Energy storage battery DCR detection method based on temperature induction and energy storage system

Abstract

According to the energy storage battery DCR detection method based on temperature induction and the energy storage system, when constant-current charging and discharging are carried out on an energy storage battery, the temperature of a battery cell is sampled in at least one charging and discharging period; acquiring an environment temperature, and determining an environment temperature scale factor according to predetermined factor contrast data; obtaining the temperature of the battery cell at two moments, and calculating the temperature rise energy required by the temperature rise of the battery cell by combining the mass and the specific heat capacity of the battery cell; calculating the DCR of each battery cell of the energy storage battery based on the temperature rise energy in combination with the current magnitude, the interval duration of the two moments and the environment temperature scale factor; according to the method, the influence of circuit noise and dynamic working conditions can be effectively reduced, external temperature fluctuation interference is eliminated, meanwhile, errors caused by the nonlinear characteristic of the battery in a traditional method are avoided, and the DCR measurement precision can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of DCR detection, and in particular to a DCR detection method of an energy storage battery based on temperature sensing and an energy storage system. Background Art

[0002] With the popularity of energy storage systems and the rapid growth in sales, the number of energy storage devices on the market has increased rapidly. Many energy storage systems have been in operation for many years, so battery safety warning and monitoring have become a key issue in the field of energy storage. The DC internal resistance (DCR) of a battery is one of the important indicators for evaluating battery performance and safety. DCR reflects the resistance generated internally by the battery during discharge. The lower this value is, the smaller the energy loss of the battery during use and the higher the efficiency. Generally, as the battery ages or the conditions of use change (such as increased temperature), its DCR gradually increases. In applications such as electric vehicles and energy storage systems, monitoring the DCR of each cell in the battery pack is crucial to promptly detect potential problems. For example, inconsistent aging of individual battery cells may lead to performance degradation of the entire battery pack.

[0003] Existing DCR detection methods often rely on static or dynamic voltage and current measurements, which are easily affected by external interference in actual operation, resulting in inaccurate measurement results. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a DCR detection method and an energy storage system for an energy storage battery based on temperature sensing, which can more accurately achieve DCR measurement.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for detecting DCR of an energy storage battery based on temperature sensing comprises the following steps: S1. When the energy storage battery is charged and discharged at a constant current, the temperature of the battery cell is sampled during at least one charging and discharging period; S2, obtaining the ambient temperature, and determining the ambient temperature proportional factor according to the predetermined factor control data; S3, obtaining the temperature of the battery cell at two moments, and calculating the heating energy required for heating the battery cell based on the mass and specific heat capacity of the battery cell; S4. Based on the heating energy, combined with the current size, the interval between two moments and the ambient temperature proportional factor, calculate the DCR of each cell of the energy storage battery: DCR=Q / (I 2 ×t×K); Among them, Q represents the heating energy, I represents the current, t represents the interval between two moments, and K represents the ambient temperature proportional factor.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: An energy storage system includes an energy storage battery and a battery management system connected to the energy storage battery, and the battery management system implements the following steps: S1. When the energy storage battery is charged and discharged at a constant current, the temperature of the battery cell is sampled during at least one charging and discharging period; S2, obtaining the ambient temperature, and determining the ambient temperature proportional factor according to the predetermined factor control data; S3, obtaining the temperature of the battery cell at two moments, and calculating the heating energy required for heating the battery cell based on the mass and specific heat capacity of the battery cell; S4. Based on the heating energy, combined with the current size, the interval between two moments and the ambient temperature proportional factor, calculate the DCR of each cell of the energy storage battery: DCR=Q / (I 2 ×t×K); Among them, Q represents the heating energy, I represents the current, t represents the interval between two moments, and K represents the ambient temperature proportional factor.

[0007] The beneficial effects of the present invention are as follows: a DCR detection method and energy storage system of an energy storage battery based on temperature sensing of the present invention monitors the temperature change of a battery cell in real time and combines the correction of an ambient temperature proportional factor. Since the temperature change is a stable physical quantity, it is less affected by circuit noise and dynamic working conditions than the traditional voltage / current measurement. The ambient temperature proportional factor is used to compensate for the influence of the ambient temperature on the energy loss, and the interference of external temperature fluctuations is eliminated. At the same time, based on the inherent parameters such as the battery cell mass and specific heat capacity, the error caused by the nonlinear characteristics of the battery in the traditional method is avoided, and the accuracy of DCR measurement can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a flow chart of an energy storage battery DCR detection method based on temperature sensing according to an embodiment of the present invention; Figure 2 The present invention is a block diagram of an energy storage system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0009] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0010] Please refer to Figure 1 , a DCR detection method for energy storage batteries based on temperature sensing, comprising the steps of: S1. When the energy storage battery is charged and discharged at a constant current, the temperature of the battery cell is sampled during at least one charging and discharging period; S2, obtaining the ambient temperature, and determining the ambient temperature proportional factor according to the predetermined factor control data; S3, obtaining the temperature of the battery cell at two moments, and calculating the heating energy required for heating the battery cell based on the mass and specific heat capacity of the battery cell; S4. Based on the heating energy, combined with the current size, the interval between two moments and the ambient temperature proportional factor, calculate the DCR of each cell of the energy storage battery: DCR=Q / (I 2 ×t×K); Among them, Q represents the heating energy, I represents the current, t represents the interval between two moments, and K represents the ambient temperature proportional factor.

[0011] From the above description, it can be seen that the beneficial effects of the present invention are: a DCR detection method for energy storage batteries based on temperature sensing of the present invention monitors the temperature change of the battery cell in real time and combines the correction of the ambient temperature proportional factor. Since the temperature change is a stable physical quantity, it is less affected by circuit noise and dynamic working conditions than the traditional voltage / current measurement. The ambient temperature proportional factor is used to compensate for the influence of the ambient temperature on the energy loss, and the interference of external temperature fluctuations is eliminated. At the same time, based on the inherent parameters such as the battery cell mass and specific heat capacity, the error caused by the nonlinear characteristics of the battery in the traditional method is avoided, and the accuracy of DCR measurement can be effectively improved.

[0012] Furthermore, the calculation of the heating energy is specifically as follows: Q=m*c*(T_2-T_1); Among them, m represents the mass of the battery cell, c represents the specific heat capacity of the battery cell, T_2 represents the temperature of the battery cell at time t2 of the two moments, T_1 represents the temperature of the battery cell at time t1 of the two moments, and t2>t1.

[0013] From the above description, it can be seen that the calculation of heating energy is realized based on the above formula, ensuring that the energy calculation is based on the physical properties of the battery cell, avoiding errors caused by assumptions or approximations. This formula takes the measured temperature difference as the core, combined with inherent parameters such as battery cell mass and specific heat capacity, to provide a stable and reliable energy benchmark for DCR calculation, so that the DCR comparison under different temperature conditions has a unified physical standard, thereby improving the credibility and consistency of the test results.

[0014] Furthermore, the sampling of the battery cell temperature in step S1 is specifically as follows: Sample the battery cell temperature at a preset frequency; The specific temperature of the battery cell at two time points is obtained as follows: The temperature sampling data of the battery cell at two moments are obtained, wherein the temperature sampling data at each moment includes the temperature data sampled at the current moment and the temperature data sampled a preset number of times before, and the average value is calculated after filtering to serve as the battery cell temperature at the current moment.

[0015] From the above description, it can be seen that by sampling and filtering at preset frequencies, sudden noise and outliers in the temperature data are filtered out to ensure the smoothness and accuracy of the temperature data. This method reduces the impact of single sampling errors by calculating the mean of multiple sets of sampled data, improves the accuracy of temperature measurement, provides more reliable input for subsequent energy and DCR calculations, and indirectly enhances the anti-interference ability of the overall detection method.

[0016] Furthermore, the filtering includes: The maximum value and the minimum value of the temperature data sampled at the current moment and the temperature data sampled a preset number of times before are removed.

[0017] From the above description, it can be seen that the extreme value filtering method can effectively filter out sudden noise and abnormal values ​​in the temperature data, ensuring the smoothness and accuracy of the temperature data.

[0018] Furthermore, in step S1, in sampling the current and the battery cell temperature in at least one charging period, the charging period for sampling is a charging period in which the battery SOC is in the range of [20%, 80%].

[0019] From the above description, we can know that by limiting the sampling period to the SOC20%-80% range, and taking advantage of the stable DCR resistance in this range, we can avoid the resistance fluctuation caused by the battery polarization effect at low or high SOC, and ensure the effectiveness of DCR comparison at the same latitude. This design reduces the interference factors introduced by SOC changes, making the DCR comparison of different cells or the same cell at different time points more meaningful, and improving the reliability of the test results.

[0020] Please refer to Figure 2 , an energy storage system, comprising an energy storage battery and a battery management system connected to the energy storage battery, wherein the battery management system implements the following steps: S1. When the energy storage battery is charged and discharged at a constant current, the temperature of the battery cell is sampled during at least one charging and discharging period; S2, obtaining the ambient temperature, and determining the ambient temperature proportional factor according to the predetermined factor control data; S3, obtaining the temperature of the battery cell at two moments, and calculating the heating energy required for heating the battery cell based on the mass and specific heat capacity of the battery cell; S4. Based on the heating energy, combined with the current size, the interval between two moments and the ambient temperature proportional factor, calculate the DCR of each cell of the energy storage battery: DCR=Q / (I 2 ×t×K); Among them, Q represents the heating energy, I represents the current, t represents the interval between two moments, and K represents the ambient temperature proportional factor.

[0021] From the above description, it can be seen that the beneficial effects of the present invention are: an energy storage system of the present invention monitors the temperature change of the battery cell in real time and combines the correction of the ambient temperature proportional factor. Since the temperature change is a stable physical quantity, it is less affected by circuit noise and dynamic working conditions than the traditional voltage / current measurement. The ambient temperature proportional factor is used to compensate for the influence of the ambient temperature on the energy loss, and the interference of external temperature fluctuations is eliminated. At the same time, based on the inherent parameters such as the battery cell mass and specific heat capacity, the error caused by the nonlinear characteristics of the battery in the traditional method is avoided, and the accuracy of DCR measurement can be effectively improved.

[0022] Furthermore, the calculation of the heating energy is specifically as follows: Q=m*c*(T_2-T_1); Among them, m represents the mass of the battery cell, c represents the specific heat capacity of the battery cell, T_2 represents the temperature of the battery cell at time t2 of the two moments, T_1 represents the temperature of the battery cell at time t1 of the two moments, and t2>t1.

[0023] From the above description, it can be seen that the calculation of heating energy is realized based on the above formula, ensuring that the energy calculation is based on the physical properties of the battery cell, avoiding errors caused by assumptions or approximations. This formula takes the measured temperature difference as the core, combined with inherent parameters such as battery cell mass and specific heat capacity, to provide a stable and reliable energy benchmark for DCR calculation, so that the DCR comparison under different temperature conditions has a unified physical standard, thereby improving the credibility and consistency of the test results.

[0024] Furthermore, the sampling of the battery cell temperature in step S1 is specifically as follows: Sample the battery cell temperature at a preset frequency; The specific temperature of the battery cell at two time points is obtained as follows: The temperature sampling data of the battery cell at two moments are obtained, wherein the temperature sampling data at each moment includes the temperature data sampled at the current moment and the temperature data sampled a preset number of times before, and the average value is calculated after filtering to serve as the battery cell temperature at the current moment.

[0025] From the above description, it can be seen that by sampling and filtering at preset frequencies, sudden noise and outliers in the temperature data are filtered out to ensure the smoothness and accuracy of the temperature data. This method reduces the impact of single sampling errors by calculating the mean of multiple sets of sampled data, improves the accuracy of temperature measurement, provides more reliable input for subsequent energy and DCR calculations, and indirectly enhances the anti-interference ability of the overall detection method.

[0026] Furthermore, the filtering includes: The maximum value and the minimum value of the temperature data sampled at the current moment and the temperature data sampled a preset number of times before are removed.

[0027] From the above description, it can be seen that the extreme value filtering method can effectively filter out sudden noise and abnormal values ​​in the temperature data, ensuring the smoothness and accuracy of the temperature data.

[0028] Furthermore, in step S1, in sampling the current and the battery cell temperature in at least one charging period, the charging period for sampling is a charging period in which the battery SOC is in the range of [20%, 80%].

[0029] From the above description, we can know that by limiting the sampling period to the SOC20%-80% range, and taking advantage of the stable DCR resistance in this range, we can avoid the resistance fluctuation caused by the battery polarization effect at low or high SOC, and ensure the effectiveness of DCR comparison at the same latitude. This design reduces the interference factors introduced by SOC changes, making the DCR comparison of different cells or the same cell at different time points more meaningful, and improving the reliability of the test results.

[0030] The present invention discloses a temperature-sensing-based energy storage battery DCR detection method and energy storage system, which are suitable for energy storage battery DCR detection of an energy storage system with high-precision and real-time battery status monitoring.

[0031] Please refer to Figure 1 , Embodiment 1 of the present invention is: A method for detecting DCR of an energy storage battery based on temperature sensing comprises the following steps: S1. When the energy storage battery is charged and discharged at a constant current, the temperature of the battery cell is sampled during at least one charging and discharging period; Step S1 samples the current and the battery cell temperature during at least one charging period, wherein the charging period for sampling is a charging period when the battery SOC is within the range of [20%, 80%]; The sampling of the battery cell temperature in step S1 is specifically as follows: The battery cell temperature is sampled at a preset frequency.

[0032] In this embodiment, since the resistance value of the battery DCR is relatively consistent when the SOC is 20%~80%, and when judging the DCR, it is meaningful to compare the DCR at the same latitude, therefore, during the battery working period when the battery SOC is 20%~80%, the battery cell temperature is sampled at a frequency of once every 100ms.

[0033] In this embodiment, the AFE front-end chip is used to sample the battery cell temperature.

[0034] S2. Obtain the ambient temperature, and determine the ambient temperature proportional factor according to the predetermined factor control data.

[0035] In this embodiment, K is the ambient temperature proportional factor, which will affect the rise or fall of the battery cell temperature. Its value will be calculated and obtained from experimental tests. In this embodiment, the factor control data is in a table form and is represented by letters for example: Table 1

[0036] The experimental test calculation steps are as follows: According to formula I 2 ×t_a×K×DCR = Q, select a battery cell, use the equipment to measure the DCR, and combine the formula Q=m*c*(T_2-T_1), you can calculate K at different ambient temperatures.

[0037] S3, obtaining the temperature of the battery cell at two moments, and calculating the heating energy required for heating the battery cell based on the mass and specific heat capacity of the battery cell; The specific temperature of the battery cell at two time points is obtained as follows: The temperature sampling data of the battery cell at two moments are obtained, wherein the temperature sampling data at each moment includes the temperature data sampled at the current moment and the temperature data sampled a preset number of times before, and the average value is calculated after filtering to serve as the battery cell temperature at the current moment.

[0038] In this embodiment, the cell temperatures at any two moments on a time axis are taken, such as the temperature T_1 at moment t1 and the temperature T_2 at moment t2. The greater the temperature difference between T2 and T1 in a shorter time, the easier it is to calculate, such as in a high rate charging condition.

[0039] In this embodiment, the temperature T_1 at time t1 and the temperature T_2 at time t2 are obtained by extreme value filtering and averaging. Taking the acquisition of T_1 as an example, when the temperature acquisition period is 100ms, a preset number of sampling data are obtained at time t1 (5 times in this embodiment, i.e., the sampling data at time t1 and the previous 4 sampling data), and the highest and lowest values ​​are removed, and then the average calculation is performed to obtain the temperature T_1.

[0040] The calculation of the heating energy is specifically as follows: Q=m*c*(T_2-T_1); Among them, m represents the mass of the battery cell, c represents the specific heat capacity of the battery cell, T_2 represents the temperature of the battery cell at time t2 of the two moments, T_1 represents the temperature of the battery cell at time t1 of the two moments, and t2>t1.

[0041] In this embodiment, assuming that the mass of the battery cell is (m=100) grams, the specific heat capacity is (c=0.836) joules / gram·degrees Celsius (the typical specific heat capacity of lithium-ion batteries), the initial temperature (at time t1) is (T_1=20) degrees Celsius, and the target temperature (at time t2) is (T_2=30) degrees Celsius, it can be calculated that Q=100*0.836*10=836 joules.

[0042] S4. Based on the heating energy, combined with the current size, the interval between two moments and the ambient temperature proportional factor, calculate the DCR of each cell of the energy storage battery: DCR=Q / (I 2 ×t×K); Among them, Q represents the heating energy, I represents the current, t represents the interval between two moments, and K represents the ambient temperature proportional factor.

[0043] In this embodiment, based on the above formula, the DCR calculation of each cell of the energy storage battery is realized. Specifically, its calculation formula can be combined with the calculation formula of the heating energy Q mentioned above as follows: DCR=Q / I 2 *t*K=m*c*(T_2-T_1) / (I 2 *t*K); In actual use, the DCR of the battery cell can be directly calculated based on this formula and the collected data.

[0044] In this embodiment, I is the current flowing through the battery, which can be measured by a Hall sensor provided at the front end of the bus main negative relay.

[0045] In addition, if you need to consider the different DCR values ​​at different temperatures: Table 2

[0046] The formula can be optimized as follows: (I 2 × t_a × K × DCR_A) + (I 2 × t_b × K × DCR_B) + (I 2 × t_c × K × DCR_C) + (I 2× t_d × K × DCR_D) + (I 2 × t_e × K × DCR_E) + (I 2 × t_f × K × DCR_F) = Q; t_a, t_b, t_c, t_d, t_e, and t_f are the time that the battery cell is in the temperature range in Table 2 above.

[0047] Since DCR changes with temperature, it needs to be accumulated in sections, and based on this equation, the equation group of the battery cell can be obtained through multiple data collection. That is, by using the equation solving method, through multiple data collection, the same number of equations as unknowns (DCR values ​​at different temperatures) can be obtained, so as to calculate the DCR at different temperatures.

[0048] Please refer to Figure 2 , Embodiment 2 of the present invention is: An energy storage system includes an energy storage battery and a battery management system connected to the energy storage battery, and the battery management system implements the steps in the energy storage battery DCR detection method based on temperature sensing in the above embodiment 1.

[0049] In this embodiment, the energy storage system is mainly composed of PCS, EMS, BMS, etc., and is connected to the power grid and load through PCS.

[0050] in: PCS: It can draw electricity from the grid, battery or photovoltaic power, discharge it to the load (household electricity), and charge the battery; BMS: It mainly realizes the signal acquisition of the battery system, performs logical calculations through the data collected by the signals, and realizes the power on and off of the battery by controlling the relays. In terms of energy storage, BMS is generally divided into a three-level architecture or a two-level architecture. The three-level architecture consists of MBMU (master battery management unit), SBMU (slave battery management unit) and CSC (cell monitoring circuit); the two-level architecture generally consists of SBMU and CSC.

[0051] EMS: Storage Energy Management System (SEMS) is a specific application area of ​​EMS that focuses on managing and optimizing the performance of energy storage systems (such as battery storage). SEMS plays an increasingly important role in power systems, especially in integrating renewable energy and improving grid flexibility and stability.

[0052] In summary, the present invention provides a DCR detection method and energy storage system for energy storage batteries based on temperature sensing. By real-time monitoring of the temperature change of the battery cell and combining with the correction of the ambient temperature proportional factor, the temperature change is a stable physical quantity. Compared with the traditional voltage / current measurement, it is less affected by circuit noise and dynamic working conditions. The ambient temperature proportional factor is used to compensate for the influence of the ambient temperature on the energy loss, and the interference of external temperature fluctuations is eliminated. At the same time, based on the inherent parameters such as the battery cell mass and specific heat capacity, the error caused by the nonlinear characteristics of the battery in the traditional method is avoided, and the accuracy of DCR measurement can be effectively improved.

[0053] The present invention realizes multi-dimensional optimization of DCR detection of energy storage batteries, and its beneficial effects are specifically reflected in the following aspects: High-precision anti-interference: DCR is calculated indirectly through temperature changes to avoid the defect that traditional voltage / current measurement is easily interfered by noise. The environmental factor K is introduced to correct energy loss. Combined with the inherent parameters of the battery cell (mass, specific heat capacity), the DCR measurement error is controlled within 5%, which is significantly better than the existing technology.

[0054] Fast response and real-time performance: Based on high-frequency sampling of the temperature sensor (e.g. 1 Hz) and direct calculation of the energy formula, the time required for a single DCR measurement is shortened to minutes, which can capture battery status changes in real time and is suitable for dynamic operating scenarios such as electric vehicles.

[0055] System simplification and low cost: Only a new temperature sensor (1:1 configuration is required by regulations) and a simple data processing module are required, without complex circuits or algorithms, which reduces system complexity and cost while being compatible with existing BMS architectures (three-level / two-level).

[0056] Wide adaptability and standardization: Through the experimentally calibrated temperature-K value mapping table, it adapts to different battery types (lithium-ion, lead-acid, etc.) and wide temperature range environments (-20℃~45℃), and limits SOC sampling to the range of 20%-80%, ensuring the consistency of DCR comparison and improving the universality of detection.

[0057] Efficient data processing: Improve the quality of temperature data through filtering algorithms (such as extreme value elimination) and reduce invalid data interference. At the same time, DCR calculation is based on direct physical formulas, with small data volume and simple processing, supporting real-time analysis and feedback, and enhancing system reliability.

[0058] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A DCR detection method for energy storage batteries based on temperature sensing, characterized in that: Includes steps: S1. When the energy storage battery is charged and discharged at a constant current, the temperature of the battery cell is sampled during at least one charging and discharging period; S2, obtaining the ambient temperature, and determining the ambient temperature proportional factor according to the predetermined factor control data; S3, obtaining the temperature of the battery cell at two moments, and calculating the heating energy required for heating the battery cell based on the mass and specific heat capacity of the battery cell; S4. Based on the heating energy, combined with the current size, the interval between two moments and the ambient temperature proportional factor, calculate the DCR of each cell of the energy storage battery: DCR=Q / (I 2 ×t×K); Among them, Q represents the heating energy, I represents the current, t represents the interval between two moments, and K represents the ambient temperature proportional factor.

2. A method for detecting DCR of an energy storage battery based on temperature sensing according to claim 1, characterized in that: The calculation of the heating energy is specifically as follows: Q=m*c*(T_2-T_1); Among them, m represents the mass of the battery cell, c represents the specific heat capacity of the battery cell, T_2 represents the temperature of the battery cell at time t2 of the two moments, T_1 represents the temperature of the battery cell at time t1 of the two moments, and t2>t1.

3. The DCR detection method of energy storage battery based on temperature sensing according to claim 1 is characterized in that: The sampling of the battery cell temperature in step S1 is specifically as follows: Sample the battery cell temperature at a preset frequency; The specific temperature of the battery cell at two time points is obtained as follows: The temperature sampling data of the battery cell at two moments are obtained, wherein the temperature sampling data at each moment includes the temperature data sampled at the current moment and the temperature data sampled a preset number of times before, and the average value is calculated after filtering to serve as the battery cell temperature at the current moment.

4. A method for detecting DCR of an energy storage battery based on temperature sensing according to claim 3, characterized in that: The filtering includes: The maximum value and the minimum value of the temperature data sampled at the current moment and the temperature data sampled a preset number of times before are removed.

5. The method for detecting DCR of a storage battery based on temperature sensing according to claim 1, characterized in that: In step S1, the current and the battery cell temperature are sampled in at least one charging period, and the charging period for sampling is a charging period when the battery SOC is in the range of [20%, 80%].

6. An energy storage system, characterized in that: The invention comprises an energy storage battery and a battery management system connected to the energy storage battery, and the battery management system implements the following steps: S1. When the energy storage battery is charged and discharged at a constant current, the temperature of the battery cell is sampled during at least one charging and discharging period; S2, obtaining the ambient temperature, and determining the ambient temperature proportional factor according to the predetermined factor control data; S3, obtaining the temperature of the battery cell at two moments, and calculating the heating energy required for heating the battery cell based on the mass and specific heat capacity of the battery cell; S4. Based on the heating energy, combined with the current size, the interval between two moments and the ambient temperature proportional factor, calculate the DCR of each cell of the energy storage battery: DCR=Q / (I 2 ×t×K); Among them, Q represents the heating energy, I represents the current, t represents the interval between two moments, and K represents the ambient temperature proportional factor.

7. An energy storage system according to claim 6, characterized in that: The calculation of the heating energy is specifically as follows: Q=m*c*(T_2-T_1); Among them, m represents the mass of the battery cell, c represents the specific heat capacity of the battery cell, T_2 represents the temperature of the battery cell at time t2 of the two moments, T_1 represents the temperature of the battery cell at time t1 of the two moments, and t2>t1.

8. An energy storage system according to claim 6, characterized in that: The sampling of the battery cell temperature in step S1 is specifically as follows: Sample the battery cell temperature at a preset frequency; The specific temperature of the battery cell at two time points is obtained as follows: The temperature sampling data of the battery cell at two moments are obtained, wherein the temperature sampling data at each moment includes the temperature data sampled at the current moment and the temperature data sampled a preset number of times before, and the average value is calculated after filtering to serve as the battery cell temperature at the current moment.

9. An energy storage system according to claim 8, characterized in that: The filtering includes: The maximum value and the minimum value of the temperature data sampled at the current moment and the temperature data sampled a preset number of times before are removed.

10. An energy storage system according to claim 6, characterized in that: In step S1, the current and the battery cell temperature are sampled in at least one charging period, and the charging period for sampling is a charging period when the battery SOC is in the range of [20%, 80%].

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