Battery health state estimation method based on semi-supervised regression and cooperative training

Abstract

The invention relates to a battery health state estimation method based on semi-supervised regression and cooperative training, and belongs to the technical field of batteries. The method comprises the following steps: S1, collecting operation data of the electric vehicle, and establishing a battery operation database; s2, calculating a battery health state label by adopting an ampere integral formula and an open-circuit voltage correction strategy; s3, extracting a health index set highly related to the state of health of the battery based on the Pearson correlation coefficient and the gray correlation gradient index; s4, according to the selected health index set, utilizing semi-supervised cooperative training to estimate a pseudo label of unmarked data so as to enhance a training data set, and constructing a battery health state estimation model based on a multi-layer perceptron; and S5, taking the test data from which the health index set is extracted as input of a battery health state estimation model to obtain an estimated battery health state. According to the method, the battery health state estimation precision under the condition of limited mark data is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries and relates to a battery health state estimation method based on semi-supervised regression and collaborative training. Background Art

[0002] Methods for battery health state estimation can generally be divided into two categories: model-based and data-driven. Model-based methods describe battery aging by building mathematical models, which usually include equivalent circuit models and electrochemical models. Equivalent circuit model-based methods estimate battery health state online by using advanced filtering algorithms, such as the least squares method. In the battery health state estimation model based on the electrochemical model, the battery health state can be directly calculated by identifying the electrochemical parameters related to battery aging. Data-driven methods have received widespread attention in recent years because they rely on sufficient data to achieve high-precision estimation. Data-driven methods usually regard battery health state estimation as a supervised regression problem, using advanced machine learning algorithms to build models and learn the nonlinear relationship between input variables and battery health state without prior knowledge of battery parameters. Typically, the input variables are statistical features of directly measured parameters that are used to reflect the health state of the battery. Lightweight and deep machine learning models have become popular in battery health state estimation. However, the successful application of these methods relies on the availability of a large number of battery health state labels, which is usually not available for field data. In contrast to supervised learning methods that rely entirely on labeled data for model training, semi-supervised learning methods utilize both labeled and unlabeled data by assigning pseudo labels to unlabeled samples. Current research is mainly developed and evaluated using laboratory data under controlled conditions, lacking the implementation of battery health status estimation for real-world scenarios.

[0003] To address the above issues, there is currently no effective semi-supervised learning and collaborative training method based on field data to efficiently and accurately estimate the health status of battery packs. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a battery health state estimation method based on semi-supervised regression and collaborative training, which can accurately estimate the battery health state based on field data.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A battery health state estimation method based on semi-supervised regression and collaborative training, the method comprising the following steps:

[0007] S1: Collect the operation data of electric vehicles, including the operation data of each cell in the battery pack, and establish a battery operation database;

[0008] S2: Analyze the charging data of electric vehicles, select the charging segments that meet the needs, and calculate the battery health status label using the ampere integral formula and open circuit voltage correction strategy;

[0009] S3: Extract the health indicator set highly correlated with the battery health status based on the Pearson correlation coefficient PCC and the grey relational gradient GRG indicator;

[0010] S4: Based on the selected set of health indicators, semi-supervised co-training is used to estimate pseudo labels of unlabeled data to enhance the training dataset, and a battery health state estimation model is constructed based on a multi-layer perceptron (MLP).

[0011] S5: The test data after extracting the health indicator set is used as the input of the battery health state estimation model, and all regressors are integrated to obtain the estimated battery health state.

[0012] Further, the S1 is specifically:

[0013] S11: Collect the operating data of a battery pack of an electric vehicle, including charging and discharging current, voltage, temperature, time and power parameters;

[0014] S12: Collecting the cell operation data in the battery pack, including the cell voltages of all cells and the temperature data sets of each temperature probe placed near the cells;

[0015] S13: Establish a battery pack operation database for a certain vehicle based on the collected operation data of the battery cells and battery packs.

[0016] Further, the S2 is specifically:

[0017] S21: analyzing charging data of the electric vehicle and screening charging segments that meet the rules of using the open circuit voltage correction strategy;

[0018] S22: Collecting an open circuit voltage-state of charge lookup table provided by a battery manufacturer, adopting an open circuit voltage correction strategy, and correcting the initial battery state of charge according to the minimum battery voltage at the initial time;

[0019] S23: According to the historical operation data of the battery pack, the maximum available capacity of the battery pack is calculated using the ampere integral formula to obtain a battery health status label.

[0020] Further, the S3 is specifically:

[0021] S31: performing a charging curve analysis based on the maximum voltage of the battery on the charging segment of the battery pack to determine a voltage interval extracted from the health indicator set;

[0022] S32: Calculate statistical characteristics of charging data according to the selected voltage range, including average value and standard deviation value, and extract a health indicator set;

[0023] S33: A set of health indicators that are highly correlated with the battery health status are screened out through correlation analysis based on the Pearson correlation coefficient PCC and the grey correlation gradient GRG.

[0024] Further, the health indicator set includes current, battery pack voltage, state of charge, maximum and minimum battery voltage, maximum and minimum battery probe temperature, battery voltage difference, probe temperature and battery capacity calculated by the ampere integral formula;

[0025] In order to ensure the diversity of health indicator combinations, two health indicator sets are selected to simultaneously establish a battery health state estimation model to achieve battery health state estimation.

[0026] Further, the S4 is specifically:

[0027] S41: establishing independent pseudo battery health state estimation models according to the two selected health indicator sets;

[0028] S42: Based on a multi-layer perceptron, a set of estimated pseudo battery health states are respectively integrated into an enhanced training set of another set of battery health state estimation models, and an independent battery health state estimation model is established by using a semi-supervised regression and collaborative training method;

[0029] Furthermore, the multilayer perceptron is composed of a plurality of fully connected layers, wherein each layer is composed of a plurality of units, and the units are interconnected with all units of the previous layer.

[0030] Further, the S5 is specifically:

[0031] S51: input the test data after the health indicator set is extracted into the trained battery health state estimation model to estimate the battery health state;

[0032] S52: The average value estimated by the battery health state estimation model using the enhanced training set is considered as the final estimated battery health state.

[0033] The beneficial effects of the present invention are:

[0034] (1) The semi-supervised regression and collaborative training methods can effectively utilize labeled and unlabeled data to improve the accuracy and robustness of battery health status estimation in scenarios with limited labeled data.

[0035] (2) The method of combining the ampere-hour integral formula and the open circuit voltage correction strategy in the present invention can reduce the battery health status calculation error caused by inaccurate charge state.

[0036] (3) The present invention can overcome the obstacles of on-site battery health status data sets caused by complex conditions of electric vehicles and high data collection costs, and accurately estimate the battery health status, thereby ensuring timely maintenance and safe operation of the battery pack.

[0037] (4) The present invention retains the diversity of health indicators and provides multi-dimensional correlation criteria to determine the correlation between the battery health status and multiple health indicators.

[0038] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0040] Figure 1 A flow chart of the overall method of the present invention;

[0041] Figure 2 A schematic diagram of the analysis of the correlation between the health indicator set and the battery health status in the embodiment; Figure 2 (a) is a schematic diagram of the correlation analysis results based on the Pearson correlation coefficient PCC; Figure 2 (b) is a schematic diagram of the correlation analysis results based on grey relational gradient GRG;

[0042] Figure 3 It is the overall framework diagram of the embodiment method;

[0043] Figure 4 Create a flow chart for the implementation model. DETAILED DESCRIPTION

[0044] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0045] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0046] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0047] See also Figure 1 ,A battery health state estimation method based on semi-supervised regression and ,co-training can be divided into the following steps:

[0048] S1: Collect the operation data of electric vehicles, including the operation data of each cell in the battery pack, and establish a battery operation database;

[0049] S2: Analyze the charging data of electric vehicles, select the charging segments that meet the needs, and calculate the battery health status label using the ampere integral formula and open circuit voltage correction strategy;

[0050] S3: Extract the health indicator set highly correlated with the battery health status based on the Pearson correlation coefficient (PCC) and the grey relational gradient (GRG) indicator respectively;

[0051] S4: Based on the selected set of health indicators, semi-supervised co-training is used to estimate pseudo labels of unlabeled data to enhance the training dataset, and a battery health state estimation model is constructed based on a multi-layer perceptron (MLP);

[0052] S5: The test data after extracting the health indicator set is used as the input of the battery health state estimation model, and all regressors are integrated to obtain the estimated battery health state.

[0053] As an optional embodiment, the complete technical roadmap of this solution is as follows: Figure 3 shown.

[0054] As an optional embodiment, the above S1 specifically includes S11-S13:

[0055] S11: Collect the operating data of a battery pack of an electric vehicle, including charging and discharging current, voltage, temperature, time and power parameters;

[0056] S12: Collecting the cell operation data in the battery pack, including the cell voltages of all cells and the temperature data sets of each temperature probe placed near the cells;

[0057] S13: Establish a battery pack operation database for a certain vehicle based on the collected operation data of the battery cells and battery packs.

[0058] As an optional embodiment, the above S2 specifically includes S21-S23:

[0059] S21: analyzing charging data of the electric vehicle and screening charging segments that meet the rules of using the open circuit voltage correction strategy;

[0060] S22: Collecting an open circuit voltage-state of charge lookup table provided by a battery manufacturer, adopting an open circuit voltage correction strategy, and correcting the initial battery state of charge according to the minimum battery voltage at the initial time;

[0061] S23: According to the historical operation data of the battery pack, the maximum available capacity of the battery pack is calculated using the ampere integral formula to obtain a battery health status label.

[0062] As an optional embodiment, an open circuit voltage-state of charge lookup table provided by a battery manufacturer is collected, and an open circuit voltage correction strategy is adopted to correct the initial battery state of charge according to the minimum battery voltage at the initial time, specifically including:

[0063] First, a charging process with a 2-hour standstill is selected to eliminate the influence of polarization effect and obtain an accurate open circuit voltage value; then, the terminal state of charge is set to 100% to ensure that the battery is fully charged and the terminal state of charge is accurate; in addition, the initial state of charge is set to less than 30% to mitigate the influence of voltage fluctuations on the voltage platform; finally, the open circuit voltage-state of charge lookup table is used to correct the initial state of charge according to the minimum battery voltage at the initial time.

[0064] The battery health status tag is obtained by:

[0065] The health status of a battery pack is defined as the ratio of the current maximum available capacity of the battery pack to its rated capacity, as shown in the following formula:

[0066]

[0067] The charging process using field data is calculated according to the Ampere integral formula Get the battery health status tag:

[0068]

[0069] Where ΔQ(i) and ΔSOC(i) represent the cumulative charge and state of charge change of the i-th charging segment, respectively. SOC(t1) and SOC(t2) represent the initial state of charge at t1 and the final state of charge at t2, respectively.

[0070] As an optional embodiment, the above S3 specifically includes S31-S33:

[0071] S31: performing a charging curve analysis based on the maximum voltage of the battery on the charging segment of the battery pack to determine a voltage interval extracted from the health indicator set;

[0072] S32: Calculate statistical characteristics of charging data according to the selected voltage range, including average value and standard deviation value, and extract a health indicator set;

[0073] S33: A set of health indicators highly correlated with the battery health status is screened out through correlation analysis based on Pearson correlation coefficient (PCC) and grey relational gradient (GRG).

[0074] As an optional embodiment, the health indicator set includes current, battery pack voltage, state of charge, maximum and minimum battery voltages, maximum and minimum battery probe temperatures, battery voltage difference, probe temperature, and battery capacity calculated by an ampere integral formula;

[0075] In order to ensure the diversity of health indicator combinations, two health indicator sets are selected to simultaneously establish a battery health state estimation model to achieve battery health state estimation.

[0076] As an optional embodiment, two commonly used indicators, Pearson correlation coefficient (PCC) and grey relation gradient (GRG), are used to evaluate the correlation between the battery health status and the health indicator set, such as Figure 2 As shown, it is a schematic diagram of the analysis result of the correlation between the health indicator set and the battery health status of the embodiment; Figure 2 (a) is a schematic diagram of the correlation analysis results based on the Pearson correlation coefficient (PCC); Figure 2 (b) is a schematic diagram of the correlation analysis results based on grey relational gradient (GRG);

[0077] The Pearson correlation coefficient (PCC) gives information about the direction and strength of linear correlation. The calculation formula is as follows:

[0078]

[0079] In the formula, x i represents the i-th health indicator, y represents the health status of the marker, and Represent the average values ​​of the corresponding health indicator series and health status series respectively.

[0080] Grey relational gradient (GRG) provides a quantitative assessment of system evolution, and the calculation formula can be defined as follows:

[0081]

[0082]

[0083] In the formula, ξ i (k) is the grey correlation coefficient between the ith health index and the marked health status at time k, and ρ represents the discrimination coefficient, which is usually set to 0.5.

[0084] As an optional embodiment, in order to select a highly correlated effective health indicator as an input of the health status estimation model, the high correlation threshold is set to 0.8.

[0085] As an optional embodiment, the above S4 specifically includes S41-S42:

[0086] S41: establishing independent pseudo battery health state estimation models according to the two selected health indicator sets;

[0087] S42: Based on a multi-layer perceptron, a set of estimated pseudo battery health states are respectively integrated into an enhanced training set of another set of battery health state estimation models, and an independent battery health state estimation model is established by using a semi-supervised regression and collaborative training method;

[0088] As an optional embodiment, the multilayer perceptron is composed of a plurality of fully connected layers, wherein each layer is composed of a plurality of units, and these units are interconnected with all units of the previous layer, that is, a unit j∈(1,2,…,J l ) In any hidden layer l∈(0,1,…,L), the mathematical formula for propagation from one unit to another is as follows:

[0089]

[0090] h j,l =a l (z j,l )

[0091] Where w, b, and a represent unit weight, bias factor, and activation function, respectively.

[0092] As an optional embodiment, mean absolute error and root mean square error are used to evaluate the battery health state estimation effect.

[0093] The specific technical implementations described in S2-S4 are as follows Figure 4 shown.

[0094] As an optional embodiment, S5 specifically includes S51-S52:

[0095] S51: input the test data after the health indicator set is extracted into the trained battery health state estimation model to estimate the battery health state;

[0096] S52: The average value estimated by the battery health state estimation model using the enhanced training set is considered as the final estimated battery health state.

[0097] As an optional embodiment, the semi-supervised regression and co-training method will perform semi-supervised co-training based on two selected health indicator sets, assign pseudo battery health states to unlabeled data to enhance the training set, and finally fuse all regressors to obtain the estimated battery health state.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A battery health state estimation method based on semi-supervised regression and collaborative training, characterized in that: The method comprises the following steps: S1: Collect the operation data of electric vehicles, including the operation data of each cell in the battery pack, and establish a battery operation database; S2: Analyze the charging data of electric vehicles, select the charging segments that meet the needs, and calculate the battery health status label using the ampere integral formula and open circuit voltage correction strategy; S3: Extract the health indicator set highly correlated with the battery health status based on the Pearson correlation coefficient PCC and the grey relational gradient GRG indicator; S4: Based on the selected set of health indicators, semi-supervised co-training is used to estimate pseudo labels of unlabeled data to enhance the training dataset, and a battery health state estimation model is constructed based on a multi-layer perceptron (MLP). S5: The test data after extracting the health indicator set is used as the input of the battery health state estimation model, and all regressors are integrated to obtain the estimated battery health state.

2. The method for estimating battery health status based on semi-supervised regression and collaborative training according to claim 1, characterized in that: The S1 is specifically: S11: Collect the operating data of a battery pack of an electric vehicle, including charging and discharging current, voltage, temperature, time and power parameters; S12: Collecting the cell operation data in the battery pack, including the cell voltages of all cells and the temperature data sets of each temperature probe placed near the cells; S13: Establish a battery pack operation database for a certain vehicle based on the collected operation data of the battery cells and battery packs.

3. The method for estimating battery health status based on semi-supervised regression and collaborative training according to claim 2, characterized in that: The S2 is specifically: S21: analyzing charging data of the electric vehicle and screening charging segments that meet the rules of using the open circuit voltage correction strategy; S22: Collecting an open circuit voltage-state of charge lookup table provided by a battery manufacturer, adopting an open circuit voltage correction strategy, and correcting the initial battery state of charge according to the minimum battery voltage at the initial time; S23: According to the historical operation data of the battery pack, the maximum available capacity of the battery pack is calculated using the ampere integral formula to obtain a battery health status label.

4. The method for estimating battery health status based on semi-supervised regression and collaborative training according to claim 3, characterized in that: The S3 is specifically: S31: performing a charging curve analysis based on the maximum voltage of the battery on the charging segment of the battery pack to determine a voltage interval extracted from the health indicator set; S32: Calculate statistical characteristics of charging data according to the selected voltage range, including average value and standard deviation value, and extract a health indicator set; S33: A set of health indicators that are highly correlated with the battery health status are screened out through correlation analysis based on the Pearson correlation coefficient PCC and the grey correlation gradient GRG.

5. The method for estimating battery health status based on semi-supervised regression and collaborative training according to claim 4, characterized in that: The health indicator set includes current, battery pack voltage, state of charge, maximum and minimum battery voltage, maximum and minimum battery probe temperature, battery voltage difference, probe temperature and battery capacity calculated by the ampere integral formula; In order to ensure the diversity of health indicator combinations, two health indicator sets are selected to simultaneously establish a battery health state estimation model to achieve battery health state estimation.

6. The method for estimating battery health status based on semi-supervised regression and collaborative training according to claim 4, characterized in that: The S4 is specifically: S41: establishing independent pseudo battery health state estimation models according to the two selected health indicator sets; S42: Based on a multilayer perceptron, a set of estimated pseudo battery health states are respectively integrated into an enhanced training set of another set of battery health state estimation models, and an independent battery health state estimation model is established using a semi-supervised regression and collaborative training method.

7. The method for estimating battery health status based on semi-supervised regression and collaborative training according to claim 6, characterized in that: The multilayer perceptron consists of multiple fully connected layers, where each layer consists of multiple units, and these units are interconnected with all the units in the previous layer.

8. The method for estimating battery health status based on semi-supervised regression and collaborative training according to claim 6, characterized in that: The S5 is specifically: S51: input the test data after the health indicator set is extracted into the trained battery health state estimation model to estimate the battery health state; S52: The average value estimated by the battery health state estimation model using the enhanced training set is considered as the final estimated battery health state.

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