A method for predicting the internal temperature of a battery based on variable frequency impedance measurement

Abstract

This invention relates to a method for predicting the internal temperature of a battery based on variable frequency impedance measurement, belonging to the field of battery thermal management technology. The invention measures the impedance of the battery in its offline state at different temperatures and at different excitation frequencies, plots the battery excitation frequency-impedance relationship at different temperatures, divides temperature ranges according to the test accuracy, finds the optimal excitation frequency for each temperature range, and fits the battery impedance-temperature function relationship at the optimal excitation frequency for each temperature range. Using the one-dimensional heat transfer principle, the measured battery surface temperature and ambient temperature are used as inputs to perform the first estimation of the battery's internal temperature, obtaining the internal temperature T. in 1; Based on the internal temperature T in 1. Select the optimal excitation frequency for the corresponding temperature range, i.e., the internal temperature T. in The optimal excitation frequency for the specified temperature range is used to perform a second estimation based on the battery impedance-temperature function relationship at the optimal excitation frequency, thus obtaining the battery internal temperature T. in 2. This invention can more accurately predict the internal temperature of a battery.

Description

A method for predicting battery internal temperature based on frequency conversion impedance measurement Technical Field

[0001] This invention relates to a method for predicting the internal temperature of a battery based on variable frequency impedance measurement, belonging to the field of battery thermal management technology. Background Technology

[0002] When a battery cell operates abnormally, the conversion of electrical energy can quickly spiral out of control and generate a large amount of heat. If the released heat exceeds the heat dissipation capacity of the battery casing and cooling system, irreversible thermal runaway can occur, leading to fire or even explosion. Therefore, it is necessary to monitor the temperature of each cell in a lithium-ion battery pack in real time. Existing methods for monitoring cell temperature measure the surface temperature of the battery cell and use it as its core temperature. However, this method is inaccurate and unreliable because the temperature difference between the surface and the core can be as high as 30°C.

[0003] Placing a temperature sensor inside the battery can measure its internal temperature, but this method damages the battery's original structure, and the implantation of the internal sensor is a complex process that increases costs. The internal temperature can also be predicted by establishing a battery thermoelectric model, but many parameters involved in the model are difficult to obtain. Electrochemical impedance spectroscopy can achieve sensorless temperature measurement, but traditional single-frequency temperature measurement has drawbacks such as large prediction errors or even failure at certain temperatures. Summary of the Invention

[0004] To address the problem of large prediction errors in battery internal temperature using existing technologies, this invention proposes a method for predicting battery internal temperature based on variable frequency impedance measurement. This method involves selecting different excitation frequencies within different temperature ranges to measure battery impedance and predict the battery internal temperature, thereby improving prediction accuracy and expanding the range of predictable temperatures.

[0005] A method for predicting the internal temperature of a battery based on frequency conversion impedance measurement, the specific steps of which are as follows:

[0006] (1) At different temperatures, the electrochemical impedance spectroscopy (EIS) of the battery at different excitation frequencies in the offline state was tested to obtain the impedance of the battery at different excitation frequencies in the offline state at different temperatures. The test temperature range was -20℃ to 60℃ (the normal operating temperature of the battery is 5℃ to 55℃), and the excitation frequency range was 0.01Hz to 1000Hz. The battery excitation frequency-impedance relationship graph at different temperatures was plotted. The temperature range was divided according to the test accuracy, the optimal excitation frequency of each temperature range was found, and the battery impedance-temperature function relationship at the optimal excitation frequency in each temperature range was fitted.

[0007] (2) Using the one-dimensional heat transfer principle, the measured battery surface temperature and ambient temperature are used as inputs to make the first estimate of the battery internal temperature, and the internal temperature T is obtained. in 1;

[0008] (3) Based on the internal temperature T in 1. Select the optimal excitation frequency for the corresponding temperature range, i.e., the internal temperature T. in The optimal excitation frequency for the specified temperature range is then used to perform a second estimation based on the battery impedance-temperature function relationship at the optimal excitation frequency, thus obtaining the battery internal temperature T. in 2.

[0009] The optimal excitation frequency in step (1) is the excitation frequency at which the impedance is most sensitive to temperature within the temperature range, that is, at this frequency, the battery impedance is most sensitive to temperature changes.

[0010] The battery impedance-temperature function relationship in step (1) is a functional expression of the battery impedance and battery internal temperature fitted by a quadratic polynomial at the optimal frequency.

[0011] The one-dimensional heat transfer principle of step (2) is as follows:

[0012]

[0013] In the formula, T in T represents the internal temperature of the battery. surf T represents the surface temperature of the battery. amb ν is the ambient temperature; K is the internal heat transfer coefficient of the battery; h is the convective heat transfer coefficient of the battery surface.

[0014] The beneficial effects of this invention are:

[0015] (1) The present invention measures battery impedance to predict battery internal temperature by selecting different excitation frequencies in different temperature ranges, thereby improving the accuracy of prediction and expanding the range of predictable temperatures.

[0016] (2) Compared with traditional temperature measurement methods, such as embedded temperature sensors and thermal model estimation, the method of the present invention will not damage the battery and is not affected by thermal delay.

[0017] (3) When measuring battery impedance, the method of the present invention selects the excitation frequency used, which has higher accuracy and can also expand the range of temperature estimation to a greater extent compared with single-frequency impedance measurement to estimate internal temperature. Attached Figure Description

[0018] Figure 1 is a schematic diagram of the battery internal temperature prediction device and process;

[0019] Figure 2 is a flowchart for predicting the internal temperature of the battery.

[0020] Figure 3 shows the results of frequency range selection.

[0021] Figure 4 shows the frequency selection results within different temperature ranges;

[0022] Figure 5 shows the impedance-temperature relationship fitting results at 1 Hz.

[0023] Figure 6 is a schematic diagram of a single temperature estimation;

[0024] Figure 7 shows the internal temperature of the battery during a 10A current discharge process.

[0025] Figure 8 shows the internal temperature results of the battery during a 20A discharge process. Detailed Implementation

[0026] The present invention will be further described in detail below with reference to specific embodiments, but the scope of protection of the present invention is not limited to the content described.

[0027] Example 1: A method for predicting the internal temperature of a battery based on frequency conversion impedance measurement (see Figure 2), the specific steps are as follows:

[0028] (1) At different temperatures, the electrochemical impedance spectroscopy (EIS) of the battery at different excitation frequencies in the offline state was tested to obtain the impedance of the battery at different excitation frequencies in the offline state at different temperatures. The battery excitation frequency-impedance relationship graph at different temperatures was plotted. The temperature range was divided according to the test accuracy, and the optimal excitation frequency of each temperature range was found. The optimal excitation frequency is the excitation frequency at which the impedance is most sensitive to temperature in the temperature range, that is, at this frequency, the battery impedance is most sensitive to temperature changes. The battery impedance-temperature function relationship at the optimal excitation frequency in each temperature range was fitted. The battery impedance-temperature function relationship is the functional relationship expression between the battery impedance and the battery internal temperature fitted by a quadratic polynomial at the optimal frequency.

[0029] (2) Using the one-dimensional heat transfer principle, the measured battery surface temperature and ambient temperature are used as inputs to make the first estimate of the battery internal temperature, and the internal temperature T is obtained. in 1;

[0030] The one-dimensional heat transfer principle of step (2) is as follows:

[0031]

[0032] In the formula, T in T represents the internal temperature of the battery. surf T represents the surface temperature of the battery. amb Where is the ambient temperature; K is the internal heat transfer coefficient of the battery; h is the convective heat transfer coefficient of the battery surface;

[0033] (3) Based on the internal temperature T in 1. Select the optimal excitation frequency for the corresponding temperature range, i.e., the internal temperature T. in The optimal excitation frequency for the specified temperature range is then used to perform a second estimation based on the battery impedance-temperature function relationship at the optimal excitation frequency, thus obtaining the battery internal temperature T. in 2;

[0034] The battery internal temperature prediction system of the present invention (see Figure 1) includes a lithium battery 1, a thermocouple 2, an AC signal generator 3, a data acquisition unit 4, and a computer terminal 5. The thermocouple 2 is attached to the surface of the lithium battery 1 to measure the surface temperature of the battery. The AC signal generator 3 is connected to the lithium battery 1 to provide an AC excitation signal to the battery for measuring battery impedance. Both the thermocouple 2 and the AC signal generator 3 are connected to the data acquisition unit 4 to collect the battery impedance of the impedance measurement circuit and to transmit the impedance data to the computer terminal 5. The computer terminal is used for the calculation of the temperature prediction process, including the calculation of the first temperature estimate and the calculation of the second temperature estimate. That is, it receives the battery surface temperature parameter and the ambient temperature from the thermocouple 2 to perform the first temperature estimate calculation and is connected to the AC signal generator 3 to adjust the frequency of the given AC signal. At the same time, it receives the impedance data from the data acquisition unit 4 to perform the second internal temperature estimate calculation.

[0035] The temperature estimation process is specifically divided into primary estimation and secondary estimation. Primary temperature estimation provides a basis for selecting the optimal excitation frequency for secondary estimation. The primary estimation process includes obtaining the surface temperature 101 and the ambient temperature 102. Secondary estimation includes impedance measurement 201, impedance transmission 202, and outputting the predicted internal temperature 203. Temperature estimation begins by estimating the initial internal temperature based on the surface temperature and ambient temperature using the one-dimensional heat transfer principle on the computer terminal 5. Then, frequency adjustment 103 is performed to select the optimal excitation frequency within the temperature range of the initial value. The battery impedance value is measured at this excitation frequency. Finally, the internal temperature of the battery is predicted and output on the computer terminal 5 using EIS.

[0036] Example 2: The battery selected in this example is a cylindrical battery with a rated capacity of 2.3Ah (A123 model ANR26650 m1-A); the battery internal temperature prediction system of Example 1 is used to predict the internal temperature of the battery;

[0037] A method for predicting the internal temperature of a battery based on frequency conversion impedance measurement (see Figure 2) includes the following steps:

[0038] (1) At different temperatures, the electrochemical impedance spectroscopy (EIS) of the battery at different excitation frequencies in the offline state was tested to obtain the impedance of the battery at different excitation frequencies in the offline state at different temperatures. The battery excitation frequency-impedance relationship graph at different temperatures was plotted. The temperature range was divided according to the test accuracy, and the optimal excitation frequency of each temperature range was found. The optimal excitation frequency is the excitation frequency at which the impedance is most sensitive to temperature in the temperature range, that is, at this frequency, the battery impedance is most sensitive to temperature changes. The battery impedance-temperature function relationship at the optimal excitation frequency in each temperature range was fitted. The battery impedance-temperature function relationship is the functional relationship expression between the battery impedance and the battery internal temperature fitted by a quadratic polynomial at the optimal frequency.

[0039] The frequency range selection results are shown in Figure 3. Figure 3 shows that in the low-frequency region, the battery impedance exhibits good sensitivity to temperature. However, in the high-frequency region, the battery impedance is not strongly dependent on temperature. When the frequency is above 100Hz, the Nyquist curves at different temperatures tend to be consistent, indicating that the battery impedance has almost no sensitivity to temperature. It is evident that the battery impedance's dependence on temperature varies at different frequencies. At excitation frequencies of 0.1Hz, 1Hz, and 10Hz, the impedance value changes significantly with increasing temperature, while at 100Hz, especially at 1000Hz, the battery impedance hardly changes.

[0040] The results of selecting the optimal frequency in different temperature ranges are shown in Figure 4. In the range of 0 to 15℃, the slope of the temperature impedance curve at the frequency of 0.1Hz is the largest, indicating that there is a strong correlation between temperature and battery impedance. The optimal frequencies corresponding to the temperature ranges of 15 to 30℃, 30 to 45℃ and 45 to 60℃ are 1Hz, 10Hz and 100Hz, respectively.

[0041] Figure 5 shows the impedance-temperature relationship fitting results at 1Hz. The quadratic polynomial fitting can well reflect the impedance-temperature relationship under this condition.

[0042] (2) Using the one-dimensional heat transfer principle, the measured battery surface temperature and ambient temperature are used as inputs to make the first estimate of the battery internal temperature (see Figure 6), and the internal temperature T is obtained. in 1;

[0043] The one-dimensional heat transfer principle of step (2) is as follows:

[0044]

[0045] In the formula, T in T represents the internal temperature of the battery. surf T represents the surface temperature of the battery. ambThe ambient temperature is K; the internal heat transfer coefficient of the battery is h; the convective heat transfer coefficient of the battery surface is h; in this embodiment, K is taken as 1 W / m. 2 K and h are taken as 5W / m 2 K;

[0046] (3) Based on the internal temperature T in 1. Select the optimal excitation frequency for the corresponding temperature range, i.e., the internal temperature T. in The optimal excitation frequency for the specified temperature range is then used to perform a second estimation based on the battery impedance-temperature function relationship at the optimal excitation frequency, thus obtaining the battery internal temperature T. in 2;

[0047] The battery internal temperature estimation results corresponding to the 10A current discharge process in this embodiment are shown in Figure 7, and the battery internal temperature estimation results corresponding to the 20A current discharge process are shown in Figure 8. As can be seen from Figures 7 and 8, the battery internal temperature estimated in this embodiment has high accuracy. The estimation error for 10A current discharge is less than 5%, while the estimation error for 20A current discharge is less than 4%. It can achieve relatively accurate temperature estimation.

[0048] Compared to traditional temperature measurement methods, such as embedded temperature sensors and thermal model estimation, this method does not damage the battery and is not affected by thermal delay. In addition, when measuring battery impedance, this method selects the excitation frequency used, which has higher accuracy and can expand the temperature estimation range to a greater extent compared to single-frequency impedance measurement for internal temperature estimation.

[0049] The specific embodiments of the present invention have been described in detail above. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for predicting the internal temperature of a battery based on frequency conversion impedance measurement, characterized in that, The specific steps are as follows: (1) At different temperatures, the electrochemical impedance spectroscopy (EIS) of the battery at different excitation frequencies in the offline state is tested to obtain the impedance of the battery at different excitation frequencies in the offline state at different temperatures. The test temperature range is -20℃~60℃, the normal operating temperature of the battery is 5℃~55℃, and the range of excitation frequency is 0.01Hz~1000Hz. Because at low frequencies, the battery impedance is almost a function of temperature as a single variable, the battery excitation frequency-impedance relationship graph at different temperatures is plotted, the temperature range is divided according to the test accuracy, the optimal excitation frequency of each temperature range is found, and the battery impedance-temperature function relationship at the optimal excitation frequency of each temperature range is fitted; (2) Using the one-dimensional heat transfer principle, the measured battery surface temperature and ambient temperature are used as input quantities to make the first estimate of the battery internal temperature and obtain the internal temperature T. in 1; (3) Based on the internal temperature T in 1. Select the optimal excitation frequency for the corresponding temperature range, i.e., the internal temperature T. in The optimal excitation frequency for the specified temperature range is then used to perform a second estimation based on the battery impedance-temperature function relationship at the optimal excitation frequency, thus obtaining the battery internal temperature T. in 2.

2. The method for predicting the internal temperature of a battery based on frequency conversion impedance measurement according to claim 1, characterized in that: Step (2) The one-dimensional heat transfer principle is as follows: In the formula, T in T represents the internal temperature of the battery. surf T represents the surface temperature of the battery. amb ν is the ambient temperature; K is the internal heat transfer coefficient of the battery; h is the convective heat transfer coefficient of the battery surface.

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