Lithium battery health state estimation method, system and device and storage medium

Abstract

The invention discloses a lithium battery health state estimation method, system and device and a storage medium, and the method comprises the steps: obtaining a preset charge-discharge cycle data set, and carrying out the analysis and processing of the data set, and obtaining a preprocessing data set; performing IC, TIECVD and DT feature extraction on the preprocessed data set to obtain a health feature information set, and performing random division on the health feature information set to obtain a training set and a test set; constructing a lithium battery health state prediction model based on an LSTM framework, inputting the training set into the lithium battery health state prediction model for training, and performing test verification on the trained lithium battery health state prediction model through the test set to obtain a trained lithium battery health state prediction model; and performing health state estimation on the lithium battery to be detected according to the trained lithium battery health state prediction model. The accuracy of lithium battery health state estimation is improved, so that the safety and reliability of lithium battery operation are improved, and the method can be applied to the technical field of lithium batteries.

Description

Technical Field

[0001] The present invention relates to the field of lithium battery technology, and in particular to a lithium battery health state estimation method, system, device and storage medium. Background Art

[0002] As a clean energy source, lithium batteries are used in many fields, such as portable processing terminals, electric vehicles, aerospace, etc., because of their advantages such as high energy density, low self-discharge rate, fast charging speed and long service life. In actual application scenarios, how to ensure the safety and reliability of lithium batteries is an important and complex issue. With the continuous use of lithium batteries, that is, the increase in the number of charge and discharge times, lithium batteries will undergo irreversible performance degradation, which may cause lithium batteries to fail during operation, thus posing a safety hazard. The state of health (SOH) of the battery is an important parameter that reflects the current aging state of the battery. Battery aging is a gradual process that is affected by a variety of complex internal and external factors, such as chemical degradation inside the battery, ambient temperature, and usage conditions. Battery aging seriously affects the normal and safe use of the power system. Predicting the battery's SOH can assess the battery aging so that the battery can be replaced in time to prevent safety problems.

[0003] In order to avoid a series of problems caused by battery health issues, many studies on battery SOH prediction have emerged in recent years. There is a close relationship between temperature and lithium battery aging. For example, high temperature will accelerate the chemical reactions inside the battery and promote the occurrence of some adverse irreversible chemical reactions. Among these studies, only a few studies have conducted in-depth analysis of the relationship between temperature and lithium battery aging to explore the relationship between temperature and SOH. Most studies did not use temperature or simply used surface temperature as a feature to predict SOH, resulting in insufficient accuracy in estimating the health status of lithium batteries. Summary of the invention

[0004] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0005] To this end, an object of an embodiment of the present invention is to provide a method for estimating the health state of a lithium battery, which improves the accuracy of estimating the health state of a lithium battery, thereby improving the safety and reliability of the operation of the lithium battery.

[0006] Another object of an embodiment of the present invention is to provide a lithium battery health status estimation system.

[0007] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

[0008] In one aspect, an embodiment of the present invention provides a method for estimating the health status of a lithium battery, comprising the following steps:

[0009] Acquire a preset charge-discharge cycle data set, and analyze and process the charge-discharge cycle data set to obtain a preprocessed data set;

[0010] Perform IC, TIECVD and DT feature extraction on the preprocessed data set to obtain a health feature information set, randomly divide the health feature information set, and determine the feature parameters of the IC, TIECVD and DT feature extraction processes through comparative experiments to obtain a training set and a test set;

[0011] Building a lithium battery health state prediction model based on the LSTM framework, inputting the training set into the lithium battery health state prediction model for training to obtain the trained lithium battery health state prediction model, and testing and verifying the trained lithium battery health state prediction model through the test set to obtain the trained lithium battery health state prediction model;

[0012] The health state of the lithium battery to be tested is estimated according to the trained lithium battery health state prediction model.

[0013] Further, in one embodiment of the present invention, the step of analyzing and processing the charge-discharge cycle data set to obtain a preprocessed data set specifically includes:

[0014] Performing data cleaning on the charge-discharge cycle data set to obtain the cleaned charge-discharge cycle data set;

[0015] The cleaned charge-discharge cycle data set is smoothed to obtain the pre-processed data set.

[0016] Further, in one embodiment of the present invention, the step of obtaining audio sample data, extracting IC, TIECVD and DT features from the preprocessed data set to obtain a health feature information set specifically includes:

[0017] Calculating according to the current-time curve in the preprocessed data set to obtain an electric quantity-time curve;

[0018] Calculating based on the charge-time curve and the voltage-time curve in the preprocessed data set to obtain an IC curve;

[0019] Performing MASF filtering and smoothing processing on the IC curve to obtain IC curve characteristics;

[0020] According to the charging time and terminal voltage in the preprocessed data set, a charging time curve in an equal voltage range is obtained;

[0021] Performing equidistant sampling on the charging time curve within the equal voltage range to obtain a TIECVD feature vector;

[0022] Performing SG filtering and smoothing processing on the DT curve in the preprocessed data set to obtain the smoothed DT curve;

[0023] Performing feature extraction on the smoothed DT curve to obtain a DT feature vector;

[0024] The health feature information set is generated according to the IC curve feature, the TIECVD feature vector and the DT feature vector.

[0025] Furthermore, in one embodiment of the present invention, the step of constructing a lithium battery health status prediction model based on the LSTM framework specifically includes:

[0026] Construct a forget gate based on the sigmoid neural network layer and multiplication operation structure;

[0027] Construct input gate based on sigmoid neural network layer, tanh neural network layer, multiplication operation structure and addition operation structure;

[0028] Construct output gate based on sigmoid neural network layer, tanh neural network layer and addition operation structure;

[0029] Based on the LSTM framework, the lithium battery health status prediction model is constructed according to the forget gate, the input gate and the output gate.

[0030] Furthermore, in one embodiment of the present invention, the calculation formula of the lithium battery health status prediction model is as follows:

[0031]

[0032] Among them, sigmoid(x) means to perform sigmoid function operation on x, tanh(x) means to perform tanh function operation on x, and x t Indicates the input at the current moment, h t-1 Represents the output signal at the previous moment, h t Represents the output signal at the current moment, C t-1 Indicates the unit state at the previous moment, Indicates the backup information used to update the unit status, C t represents the unit state at the current moment, f t 、i t , o t Respectively represent the output of the forget gate, input gate, and output gate at the current moment, W f , Wi , W o Respectively represent the weight parameters of the forget gate, input gate and output gate, b f 、b i and b o Respectively represent the bias parameters of the forget gate, input gate, and output gate, W C represents the weight parameter used to update the unit state, b C Represents the bias parameter used to update the cell state.

[0033] Further, in one embodiment of the present invention, the step of inputting the training set into the lithium battery health state prediction model for training to obtain the trained lithium battery health state prediction model specifically includes:

[0034] Inputting the training set into the lithium battery health status prediction model to obtain a model output value;

[0035] Calculating the model output value and the true value of the training set using a preset loss function to obtain a loss value;

[0036] Based on the loss value, the model parameters of the lithium battery health status prediction model are optimized by the BP algorithm and the gradient descent method, and the step of inputting the training set into the lithium battery health status prediction model is returned until a preset number of training times is reached, and the training is stopped to obtain the trained lithium battery health status prediction model.

[0037] Further, in one embodiment of the present invention, the step of estimating the health state of the lithium battery to be tested according to the trained lithium battery health state prediction model specifically includes:

[0038] Obtain real-time charging and discharging information of the lithium battery to be tested;

[0039] The real-time charge and discharge information is input into the trained lithium battery health status prediction model to obtain the health status estimation result of the lithium battery to be tested.

[0040] On the other hand, an embodiment of the present invention provides a lithium battery health status estimation system, including:

[0041] A data set acquisition module is used to acquire a preset charge-discharge cycle data set, and analyze and process the charge-discharge cycle data set to obtain a preprocessed data set;

[0042] A feature extraction module is used to extract IC, TIECVD and DT features from the preprocessed data set to obtain a health feature information set, randomly divide the health feature information set, and determine the feature parameters of the IC, TIECVD and DT feature extraction processes through comparative experiments to obtain a training set and a test set;

[0043] A model training module is used to build a lithium battery health state prediction model based on the LSTM framework, input the training set into the lithium battery health state prediction model for training, obtain the trained lithium battery health state prediction model, and test and verify the trained lithium battery health state prediction model through the test set to obtain the trained lithium battery health state prediction model;

[0044] The health state estimation module is used to estimate the health state of the lithium battery to be tested according to the trained lithium battery health state prediction model.

[0045] On the other hand, an embodiment of the present invention provides a lithium battery health status estimation device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the lithium battery health status estimation method as described above is realized.

[0046] On the other hand, an embodiment of the present invention also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage, and the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the lithium battery health status estimation method as described above.

[0047] The advantages and beneficial effects of the present invention will be partly given in the following description, partly become apparent from the following description, or be understood through the practice of the present invention:

[0048] The embodiment of the present invention obtains a preset charge and discharge cycle data set, analyzes and processes the charge and discharge cycle data set to obtain a preprocessed data set, then performs IC, TIECVD and DT feature extraction on the preprocessed data set to obtain a health feature information set, randomly divides the health feature information set, and determines the feature parameters of the IC, TIECVD and DT feature extraction processes through comparative experiments to obtain a training set and a test set, then builds a lithium battery health status prediction model based on an LSTM framework, inputs the training set into the lithium battery health status prediction model for training, obtains a trained lithium battery health status prediction model, and tests and verifies the trained lithium battery health status prediction model through a test set to obtain a trained lithium battery health status prediction model, and then the health status of the lithium battery to be tested can be estimated according to the trained lithium battery health status prediction model. The embodiment of the present invention obtains a charge and discharge cycle data set and performs pre-analysis processing, extracts features from an IC curve, an equal voltage range charging time curve, and a DT curve to obtain multi-dimensional health feature information, divides the extracted health feature information into time series to obtain health feature information with time series information, fuses the health feature information, and inputs it into a lithium battery health status prediction model built based on an LSTM framework for model training and testing, thereby obtaining a trained lithium battery health status prediction model. The lithium battery health status prediction model fully considers the relationship between temperature and the health status of the lithium battery based on DT feature extraction, thereby improving the accuracy of lithium battery health status estimation, thereby improving the safety and reliability of lithium battery operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solution in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solution of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0050] Figure 1 A flowchart of the steps of a lithium battery health status estimation method provided by an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of an IC curve provided by an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of a charging time curve in an equal voltage range provided by an embodiment of the present invention;

[0053] Figure 4 A schematic diagram of a DT curve provided by an embodiment of the present invention;

[0054] Figure 5Another schematic diagram of a DT curve provided by an embodiment of the present invention;

[0055] Figure 6 A schematic diagram of the structure of a lithium battery health status prediction model provided by an embodiment of the present invention;

[0056] Figure 7 A schematic diagram of test results of a lithium battery health status prediction model provided by an embodiment of the present invention;

[0057] Figure 8 A schematic diagram of the structure of a lithium battery health status estimation system provided by an embodiment of the present invention;

[0058] Fig. 9 A schematic diagram of the hardware structure of a lithium battery health status estimation device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] Embodiments of the present invention are described in detail below, and examples of the embodiments 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 only used to explain the present application, and cannot be understood as limitations on the present application. It should be noted that, although the functional module division is performed in the system schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order from the module division in the system schematic diagram or the flow chart. For the step numbers in the following embodiments, they are only set for the convenience of explanation, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0060] In the description of the present invention, the meaning of "a plurality" is two or more. If there is a description of the first or the second, it is only for the purpose of distinguishing the technical features, and it cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those commonly understood by technicians in the technical field of this application. The terms used in this document are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0061] The lithium battery health status estimation method provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a set-top box, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the lithium battery health status estimation method, etc., but is not limited to the above forms.

[0062] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0063] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with the relevant laws, regulations, and standards of the relevant countries and regions. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0064] like Figure 1 FIG. 1 is a flow chart showing a method for estimating the health status of a lithium battery according to an embodiment of the present invention. Figure 1 The embodiment of the present invention provides a method for estimating the health status of a lithium battery, which specifically includes the following steps:

[0065] S101 , obtaining a preset charge-discharge cycle data set, and analyzing and processing the charge-discharge cycle data set to obtain a preprocessed data set.

[0066] Specifically, time variable parameters are obtained, including the terminal voltage at each moment in the lithium battery charging cycle, the charging current at each moment in the lithium battery charging cycle, the temperature corresponding to each moment in the lithium battery charging cycle, and the time corresponding to each moment in the lithium battery charging cycle. The obtained time variable parameters are used as data of the corresponding charging cycle, and the data of multiple consecutive charging cycles are spliced ​​together as original data to obtain a charge and discharge cycle data set.

[0067] As an optional implementation, the step of analyzing and processing the charge and discharge cycle data set to obtain a preprocessed data set specifically includes:

[0068] S1011, performing data cleaning on the charge-discharge cycle data set to obtain a cleaned charge-discharge cycle data set;

[0069] S1012, smoothing the cleaned charge and discharge cycle data set to obtain a preprocessed data set.

[0070] S102, perform IC, TIECVD and DT feature extraction on the preprocessed data set to obtain a health feature information set, randomly divide the health feature information set, and determine the feature parameters of the IC, TIECVD and DT feature extraction process through comparative experiments to obtain a training set and a test set.

[0071] Specifically, for lithium-ion battery SOH prediction, it is a key step to study how to effectively extract HFs by analyzing the characteristics of battery measurement data. Observe the curves of terminal voltage change over time under different charge and discharge cycles, the curves of charging current change over time under different charge and discharge cycles, and the curves of temperature change over time under different charge and discharge cycles, and analyze and select the IC, TIECVD and DT features extracted after some conversion of charging time, current, voltage and temperature as the features for model training.

[0072] As an optional implementation, the step of extracting IC, TIECVD and DT features from the preprocessed data set to obtain a health feature information set specifically includes:

[0073] S1021, calculating according to the current-time curve in the preprocessing data set to obtain an electric quantity-time curve;

[0074] S1022, performing calculation based on the charge-time curve and the voltage-time curve in the preprocessed data set to obtain an IC curve;

[0075] S1023, performing MASF filtering and smoothing processing on the IC curve to obtain IC curve characteristics;

[0076] S1024, obtaining a charging time curve in an equal voltage range according to the charging time and terminal voltage in the preprocessing data set;

[0077] S1025, performing equidistant sampling on charging time curves within equal voltage ranges to obtain a TIECVD feature vector;

[0078] S1026, performing SG filtering and smoothing processing on the DT curve in the preprocessing data set to obtain a smoothed DT curve;

[0079] S1027, extracting features from the smoothed DT curve to obtain a DT feature vector;

[0080] S1028. Generate a health feature information set according to the IC curve features, the TIECVD feature vector, and the DT feature vector.

[0081] Specifically, the processes of IC, TIECVD and DT feature extraction are described below respectively.

[0082] 1) IC feature extraction:

[0083] The IC curve can be obtained by taking the derivative of the capacity-voltage curve with respect to voltage. The IC curve reflects the relationship between the charge and discharge capacity and the voltage. Figure 2 FIG. 1 is a schematic diagram of an IC curve provided by an embodiment of the present invention, wherein Figure 2 It can be seen that the IC curves under different charge and discharge cycles have obvious differences in the area near the peak. The features extracted based on the IC curve can reflect the relationship between voltage and battery capacity. The calculation expression of the IC curve is as follows:

[0084]

[0085] In the formula, Q k is the total amount of electricity charged to time k, η is the charging efficiency, I is the charging current, t is the charging time, and V is the terminal voltage. From formula (2), we can see that when the sampling interval is small, the denominator V k+1 -V k If the value of is small, the calculation result will fluctuate greatly. In practical applications, due to the noise of the measurement data sensor and the instability of the equipment, the measurement voltage will fluctuate, which will lead to V k+1 -V kAbnormal values, such as zero or negative values, will cause abnormal calculation results and are detrimental to subsequent data processing. Smoothing filtering algorithms can be used to solve this problem, such as Kalman filtering, Gaussian filtering, etc. In order to simplify the calculation, Moving Average Smoothing Filter (MASF) is selected. The formula is as follows:

[0086]

[0087] Where x is the original input, y is the processed output, and M is the smoothing window size. Similar to the mean filter algorithm, MASF essentially replaces the value of the element with the mean of the M neighboring elements of the current position element.

[0088] The specific steps of using MASF filtering to smooth the IC curve and extract IC features are as follows:

[0089] ① Calculate the charged quantity Q at all times according to the current curve;

[0090] ② Select the window size M1, and use MASF to smooth the voltage V and the charged power Q respectively;

[0091] ③ Calculate dQ / dV, select △V=0.1mV as the unit of voltage V, then interpolate voltage V, that is, expand the data of voltage V, and then perform the same operation on dQ / dV. This process ensures that dQ / dV can take the corresponding value for any voltage point (the smallest unit should be no less than 0.1mV, such as 3.8625V);

[0092] ④ Select the window size M2, and use MASF to smooth the dQ / dV obtained in step ③. The dQ / dV-V curve at this time is the smoothed IC curve;

[0093] ⑤ Select the voltage range V1~V2, the number of sampling points N, and perform equidistant sampling on the IC curve obtained in step ④ to obtain the IC feature vector: [IC1, IC2, ..., IC N ].

[0094] 2) TIECVD feature extraction:

[0095] In the constant current charging stage, the relationship between the charging time and terminal voltage required for the battery terminal voltage to reach the cut-off voltage of 4.2V under different charge and discharge cycles (i.e., the charging time curve in the equal voltage range) is as follows: Figure 3As shown, it can be seen that in the constant current charging stage, as the number of charge and discharge cycles increases, the terminal voltage of the battery rises faster and the time to reach the cut-off voltage gradually shortens. This shows that the time it takes to fully charge the battery is gradually shortening, which means that the battery capacity is gradually decaying. Therefore, the TIECVD feature extracted based on the charging time curve in the equal voltage range can effectively characterize the capacity degradation characteristics of the battery.

[0096] The specific steps to extract TIECVD features from the equal voltage range charging time curve are as follows:

[0097] ① Based on the battery measurement data, the charging time t and the terminal voltage V are used to find the time t when the terminal voltage first reaches the cut-off voltage of 4.2V. e , then before the cut-off voltage t i The terminal voltage V i The corresponding charging time in the equal voltage range is t e -t i ;

[0098] ② According to the calculation method of step ①, the equal voltage range charging time corresponding to the terminal voltage at any time before the cut-off voltage is calculated to obtain the equal voltage range charging time curve;

[0099] ③ Select the voltage range V1~V2, the number of sampling points N, and perform equidistant sampling on the charging time curve of the equal voltage range obtained in step ② to obtain the TIECVD feature vector: [TIECVD1, TIECVD2, ..., TIECVD N ].

[0100] 3) DT feature extraction:

[0101] The formula of DT physical model is as follows:

[0102]

[0103] Where T is temperature, I is current, R i is the internal resistance of the battery, F is the Faraday constant, n is the number of exchanged electrons, △S is the entropy change, m c and A are the mass area and surface area of ​​the battery under test, respectively, c p and h are heat capacity and equivalent heat transfer coefficient respectively, T amb is the ambient temperature. 2 R i represents the Joule heat caused by the internal resistance of the battery, represents the reversible reaction heat caused by the entropy change at the electrode during lithium ion insertion or extraction, hA(TT amb ) represents the heat exchange between the battery and the environment. During constant current charging, only R iand △S will change, and other parameters remain constant. At this time, the change of DT is only related to R i Since the change of battery internal resistance and the change of battery internal entropy are closely related to battery aging, the DT curve has a great relationship with battery aging. Extracting features based on the DT curve can effectively characterize the battery aging characteristics.

[0104] Due to the limited resolution of the temperature sensor, direct calculation of DT is susceptible to measurement noise. To avoid this problem, the DT curve is approximately calculated using the following formula, where L is the sampling interval.

[0105]

[0106] There is a lot of noise in the DT curve of lithium batteries under different charge and discharge cycles when it is not processed. It is difficult to extract features from the DT curve with a lot of noise. Therefore, filtering algorithms such as Kalman filtering and Gaussian filtering can be used to smooth the temperature curve and DT curve at the same time.

[0107] In order to reduce the amount of calculation, this study uses the SG filtering algorithm. When the temperature curve has a lot of noise, it is necessary to smooth the temperature curve first, then use the smoothed temperature curve to calculate the DT curve, and then smooth the DT curve to get the final DT curve; when the temperature curve has less noise, it is only necessary to smooth the DT curve. The specific steps are as follows:

[0108] ① Use SG filter to smooth the original temperature curve and select the sampling interval L A , the DT curve is calculated using the smoothed temperature curve according to formula (5);

[0109] ② Use SG filtering to smooth the DT curve obtained in step ① to obtain a smoothed DT curve;

[0110] ③ Such as Figure 4 FIG. 1 is a schematic diagram of feature extraction of a DT curve provided by an embodiment of the present invention, that is, respectively extracting the amplitude H1 of the peak 1, the amplitude H2 of the trough 2, the amplitude H3 of the peak 2, the amplitude difference H4 between the peak 1 and the trough 2, the amplitude difference H5 between the peak 2 and the trough 2, the voltage value V A , the voltage value of valley 2 V B , the voltage value of peak 2 is V C , the voltage difference D1 between the peak 1 and the trough 2, and the voltage difference D2 between the peak 2 and the trough 2, such as Figure 5Another schematic diagram of feature extraction of DT curve provided by an embodiment of the present invention is shown, that is, the area A1 enclosed by the curve from trough 1 to peak 1, the horizontal line where the amplitude of trough 1 is located, and the vertical line where the voltage value of peak 1 is located, the area A2 enclosed by the curve from peak 1 to trough 2, the horizontal line where the amplitude of trough 2 is located, and the vertical line where the voltage value of peak 1 is located, and the area A3 enclosed by the curve from trough 2 to peak 2, the horizontal line where the amplitude of trough 2 is located, and the vertical line where the voltage value of peak 2 is located. The DT curve obtained in step ② is used Figure 3 and Figure 4 The feature extraction method shown in FIG. 1 selects multiple health features that are highly correlated with SOH from the smoothed DT curve to obtain a DT feature vector, such as [V A , V C , H4, D1, D2, A1, A2].

[0111] In some optional embodiments, after extracting the IC curve features, TIECVD feature vectors and DT feature vectors, in order to avoid the problem of weight tilt due to different feature units and orders of magnitude, it is necessary to perform dimensionless processing on the IC, TIECVD and DT features, such as normalization, standardization and unit scaling, so that the orders of magnitude of all features remain consistent. The calculation formulas for normalization and standardization are as follows:

[0112]

[0113] in:

[0114]

[0115] Since the dimensions of IC, TIECVD and DT feature vectors are inconsistent and there is no need to keep them consistent, a weight coefficient can be assigned to each feature vector and then vector concatenation can be performed. The weight coefficient can be obtained by manual assignment or by using algorithms such as β distribution. In order to simplify the algorithm, the embodiment of the present invention uses a manual assignment method to define the weight coefficient.

[0116] In some optional embodiments, this embodiment adopts the holdout method to randomly divide the data set into a training set and a test set in a ratio of 3:1, and then uses the training set to train the model. By evaluating the performance of the model on the test set, the effectiveness and generalization ability of the model can be judged, and necessary adjustments and improvements can be made.

[0117] In some optional embodiments, the embodiments of the present invention determine the parameters of the IC, TIECVD and DT feature extraction processes through comparative experiments on the data set. The specific process is as follows:

[0118] 1) Selection of the number of IC feature points:

[0119] The voltage range of the IC curve of the battery of the embodiment of the present invention in the region with large differences under different charge and discharge cycles is 3.5V to 4.1V, and this voltage range is selected as the interval for extracting IC features. Using the IC feature extraction method introduced in the previous steps, the voltage range is 3.5V to 4.2V, and the number of sampling points N is 5, 10, 15, and 20 respectively.

[0120] The embodiment of the present invention determines that the number N of sampling points of the IC feature used is 10.

[0121] 2) Selection of the number of TIECVD feature points:

[0122] The voltage range of the charging time curve of the battery in the embodiment of the present invention is 3.2V to 4.1V, and the voltage range in the region with large differences under different charge and discharge cycles is selected as the interval for extracting TIECVD features. The TIECVD feature extraction method introduced in the above steps is used, the voltage range is 3.2V to 4.1V, and the number of sampling points N is 5, 10, 15, and 20 respectively.

[0123] The embodiment of the present invention determines that the number of sampling points N of the TIECVD feature used is 10.

[0124] 3) DT feature selection:

[0125] In the DT feature extraction method, the features involved are V B 、V C 、V D , H1, H2, H3, H4, H5, D1, D2, A1, A2 and A3. In order to determine which features to use, the Pearson correlation analysis method can be used. The Pearson correlation coefficient is independent of the dimension or order of magnitude of the data and is usually used to measure the degree of linear correlation between two sets of data. The value of the Pearson correlation coefficient ranges from -1 to 1. The larger the absolute value of the correlation coefficient, the stronger the linear correlation between the two sets of data. For example, 1 indicates positive linear correlation, -1 indicates negative linear correlation, and 0 indicates linear irrelevance. The calculation formula is as follows:

[0126]

[0127] in:

[0128]

[0129] where r xy represents the Pearson correlation coefficient between vector x and vector y, n represents the length of vector x and vector y, x i and i Represents the value of the i-th element of vector x and vector y respectively, and Respectively represent the mean of vector x and vector y. In the analysis of DT features, vector x corresponds to a vector composed of a certain feature under different charge and discharge cycles, and vector y corresponds to a vector composed of SOH under different charge and discharge cycles.

[0130] The final feature selected in the embodiment of the present invention is V B 、V D , H1, H4, A2 and A3.

[0131] 4) Selection of the length of the time series

[0132] The feature matrix size of the input LSTM network is (samples, sequence_len, features_dim), where samples represents the number of samples, which represents the number of different charge and discharge cycles in the embodiment of the present invention, sequence_len represents the time series length of each sample, and features_dim represents the number of features.

[0133] For lithium batteries, only one feature vector of length features_dim can be extracted from each charge and discharge cycle (the feature vector X of the i-th charge and discharge cycle i =[X i，1 , X i,2 ,......,X i，features_dim ]), then the characteristic matrix of each battery is X = [X1, X2, ..., X C ], where C is the number of charge and discharge cycles. Therefore, it is necessary to divide the time series, that is, divide X into [X 1：sequence_len , X 2：sequence_len+1 ,......,X C-sequence_len+1：C ].

[0134] The time series lengths are 5, 10, 15, and 20 respectively, and the optimal feature parameters obtained previously are used to extract IC, TIECVD, and DT features as the input of the model for experiments.

[0135] The time series length used in the embodiment of the present invention is determined to be 10.

[0136] S103. Build a lithium battery health status prediction model based on the LSTM framework, input the training set into the lithium battery health status prediction model for training, obtain the trained lithium battery health status prediction model, and test and verify the trained lithium battery health status prediction model through the test set to obtain the trained lithium battery health status prediction model.

[0137] Specifically, since the lithium battery measurement data is related to the number of charge and discharge cycles and has information on the time scale, the extracted HFs also have information on the time scale, so the LSTM network can be used and the problems of gradient vanishing and gradient exploding when facing long-term memory can be avoided.

[0138] As an optional implementation, the step of building a lithium battery health status prediction model based on the LSTM framework specifically includes:

[0139] S1031. Construct a forget gate based on the sigmoid neural network layer and multiplication operation structure;

[0140] S1032, constructing an input gate based on a sigmoid neural network layer, a tanh neural network layer, a multiplication operation structure, and an addition operation structure;

[0141] S1033, constructing an output gate based on a sigmoid neural network layer, a tanh neural network layer, and an addition operation structure;

[0142] S1034. Based on the LSTM framework, a lithium battery health status prediction model is constructed according to the forget gate, input gate and output gate.

[0143] Specifically, the core of LSTM is the cell state, denoted by C t , which is used to save the current LSTM state information and pass it to the LSTM at the next moment. The current LSTM receives the cell state C from the previous moment t-1 , the input x received by the current LSTM t Together they generate the current LSTM cell state C t , and C t Passed to the LSTM at the next moment.

[0144] like Figure 6 The figure shows a schematic diagram of the structure of the lithium battery health state prediction model provided by an embodiment of the present invention. The lithium battery health state prediction model based on LSTM includes a forget gate, a memory gate and an output gate; wherein the forget gate is constructed by a sigmoid neural network layer and a bitwise multiplication structure to determine the cell state C t-1 What information in the memory is forgotten; the memory gate is constructed by the input gate, tanh neural network layer, bitwise multiplication structure and bitwise addition structure. The role of the memory gate is to determine the new input information x t and h t-1 Which information in will be retained? The output gate is constructed by the stgmoid neural network layer, the tanh neural network layer and the bitwise addition structure. The output gate transmits the cell state C after the forget gate and the memory gate at time t-1. t-1 , and the output signal h at time t-1 t-1and the input signal x at time t t Integrate them together as the output signal at the current moment. t and h t-1 After a sigmoid neural network layer, a value between 0 and 1 is output. t , C t After a tanh function, and at o t Multiply to get the output signal h t , while h t It is also passed to the next stage as the input signal at the next moment. Multiple LSTM networks can be stacked, and their output results are then passed through a fully connected layer FC to map the feature information learned by the LSTM network to the sample label space. The final output result is the predicted value of the remaining capacity of the lithium battery. In this embodiment, the number of LSTM network layers num_layers = 5, and the feature dimension hidden_size of the hidden layer = 50.

[0145] As an optional implementation, the calculation formula of the lithium battery health status prediction model is as follows:

[0146]

[0147] Among them, sigmoid(x) means to perform sigmoid function operation on x, tanh(x) means to perform tanh function operation on x, and x t Indicates the input at the current moment, h t-1 Represents the output signal at the previous moment, h t Represents the output signal at the current moment, C t-1 Indicates the unit state at the previous moment, Indicates the backup information used to update the unit status, C t represents the unit state at the current moment, f t 、i t , o t Respectively represent the output of the forget gate, input gate, and output gate at the current moment, W f , W i , W o Respectively represent the weight parameters of the forget gate, input gate and output gate, b f , b i and b o Respectively represent the bias parameters of the forget gate, input gate, and output gate, W C represents the weight parameter used to update the unit state, b C Represents the bias parameter used to update the cell state.

[0148] As an optional implementation, the step of inputting the training set into the lithium battery health state prediction model for training to obtain the trained lithium battery health state prediction model specifically includes:

[0149] S1035, inputting the training set into the lithium battery health status prediction model to obtain the model output value;

[0150] S1036, using a preset loss function to calculate the model output value and the true value of the training set to obtain a loss value;

[0151] S1037. Optimize the model parameters of the lithium battery health status prediction model through the BP algorithm and the gradient descent method based on the loss value, and return to the step of inputting the training set into the lithium battery health status prediction model until the preset number of training times is reached, stop the training, and obtain the trained lithium battery health status prediction model.

[0152] Specifically, the dimensionless method of order of magnitude scaling is used, and the weight coefficient of feature fusion is IC:TIECVD:DT=1:1:2. The voltage range of IC feature extraction is 3.8V~4.1V, the number of sampling points is 10, and the voltage range of TIECVD feature extraction is 3.4V~4.2V, and the number of sampling points is 10. The training set is input into the lithium battery health status prediction model, and the mean square error loss function is used to calculate the model output value and the true value of the training set to obtain the error value. The calculation formula is as follows:

[0153]

[0154] Where MSE stands for mean square error, Represents the model output value, y i represents the true value of the training set, and n is the number of samples.

[0155] Through the BP algorithm consisting of two processes, information forward propagation and error back propagation, the model parameters are optimized to minimize the error, and finally a trained lithium battery health status prediction model is obtained.

[0156] In some optional embodiments, after 5 training cycles, the test data set is input into the trained lithium battery health status prediction model for testing to obtain test results.

[0157] The embodiment of the present invention uses K-fold cross validation, i.e., taking the average error of K validation results to evaluate the prediction performance of the model. The root mean square error, mean square error, and mean absolute percentage error between the prediction result and the true value are calculated as the performance index of the current model. The smaller the error, the better the model performance. The calculation formula of the performance index is as follows:

[0158] Root Mean Square Error:

[0159] Mean absolute error:

[0160] Mean absolute percentage error:

[0161] in, is the predicted SOH value, y i is the actual value of SOH, and n is the number of charge and discharge cycles.

[0162] The present invention uses a dimensionless method of order of magnitude scaling, and the weight coefficient of feature fusion is IC:TIECVD:DT=1:1:2. The voltage range for extracting IC features is 3.8V~4.1V, the number of sampling points is 10, and the voltage range for extracting TIECVD features is 3.4V~4.2V, the number of sampling points is 10. The test results of the final trained model on the test set are as follows: Figure 7 As shown, the errors of RMSE, MAE, and MAPE using only IC and TIECVD features are 0.748%, 0.574%, and 0.692%, respectively, and the errors of using IC, TIECVD, and DT features are 0.623%, 0.499%, and 0.599%, respectively.

[0163] S104, estimating the health status of the lithium battery to be tested according to the trained lithium battery health status prediction model.

[0164] Specifically, the lithium battery health status prediction model trained based on the above steps can be used to estimate the health status of the lithium battery to be tested, and the real-time charge and discharge information of the lithium battery to be tested is input into the lithium battery health status prediction model to obtain the health status estimation result output by the model.

[0165] As an optional implementation, the step of estimating the health state of the lithium battery to be tested according to the trained lithium battery health state prediction model specifically includes:

[0166] S1041, obtaining real-time charge and discharge information of the lithium battery to be tested;

[0167] S1042: input the real-time charge and discharge information into the trained lithium battery health status prediction model to obtain the health status estimation result of the lithium battery to be tested.

[0168] The above is a description of the method steps of the embodiment of the present invention. It can be understood that the embodiment of the present invention obtains a charge and discharge cycle data set and performs pre-analysis processing, extracts features from the IC curve, the equal voltage range charging time curve, and the DT curve to obtain multi-dimensional health feature information, divides the extracted health feature information into time series to obtain health feature information with time series information, and fuses the health feature information and inputs it into a lithium battery health status prediction model built based on the LSTM framework for model training and testing, thereby obtaining a trained lithium battery health status prediction model, which fully considers the relationship between temperature and lithium battery health status based on DT feature extraction, improves the accuracy of lithium battery health status estimation, and thus improves the safety and reliability of lithium battery operation.

[0169] like Figure 8 FIG. 1 is a schematic diagram of a lithium battery health status estimation system according to an embodiment of the present invention. Figure 8 The embodiment of the present invention provides a lithium battery health status estimation system, comprising:

[0170] A data set acquisition module is used to acquire a preset charge-discharge cycle data set, and analyze and process the charge-discharge cycle data set to obtain a preprocessed data set;

[0171] The feature extraction module is used to extract IC, TIECVD and DT features from the preprocessed data set to obtain a health feature information set, randomly divide the health feature information set, and determine the feature parameters of the IC, TIECVD and DT feature extraction process through comparative experiments to obtain a training set and a test set;

[0172] The model training module is used to build a lithium battery health state prediction model based on the LSTM framework, input the training set into the lithium battery health state prediction model for training, obtain the trained lithium battery health state prediction model, and test and verify the trained lithium battery health state prediction model through the test set to obtain the trained lithium battery health state prediction model;

[0173] The health state estimation module is used to estimate the health state of the lithium battery to be tested based on the trained lithium battery health state prediction model.

[0174] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0175] The embodiment of the present invention also provides a lithium battery health state estimation device, which includes: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program is executed by the processor to realize the above-mentioned lithium battery health state estimation method. The lithium battery health state estimation device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0176] like Fig. 9 FIG. 1 is a schematic diagram of the hardware structure of a lithium battery health status estimation device provided by an embodiment of the present invention, referring to FIG. Fig. 9 The embodiment of the present invention provides a lithium battery health status estimation device, comprising:

[0177] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention;

[0178] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the lithium battery health status estimation method of the embodiment of the present invention;

[0179] Input / output interface 903, used to implement information input and output;

[0180] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0181] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0182] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0183] An embodiment of the present invention also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned lithium battery health status estimation method.

[0184] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0185] The embodiment of the present invention also discloses a computer program product or a computer program, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 1 The method shown.

[0186] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0187] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified to the contrary, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0188] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0189] 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 conjunction with such instruction execution systems, devices or apparatuses. For the purposes 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 conjunction with such instruction execution systems, devices or apparatuses.

[0190] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the above-mentioned program is printed, since the above-mentioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in other suitable ways as necessary, and then stored in a computer memory.

[0191] 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-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, 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.

[0192] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / 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 representation of the above terms does 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 more embodiments or examples in a suitable manner.

[0193] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0194] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for estimating the health status of a lithium battery, characterized in that: The following steps are involved: Acquire a preset charge-discharge cycle data set, and analyze and process the charge-discharge cycle data set to obtain a preprocessed data set; Perform IC, TIECVD and DT feature extraction on the preprocessed data set to obtain a health feature information set, randomly divide the health feature information set, and determine the feature parameters of the IC, TIECVD and DT feature extraction processes through comparative experiments to obtain a training set and a test set; Building a lithium battery health state prediction model based on the LSTM framework, inputting the training set into the lithium battery health state prediction model for training to obtain the trained lithium battery health state prediction model, and testing and verifying the trained lithium battery health state prediction model through the test set to obtain the trained lithium battery health state prediction model; The health state of the lithium battery to be tested is estimated according to the trained lithium battery health state prediction model.

2. A lithium battery health status estimation method according to claim 1, characterized in that: The step of analyzing and processing the charge-discharge cycle data set to obtain a preprocessed data set specifically includes: Performing data cleaning on the charge-discharge cycle data set to obtain the cleaned charge-discharge cycle data set; The cleaned charge-discharge cycle data set is smoothed to obtain the pre-processed data set.

3. A lithium battery health status estimation method according to claim 1, characterized in that: The step of extracting IC, TIECVD and DT features from the preprocessed data set to obtain a health feature information set specifically includes: Calculating according to the current-time curve in the preprocessed data set to obtain an electric quantity-time curve; Calculating based on the charge-time curve and the voltage-time curve in the preprocessed data set to obtain an IC curve; Performing MASF filtering and smoothing processing on the IC curve to obtain IC curve characteristics; According to the charging time and terminal voltage in the preprocessed data set, a charging time curve in an equal voltage range is obtained; Performing equidistant sampling on the charging time curve within the equal voltage range to obtain a TIECVD feature vector; Performing SG filtering and smoothing processing on the DT curve in the preprocessed data set to obtain the smoothed DT curve; Performing feature extraction on the smoothed DT curve to obtain a DT feature vector; The health feature information set is generated according to the IC curve feature, the TIECVD feature vector and the DT feature vector.

4. A lithium battery health status estimation method according to claim 1, characterized in that: The step of building a lithium battery health status prediction model based on the LSTM framework specifically includes: Construct a forget gate based on the sigmoid neural network layer and multiplication operation structure; Construct input gate based on sigmoid neural network layer, tanh neural network layer, multiplication operation structure and addition operation structure; Construct output gate based on sigmoid neural network layer, tanh neural network layer and addition operation structure; Based on the LSTM framework, the lithium battery health status prediction model is constructed according to the forget gate, the input gate and the output gate.

5. A lithium battery health status estimation method according to claim 4, characterized in that: The calculation formula of the lithium battery health status prediction model is as follows: Among them, sigmoid(x) means to perform sigmoid function operation on x, tanh(x) means to perform tanh function operation on x, and x t Indicates the input at the current moment, h t-1 Represents the output signal at the previous moment, h t Represents the output signal at the current moment, C t-1 Indicates the unit state at the previous moment, Indicates the backup information used to update the unit status, C t represents the unit state at the current moment, f t 、i t , o t Respectively represent the output of the forget gate, input gate, and output gate at the current moment, W f , W i , W o Respectively represent the weight parameters of the forget gate, input gate and output gate, b f 、b i and b o Respectively represent the bias parameters of the forget gate, input gate, and output gate, W C represents the weight parameter used to update the unit state, b C Represents the bias parameter used to update the cell state.

6. A lithium battery health status estimation method according to claim 1, characterized in that: The step of inputting the training set into the lithium battery health state prediction model for training to obtain the trained lithium battery health state prediction model specifically includes: Inputting the training set into the lithium battery health status prediction model to obtain a model output value; Calculating the model output value and the true value of the training set using a preset loss function to obtain a loss value; Based on the loss value, the model parameters of the lithium battery health status prediction model are optimized by the BP algorithm and the gradient descent method, and the step of inputting the training set into the lithium battery health status prediction model is returned until a preset number of training times is reached, and the training is stopped to obtain the trained lithium battery health status prediction model.

7. A lithium battery health status estimation method according to any one of claims 1 to 6, characterized in that: The step of estimating the health state of the lithium battery to be tested according to the trained lithium battery health state prediction model specifically includes: Obtain real-time charging and discharging information of the lithium battery to be tested; The real-time charge and discharge information is input into the trained lithium battery health status prediction model to obtain the health status estimation result of the lithium battery to be tested.

8. A lithium battery health status estimation system, characterized in that: include: A data set acquisition module is used to acquire a preset charge-discharge cycle data set, and analyze and process the charge-discharge cycle data set to obtain a preprocessed data set; A feature extraction module is used to extract IC, TIECVD and DT features from the preprocessed data set to obtain a health feature information set, randomly divide the health feature information set, and determine the feature parameters of the IC, TIECVD and DT feature extraction processes through comparative experiments to obtain a training set and a test set; A model training module is used to build a lithium battery health state prediction model based on the LSTM framework, input the training set into the lithium battery health state prediction model for training, obtain the trained lithium battery health state prediction model, and test and verify the trained lithium battery health state prediction model through the test set to obtain the trained lithium battery health state prediction model; The health state estimation module is used to estimate the health state of the lithium battery to be tested according to the trained lithium battery health state prediction model.

9. A lithium battery health status estimation device, characterized in that: The lithium battery health status estimation device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the lithium battery health status estimation method as described in any one of claims 1 to 7 are realized.

10. A storage medium, the storage medium being a computer-readable storage medium, used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the lithium battery health status estimation method as described in any one of claims 1 to 7.

Images