A battery pack temperature estimation method based on limited reference temperature information

Abstract

The application discloses a battery pack temperature estimation method based on limited reference temperature information and relates to the technical field of lithium ion batteries. The correlation between the terminal voltage of a cell without a temperature sensor and the terminal voltage of all cells with temperature sensors is calculated based on Pearson, and a target cell is found through a cell index; the current, terminal voltage, state of charge, open circuit voltage and measured temperature of the target cell are collected to serve as modeling features, and the measured temperature serves as a modeling target; a data-driven temperature estimation model is established; input features of the cell without the temperature sensor are selected; and the input features are input into the data-driven temperature estimation model to obtain a temperature estimation value. The correlation between measurable voltage parameters is used to obtain the cell with the temperature sensor as the target cell, a data-driven model is established based on the target cell, and the parameter information of the corresponding cell without the temperature sensor is selected as the input feature to obtain the battery temperature estimation result.

Description

Technical Field

[0001] This invention relates to the field of lithium-ion battery technology, specifically a battery pack temperature estimation method based on limited reference temperature information. Background Technology

[0002] Data-driven temperature estimation methods have attracted widespread attention because they do not need to consider the complex heat generation and heat transfer mechanisms of batteries. Traditional data-driven temperature estimation methods use data-driven algorithms to establish the mapping relationship between parameters such as voltage, current, and state of charge and temperature. However, the established data-driven model depends on a large amount of modeling data. In addition, the heat generation and heat transfer mechanisms of individual cells in different locations of the battery pack are inconsistent, which leads to poor robustness of the established data-driven temperature estimation model.

[0003] A battery pack contains numerous individual cells, and due to limitations in cost, space, and wiring, only a limited number of these cells have temperature sensors on their surfaces, leaving the temperatures of other cells unmonitored. Furthermore, the temperature variation patterns among different cells differ depending on their location and whether the cooling system is operational. Mixing temperature data from different patterns in a single model can lead to a decrease in the adaptability of the temperature estimation model under specific environmental conditions. Therefore, there is an urgent need for a method that uses the limited measurable temperature information within the battery pack to obtain the most relevant target cell data, thereby enabling temperature estimation for all individual cells within the pack. Summary of the Invention

[0004] To address the shortcomings of the prior art, this invention provides a battery pack temperature estimation method based on limited reference temperature information. It obtains a target battery by measuring the correlation between measurable voltage parameters and identifying individual cells equipped with temperature sensors. A data-driven model is then established based on the target battery, and parameter information of corresponding individual cells without temperature sensors is selected as input features to obtain the battery temperature estimation result, which is fast and efficient.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a battery pack temperature estimation method based on limited reference temperature information, comprising the following steps:

[0006] Step 1: Select a target battery with measurable temperature information based on the correlation index between measurable voltage parameters.

[0007] The correlation between the terminal voltage of each cell without a temperature sensor and the terminal voltages of all cells with temperature sensors was calculated based on the Pearson correlation coefficient, and is expressed as follows:

[0008]

[0009] In the formula, Per represents the calculation of the Pearson correlation coefficient. This indicates the terminal voltage of a single battery cell without a temperature sensor. This represents the terminal voltage of a single cell equipped with a temperature sensor, M represents the number of single cells without a temperature sensor, and ω represents the number of single cells equipped with a temperature sensor.

[0010] The battery index with the highest correlation is obtained based on the calculated correlation magnitude. Best as follows:

[0011]

[0012] The target battery is the single cell equipped with a temperature sensor, which is found by the battery index with the largest correlation size.

[0013] Step 2: Establishing a data-driven temperature estimation model based on the target battery

[0014] S2.1. Modeling Feature Selection and Modeling Target Establishment for Data-Driven Temperature Estimation Model Establishment: Based on the target battery obtained in step one, select the target battery's current I and terminal voltage. State of charge (SOC) s Open circuit voltage OCV s and measuring temperature T s As input variables, the above set of input variables is as follows: Used for modeling features;

[0015] The target battery's measured temperature T s As an output variable used to model the objective;

[0016] S2.2 Establishment of a data-driven temperature estimation model

[0017] Based on the modeling features and modeling objectives obtained in step S2.1, a data-driven temperature estimation model is established using a data-driven algorithm, as shown in the following formula:

[0018] M data =f(Fea,T s (3)

[0019] In the formula, M data Here, f represents the data-driven temperature estimation model, and f is the data-driven algorithm.

[0020] Step 3: Temperature estimation of individual cells without temperature sensors

[0021] S3.1 Input characteristic selection for single-cell batteries without temperature sensors

[0022] Select the current I and terminal voltage of the single cell without a temperature sensor. State of charge (SOC) n Open circuit voltage OCV n And the measured temperature T of the target battery obtained in step one. s As input variables, the above set of input variables is as follows:

[0023] S3.2 Temperature estimation of a single cell without a temperature sensor

[0024] Input the selected input feature Fea' from step S3.1 into the data-driven temperature estimation model established in step S2.2 to obtain the estimated temperature of a single cell without a temperature sensor, as shown in the following formula:

[0025]

[0026] In the formula, This is the temperature estimate for a single battery cell that does not have a temperature sensor installed.

[0027] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention obtains the individual cells equipped with temperature sensors as target cells by measuring the correlation between measurable voltage parameters, and then establishes a data-driven model based on the target cells. This can make full use of the limited measurable temperature information within the battery pack, select the parameter information of the corresponding individual cells without temperature sensors as input features, and quickly and efficiently obtain the temperature estimation results of all individual cells in the battery pack. It has the following advantages:

[0028] 1. It can maximize the use of temperature information from the limited number of individual cells in the battery pack that are equipped with temperature sensors, and achieve temperature estimation for all individual cells in the pack that are not equipped with temperature sensors.

[0029] 2. It can be quickly deployed and applied to achieve efficient temperature estimation and monitoring of all individual cells within the battery pack;

[0030] 3. No need to consider complex heat generation and heat transfer mechanisms, no need for a large amount of operating data modeling, the temperature of all individual cells in the battery pack can be estimated quickly and effectively based on the relationship between temperature and measurable parameters.

[0031] 4. It eliminates the need to consider complex information such as the actual structure of the battery pack and whether the cooling system is on, making it more suitable for practical applications;

[0032] 5. It can provide a basis for predicting driving range and estimating aging status, and is also beneficial to the thermal safety and safe driving of electric vehicles. Attached Figure Description

[0033] Figure 1This is a flowchart of the present invention;

[0034] Figure 2 This is a comparison chart of the temperature estimation results of individual cells 1-8 in the embodiment;

[0035] Figure 3 This is a comparison chart of the temperature estimation results of individual cells 9-16 in the embodiment. Detailed Implementation

[0036] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0037] like Figure 1 As shown, a battery pack temperature estimation method based on limited reference temperature information includes the following steps:

[0038] Step 1: Select a target battery with measurable temperature information based on the correlation index between measurable voltage parameters.

[0039] The correlation between the terminal voltages of individual cells determines the similarity between their temperatures. A higher correlation indicates more consistent internal reaction characteristics and smaller temperature differences between individual cells. To measure the correlation between terminal voltages, the correlation between the terminal voltage of each cell without a temperature sensor and the terminal voltages of all cells with temperature sensors is calculated based on the Pearson correlation coefficient, as shown below:

[0040]

[0041] In the formula, Per represents the calculation of the Pearson correlation coefficient. This indicates the terminal voltage of a single battery cell without a temperature sensor. This represents the terminal voltage of a single battery cell equipped with a temperature sensor, M represents the number of single batteries cell cells without a temperature sensor, and ω represents the number of single batteries cell cells equipped with a temperature sensor.

[0042] The battery index with the highest correlation is obtained based on the calculated correlation magnitude. Best as follows:

[0043]

[0044] The target battery is the single cell equipped with a temperature sensor, which is found by the battery index with the largest correlation size.

[0045] Step 2: Establishing a data-driven temperature estimation model based on the target battery

[0046] S2.1. Modeling Feature Selection and Modeling Target Establishment for Data-Driven Temperature Estimation Model Establishment: Based on the target battery obtained in step one, select the target battery's current I and terminal voltage. State of charge (SOC) s Open circuit voltage OCV s and measuring temperature T s As input variables, the above set of input variables is as follows: Used for modeling features;

[0047] The target battery's measured temperature T s As an output variable used to model the objective;

[0048] S2.2 Establishment of a data-driven temperature estimation model

[0049] Based on the modeling features and modeling objectives obtained in step S2.1, a data-driven temperature estimation model is established using a data-driven algorithm, as shown in the following formula:

[0050] M data =f(Fea,T s (3)

[0051] In the formula, M data Here, f represents the data-driven temperature estimation model, and f is the data-driven algorithm.

[0052] Step 3: Temperature estimation of individual cells without temperature sensors

[0053] S3.1 Input characteristic selection for single-cell batteries without temperature sensors

[0054] Select the current I and terminal voltage of the single cell without a temperature sensor. State of charge (SOC) n Open circuit voltage OCV n And the measured temperature T of the target battery obtained in step one. s As input variables, the above set of input variables is as follows: This will provide a basis for subsequent temperature estimation of individual cells without temperature sensors.

[0055] S3.2 Temperature estimation of a single cell without a temperature sensor

[0056] Input the selected input feature Fea' from step S3.1 into the data-driven temperature estimation model established in step S2.2 to obtain the estimated temperature of a single cell without a temperature sensor, as shown in the following formula:

[0057]

[0058] In the formula, This is the temperature estimate for a single battery cell that does not have a temperature sensor installed.

[0059] Example

[0060] This embodiment verifies the results based on battery pack data under low-temperature heating. The experiment is based on a battery pack with a built-in temperature sensor, which is a 133Ah ternary lithium-ion battery. The temperature estimation results of different individual cells under low-temperature heating conditions obtained by the method of this invention are shown in Table 1:

[0061] Table 1 Temperature estimation results for different individual cells

[0062]

[0063]

[0064] Specific temperature estimation results comparison chart Figures 2-3 As shown in the figure, the reference value curve is the actual temperature curve measured by the temperature sensor, and the estimated value curve is the estimated temperature curve obtained by the method of the present invention. It can be seen from the above experimental results that the method proposed in this invention can accurately estimate the temperature of individual cells in the battery pack.

[0065] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0066] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A battery pack temperature estimation method based on limited reference temperature information, characterized in that: Includes the following steps: Step 1: Select a target battery with measurable temperature information based on the correlation index between measurable voltage parameters; The correlation between the terminal voltage of each cell without a temperature sensor and the terminal voltages of all cells with temperature sensors was calculated based on the Pearson correlation coefficient, and is expressed as follows: （1） In the formula, This indicates the calculation of the Pearson correlation coefficient. This indicates the terminal voltage of a single battery cell without a temperature sensor. This indicates the terminal voltage of a single battery cell equipped with a temperature sensor. This indicates the number of individual battery cells without temperature sensors. This indicates the number of individual battery cells equipped with temperature sensors; The battery index with the highest correlation is obtained based on the calculated correlation magnitude. as follows: （2） The target battery is the single cell equipped with a temperature sensor, which is found by the battery index with the largest correlation size. Step 2: Establishing a temperature estimation model based on data from the target battery; S2.1, Selection of modeling features and establishment of modeling objectives for establishing data-driven temperature estimation models; Based on the target battery obtained in step one, select the current of the target battery. Terminal voltage State of charge Open circuit voltage and measuring temperature As input variables, the above set of input variables is as follows: Used for modeling features; The target battery's measured temperature As an output variable used to model the objective; S2.2 Establishment of a data-driven temperature estimation model; Based on the modeling features and modeling objectives obtained in step S2.1, a data-driven temperature estimation model is established using a data-driven algorithm, as shown in the following formula: （3） In the formula, For data-driven temperature estimation models, Data-driven algorithms; Step 3: Temperature estimation of individual battery cells without temperature sensors; S3.1 Input characteristic selection for single-cell batteries without a temperature sensor; Select the current of the single cell without a temperature sensor. Terminal voltage State of charge Open circuit voltage And the measured temperature of the target battery obtained in step one. As input variables, the above set of input variables is as follows: ; S3.2 Temperature estimation of individual cells without temperature sensors; The input features selected in step S3.1 The data is input into the data-driven temperature estimation model established in step S2.2 to obtain the estimated temperature of a single cell without a temperature sensor, as shown in the following formula: （4） In the formula, This is the temperature estimate for a single battery cell that does not have a temperature sensor installed.

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