A safety assessment method, device and electronic equipment for second-life batteries

Abstract

The present invention provides a safety assessment method, device and electronic device for cascade batteries, wherein the method includes: obtaining operating data of multiple cascade batteries in an energy storage system; predicting the current remaining life, current state of charge, and current temperature rise rate of the cascade batteries based on the operating data; and determining the safety factor of the cascade batteries based on multiple evaluation indicators of the cascade batteries, wherein the current remaining life, current state of charge, and current temperature rise rate are all evaluation indicators. The safety assessment method, device and electronic device for cascade batteries provided by the embodiments of the present invention comprehensively consider the state of charge of the cascade batteries, as well as multiple evaluation indicators such as the remaining life and temperature that are likely to cause battery short circuit problems. The safety of the cascade batteries is comprehensively assessed based on multiple evaluation indicators, and the safety factor that can characterize the health of the cascade batteries can be determined more accurately. By performing safety assessments on multiple cascade batteries in the energy storage system, the overall safety of the energy storage system can also be assessed.

Description

Technical Field

[0001] The present invention relates to the technical field of second-life power batteries, and in particular to a safety assessment method, device, electronic device, and computer-readable storage medium for second-life batteries. Background Art

[0002] In recent years, my country's new energy vehicle industry has flourished, with both production and sales booming. This has been accompanied by a surge in the retirement of new energy vehicle power batteries, with estimates suggesting that by 2025, my country's retired battery stock will reach nearly 800,000 tons. A technical report from a renewable energy laboratory indicates that retired power batteries still have over 70% remaining capacity. Therefore, the trend toward second-life utilization of retired power batteries (abbreviated as "second-life batteries") is a key approach to fully utilize power lithium batteries and reduce the environmental impact of second-life batteries. Currently, applying second-life batteries to large-scale energy storage systems is one of the best ways to effectively utilize the remaining life of second-life batteries and represents a profound implementation of the full life cycle concept.

[0003] Compared with traditional energy storage systems that use new batteries as energy storage elements, the performance of retired power batteries has been attenuated to a certain extent by the use of new energy vehicles, and the safety issues when they are used in large-scale energy storage systems are more prominent. Therefore, conducting a comprehensive safety assessment of the energy storage system, mastering its safety status during operation, ensuring its safety and preventing the occurrence of safety accidents have become major issues that need to be solved urgently. In order to improve the safety of cascade battery energy storage systems, existing methods mostly focus on a certain characteristic of cascade batteries. Taking into account that the state of charge (SOC) of cascade batteries has a great influence on the safety of energy storage systems, it is generally established by establishing a mathematical equivalent model of cascade batteries, calculating the SOC of cascade batteries, and setting the SOC safety margin of cascade battery energy storage systems based on the attenuation of the batteries.

[0004] Existing technologies cannot fully consider the causes of safety accidents, cannot conduct systematic safety assessments from a holistic perspective, and may lead to misjudgments of the overall system safety. Summary of the Invention

[0005] To solve the existing technical problems, embodiments of the present invention provide a safety assessment method, device, electronic device, and computer-readable storage medium for a second-life battery.

[0006] In a first aspect, an embodiment of the present invention provides a safety assessment method for a second-life battery, comprising:

[0007] Acquire operating data of multiple secondary batteries in the energy storage system, the operating data including current, voltage, temperature and sampling time;

[0008] Predicting the current remaining life of the second-life battery based on the operating data, determining the current state of charge of the second-life battery, and determining the current temperature rise rate of the second-life battery;

[0009] The safety factor of the second-life battery is determined based on multiple evaluation indicators of the second-life battery, wherein the current remaining life, the current state of charge, and the current temperature rise rate are each an evaluation indicator.

[0010] In a second aspect, an embodiment of the present invention further provides a safety assessment device for a second-life battery, comprising:

[0011] An acquisition module is used to acquire operating data of multiple secondary batteries in the energy storage system, wherein the operating data includes current, voltage, temperature and sampling time;

[0012] a processing module, configured to predict the current remaining life of the secondary battery based on the operating data, determine the current state of charge of the secondary battery, and determine the current temperature rise rate of the secondary battery;

[0013] An evaluation module is used to determine the safety factor of the second-life battery according to multiple evaluation indicators of the second-life battery, wherein the current remaining life, the current state of charge, and the current temperature rise rate are each an evaluation indicator.

[0014] In a third aspect, an embodiment of the present invention provides an electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the steps of the safety assessment method for a second-life battery as described above are implemented.

[0015] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the above-mentioned methods for safety assessment of second-life batteries.

[0016] The safety assessment method, device, electronic device and computer-readable storage medium for a secondary battery provided in an embodiment of the present invention, after obtaining the operating data of the secondary battery, determines the current remaining life, current state of charge and current temperature rise rate of the secondary battery based on the operating data, thereby determining multiple evaluation indicators of the secondary battery, and then comprehensively determining the safety factor of the secondary battery by combining multiple evaluation indicators. This method comprehensively considers multiple evaluation indicators such as the state of charge of the secondary battery, as well as the remaining life and temperature that are likely to cause battery short circuit problems, and comprehensively evaluates the safety of the secondary battery based on multiple evaluation indicators, which can more accurately determine the safety factor that can characterize the health of the secondary battery; safety assessments are performed on multiple secondary batteries in the energy storage system, and the overall safety of the energy storage system can also be evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0018] Figure 1 A flow chart showing a safety assessment method for a second-life battery provided by an embodiment of the present invention is shown;

[0019] Figure 2 A schematic diagram of an equivalent model of a second-life battery provided by an embodiment of the present invention is shown;

[0020] Figure 3 A schematic diagram of the structure of a neural network provided by an embodiment of the present invention is shown;

[0021] Figure 4 A schematic structural diagram of a safety assessment device for a second-life battery provided by an embodiment of the present invention is shown;

[0022] Figure 5 A schematic structural diagram of an electronic device for executing a safety assessment method for a second-life battery provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0023] The embodiments of the present invention are described below with reference to the accompanying drawings.

[0024] Figure 1 FIG1 shows a flow chart of a safety assessment method for a second-life battery provided by an embodiment of the present invention. Figure 1 As shown, the method includes:

[0025] Step 101: Acquire operating data of multiple secondary batteries in an energy storage system, where the operating data includes current, voltage, temperature, and sampling time.

[0026] In an embodiment of the present invention, the second-life battery is a retired power battery that has been used in a second-life manner, and the energy storage system is a battery system having multiple second-life batteries. For example, the energy storage system includes multiple battery modules, and each battery module includes multiple second-life batteries. During the operation of the energy storage system, the current, voltage, temperature and other data of each second-life battery in the energy storage system can be collected, and each collection corresponds to a time, that is, the sampling time, so that the operating data of each second-life battery can be obtained. Among them, data can be collected periodically, that is, the operating data of the second-life battery can be obtained periodically; and if the time interval between two adjacent collections is small, it can be considered that the operating data can be collected in real time, and based on the safety assessment method provided in this embodiment, real-time online safety assessment can be performed. Among them, existing mature technologies can be used to collect the current, voltage, temperature, etc. of each second-life battery, and this embodiment does not limit the specific collection method.

[0027] Step 102: predict the current remaining life of the second-life battery according to the operating data, determine the current state of charge of the second-life battery, and determine the current temperature rise rate of the second-life battery.

[0028] In the embodiment of the present invention, the current state of charge (SOC) of the secondary battery can be determined based on the operating data of the secondary battery. Alternatively, the current state of charge of the secondary battery can be determined based on the equivalent model of the secondary battery. For example, the equivalent model of the secondary battery can be found in Figure 2 As shown, the current state of charge of the secondary battery satisfies the following formula (1):

[0029]

[0030] Among them, α bj is the amplitude within the index range of the secondary battery bj; G is the number of secondary batteries in the battery module; i bj is the current value of charge and discharge; U bj_max is the full charge voltage amplitude; U bj_e is the voltage limit value of the index area; U bj_last is the nominal area voltage limit value, U bj_0 is the standard value of charging voltage; E bj_e is the capacity limit of the index area; E bj_last is the nominal area capacity limit value; S bj Indicates the state of charge of the secondary battery bj; U bj is the measured voltage value of the secondary battery bj, E bj It is the remaining capacity of the secondary battery bj after the last round of discharge and charge.

[0031] Furthermore, the temperature change rate, i.e., the current temperature rise rate, can be determined by using multiple temperatures collected at different sampling times. Optionally, the step of "determining the current temperature rise rate of the echelon battery" includes: determining the current temperature rise rate of the echelon battery based on the sampling time and temperature determined by two consecutive samplings, and the current temperature rise rate satisfies the following formula (2):

[0032]

[0033] Where ΔT i is the current temperature rise rate corresponding to the i-th sampling time, t i , t i-1 are the i-th and i-1-th sampling times, T i , T i-1 are the temperatures corresponding to the i-th and i-1-th sampling times respectively. If the current temperature rise rate ΔT i is positive, indicating that the temperature of the cascade battery is increasing, and ΔT i The larger it is, the faster the temperature rises, and the more likely the battery is to be dangerous.

[0034] In the embodiment of the present invention, the remaining useful life (RUL) of the second-life battery is also determined, that is, the remaining useful life of the second-life battery. In this embodiment, the remaining useful life of the second-life battery determined based on the current operating data is referred to as the current remaining useful life.

[0035] Compared with traditional solutions, after determining the current state of charge of the second-life battery, the embodiment of the present invention does not need to determine the attenuation degree of the second-life battery (for example, the SOC change rate, etc.); in addition, the temperature rise rate and remaining life of the second-life battery are determined to be able to comprehensively evaluate the health of the second-life battery from multiple dimensions.

[0036] Step 103: Determine the safety factor of the second-life battery based on multiple evaluation indicators of the second-life battery, where the current remaining life, the current state of charge, and the current temperature rise rate are each an evaluation indicator.

[0037] The remaining life and temperature of the battery are important reasons for causing short circuits in the battery and thus safety accidents. In the embodiment of the present invention, the current remaining life, current state of charge, and current temperature rise rate of the echelon battery are used as evaluation indicators. Based on multiple evaluation indicators and from multiple dimensions, a comprehensive evaluation of the health of the echelon battery is performed, which can more accurately determine the safety factor of the echelon battery. The safety factor can represent the safety level of the echelon battery; for example, the larger the safety factor, the safer the echelon battery. Since the energy storage system contains multiple echelon batteries, the above method can be executed on each echelon battery to determine the safety factor of each echelon battery.

[0038] Those skilled in the art will understand that since the operating data at different moments (sampling times) can be discretely sampled, each time the current operating data is obtained, the current safety factor of the echelon battery can be determined based on the above steps 101-103, thereby enabling real-time determination of the safety factor of the echelon battery, timely suggestions for the operation of the echelon battery, and timely detection of dangerous echelon batteries.

[0039] The safety assessment method for a secondary battery provided in an embodiment of the present invention, after obtaining the operating data of the secondary battery, determines the current remaining life, current state of charge, and current temperature rise rate of the secondary battery based on the operating data, thereby determining multiple evaluation indicators of the secondary battery, and then comprehensively determining the safety factor of the secondary battery by combining multiple evaluation indicators. This method comprehensively considers multiple evaluation indicators such as the state of charge of the secondary battery, as well as the remaining life and temperature that are likely to cause battery short circuit problems, and comprehensively evaluates the safety of the secondary battery based on multiple evaluation indicators. It can more accurately determine the safety factor that can characterize the health of the secondary battery; it performs safety assessments on multiple secondary batteries in the energy storage system, and can also evaluate the overall safety of the energy storage system.

[0040] Optionally, the process of “predicting the current remaining life of the second-life battery based on the operating data” in step 102 includes:

[0041] Step A1: Determine the last remaining life R of the battery f and the time T of the current discharge process of the secondary battery f .

[0042] Step A2: Set the last remaining life span R f and time T f The input is fed into a pre-trained neural network for predicting the remaining life of the second-life battery, and the current remaining life R of the second-life battery is determined based on the output of the neural network. p .

[0043] In an embodiment of the present invention, a neural network capable of predicting the remaining life of a second-life battery is pre-trained, wherein the neural network can realize a nonlinear mapping between input and output; for example, the neural network can be a BP neural network, which has good robustness. In this embodiment, the neural network can predict the remaining life of the second-life battery after a discharge process. Specifically, the neural network is based on the remaining life R f And the time T of the current discharge process f Prediction, current remaining life R p , that is, the neural network can predict the remaining life after a discharge process.

[0044] Furthermore, the difference E between the predicted value and the actual value of the remaining life can also be used as an output of the neural network. The neural network has the function of minimizing the difference E so that the predicted remaining life (for example, the current remaining life R p ) as accurately as possible. Alternatively, see Figure 3 As shown in Figure 3, the neural network includes two input layers, m hidden layers and two output layers.

[0045] like Figure 3 As shown, the two input layers are used to input the last remaining life R f and time T f . Figure 3 In the example, I1 represents the input of the first input layer, which can be used to input the last remaining life R f ; I2 represents the input of the second input layer, which can be used to input the time T of the current discharge process of the cascade battery f .

[0046] The hidden layer is used to determine its output based on the input of the input layer and the connection weight between the input layer and the hidden layer. In this embodiment, the output of the hidden layer satisfies the following formula (3):

[0047]

[0048] Among them, I i represents the input of the i-th input layer of the neural network, h j represents the output of the jth hidden layer, W ij represents the connection weight from the i-th input layer to the j-th hidden layer, b j is the threshold; j ranges from 1 to m; m is the number of hidden layers in the neural network.

[0049] The output layer is used to determine its output based on the output of the hidden layer and the connection weight between the hidden layer and the output layer, so as to realize the layer-by-layer processing of the input signal (including the last residual life R f and time T f In this embodiment, O1 represents the output of the first output layer, which can be used to output the current remaining life span R p O2 represents the output of the second output layer, which can be used to output the difference E between the predicted value and the actual value of the remaining life. In addition, the output of the output layer satisfies the following formula (4):

[0050]

[0051] Among them, O k represents the output of the kth output layer of the neural network, w jkRepresents the connection weight from the jth hidden layer to the kth output layer. Since the output of the neural network includes two (current remaining life R p and the error E between the predicted value and the actual value of the remaining life), so the neural network includes two output layers, correspondingly, k = 1, 2.

[0052] In this embodiment, the connection weight W of the neural network can be continuously adjusted according to the error E between the predicted value and the actual value. ij 、w jk and threshold b j After convergence, the training is considered to be completed, and the remaining life of the battery is predicted based on the trained neural network.

[0053] Optionally, the embodiment of the present invention uses a weighted approach to determine the safety factor of a second-life battery; wherein, this embodiment combines subjective and objective weights for comprehensive evaluation, which can avoid the defects of a single weight being too subjective and insufficiently considering factors, and can improve the evaluation accuracy. Specifically, the above step 103 "determining the safety factor of a second-life battery based on multiple evaluation indicators of the second-life battery" includes the following steps B1-B2:

[0054] Step B1: Determine the subjective weight of each evaluation indicator and the objective weight of each evaluation indicator; each evaluation indicator is determined after normalization.

[0055] In the embodiment of the present invention, a subjective weight and an objective weight are set for each evaluation indicator. The subjective weight can be a weight determined based on the recognition of the importance of each evaluation indicator, and the objective weight can be a weight objectively determined based on the evaluation value of each evaluation indicator. Furthermore, since different evaluation indicators have different meanings, in order to unify the various evaluation indicators, the evaluation indicators in this embodiment are normalized indicators. That is, the original evaluation indicators are first normalized, and the normalized indicators are used as the evaluation indicators required in the process of determining the two weights.

[0056] For example, for the i-th evaluation index, its original index is e i , the evaluation index determined after normalization is N(e i ), the evaluation index N(e i ) can satisfy the following formula:

[0057]

[0058] In the above formula (5), n represents the number of types of evaluation indicators. For example, if the evaluation indicators include the current remaining life, the current state of charge, and the current temperature rise rate, then n=3.

[0059] Step B2: Weighting multiple evaluation indicators according to subjective weights and objective weights to determine the safety factor of the second-life battery.

[0060] In the embodiment of the present invention, each evaluation index is weighted based on subjective weight and objective weight. For example, the i-th evaluation index p i The subjective weight is w a,i , the objective weight is w k,i , this embodiment combines the subjective weight w a,i and objective weight w k,i For the i-th evaluation index p i Optionally, the safety factor of the secondary battery satisfies the following formula (6):

[0061]

[0062] Where R represents the safety factor of the second-life battery, n represents the number of types of evaluation indicators; W A 、W K Represent the vector of subjective weight and the vector of objective weight respectively, and W A =[w a,1 ,w a,2 ,…,w a,n ],W K =[w k,1 ,w k,2 ,…,w k,n ],w a,i represents the subjective weight of the i-th evaluation index, w k,i represents the objective weight of the i-th evaluation index; P represents the vector of all evaluation indicators of the cascade battery, and P=[p1,p2,…,p n ] T , p i represents the value of the i-th evaluation indicator; i ranges from 1 to n; α and β are preset adjustment coefficients, ⊙ represents the Hadamard product, and · represents the scalar product. Similar to the subjective and objective weight vectors described above, α and β are also n-dimensional vectors.

[0063] Optionally, the above step B1 "determining the subjective weight of each evaluation indicator" includes the following steps B11-B14:

[0064] Step B11: Construct the comparison matrix A of the evaluation index, and compare the elements a in the matrix pq It indicates the importance of the p-th evaluation index compared with the q-th evaluation index, and the greater the importance, the greater the pq The bigger; a pq ×a qp =1.

[0065] In the embodiment of the present invention, apq Indicates the importance of the p-th evaluation index compared to the q-th evaluation index. For example, the p-th evaluation index is more important than the q-th evaluation index, and the element a pq The value can be 3; conversely, if the qth evaluation index is not as important as the pth evaluation index, then the element a qp The value can be 1 / 3 to ensure a pq ×a qp = 1. For example, the element a pq The value of can be selected according to the following table 1.

[0066] Table 1

[0067]

[0068]

[0069] Step B12: Construct a judgment matrix B based on the comparison matrix A, and the element b in the judgment matrix B pq satisfy:

[0070]

[0071] Among them, r p Represents the sum of all elements in the p-th row of the judgment matrix B, r q represents the sum of all elements in the qth row of the judgment matrix B, and the value range of p and q is 1 to n, where n represents the number of evaluation indicators; r max Represents all r p The maximum value in r min Represents all r p The minimum value in .

[0072] In the embodiment of the present invention, the original comparison matrix A can be determined based on the importance of each evaluation index. For example, if this embodiment involves n evaluation indexes, the comparison matrix A is an n-order square matrix. Then, the above formula (7) can be used to determine each element b in the judgment matrix B. pq , and then the judgment matrix B can be determined. Among them, it can be determined whether the consistency of the judgment matrix B meets the requirements, otherwise the comparison matrix A needs to be reconstructed. In this embodiment, r p is the importance ranking index, which can be specifically:

[0073] Step B13: Determine the quasi-optimal transfer matrix E according to the judgment matrix B, and the element e in the quasi-optimal transfer matrix E pq satisfy:

[0074]

[0075] Wherein, a is a preset coefficient; for example, a=10.

[0076] Step B14: Determine the subjective weight of each evaluation indicator based on the optimal transfer matrix E, and the subjective weight satisfies:

[0077]

[0078] Among them, w a,q Represents the subjective weight of the qth evaluation indicator.

[0079] In the embodiment of the present invention, the comparison matrix A can be converted into the optimal transfer matrix E using the above formula (7) and the above formula (8), and finally the element e of each column in the optimal transfer matrix E is used. pq (p=1,2,…,n) Determine the subjective weight corresponding to the corresponding row, that is, the subjective weight w of the qth evaluation indicator corresponding to the qth column a,q , which can be determined based on the above formula (9).

[0080] Additionally, optionally, the above step B1 of "determining the objective weight of each evaluation indicator" includes the following steps B15-B17:

[0081] Step B15: forming an attribute vector of the second-life battery according to the multiple evaluation indicators of the second-life battery, wherein an attribute element in the attribute vector corresponds to an indicator value of an evaluation indicator.

[0082] In the embodiment of the present invention, the evaluation values ​​of multiple evaluation indicators of the echelon battery are used to represent the attribute vector, and the attribute vector can represent the characteristics of each echelon battery. For example, the echelon battery has three attributes (evaluation indicators): remaining life RUL, state of charge SOC, and temperature rise rate ΔT; for the i-th echelon battery, its attribute vector can be expressed as [x i1 ,x i2 ,x i3 ], x i1 、x i2 、x i3 They represent the RUL, SOC, and ΔT of the i-th battery respectively.

[0083] Step B16: The distance between the two echelon batteries is represented by the distance between their attribute vectors, and N echelon batteries are clustered to divide the multiple echelon batteries into n clusters, where n is the same as the number of types of evaluation indicators.

[0084] In the embodiment of the present invention, N secondary batteries are clustered. In the clustering process, the attribute vector represents the secondary battery, and the distance between the attribute vectors is the distance between the corresponding secondary batteries. The distance can be the Euclidean distance. For example, for the i-th and j-th secondary batteries, the distance d(x i,x j ) satisfies the following formula:

[0085]

[0086] Among them, x i 、x j Respectively represent the i-th and j-th battery; x iu is x i The u-th attribute value, x ju is x j The u-th attribute value of .

[0087] Step B17: Determine the cluster corresponding to each evaluation indicator, and determine the objective weight of each evaluation indicator. The objective weight satisfies:

[0088]

[0089] Among them, w k,i represents the objective weight of the i-th evaluation index, s i It represents the number of echelon batteries in the cluster corresponding to the i-th evaluation index, and the value of i ranges from 1 to n.

[0090] In an embodiment of the present invention, after N second-life batteries are clustered into n clusters, the mean of the attribute vectors of all second-life batteries in each cluster is the center of the cluster. In addition, the number of second-life batteries in each cluster is recorded. In this embodiment, an association is established between each cluster and each evaluation indicator. For example, the cluster closest to the i-th evaluation indicator is used as the cluster corresponding to the i-th evaluation indicator. The distance between the cluster center and the overall data of the i-th evaluation indicator can be used to determine which cluster is closest to the i-th evaluation indicator. When determining the number s of second-life batteries in the cluster corresponding to the i-th evaluation indicator, i Then, based on the above formula (11), the objective weight w of each evaluation index can be determined k,i , i=1,2,…,n.

[0091] In the embodiment of the present invention, after determining the subjective weight and objective weight of each evaluation indicator of the echelon battery, the safety factor R of the echelon battery can be determined based on the above formula (6). The larger the safety factor R, the safer the echelon battery. The safety of the energy storage system can be evaluated based on the real-time safety factor of the echelon battery, and recommendations can be given for its continued operation. For example, operation recommendations for different safety factors can be found in Table 2 below:

[0092] Table 2

[0093]

[0094] The above describes in detail the safety assessment method for a second-life battery provided by an embodiment of the present invention. This method can also be implemented by a corresponding device. The following describes in detail the safety assessment device for a second-life battery provided by an embodiment of the present invention.

[0095] Figure 4 FIG1 shows a schematic structural diagram of a safety assessment device for a secondary battery provided by an embodiment of the present invention. Figure 4 As shown, the safety assessment device for the second-life battery includes:

[0096] An acquisition module 41 is configured to acquire operating data of multiple secondary batteries in the energy storage system, wherein the operating data includes current, voltage, temperature, and sampling time;

[0097] a processing module 42 for predicting the current remaining life of the second-life battery based on the operating data, determining the current state of charge of the second-life battery, and determining the current temperature rise rate of the second-life battery;

[0098] The evaluation module 43 is used to determine the safety factor of the second-life battery according to multiple evaluation indicators of the second-life battery, wherein the current remaining life, the current state of charge, and the current temperature rise rate are each an evaluation indicator.

[0099] In a possible implementation, the processing module 42 predicts the current remaining life of the second-life battery according to the operating data, including:

[0100] Determine the last remaining life R of the cascade battery f and the time T of the current discharge process of the secondary battery f ;

[0101] The last remaining life R f and the time T f The input is fed into a pre-trained neural network for predicting the remaining life of a second-life battery, and the current remaining life R of the second-life battery is determined based on the output of the neural network. p .

[0102] In one possible implementation, the neural network includes two input layers, m hidden layers, and two output layers;

[0103] The two input layers are used to input the last remaining life span R f and the time T f ;

[0104] The output of the hidden layer satisfies:

[0105]

[0106] Among them, Ii represents the input of the ith input layer of the neural network, h j represents the output of the jth hidden layer, W ij represents the connection weight from the i-th input layer to the j-th hidden layer, b j is the threshold; j ranges from 1 to m;

[0107] The output of the output layer satisfies:

[0108]

[0109] Among them, O k represents the output of the kth output layer of the neural network, w jk represents the connection weight from the jth hidden layer to the kth output layer.

[0110] In one possible implementation, the processing module 42 determines the current state of charge of the secondary battery, including:

[0111] The current state of charge of the secondary battery is determined according to an equivalent model of the secondary battery.

[0112] In a possible implementation, the processing module 42 determines the current temperature rise rate of the secondary battery, including:

[0113] The current temperature rise rate of the cascade battery is determined according to the sampling time and temperature determined by two consecutive samplings, and the current temperature rise rate satisfies:

[0114]

[0115] Where ΔT i is the current temperature rise rate corresponding to the i-th sampling time, t i , t i-1 are the i-th and i-1-th sampling times, T i , T i-1 are the temperatures corresponding to the i-th and i-1-th sampling times respectively.

[0116] In a possible implementation, the evaluation module 43 includes:

[0117] A weight determination unit, used to determine the subjective weight of each evaluation indicator and determine the objective weight of each evaluation indicator; each evaluation indicator is determined after normalization processing;

[0118] An evaluation unit is used to perform weighted processing on the multiple evaluation indicators according to the subjective weight and the objective weight to determine the safety factor of the second-life battery.

[0119] In a possible implementation, the weight determination unit determines the subjective weight of each evaluation indicator, including:

[0120] Construct the comparison matrix A of the evaluation index, and the element a in the comparison matrix pq It indicates the importance of the p-th evaluation index compared with the q-th evaluation index, and the greater the importance, the greater the pq The bigger; a pq ×a qp =1;

[0121] The judgment matrix B is constructed based on the comparison matrix A, and the element b in the judgment matrix B is pq satisfy:

[0122]

[0123] Among them, r p represents the sum of all elements in the p-th row of the judgment matrix B, r q represents the sum of all elements in the qth row of the judgment matrix B, and the value range of p and q is 1 to n, and n represents the number of types of the evaluation indicators; r max Represents all r p The maximum value in r min Represents all r p The minimum value in ;

[0124] The quasi-optimal transfer matrix E is determined according to the judgment matrix B, and the element e in the quasi-optimal transfer matrix E is pq satisfy:

[0125]

[0126] Among them, a is the preset coefficient;

[0127] The subjective weight of each evaluation indicator is determined according to the optimal transfer matrix E, and the subjective weight satisfies:

[0128]

[0129] Among them, w a,q Represents the subjective weight of the qth evaluation indicator.

[0130] In a possible implementation, the weight determination unit determines the objective weight of each evaluation indicator, including:

[0131] forming an attribute vector of the second-life battery according to a plurality of evaluation indicators of the second-life battery, wherein one attribute element in the attribute vector corresponds to an indicator value of one evaluation indicator;

[0132] The distance between the two echelon batteries is represented by the distance between the attribute vectors of the two echelon batteries, and the N echelon batteries are clustered to divide the multiple echelon batteries into n clusters, where n is the same as the number of types of the evaluation indicators;

[0133] Determine the cluster corresponding to each evaluation indicator, and determine the objective weight of each evaluation indicator, where the objective weight satisfies:

[0134]

[0135] Among them, w k,i represents the objective weight of the i-th evaluation index, s i It represents the number of the second-life batteries in the cluster corresponding to the i-th evaluation index, and the value of i ranges from 1 to n.

[0136] In one possible implementation, the safety factor of the secondary battery satisfies:

[0137]

[0138] Wherein, R represents the safety factor of the secondary battery, n represents the number of types of the evaluation indicators; W A 、W K Respectively represent the vector of the subjective weight and the vector of the objective weight, and W A =[w a,1 ,w a,2 ,…,w a,n ],W K =[w k,1 ,w k,2 ,…,w k,n ],w a,i represents the subjective weight of the i-th evaluation index, w k,i represents the objective weight of the i-th evaluation index; P represents the vector of all evaluation indicators of the cascade battery, and P = [p1, p2, ..., p n ] T , p i Indicates the index value of the i-th evaluation index; the value range of i is 1 to n;

[0139] α and β are both preset adjustment coefficients, ⊙ represents the Hadamard product, and · represents the scalar product.

[0140] In addition, an embodiment of the present invention further provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and runnable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, the various processes of the above-mentioned embodiment of the safety assessment method for second-life batteries are implemented, and the same technical effect can be achieved. To avoid repetition, they will not be described here.

[0141] For details, see Figure 5 As shown, an embodiment of the present invention further provides an electronic device, which includes a bus 1110 , a processor 1120 , a transceiver 1130 , a bus interface 1140 , a memory 1150 and a user interface 1160 .

[0142] In an embodiment of the present invention, the electronic device further includes: a computer program stored in the memory 1150 and executable on the processor 1120, which implements the various processes of the above-mentioned second-life battery safety assessment method embodiment when executed by the processor 1120.

[0143] The transceiver 1130 is configured to receive and send data under the control of the processor 1120 .

[0144] In an embodiment of the present invention, a bus architecture (represented by bus 1110) may include any number of interconnected buses and bridges, and bus 1110 connects various circuits including one or more processors represented by processor 1120 and a memory represented by memory 1150.

[0145] Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an Accelerated Graphical Port (AGP), a processor, or a local bus using any of a variety of bus architectures. By way of example and not limitation, such architectures include an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA), and a Peripheral Component Interconnect (PCI) bus.

[0146] The processor 1120 can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above-mentioned processor includes: a general-purpose processor, a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a complex programmable logic device (CPLD), a programmable logic array (PLA), a microcontroller unit (MCU) or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The various methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. For example, the processor can be a single-core processor or a multi-core processor, and the processor can be integrated into a single chip or located on multiple different chips.

[0147] The processor 1120 can be a microprocessor or any conventional processor. The method steps disclosed in conjunction with the embodiments of the present invention can be directly executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a readable storage medium known in the art, such as a random access memory (RAM), a flash memory (Flash Memory), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), or a register. The readable storage medium is located in a memory, and the processor reads the information in the memory and performs the steps of the above method in conjunction with its hardware.

[0148] The bus 1110 may also connect various other circuits, such as peripheral devices, voltage regulators, or power management circuits. The bus interface 1140 provides an interface between the bus 1110 and the transceiver 1130. These are all well known in the art and are therefore not further described in this embodiment of the present invention.

[0149] The transceiver 1130 can be a single component or multiple components, such as multiple receivers and transmitters, providing a means for communicating with various other devices over a transmission medium. For example, the transceiver 1130 receives external data from other devices and transmits data processed by the processor 1120 to other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, such as a touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, or stylus.

[0150] It should be understood that in an embodiment of the present invention, the memory 1150 may further include a memory remotely located relative to the processor 1120, and these remotely located memories may be connected to a server via a network. One or more parts of the aforementioned network may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless local area network (WLAN), a wide area network (WAN), a wireless wide area network (WWAN), a metropolitan area network (MAN), the Internet, a public switched telephone network (PSTN), a plain old telephone service network (POTS), a cellular telephone network, a wireless network, a wireless fidelity (Wi-Fi) network, or a combination of two or more of the aforementioned networks. For example, the cellular telephone network and the wireless network can be a Global System for Mobile Communications (GSM) system, a Code Division Multiple Access (CDMA) system, a Worldwide Interoperability for Microwave Access (WiMAX) system, a General Packet Radio Service (GPRS) system, a Wideband Code Division Multiple Access (WCDMA) system, a Long Term Evolution (LTE) system, an LTE Frequency Division Duplex (FDD) system, an LTE Time Division Duplex (TDD) system, an Advanced Long Term Evolution (LTE-A) system, a Universal Mobile Telecommunications (UMTS) system, an Enhanced Mobile Broadband (eMBB) system, a Massive Machine Type of Communication (mMTC) system, an Ultra Reliable Low Latency Communications (uRLLC) system, and the like.

[0151] It should be understood that the memory 1150 in the embodiment of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Non-volatile memories include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0152] Volatile memory includes random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 1150 of the electronic device described in the embodiments of the present invention includes, but is not limited to, the above and any other suitable types of memory.

[0153] In the embodiment of the present invention, the memory 1150 stores the following elements of the operating system 1151 and the application 1152: executable modules, data structures, or subsets thereof, or extended sets thereof.

[0154] Specifically, the operating system 1151 includes various system programs, such as a framework layer, a core library layer, and a driver layer, which are used to implement various basic services and process hardware-based tasks. The application 1152 includes various application programs, such as a media player and a browser, which are used to implement various application services. The program that implements the method of the embodiment of the present invention may be included in the application 1152. The application 1152 includes applets, objects, components, logic, data structures, and other computer system executable instructions that perform specific tasks or implement specific abstract data types.

[0155] In addition, 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 various processes of the above-mentioned embodiment of the safety assessment method for second-life batteries are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0156] Computer-readable storage media include: permanent and non-permanent, removable and non-removable media, which are tangible devices that can retain and store instructions for use by instruction execution devices. Computer-readable storage media include: electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, and any suitable combination of the above. Computer-readable storage media include: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette storage, magnetic disk storage or other magnetic storage devices, memory sticks, mechanical encoding devices (such as punched cards or raised structures with grooves in which instructions are recorded), or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined in the embodiments of the present invention, computer-readable storage media does not include temporary signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (such as light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0157] In the several embodiments provided in this application, it should be understood that the disclosed devices, electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or can be an electrical, mechanical or other form of connection.

[0158] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in a single location or distributed across multiple network units. Some or all of these units may be selected based on actual needs to address the issues addressed by the embodiments of the present invention.

[0159] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0160] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (including: a personal computer, a server, a data center or other network device) to perform all or part of the steps of the method described in each embodiment of the present invention. The above-mentioned storage medium includes the various media that can store program codes as listed above.

[0161] In describing the embodiments of the present invention, those skilled in the art should understand that the embodiments of the present invention can be implemented as methods, apparatuses, electronic devices, and computer-readable storage media. Therefore, the embodiments of the present invention can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. In addition, in some embodiments, the embodiments of the present invention can also be implemented in the form of a computer program product in one or more computer-readable storage media, wherein the computer-readable storage medium contains computer program code.

[0162] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or components, or any combination thereof. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM), flash memories (Flash Memory), optical fibers, compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices or any combination thereof. In an embodiment of the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component.

[0163] The computer program code contained in the computer-readable storage medium may be transmitted using any appropriate medium, including wireless, wire, optical cable, radio frequency (RF), or any suitable combination thereof.

[0164] The computer program code for performing the operations of the embodiments of the present invention can be written in assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or in one or more programming languages ​​or a combination thereof, wherein the programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and also includes a conventional procedural programming language, such as C language or a similar programming language. The computer program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, and entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer or to an external computer via any type of network, including a local area network (LAN) or a wide area network (WAN).

[0165] The embodiments of the present invention describe the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0166] It should be understood that each block in the flowchart and / or block diagram, as well as combinations of blocks in the flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine. These computer-readable program instructions are executed by the computer or other programmable data processing device to produce a device that implements the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0167] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to operate in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product that implements the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0168] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process that implements the functions / operations specified by the blocks in the flowchart and / or block diagram.

[0169] The above description is merely a specific implementation of the embodiments of the present invention, but the scope of protection of the embodiments of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the embodiments of the present invention should be included in the scope of protection of the embodiments of the present invention. Therefore, the scope of protection of the embodiments of the present invention should be based on the scope of protection of the claims.

Claims

1. A safety assessment method for a second-life battery, characterized in that: include: Acquire operating data of multiple secondary batteries in the energy storage system, the operating data including current, voltage, temperature and sampling time; Predicting the current remaining life of the second-life battery based on the operating data, determining the current state of charge of the second-life battery, and determining the current temperature rise rate of the second-life battery; Determining a safety factor of the second-life battery according to multiple evaluation indicators of the second-life battery, wherein the current remaining life, the current state of charge, and the current temperature rise rate are each an evaluation indicator; The determining of the safety factor of the second-life battery according to the multiple evaluation indicators of the second-life battery includes: Determine the subjective weight of each evaluation indicator and determine the objective weight of each evaluation indicator; each evaluation indicator is determined after normalization; Performing weighted processing on the plurality of evaluation indicators according to the subjective weight and the objective weight to determine the safety factor of the second-life battery; The safety factor of the secondary battery satisfies: Wherein, R represents the safety factor of the secondary battery, n represents the number of types of the evaluation indicators; W A 、W K Respectively represent the vector of the subjective weight and the vector of the objective weight, and W A =[w a,1 ,w a,2 ,…,w a,n ],W K =[w k,1 ,w k,2 ,…,w k,n ],w a,i represents the subjective weight of the i-th evaluation index, w k,i represents the objective weight of the i-th evaluation index; P represents the vector of all evaluation indicators of the cascade battery, and P = [p1, p2, ..., p n ] T , p i Indicates the index value of the i-th evaluation index; the value range of i is 1 to n; α and β are both preset adjustment coefficients, ⊙ represents the Hadamard product, and · represents the scalar product.

2. The method according to claim 1, characterized in that The predicting the current remaining life of the second-life battery according to the operating data includes: Determine the last remaining life R of the cascade battery f and the time T of the current discharge process of the secondary battery f ; The last remaining life R f and the time T f The input is fed into a pre-trained neural network for predicting the remaining life of a second-life battery, and the current remaining life R of the second-life battery is determined based on the output of the neural network. p .

3. The method according to claim 2, characterized in that The neural network includes two input layers, m hidden layers and two output layers; The two input layers are used to input the last remaining life span R f and the time T f ; The output of the hidden layer satisfies: Among them, I i represents the input of the ith input layer of the neural network, h j represents the output of the jth hidden layer, W ij represents the connection weight from the i-th input layer to the j-th hidden layer, b j is the threshold; j ranges from 1 to m; The output of the output layer satisfies: Among them, O k represents the output of the kth output layer of the neural network, w jk represents the connection weight from the jth hidden layer to the kth output layer.

4. The method according to claim 1, wherein Determining the current state of charge of the secondary battery includes: The current state of charge of the secondary battery is determined according to an equivalent model of the secondary battery.

5. The method according to claim 1, wherein Determining the current temperature rise rate of the secondary battery includes: The current temperature rise rate of the cascade battery is determined according to the sampling time and temperature determined by two consecutive samplings, and the current temperature rise rate satisfies: Where, ΔT i is the current temperature rise rate corresponding to the i-th sampling time, t i , t i-1 are the i-th and i-1-th sampling times, T i , T i-1 are the temperatures corresponding to the i-th and i-1-th sampling times respectively.

6. The method according to claim 1, characterized in that Determining the subjective weight of each evaluation indicator includes: Construct the comparison matrix A of the evaluation index, and the element a in the comparison matrix pq It indicates the importance of the p-th evaluation index compared with the q-th evaluation index, and the greater the importance, the greater the pq The bigger; a pq ×a qp =1; The judgment matrix B is constructed based on the comparison matrix A, and the element b in the judgment matrix B is pq satisfy: Among them, r p represents the sum of all elements in the p-th row of the judgment matrix B, r q represents the sum of all elements in the qth row of the judgment matrix B, and the value range of p and q is 1 to n, and n represents the number of types of the evaluation indicators; r max Represents all r p The maximum value in r min Represents all r p The minimum value in ; The quasi-optimal transfer matrix E is determined according to the judgment matrix B, and the element e in the quasi-optimal transfer matrix E is pq satisfy: Among them, a is the preset coefficient; The subjective weight of each evaluation indicator is determined according to the optimal transfer matrix E, and the subjective weight satisfies: Among them, w a,q Represents the subjective weight of the qth evaluation indicator.

7. The method according to claim 1, characterized in that Determining the objective weight of each evaluation indicator includes: forming an attribute vector of the second-life battery according to a plurality of evaluation indicators of the second-life battery, wherein one attribute element in the attribute vector corresponds to an indicator value of one evaluation indicator; The distance between the two echelon batteries is represented by the distance between the attribute vectors of the two echelon batteries, and the N echelon batteries are clustered to divide the multiple echelon batteries into n clusters, where n is the same as the number of types of the evaluation indicators; Determine the cluster corresponding to each evaluation indicator, and determine the objective weight of each evaluation indicator, where the objective weight satisfies: Among them, w k,i represents the objective weight of the i-th evaluation index, s i It represents the number of the second-life batteries in the cluster corresponding to the i-th evaluation index, and the value of i ranges from 1 to n.

8. A safety assessment device for a second-life battery, characterized in that: include: An acquisition module is used to acquire operating data of multiple secondary batteries in the energy storage system, wherein the operating data includes current, voltage, temperature and sampling time; a processing module, configured to predict the current remaining life of the secondary battery based on the operating data, determine the current state of charge of the secondary battery, and determine the current temperature rise rate of the secondary battery; An evaluation module, configured to determine a safety factor of the second-life battery according to multiple evaluation indicators of the second-life battery, wherein the current remaining life, the current state of charge, and the current temperature rise rate are each an evaluation indicator; The determining of the safety factor of the second-life battery according to the multiple evaluation indicators of the second-life battery includes: Determine the subjective weight of each evaluation indicator and determine the objective weight of each evaluation indicator; each evaluation indicator is determined after normalization; Performing weighted processing on the plurality of evaluation indicators according to the subjective weight and the objective weight to determine the safety factor of the second-life battery; The safety factor of the secondary battery satisfies: Wherein, R represents the safety factor of the secondary battery, n represents the number of types of the evaluation indicators; W A 、W K Respectively represent the vector of the subjective weight and the vector of the objective weight, and W A =[w a,1 ,w a,2 ,…,w a,n ],W K =[w k,1 ,w k,2 ,…,w k,n ],w a,i represents the subjective weight of the i-th evaluation index, w k,i represents the objective weight of the i-th evaluation index; P represents the vector of all evaluation indicators of the cascade battery, and P = [p1, p2, ..., p n ] T , p i Indicates the index value of the i-th evaluation index; the value range of i is 1 to n; α and β are both preset adjustment coefficients, ⊙ represents the Hadamard product, and · represents the scalar product.

9. An electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps of the safety assessment method for a second-life battery according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the safety assessment method for a second-life battery according to any one of claims 1 to 7 are implemented.

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