Energy storage battery thermal management method and system fusing soc segment heating characteristics

Abstract

This invention relates to the field of energy storage technology, and discloses a thermal management method and system for energy storage batteries that integrates the segmented heating characteristics of State of Charge (SOC) to maximize system energy efficiency while ensuring safety. The method includes: dividing the charging and discharging process into symmetrical SOC intervals; obtaining the average heating power of each interval through single-cell thermal simulation; generating a series of simulation conditions starting from the lower limit of the liquid cooler's supply temperature and incrementally increasing the upper limit, and determining the corresponding supply temperature for each SOC interval based on an inverse control strategy; extracting the peak charging and discharging temperature moments (two sampling times) aligned with at least half of the conditions under constant power charge-rest-discharge conditions; fitting quantitative relationship functions between the cell peak temperature and the upper limit of the supply temperature at the two moments; for the battery system, substituting the system safety threshold (target peak temperature) into the two functions, taking the smaller supply temperature candidate value as the target upper limit, and then determining the actual supply temperature for each SOC interval according to the inverse strategy.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to a thermal management method and system for energy storage batteries that integrates the segmented heating characteristics of SOC. Background Technology

[0002] In the prior patent application of the applicant in this case, with publication number CN122202662A and invention title "Method and System for Thermal Management of Energy Storage Battery", a solution for dynamically adjusting the liquid cooling temperature according to the cell temperature was disclosed, which realizes linkage with BMS (Battery Management System).

[0003] As research and applications have deepened, it has been discovered that the thermal characteristics of battery cells are not only related to the current temperature, but also closely related to their state of charge (SOC). Numerous studies have shown that the thermal power of battery cells varies significantly across different SOC ranges, and this difference varies depending on factors such as battery type, material system, and charge / discharge state. Some batteries exhibit higher internal resistance and higher thermal power in the low SOC range, while others experience increased thermal power due to polarization effects in the high SOC range, and still others exhibit peak thermal power in specific SOC ranges. This objective fact that thermal characteristics dynamically change with SOC determines that the cooling requirements of battery cells change dynamically at different SOC stages.

[0004] However, existing feedback control strategies based on cell temperature still have room for further optimization in the following aspects:

[0005] (1) Control lag: Feedback control is essentially "post-event adjustment" - cooling is only started after the cell temperature rises to the threshold, and there is a time delay from the occurrence of temperature rise to the cooling response. In a specific SOC range where the heat generation power changes drastically, this lag may cause the cell temperature to exceed the safe range.

[0006] (2) The parameter setting lacks scientific basis: key control parameters such as the liquid supply temperature range and the temperature threshold for starting cooling are mostly determined by experience or repeated experiments, without fully considering the differences in heating characteristics of SOC segments, making it difficult to achieve optimal matching with the actual heat demand of the battery cell.

[0007] (3) Large temperature fluctuations: Because the supply temperature is not dynamically adjusted according to the difference in heat generation power in different SOC ranges, the cell temperature exhibits large fluctuations throughout its entire life cycle. Such frequent temperature fluctuations will accelerate cell aging and shorten system life.

[0008] (4) Non-optimal energy efficiency: Using a uniform liquid supply temperature or control threshold may lead to over-cooling in the low-heat range (energy waste) and insufficient cooling in the high-heat range (safety risk).

[0009] To address the aforementioned issues and further improve the accuracy of thermal management, an ideal solution should possess the following characteristics: in the SOC range with higher heat generation power, a lower supply temperature should be used to quickly remove heat; in the SOC range with lower heat generation power, a higher supply temperature should be used to avoid overcooling; and through an "inverse regulation" strategy, the cell temperature should always be maintained within a narrow optimization window to achieve stable temperature control.

[0010] Therefore, the industry urgently needs a thermal management strategy design method that can integrate the segmented heating characteristics of the battery cell at different SOC intervals and achieve scientific calibration of control parameters through simulation. This method should be able to simulate the temperature changes of the battery cell during actual operation using a high-fidelity thermal simulation model, based on the heating power parameters of the battery cell in different SOC intervals (which can be obtained in advance through experiments or simulations). Then, based on the temperature response data obtained from the simulation, it should calibrate key control parameters such as the optimal liquid supply temperature and the start-up cooling temperature threshold for different SOC intervals. Ultimately, it should achieve adaptive regulation of "high heating power corresponds to low water temperature, and low heating power corresponds to high water temperature," ensuring that the battery cell maintains minimal temperature fluctuations throughout its entire lifespan and maximizing system energy efficiency while ensuring safety. Summary of the Invention

[0011] The purpose of this invention is to disclose a thermal management method and system for energy storage batteries that integrates the segmented heating characteristics of SOC, so as to maximize system energy efficiency while ensuring safety.

[0012] To achieve the above objectives, the energy storage battery thermal management method integrating SOC segmented heating characteristics disclosed in this invention includes: Step S1: Divide the charging and discharging process of the battery cell into at least two SOC intervals, and the SOC intervals in the charging process and the SOC intervals in the discharging process are symmetrically distributed. Step S2: Construct a single-cell thermal simulation model and determine the average heat generation power of a single cell in each SOC range through model simulation; Step S3: Using the lower limit of the liquid supply temperature supported by the liquid cooling unit as a fixed starting point, the upper limit of the liquid supply temperature is increased sequentially with a set step size to obtain at least three simulation operating condition sequences with different liquid supply ranges. Then, based on the inverse proportional control thermal management strategy, the liquid supply temperature corresponding to each SOC interval of each simulation operating condition is determined. Step S4: Obtain the highest temperature change curve of the cell under the strategy of constant power charging-resting-constant power discharging under each simulation condition, and determine the presentation time of the cell temperature peak that is aligned with at least half of the simulation conditions during charging and discharging as the first and second sampling times. Step S5: Extract the peak cell temperature at the first sampling time and the upper limit of the liquid supply temperature in the simulation operating condition sequence as paired first-type sampling data, and extract the peak cell temperature at the second sampling time and the upper limit of the liquid supply temperature in the simulation operating condition sequence as paired second-type sampling data; then, with the peak cell temperature as the independent variable and the upper limit of the liquid supply temperature as the dependent variable, fit the first and second quantitative relationship functions between the independent and dependent variables according to each first-type sampling data and the second-type sampling data. Step S6: For a battery management system consisting of at least two single cells, the system-level safety threshold is determined as the target cell peak temperature of a single cell. The target cell peak temperature is substituted into the first and second quantitative relationship functions to obtain two candidate values ​​for the liquid supply temperature. Then, the smaller of the two candidate values ​​for the liquid supply temperature is determined as the upper limit of the target liquid supply temperature. Then, the liquid supply temperature corresponding to each SOC range in the actual operating conditions is determined based on the inverse proportional control thermal management strategy.

[0013] Preferably, the inverse proportional control thermal management strategy includes: The range of heat generation power ΔP is calculated based on the maximum and minimum heat generation power Pmax and Pmin of all charging and discharging SOC intervals. Calculate the temperature coefficient of power range The quotient is the temperature difference between the upper limit of the liquid supply temperature and the fixed starting point in the simulation condition, divided by the range of the heating power. For any SOC range under any simulated liquid supply temperature difference condition, let the average heat generation power be Pi, then the formula for calculating the corresponding liquid supply temperature setpoint Ti is: Fixed starting point + (Pmax - Pi). The calculated result is rounded down.

[0014] Preferably, the single-cell thermal simulation model consists of a cell, a liquid cooling plate, thermally conductive adhesive, and coolant. The liquid cooling plate includes an upper cooling plate and a coolant flow channel plate. Dynamic coolant is filled in the flow channel of the coolant flow channel plate. The upper cooling plate is fixed to the cell with thermally conductive adhesive.

[0015] Preferably, the system-level security threshold is defined as follows: and the maximum allowable temperature limit based on the battery pack If the maximum temperature difference between cells in the battery pack is ,but .

[0016] Preferably, the discharge time under the strategy of constant power charging-resting-constant power discharging is greater than the charging time, and the single cell thermal simulation model covers at least two charge-discharge cycles during the simulation process, and the first and second sampling times respectively occur in the cycle with the highest peak temperature of the corresponding cell.

[0017] Preferably, in step S1, the charging process is divided into 12 SOC intervals: 0~5%, 5~10%, 10~20%, 20~30%, 30~40%, 40~50%, 50~60%, 60~70%, 70~80%, 80~90%, 90~95%, and 95~100%; and the discharging process is also divided into 12 SOC intervals: 100~95%, 95~90%, 90~80%, 80~70%, 70~60%, 60~50%, 50~40%, 40~30%, 30~20%, 20~10%, 10~5%, and 5~0%.

[0018] Preferably, the method of the present invention further includes: verifying through simulation that the peak temperature of the target cell of a single cell is less than or equal to the system-level safety threshold during the cycle of constant power charging-resting-discharging in the actual working conditions corresponding to the liquid supply temperature of each SOC interval. If so, the verification is passed; otherwise, the first and second quantitative relationship functions are refitted until the verification is passed.

[0019] To achieve the above objectives, the present invention also discloses an energy storage battery thermal management system that integrates the segmented heating characteristics of SOC, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method.

[0020] The present invention has the following beneficial effects: By adopting an inverse control strategy, the liquid supply temperature is reduced in the SOC range with higher heat generation power to enhance heat dissipation, while the liquid supply temperature is increased in the SOC range with lower heat generation power to avoid overcooling. This allows for refined management of different SOC ranges, keeping the cell temperature within a relatively narrow optimization window, achieving stable temperature control, effectively mitigating aging issues, and maximizing system energy efficiency while ensuring safety.

[0021] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0022] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of the overall structure of the single-cell thermal simulation model disclosed in the embodiment of the present invention.

[0023] Figure 2 yes Figure 1 A magnified schematic diagram of a portion of the structure of A.

[0024] Figure 3 This is a schematic diagram of the heating power variation curve of a single battery cell under rated power constant power cyclic charging and discharging, as disclosed in an embodiment of the present invention.

[0025] Figure 4 This is a schematic diagram of the maximum temperature change curve of the battery cell under various simulated operating conditions disclosed in the embodiments of the present invention.

[0026] Figure 5 This is a schematic diagram illustrating the principle of determining the first and second sampling times based on the temperature peak values ​​of more than half of the battery cells under various simulation conditions.

[0027] Figure 6 Yes Figure 5 A schematic diagram illustrating the principle of fitting first and second quantitative relationship functions to two types of sampling data extracted from the cell temperature peak. Figure 6 'a' is a schematic diagram illustrating the pairing relationship between the first type of sampled data and the second type of sampled data. Figure 6 b is a schematic diagram of the first quantitative relationship function fitted based on each type of first-class sampling data; Figure 6 c is a schematic diagram of the second quantitative relationship function fitted based on each type of second-class sampling data.

[0028] Figure 7 This is a schematic diagram showing the results of simulation verification of whether the target cell peak temperature of a single cell is less than or equal to the system-level safety threshold during the cycle of constant power charging, resting, and then constant power discharging in each SOC range under actual working conditions.

[0029] Figure 8 This is a flowchart illustrating the steps of the core solution disclosed in the embodiments of the present invention. Detailed Implementation

[0030] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0031] Example 1 This embodiment discloses a thermal management method for energy storage batteries that integrates the segmented heating characteristics of SOC, and the steps are as follows.

[0032] Step 1: Construct a single-cell thermal simulation model.

[0033] The constructed single-cell thermal simulation model is as follows: Figure 1 and Figure 2 As shown, the single-cell thermal simulation model can be composed of a cell 2, a liquid cooling plate, thermally conductive adhesive 4, and coolant 3. The liquid cooling plate includes a cooling plate upper plate 6 and a coolant flow channel plate 5. The flow channel plate 5 is filled with dynamic coolant 3. The cooling plate upper plate 6 is fixed to the cell 2 with thermally conductive adhesive 4. The temperature monitoring point is the electrode post 1.

[0034] Figure 1 and Figure 2 The single-cell thermal simulation model shown uses the measured data of the cell's rated power constant power cyclic charging and discharging heat generation as the core heat source input. It simulates a typical composite cycle of "constant power charging-resting-constant power discharging-resting-constant power charging-resting-constant power discharging," and the heat generation power variation curve is shown below. Figure 3 As shown.

[0035] like Figure 3 As shown; the period from 0 to 7348s is constant power charging (0.5P, SOC from 0 to 100%), with a rest time of 10 minutes, followed by constant power discharging (0.5P, SOC from 100% to 0). The discharging time (7950s-16350s) is longer than the charging time, and this trend becomes consistent in subsequent cycles. Analysis of the simulation results reveals that in the entire composite cycle, the early and middle stages of the constant power charging phase (refer to...) Figure 3 The period from approximately 2060s to 4780s is the critical stage in which the cell generates the most heat and experiences the most dramatic temperature rise.

[0036] In-depth analysis of the data from this critical stage reveals the following patterns: throughout the entire compound cycle, the location of the highest cell temperature may change depending on the operating conditions of the liquid supply range: when the upper limit of the liquid supply temperature is low (i.e., the liquid supply range is narrow), the highest temperature usually occurs during the first discharge cycle; when the upper limit of the liquid supply temperature is high (i.e., the liquid supply range is wide), the highest temperature usually occurs during the first charging cycle; in addition, the lower the upper limit of the liquid supply temperature, the lower the overall temperature level of the cell.

[0037] Step 2: Obtain the heat generation power parameters of the battery cell in each SOC range.

[0038] The charging process is divided into 12 SOC intervals: 0~5%, 5~10%, 10~20%, 20~30%, 30~40%, 40~50%, 50~60%, 60~70%, 70~80%, 80~90%, 90~95%, and 95~100%.

[0039] The discharge process is also divided into 12 SOC intervals: 100~95%, 95~90%, 90~80%, 80~70%, 70~60%, 60~50%, 50~40%, 40~30%, 30~20%, 20~10%, 10~5%, and 5~0%.

[0040] In this embodiment, the average heating power values ​​corresponding to each SOC range are shown in Table 1.

[0041] Table 1. Average heating power values ​​corresponding to each SOC range.

[0042] Step 3: Design the liquid supply temperature range and simulate operating conditions.

[0043] With the lower limit of the liquid supply temperature supported by the liquid cooling unit at 15℃ as a fixed starting point, and the upper limit of the liquid supply temperature increasing in increments of 5℃, the liquid supply range operating condition sequence can be obtained as follows: 15~20℃, 15~25℃, 15~30℃, 15~35℃.

[0044] Step 4: Calibrate the liquid supply temperature parameter control method.

[0045] The calibration process is mainly based on the inverse proportional control thermal management strategy. The specific calculation process includes: calculating the thermal power range ΔP based on the maximum and minimum heat generation power Pmax and Pmin of all charging and discharging SOC intervals; and then calculating the power range temperature coefficient. The quotient is the temperature difference between the upper limit of the supply temperature and the fixed starting point in the simulation condition, divided by the range of heating power. Then, for any SOC interval of any supply temperature difference simulation condition, let the average heating power be Pi, then the formula for calculating the corresponding supply temperature setpoint Ti is: fixed starting point + (Pmax - Pi). The calculated result is rounded down.

[0046] According to Table 1, the maximum heating power Pmax≈25.10W, the minimum heating power Pmin≈-8.17W, and the range of heating power ΔP=Pmax-Pmin≈33.27W.

[0047] For operating conditions with a liquid supply range of 15~20℃, the power range temperature coefficient ε1≈0.15℃ / W.

[0048] For operating conditions with a liquid supply range of 15~25℃, the power range temperature coefficient ε2≈0.3℃ / W.

[0049] For operating conditions with a liquid supply range of 15~30℃, the power range temperature coefficient ε3≈0.45℃ / W.

[0050] For operating conditions with a liquid supply range of 15~35℃, the power range temperature coefficient ε4≈0.6℃ / W.

[0051] Based on the "inverse proportional control" strategy, the supply temperature for the SOC range corresponding to Pmax is set to the minimum supply temperature of 15℃. For other SOC ranges, the calculated supply temperatures are shown in Table 2.

[0052] Table 2 shows the corresponding liquid supply temperatures for the simulation operating condition sequences.

[0053] Step 5: Design and execute simulations for multiple liquid supply ranges.

[0054] Through a series of simulations, detailed data on the temperature evolution of the battery cell over time during the entire compound cycle was obtained under the above four liquid supply range conditions and when the "inverse proportional control" strategy was implemented. Figure 4 As shown.

[0055] Step Six: Simulation Data Analysis and Solution of Core Control Parameters.

[0056] The simulation data obtained in step five are processed and analyzed to extract key patterns and solve for the optimal control parameters.

[0057] (1) Extraction of physical laws.

[0058] Throughout the entire compound cycle, the highest cell temperature during the charging process under the main simulated operating conditions occurred around 4500s (peak point one, located during the first round of charging), while the highest cell temperature during the discharging process under all simulated operating condition sequences occurred around 15600s (peak point two, located during the first round of discharging). Furthermore, this confirms the fact that the lower the upper limit of the electrolyte supply temperature, the lower the overall cell temperature level.

[0059] (2) Mathematical fitting and establishment of functional relationships.

[0060] For each liquid supply range operating condition, the peak cell temperature was extracted from peak point one and peak point two, respectively. The results are as follows: Figure 5 As shown. For peak point one and peak point two, with the cell peak temperature as the independent variable x and the upper limit of the cell electrolyte supply temperature as the dependent variable y, respectively, the following is performed: Figure 6 The polynomial fitting shown yields two functions describing the quantitative relationship between y and x: ; .

[0061] (3) Definition of system-level security thresholds.

[0062] Define system-level security thresholds and the maximum allowable temperature limit based on the battery pack Considering the uneven temperature distribution among the cells within the battery pack, and that the simulation focuses on a single cell within a specific area of ​​the pack, a control safety threshold is set for each single cell to ensure the strategy is effective for all cells within the pack. In this case, a margin must be reserved for such non-uniformity. This margin is the maximum temperature difference between cells in the battery pack layer. Its value can be obtained through package-level thermal simulation or experimental statistics. Therefore, This refers to the temperature threshold that any battery cell is not allowed to exceed during operation.

[0063] (4) Solving for the optimal control parameters.

[0064] Based on the maximum permissible temperature limit of the battery pack The maximum inter-cell temperature difference at 40℃ is taken as the maximum temperature difference between cells in the battery pack. Based on simulation / actual measurement, a temperature of 4℃ is defined as the system-level safety threshold. Substitute the existing relationships into the equations. and Calculate the corresponding upper limit value of the liquid supply temperature. =32.00℃ =32.95℃. The smaller value of the two, 32℃, is taken as the upper limit of the optimal liquid supply temperature of the system to ensure that the cell temperature does not exceed the safety threshold under any peak scenario. This value is also the temperature threshold at which the cell reaches the point where cooling needs to be activated. Subsequently, based on the "inverse proportional control" strategy, the liquid supply temperature for each SOC range is calculated as shown in Table 3.

[0065] Table 3. Liquid supply temperature corresponding to the target operating conditions

[0066] To verify the effectiveness of the generated "inverse regulation" thermal management strategy, detailed data on the evolution of the cell's maximum temperature and liquid cooling temperature over time throughout the entire cycle were obtained through simulation. Figure 7 As shown in the figure. It can be seen that when the liquid supply temperature range is 15~32℃ and the initial temperature is set to 32℃, The temperature was kept stable below 36℃.

[0067] In summary, the core solution of this embodiment can be summarized as follows: Figure 8 The following steps are shown: Step S1: Divide the charging and discharging process of the battery cell into at least two SOC intervals, and the SOC intervals in the charging process and the SOC intervals in the discharging process are symmetrically distributed.

[0068] Step S2: Construct a single-cell thermal simulation model and determine the average heat generation power of a single cell in each SOC range through model simulation.

[0069] Step S3: Using the lower limit of the liquid supply temperature supported by the liquid cooling unit as a fixed starting point, the upper limit of the liquid supply temperature is increased sequentially with a set step size to obtain at least three simulation operating condition sequences with different liquid supply ranges. Then, based on the inverse proportional control thermal management strategy, the liquid supply temperature corresponding to each SOC interval of each simulation operating condition is determined.

[0070] Step S4: Obtain the highest temperature change curve of the cell under the strategy of constant power charging-resting-constant power discharging under each simulation condition, and determine the presentation time of the cell temperature peak that is aligned with at least half of the simulation conditions during charging and discharging as the first and second sampling times.

[0071] Step S5: Extract the peak cell temperature at the first sampling time and the upper limit of the liquid supply temperature in the simulation operating condition sequence as paired first-type sampling data, and extract the peak cell temperature at the second sampling time and the upper limit of the liquid supply temperature in the simulation operating condition sequence as paired second-type sampling data; then, with the peak cell temperature as the independent variable and the upper limit of the liquid supply temperature as the dependent variable, fit the first and second quantitative relationship functions between the independent and dependent variables according to each first-type sampling data and the second-type sampling data.

[0072] In steps S4 and S5, with Figure 5 For example, in operating conditions one through three, a consistent pattern emerges: a significant temperature peak appears at one peak point. However, this phenomenon is not replicated in operating condition four. The reason for this is that the temperature difference between the battery cell and the coolant is small in operating condition four, resulting in insufficient system heat dissipation to match the current heat generation demand of the battery cell. The heat generation power consistently exceeds the heat dissipation power, thus failing to reach the thermal equilibrium critical point (i.e., the turning point where the first derivative of temperature is zero), and therefore, peak point one fails to form. Nevertheless, this abnormal operating condition does not affect the reliability and universality of the method in this embodiment. On the one hand, for operating condition four, the temperature peak at subsequent peak point two can exceed the highest temperature during the entire charging process. The two fitted curves provide the system with a compensation mechanism in such cases to build sufficient safety margin and fault tolerance space. On the other hand, the trend of heat generation power change of the battery cell during charging and discharging is its inherent electrothermal characteristics. The control strategy of this embodiment is based on this inherent characteristic for feedforward design, rather than relying on the peak phenomenon under a specific operating condition. For a small number of temperature peaks that do not appear at the corresponding sampling time, they do not participate in sampling and fitting and do not affect the extraction of the overall electrothermal characteristic law. Therefore, it has high operating condition adaptability and control robustness, and can ensure a robust thermal management effect across the entire operating condition range.

[0073] Step S6: For a battery management system consisting of at least two single cells, the system-level safety threshold is determined as the target cell peak temperature of a single cell. The target cell peak temperature is substituted into the first and second quantitative relationship functions to obtain two candidate values ​​for the liquid supply temperature. Then, the smaller of the two candidate values ​​for the liquid supply temperature is determined as the upper limit of the target liquid supply temperature. Then, the liquid supply temperature corresponding to each SOC range in the actual operating conditions is determined based on the inverse proportional control thermal management strategy.

[0074] Furthermore, this embodiment can also verify through simulation that, in actual working conditions, the target cell peak temperature of a single cell is less than or equal to the system-level safety threshold during the cycle of constant power charging, resting, and then constant power discharging, corresponding to the liquid supply temperature of each SOC interval. If so, the verification is successful; otherwise, the first and second quantitative relationship functions are refitted (by changing the fitting method, etc.) until the verification is successful.

[0075] Example 2 This embodiment discloses an energy storage battery thermal management system that integrates the segmented heating characteristics of SOC, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the series of steps of the method in Embodiment 1 above.

[0076] In summary, the methods and systems disclosed in the two embodiments of the present invention have at least the following beneficial effects: By adopting an inverse control strategy, the liquid supply temperature is reduced in the SOC range with higher heat generation power to enhance heat dissipation, while the liquid supply temperature is increased in the SOC range with lower heat generation power to avoid overcooling. This allows for refined management of different SOC ranges, keeping the cell temperature within a relatively narrow optimization window, achieving stable temperature control, effectively mitigating aging issues, and maximizing system energy efficiency while ensuring safety.

[0077] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A thermal management method for energy storage batteries that integrates the segmented heating characteristics of SOC (State of Charge), characterized in that, include: Step S1: Divide the charging and discharging process of the battery cell into at least two SOC intervals, and the SOC intervals in the charging process and the SOC intervals in the discharging process are symmetrically distributed. Step S2: Construct a single-cell thermal simulation model and determine the average heat generation power of a single cell in each SOC range through model simulation; Step S3: Using the lower limit of the liquid supply temperature supported by the liquid cooling unit as a fixed starting point, the upper limit of the liquid supply temperature is increased sequentially with a set step size to obtain at least three simulation operating condition sequences with different liquid supply ranges. Then, based on the inverse proportional control thermal management strategy, the liquid supply temperature corresponding to each SOC interval of each simulation operating condition is determined. Step S4: Obtain the highest temperature change curve of the cell under the strategy of constant power charging-resting-constant power discharging under each simulation condition, and determine the presentation time of the cell temperature peak that is aligned with at least half of the simulation conditions during charging and discharging as the first and second sampling times. Step S5: Extract the peak cell temperature at the first sampling time and the upper limit of the liquid supply temperature in the simulation operating condition sequence as paired first-type sampling data, and extract the peak cell temperature at the second sampling time and the upper limit of the liquid supply temperature in the simulation operating condition sequence as paired second-type sampling data; then, with the peak cell temperature as the independent variable and the upper limit of the liquid supply temperature as the dependent variable, fit the first and second quantitative relationship functions between the independent and dependent variables according to each first-type sampling data and the second-type sampling data. Step S6: For a battery management system consisting of at least two single cells, the system-level safety threshold is determined as the target cell peak temperature of a single cell. The target cell peak temperature is substituted into the first and second quantitative relationship functions respectively to obtain two candidate values ​​of liquid supply temperature. Then, the smaller of the two candidate values ​​of liquid supply temperature is determined as the upper limit of the target liquid supply temperature. Then, based on the inverse proportional control thermal management strategy, the corresponding liquid supply temperature for each SOC range in actual working conditions is determined; Among them, the discharge time under the strategy of constant power charging-resting-constant power discharging is longer than the charging time. The single cell thermal simulation model covers at least two charge-discharge cycles in the simulation process, and the first and second sampling times occur in the cycles with the highest peak temperature of the corresponding cells.

2. The energy storage battery thermal management method integrating SOC segmented heating characteristics according to claim 1, characterized in that, The inverse proportional control thermal management strategy includes: The range of heat generation power ΔP is calculated based on the maximum and minimum heat generation power Pmax and Pmin of all charging and discharging SOC intervals. Calculate the temperature coefficient of power range The quotient is the temperature difference between the upper limit of the liquid supply temperature and the fixed starting point in the simulation condition, divided by the range of the heating power. For any SOC range under any simulated liquid supply temperature difference condition, let the average heat generation power be Pi, then the formula for calculating the corresponding liquid supply temperature setpoint Ti is: Fixed starting point + (Pmax - Pi). The calculated result is rounded down.

3. The energy storage battery thermal management method integrating SOC segmented heating characteristics according to claim 1, characterized in that, The single-cell thermal simulation model consists of a cell, a liquid cooling plate, thermally conductive adhesive, and coolant. The liquid cooling plate includes an upper cooling plate and a coolant flow channel plate. Dynamic coolant is filled in the flow channels of the coolant flow channel plate. The upper cooling plate is fixed to the cell with thermally conductive adhesive.

4. The energy storage battery thermal management method integrating SOC segmented heating characteristics according to claim 1, characterized in that, Define system-level security threshold as and the maximum allowable temperature limit based on the battery pack If the maximum temperature difference between cells in the battery pack is ,but .

5. The energy storage battery thermal management method integrating SOC segmented heating characteristics according to claim 1, characterized in that, In step S1, the charging process is divided into 12 SOC intervals: 0~5%, 5~10%, 10~20%, 20~30%, 30~40%, 40~50%, 50~60%, 60~70%, 70~80%, 80~90%, 90~95%, and 95~100%; the discharging process is also divided into 12 SOC intervals: 100~95%, 95~90%, 90~80%, 80~70%, 70~60%, 60~50%, 50~40%, 40~30%, 30~20%, 20~10%, 10~5%, and 5~0%.

6. The energy storage battery thermal management method integrating SOC segmented heating characteristics according to any one of claims 1 to 5, characterized in that, Also includes: Simulations verify that, under actual operating conditions, the target cell peak temperature of a single cell is less than or equal to the system-level safety threshold during the cycle of constant power charging, resting, and then constant power discharging, corresponding to the liquid supply temperature of each SOC range. If so, the verification is successful; otherwise, the first and second quantitative relationship functions are refitted until the verification is successful.

7. A thermal management system for an energy storage battery integrating SOC segmented heating characteristics, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method described in any one of claims 1 to 6.

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