based on the FIDES reliability guidelines, the failure rate of each component is calculated according to the preset basic failure rate parameters, thermal stress factors and temperature cycle factors of each component;

[0045] Add up the failure rates of each component to obtain the total component failure probability of the target energy storage converter;

[0046] Based on Weibull distribution, the probability density of aluminum bonding wire and solder layer of the target energy storage converter is calculated;

[0047] The wear failure probability of the target energy storage converter under the number of cycles is calculated based on the number of cycles, the probability density of the aluminum bonding line and the probability density of the solder layer.

[0048] like Figure 2 As shown in the figure, in the actual execution process, the root cause of the PCS failure of the target energy storage converter lies in the device status, charging and discharging power, and module temperature. Therefore, the embodiment of the present invention analyzes the impact of module temperature on the failure rate of PCS components, using thermal stress factor and temperature cycle factor as measurement indicators. According to the FIDES reliability guideline, the thermal stress factor of the component in the PCS in the i-th state of junction temperature fluctuation is ρ TH,i It can be expressed as:

[0049]

[0050] Among them, α is a constant, and different components have different corresponding values. a =9.89×10 -20 is the activation energy constant, T mean,i is the average temperature of the PCS components in the i-th state.

[0051] The component temperature cycle factor ρ of the PCS in the i-th state of junction temperature fluctuation TC,i , the model expression is:

[0052]

[0053] Among them, n i is the number of junction temperature cycles under component operating state i, n 0 is the reference fluctuation cycle number, t con,i is the continuous running time under running state i, θ i is the junction temperature fluctuation cycle time under operating state i, ΔT i is the junction temperature fluctuation amplitude under the PCS component operating state i, ΔT 0 is the reference temperature fluctuation amplitude, T max,i It is the maximum value of the junction temperature fluctuation of the PCS component in the i-th state.

[0054] Calculate the starting value T of the junction temperature fluctuation cycle of the PCS module by the rain flow counting method 0 and the end point value T f , use formulas (3)-(5) to calculate the fluctuation amplitude ΔT of the junction temperature fluctuation respectively i , continuous running time t con , average temperature T mean , Maximum temperature T max The estimated number of junction temperature fluctuations n i Add 1:

[0055]

[0056]

[0057]

[0058] Combining the thermal stress factor and the temperature cycle factor, the failure rate model under specific working conditions can be obtained according to the FIDES reliability guidelines to calculate the failure rate of each PCS component:

[0059]

[0060] Among them, λ TH , TC are the basic failure rates corresponding to the thermal stress factor and the temperature cycle factor, n TH is the total number of cycles, ρ pm is the impact of PCS component manufacturing quality, ρ pr is the impact of reliability quality management and control level during the life cycle of components, ρ in is the overstress contribution factor of the component. It should be noted that the calculated failure rate unit is FIT, which means every 10 9 Number of failures per hour.

[0061] PCS components are generally a series-connected, irreparable system, and the IGBT module includes IGBT and Diode. Therefore, the PCS failure rate calculation model based on the FIDES reliability guideline is used to calculate the failure rate of each component by adding up the failure rates to obtain the PCS total failure rate F based on the FIDES reliability guideline. FIDES for:

[0062] F FIDES =P FIDES,IGBT +P FIDES,Diode (7)

[0063] Where P FIDES,IGBT , P FIDES,Diode are the IGBT and Diode failure rates based on the FIDES reliability guideline, respectively, and are calculated using formula (6).

[0064] The failure rate of PCS components presents a non-constant state during the stable failure period. It changes with time and cannot meet the basic conditions of the constant failure rate model. Therefore, it is necessary to further study the impact of the number of cycles on its operating conditions.

[0065] For PCS, the failure of its component materials is a random event, and there is an uncertain relationship between its fatigue degree and failure. Theoretically, PCS failure is largely attributed to the power device IGBT used in it. However, the two main failure causes of PCS modules are aluminum alloy wire shedding and solder layer cracking. Failure may occur at any time, and the aging process can be described by Weibull distribution. Combined with the study of aging characterization methods, the reliability of IGBT is analyzed.

[0066] Assume that the failure probability distribution function of aluminum bonding wire, solder layer material and IGBT conforms to the Weibull distribution model. The specific expression is as follows:

[0067]

[0068] In the formula, m bw 、m sl are the Weibull distribution shape parameters of the aluminum alloy wire and solder layer respectively; η bw , η sl are the scale parameters of the Weibull distribution of the aluminum alloy wire and the solder layer, respectively; N is the number of cycles; f bw (N), f sl (N), f IGBT (N) are the failure probability densities of aluminum bonding wire, solder layer and IGBT respectively.

[0069] The cumulative failure probability functions of the aluminum bonding wire and solder layer in the IGBT module are:

[0070]

[0071] In the formula, F bw (N), F sl (N) are the cumulative failure probabilities of the aluminum bonding wire and solder layer, respectively.

[0072] Considering the factors affecting the failure probability of PCS with the number of cycles, the specific expression is as follows:

[0073] F CL (N) = 1-[1-F bw (N)][1-F sl (N)](10)

[0074] In the formula, F CL (N) is the PCS wear probability under different cycle numbers.

[0075] In step S102, a converter reliability assessment model is constructed according to the total failure probability and wear failure probability of components.

[0076] like Figure 2As shown in the figure, in the actual implementation process, by combining equations (7) and (10), the failure of PCS equipment caused by the number of cycles can be derived, and combined with the FIDES reliability guideline model, the overall reliability model of PCS considering the influence of the number of cycles and the thermal stress factor and the temperature cycle factor can be established, that is, the converter reliability evaluation model:

[0077] P fault,PCS =1-[1-F FIDES ][1-F CL (N)] (11)

[0078] In step S103, energy storage sample data of the target battery module is obtained, and a bidirectional long short-term memory network is trained using the energy storage sample data to obtain a plurality of reconstruction errors.

[0079] In some embodiments, energy storage sample data of a target battery module is obtained to train a bidirectional long short-term memory network using the energy storage sample data to obtain a reconstruction error, including:

[0080] Acquire energy storage sample data of the target battery module, and perform planning processing on the energy storage sample data to obtain normalized energy storage sample data;

[0081] The normalized energy storage sample data is used to train the battery energy storage data feature mining model based on the bidirectional long short-term memory network to obtain a reconstructed data set, and multiple reconstruction errors are calculated based on the reconstructed data set.

[0082] like Figure 2 As shown in the figure, in the actual execution process, in order to eliminate the influence of the dimension of each feature data when calculating the data reconstruction error and ensure the training accuracy of the basic reconstruction model, it is necessary to perform data standardization on the target battery module, adopt the mean square error normalization z-score method, and use the n battery energy storage data sets in the obtained data as the training set, and the remaining battery energy storage data sets as the test set, that is:

[0083]

[0084] Where s* is the normalized energy storage sample data, s sam is the energy storage sample data of the target battery module, s μ is the mean of the energy storage sample data, s σ is the standard deviation of battery energy storage samples.

[0085] Furthermore, an integrated model is constructed based on the Bi-LSTM battery energy storage data feature mining method. The integrated model training optimization goal is to minimize the reconstruction error of the battery energy storage input and output data, that is:

[0086]

[0087] In the formula, For input S in With output The reconstruction error, n va For S in and Dimension, s in,i,t For S in The element value of the i-th dimension at time t, s out,i,t for The element value of the i-th dimension at time t is calculated by the Bi-LSTM network method, and T is the total number of running time periods.

[0088] The n training sets are input into the integrated model, and the n integrated models are trained in sequence to calculate a plurality of reconstruction data and form a reconstruction data set, so as to calculate the reconstruction error according to the reconstruction data set.

[0089] In step S104, a plurality of reconstruction errors are input into a pre-built safety warning judgment model for discrimination to calculate a safety warning probability based on the data reconstruction error.

[0090] In the actual implementation process, multiple reconstruction errors are input into the pre-built safety warning judgment model, so that multiple reconstruction errors are compared and analyzed with the given reconstruction error threshold K to judge the output of each integrated model. That is, multiple judgment results are obtained, among which the pre-built safety warning judgment model is:

[0091]

[0092] In the formula, is the judgment result of the integrated model on the battery energy storage, and K is the threshold for judging the safety warning.

[0093] Furthermore, the safety warning probability P of each integrated model is calculated based on multiple judgment results. fault,BM ,Right now:

[0094]

[0095] In the formula, y i is the judgment result of the ith integrated model on the battery energy storage, calculated by formula (14).

[0096] Since the target battery module BC is composed of several battery modules, the thermal runaway warning result of the target battery module BC, that is, the safety warning probability based on the data reconstruction error, is:

[0097]

[0098] Where Pfault,BC is the BC safety warning probability based on data reconstruction error, P fault,BC,1 , P fault,BC,2 , …, are respectively based on the data reconstruction error n BM The safety warning probability of each battery module.

[0099] In step S105, the voltages and temperature measurement points of all the single cells in the target battery module are obtained, so as to calculate the consistency safety warning probability according to the voltages and temperature measurement points of all the single cells.

[0100] In some embodiments, the voltage and temperature measurement points of all single cells in the target battery module are obtained to calculate the consistency safety warning probability according to the voltage and temperature measurement points of all single cells, including:

[0101] Obtaining the voltages of all single cells in the target battery module to calculate the average voltage of the target battery module at a preset time;

[0102] Filter the maximum voltage value and the minimum voltage value of the target battery module at a preset time;

[0103] Calculate the extreme voltage error root mean square according to the maximum voltage value and the minimum voltage value;

[0104] The ratio of the extreme voltage error root mean square to the voltage average value is taken as the voltage consistency coefficient;

[0105] Obtaining the temperature measurement points of all single cells in the target battery module to calculate the maximum temperature value and the minimum temperature value of the target battery module at a preset time;

[0106] Calculate the extreme temperature error root mean square according to the maximum temperature value and the minimum temperature value, and use the extreme temperature error root mean square as the temperature consistency standard deviation;

[0107] The consistency safety warning probability is calculated based on the voltage consistency coefficient and the temperature consistency standard deviation.

[0108] like Figure 2 As shown in the figure, in the actual implementation process, during the battery energy storage charging and discharging process, the target battery module involves several chemical reactions. If the internal chemical reactions are balanced, the battery energy storage will have a high consistency. Unbalanced battery energy storage status may cause overcharge / over discharge, which not only shortens its life but also may cause thermal runaway, increase the probability of system failure, and reduce its reliability. Figure 3 As shown, when evaluating the operational reliability of the battery energy storage system, it is also necessary to consider consistency indicators including the current, voltage, temperature, state of charge, internal resistance, and health status of the battery module. However, due to the limitation of data availability, the embodiment of the present invention only establishes the voltage and temperature consistency model of the battery module, and does not analyze other parameters.

[0109] The battery module voltage consistency assessment should calculate the average voltage of the target battery module at time t, then determine the maximum and minimum values ​​of the terminal voltage of each single cell at time t, and calculate the ratio of the extreme voltage root mean square error to the voltage average value as the voltage consistency evaluation index. The specific model is shown in the following formulas (17)-(22):

[0110]

[0111]

[0112] u max,t = {u 1,t ,u 2,t ,...,u n,t}(19)

[0113] u min,t =min{u 1,t ,u 2,t ,...,u n,t}(20)

[0114]

[0115]

[0116] In the formula, u av,t is the average voltage of each single battery at time t, n cell is the total number of single cells in the target battery module, u n,t is the voltage of each single cell n at time t, is the average value of the terminal voltage curve of the target battery module during the charge and discharge phase, u max,t 、u min,t are the maximum and minimum voltage values ​​of each single battery at time t, σ u is the RMS error of the extreme terminal voltage during the charge and discharge phase of the target battery module, μ u Battery module voltage consistency coefficient.

[0117] The battery module temperature consistency assessment should calculate the maximum and minimum temperatures of all temperature measurement points of the battery module at time t, and calculate the root mean square of the extreme temperature error as the evaluation index of temperature consistency. The specific model is shown in the following formulas (23)-(25):

[0118] T max,t =max{T 1,t ,T 2,t ,...,T m,t}(twenty three)

[0119] T min,t=min{T 1,t ,T 2,t ,...,T m,t} (twenty four)

[0120]

[0121] Where, T max,t , T min,t is the maximum and minimum temperature values ​​of all temperature measurement points of the target battery module at time t, T 1,t , T 2,t ,…,T m,t is the temperature of the target battery module at the temperature measurement points 1, 2, ..., m, σ temp The RMS value of the extreme temperature error during the charge and discharge phase of the target battery module. The larger the value, the worse the temperature consistency.

[0122] The unweighted scores of formulas (22) and (25) are weighted to obtain the SOB safety warning probability used to evaluate the consistency of the target battery module, as follows:

[0123]

[0124]

[0125] In the formula, are the weight coefficients of the battery module voltage consistency coefficient and temperature consistency standard deviation, V U 、V T They are the unweighted safety warning scores of the voltage consistency coefficient and temperature consistency standard deviation of the battery module, are 1, 2, ..., n respectively BM The weighted consistency safety warning probability of the target battery module, P SOB,BC is the consistency safety warning probability of the target battery module BC.

[0126] In step S106, a battery energy storage safety warning model is constructed according to the safety warning probability based on the data reconstruction error and the consistency safety warning probability.

[0127] Specifically, formulas (16) and (27) are combined to form the battery energy storage safety warning model P TR&SOB .

[0128] In step S107, an operation reliability assessment model is established according to the converter reliability assessment model and the battery energy storage safety warning model, so as to use the operation reliability assessment model to perform operation reliability assessment on the target battery energy storage system.

[0129] In some embodiments, the expression of the running reliability evaluation model is:

[0130] P BESS =1-(1-P fault,PCS )(1-P TR&SOB )(1-P fault,AE )

[0131] Among them, P BESS is the integrated safety warning probability, P fault,PCS is the target energy storage converter failure rate, P TR&SOB is the battery failure probability based on data reconstruction error and consistency, P fault,AE is the failure probability of auxiliary equipment in the target battery energy storage system except the target energy storage converter and target battery module.

[0132] like Figure 2 As shown in Figure 1, in the actual implementation process, the target battery energy storage system BESS also involves several auxiliary devices, including battery energy switches, battery energy hubs, battery energy adapters, battery energy network cards and other devices. Therefore, it is also necessary to obtain the failure probability P of other auxiliary devices except BC and PCS through known means. fault,AE .

[0133] Furthermore, the PCS failure rate P is solved according to the above-mentioned converter reliability evaluation model. fault,PCS According to the battery energy storage safety warning model constructed above, the battery failure probability P based on data reconstruction error and SOB is solved TR&SOB , an operation reliability evaluation model is established based on the PCS failure rate, the battery failure probability based on data reconstruction error and SOB, and the failure probability of other auxiliary equipment, so as to evaluate the operation reliability of the target battery energy storage system using the operation reliability evaluation model, wherein the operation reliability evaluation model is:

[0134] P BESS =1-(1-P fault,PCS )(1-P TR&SOB )(1-P fault,AE )

[0135] Where P BESS is the operational reliability result, P fault,PCS is the PCS failure rate, P TR&SOB is the battery failure probability based on data reconstruction error and SOB, P fault,AE is the failure probability of other auxiliary equipment of BESS except BC and PCS.

[0136] According to the data-driven battery energy storage system operation reliability assessment method proposed in an embodiment of the present invention, a thermal stress factor and junction temperature cycle factor model of converter components based on the FIDES reliability guideline is established, and then the converter junction temperature cycle information is extracted by the rain flow counting method, and then the influence of the number of cycles of the PCS based on the Weibull probability distribution on its failure probability is introduced, thereby constructing a converter reliability assessment model; in view of the battery monitoring data and state imbalance, a battery cluster reliability method based on reconstruction error calculation and balanced state SOB is proposed to mine the potential laws of battery operation data, thereby constructing a battery energy storage safety early warning model; finally, according to the converter reliability assessment model and the battery energy storage safety early warning model, an assessment model is established that considers the influence of the pressure, temperature, and number of cycles of the PCS on the operation reliability of the DESS, and also considers the influence of the potential laws of the battery operation data on the operation reliability of the DESS, so that a more accurate operation reliability assessment result can be obtained, the resources of the DESS can be more reasonably configured, and the overall performance of the DESS can be maintained.

[0137] Next, a data-driven battery energy storage system operation reliability assessment device according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0138] Figure 4 It is a block diagram of a data-driven battery energy storage system operation reliability assessment device according to an embodiment of the present invention.

[0139] like Figure 4 As shown, the data-driven battery energy storage system operation reliability assessment device 40 includes: a first acquisition module 401, a first construction module 402, a training module 403, a discrimination module 404, a second acquisition module 405, a second construction module 406 and an assessment module 407.

[0140] Among them, the first acquisition module 401 is used to obtain the junction temperature fluctuation information and the number of cycles of the target energy storage converter, so as to calculate the total component failure probability and the wear failure probability of the target energy storage converter according to the junction temperature fluctuation information and the number of cycles. The first construction module 402 is used to construct a converter reliability assessment model according to the total component failure probability and the wear failure probability. The training module 403 is used to obtain the energy storage sample data of the target battery module, so as to train the bidirectional long short-term memory network using the energy storage sample data to obtain multiple reconstruction errors. The discrimination module 404 is used to input multiple reconstruction errors into the pre-constructed safety warning judgment model for discrimination, so as to calculate the safety warning probability based on the data reconstruction error. The second acquisition module 405 is used to obtain the voltage and temperature measurement points of all single cells in the target battery module, so as to calculate the consistency safety warning probability according to the voltage and temperature measurement points of all single cells. The second construction module 406 is used to construct a battery energy storage safety warning model according to the safety warning probability based on the data reconstruction error and the consistency safety warning probability. The evaluation module 407 is used to establish an operation reliability evaluation model according to the converter reliability evaluation model and the battery energy storage safety early warning model, so as to use the operation reliability evaluation model to evaluate the operation reliability of the target battery energy storage system.

[0141] It should be noted that the above explanation of the embodiment of the data-driven battery energy storage system operation reliability assessment method is also applicable to the data-driven battery energy storage system operation reliability assessment device of this embodiment, and will not be repeated here.

[0142] According to the data-driven battery energy storage system operation reliability assessment device proposed in the embodiment of the present invention, a thermal stress factor and junction temperature cycle factor model of the converter components based on the FIDES reliability guideline is established, and then the converter junction temperature cycle information is extracted by the rain flow counting method, and then the influence of the number of cycles of the PCS based on the Weibull probability distribution on its failure probability is introduced, thereby constructing a converter reliability assessment model; in view of the battery monitoring data and state imbalance, a battery cluster reliability method based on reconstruction error calculation and balanced state SOB is proposed to mine the potential laws of battery operation data, thereby constructing a battery energy storage safety early warning model; finally, according to the converter reliability assessment model and the battery energy storage safety early warning model, an assessment model is established that considers the influence of the pressure, temperature, and number of cycles of the PCS on the operation reliability of the DESS, and also considers the influence of the potential laws of the battery operation data on the operation reliability of the DESS, so that a more accurate operation reliability assessment result can be obtained, the resources of the DESS can be more reasonably configured, and the overall performance of the DESS can be maintained.

[0143] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0144] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0145] When the processor 502 executes the program, the data-driven battery energy storage system operation reliability assessment method provided in the above embodiment is implemented.

[0146] Furthermore, the electronic device further comprises:

[0147] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0148] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0149] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0150] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0151] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0152] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0153] An embodiment of the present invention further provides a computer program product, which, when executed by a processor, implements the above data-driven method for evaluating the operation reliability of a battery energy storage system.

[0154] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned data-driven method for evaluating the operation reliability of a battery energy storage system.

[0155] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0156] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0157] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0158] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0159] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0160] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0161] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0162] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A data-driven method for evaluating the operational reliability of a battery energy storage system, characterized in that: The following steps are involved: Acquire junction temperature fluctuation information and cycle number of the target energy storage converter, so as to calculate the total component failure probability and wear failure probability of the target energy storage converter according to the junction temperature fluctuation information and the cycle number; Constructing a converter reliability assessment model according to the total failure probability of the components and the wear failure probability; Acquire energy storage sample data of a target battery module, and use the energy storage sample data to train a bidirectional long short-term memory network to obtain a plurality of reconstruction errors; Inputting the multiple reconstruction errors into a pre-built safety warning judgment model for discrimination to calculate the safety warning probability based on the data reconstruction error; Obtaining the voltages and temperature measurement points of all single cells in the target battery module, so as to calculate the consistency safety warning probability according to the voltages and temperature measurement points of all single cells; Constructing a battery energy storage safety warning model according to the safety warning probability based on data reconstruction error and the consistency safety warning probability; An operation reliability assessment model is established according to the converter reliability assessment model and the battery energy storage safety early warning model, so as to use the operation reliability assessment model to perform operation reliability assessment on the target battery energy storage system.

2. The data-driven battery energy storage system operation reliability assessment method according to claim 1 is characterized in that: The step of obtaining the junction temperature fluctuation information and the number of cycles of the target energy storage converter to calculate the total component failure probability and the wear failure probability of the target energy storage converter according to the junction temperature fluctuation information includes: Calculating the junction temperature fluctuation information and cycle times of the target energy storage converter under different operating conditions; The total failure probability of the component is calculated according to the junction temperature fluctuation information, and the wear failure probability is calculated according to the number of cycles.

3. The data-driven battery energy storage system operation reliability assessment method according to claim 2 is characterized in that: The calculating the total failure probability of the component according to the junction temperature fluctuation information, and calculating the wear failure probability according to the number of cycles, includes: Calculate the thermal stress factor and temperature cycle factor of each component in the target energy storage converter according to the junction temperature fluctuation information; Based on the FIDES reliability guidelines, the failure rate of each component is calculated according to the preset basic failure rate parameters, thermal stress factors and temperature cycle factors of each component; Adding the failure rate of each component to obtain the total failure probability of components of the target energy storage converter; Based on Weibull distribution, the probability density of aluminum bonding wires and the probability density of solder layers of the target energy storage converter are calculated; The wear failure probability of the target energy storage converter under the number of cycles is calculated according to the number of cycles, the aluminum bonding line probability density and the solder layer probability density.

4. The data-driven battery energy storage system operation reliability assessment method according to claim 1 is characterized in that: The acquiring of energy storage sample data of the target battery module, and using the energy storage sample data to train a bidirectional long short-term memory network to obtain a reconstruction error, includes: Acquire the energy storage sample data of the target battery module, and perform planning processing on the energy storage sample data to obtain normalized energy storage sample data; The normalized energy storage sample data is used to train a battery energy storage data feature mining model constructed based on a bidirectional long short-term memory network to obtain a reconstructed data set, and the multiple reconstruction errors are calculated based on the reconstructed data set.

5. The data-driven battery energy storage system operation reliability assessment method according to claim 1 is characterized in that: The obtaining of the voltages and temperature measurement points of all the single cells in the target battery module to calculate the consistency safety warning probability according to the voltages and temperature measurement points of all the single cells includes: Obtaining the voltages of all single cells in the target battery module to calculate the average voltage of the target battery module at a preset time; Screening the maximum voltage value and the minimum voltage value of the target battery module at the preset time; Calculating an extreme voltage error root mean square according to the maximum voltage value and the minimum voltage value; The ratio of the extreme voltage error root mean square to the voltage average value is used as the voltage consistency coefficient; Acquire the temperature measurement points of all single cells in the target battery module to calculate the maximum temperature value and the minimum temperature value of the target battery module at the preset time; Calculate the extreme temperature error root mean square according to the maximum temperature value and the minimum temperature value, and use the extreme temperature error root mean square as the temperature consistency standard deviation; The consistency safety warning probability is calculated according to the voltage consistency coefficient and the temperature consistency standard deviation.

6. The data-driven battery energy storage system operation reliability assessment method according to claim 1 is characterized in that: The expression of the operation reliability evaluation model is: P BESS =1-(1-P fault,PCS )(1-P TR&SOB )(1-P fault,AE ) Among them, P BESS is the integrated safety warning probability, P fault,PCS is the target energy storage converter failure rate, P TR&SOB is the battery failure probability based on data reconstruction error and consistency, P fault,AE is the failure probability of auxiliary equipment in the target battery energy storage system except the target energy storage converter and target battery module.

7. A data-driven battery energy storage system operation reliability assessment device, characterized in that: include: A first acquisition module is used to acquire junction temperature fluctuation information and a number of cycles of a target energy storage converter, so as to calculate a total component failure probability and a wear failure probability of the target energy storage converter according to the junction temperature fluctuation information and the number of cycles; A first building module is used to build a converter reliability assessment model according to the total failure probability of the components and the wear failure probability; A training module, used for acquiring energy storage sample data of a target battery module, so as to train a bidirectional long short-term memory network using the energy storage sample data to obtain a plurality of reconstruction errors; A discrimination module, used for inputting the plurality of reconstruction errors into a pre-built safety warning judgment model for discrimination, so as to calculate a safety warning probability based on the data reconstruction error; A second acquisition module is used to acquire the voltage and temperature measurement points of all single cells in the target battery module, so as to calculate the consistency safety warning probability according to the voltage and temperature measurement points of all single cells; A second construction module is used to construct a battery energy storage safety warning model according to the safety warning probability based on the data reconstruction error and the consistency safety warning probability; An evaluation module is used to establish an operation reliability evaluation model according to the converter reliability evaluation model and the battery energy storage safety early warning model, so as to use the operation reliability evaluation model to perform an operation reliability evaluation on the target battery energy storage system.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the data-driven method for evaluating the operation reliability of a battery energy storage system as described in any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program / instruction is executed by a processor, the data-driven battery energy storage system operation reliability assessment method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the data-driven battery energy storage system operation reliability assessment method as described in any one of claims 1 to 6.

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