A thermal management method and device for an energy storage system based on temperature rise prediction

Abstract

The present disclosure provides a kind of energy storage system thermal management method and device based on temperature rise prediction, the heat generation power of battery cell under different working parameters is collected;The temperature rise prediction model corresponding to the preset time long after the temperature rise prediction of battery cell temperature is constructed with the highest temperature of battery at current time, heat generation power as variable parameter;The real-time highest temperature of battery and the real-time working parameter of battery cell are input into the temperature rise prediction model, and the temperature prediction result is determined;According to the size relationship between temperature prediction result and energy storage system limit temperature and preset refrigeration adjustment strategy, the target refrigeration medium temperature is determined, wherein, preset refrigeration adjustment strategy is previously set for different size relationship, the corresponding relationship between target refrigeration medium temperature and current refrigeration medium temperature.The future highest temperature of battery cell can be accurately predicted, the reliability of temperature regulation is improved, the energy consumption of cooling system is optimized, and the operating cost is reduced.

Description

Technical Field

[0001] This disclosure relates to the field of energy storage system control technology, and more specifically, to a thermal management method and device for energy storage systems based on temperature rise prediction. Background Technology

[0002] Energy storage systems, as a core component of new energy development, are widely used in power regulation, grid stabilization, and renewable energy storage. The operating environment of energy storage systems is typically complex and variable, operating in both high-temperature and low-temperature conditions. As a key component of energy storage systems, the performance and lifespan of battery cells are directly related to temperature. However, in practical applications, the relatively fixed control strategies of thermal management systems fail to dynamically adapt to changes in the operating status of the energy storage system and the external environment, resulting in poor thermal management performance and consequently affecting the overall system performance and energy efficiency.

[0003] Traditional thermal management systems primarily rely on a single variable—cell temperature—for control. For example, cooling is activated when the cell temperature exceeds a set threshold and deactivated when it falls below. However, this traditional thermal management strategy, designed solely based on cell temperature, can lead to several problems. At high temperatures, the system may over-rely on cooling equipment, resulting in excessive energy consumption; conversely, at low temperatures, insufficient cooling capacity may prevent timely reduction of cell temperature, potentially leading to overheating or undercooling. This not only reduces the overall efficiency of the energy storage system but also poses safety risks such as cell overheating and shortened lifespan. Summary of the Invention

[0004] This disclosure provides at least one thermal management method and apparatus for energy storage systems based on temperature rise prediction. It can accurately predict the future maximum temperature of the battery cells, improve the reliability of temperature regulation, and dynamically adjust the temperature of the cooling target medium based on the prediction results, so that the system can adapt to different operating states and external environments, avoid the problems of insufficient or excessive cooling, and optimize the energy consumption of the cooling system and reduce operating costs while meeting the temperature control requirements of the energy storage system.

[0005] This disclosure provides a thermal management method for energy storage systems based on temperature rise prediction, including:

[0006] Collect the heat generation power of the battery cell under different operating parameters;

[0007] A temperature rise prediction model is constructed, which uses the current highest battery temperature and the heat generation power as variable parameters, and corresponds to the cell temperature after a preset time for temperature rise prediction is activated.

[0008] The real-time maximum temperature of the battery and the real-time operating parameters of the battery cell are obtained, the real-time heat generation power that matches the real-time operating parameters is determined, and the real-time maximum temperature of the battery and the real-time heat generation power are input into the temperature rise prediction model to determine the temperature prediction result.

[0009] Based on the relationship between the predicted temperature and the energy storage system's limiting temperature, and a preset cooling regulation strategy, the target cooling medium temperature is determined. The preset cooling regulation strategy pre-sets a correspondence between the target cooling medium temperature and the current cooling medium temperature for different relationships.

[0010] In one optional implementation, the heat generation power of the battery cell under different operating parameters is collected, specifically including:

[0011] The heat generation power of the battery cell was measured under experimental conditions at different cell temperatures, SOC, and charge / discharge currents.

[0012] For different charging states, a charging interpolation table is constructed to represent the heat generation power corresponding to different cell temperatures, SOC, and charging current states.

[0013] For different discharge states, a discharge interpolation table is constructed to represent the heat generation power corresponding to different cell temperatures, SOC, and discharge current states.

[0014] In one optional implementation, after collecting the heat generation power of the battery cell under different operating parameters, the method further includes:

[0015] Determine the cooling start-up and cooling stop times for the refrigeration equipment to activate or deactivate the refrigeration function based on different cell temperature thresholds at different charge / discharge rates.

[0016] Based on the refrigeration performance of the refrigeration equipment, determine the time required for the refrigeration equipment to reach the target refrigeration medium temperature from the start of refrigeration;

[0017] The sum of the cooling start time and the cooling time required is determined as the predicted start time for the start temperature prediction.

[0018] In one optional implementation, the temperature rise prediction model is constructed based on the following formula:

[0019]

[0020] Among them, T qrt This represents the cell temperature after the preset time for temperature rise prediction is activated; T max The highest temperature of the battery is represented by: Cellnum; V represents the number of cells; h represents the heat generation power; S represents the heat dissipation coefficient; and T represents the cooling contact area.liq M represents the temperature of the cooling medium in the cooling equipment; C represents the battery mass. p T represents the specific heat of the battery. s This indicates that the preset time is long.

[0021] In one optional implementation, the real-time maximum temperature of the battery and the real-time operating parameters of the battery cell are obtained, a real-time heat generation power matching the real-time operating parameters is determined, and the real-time maximum temperature of the battery and the real-time heat generation power are input into the temperature rise prediction model to determine the temperature prediction result, specifically including:

[0022] The battery's real-time maximum temperature, real-time cell temperature, real-time SOC, and real-time charging and discharging current are acquired in real time from the battery management system.

[0023] Based on the real-time cell temperature, the real-time SOC, and the real-time charging and discharging current, look up the corresponding real-time heat generation power in the charging interpolation table or the discharging interpolation table;

[0024] According to the preset data acquisition frequency, the real-time maximum temperature of the battery and the real-time heat generation power collected at the corresponding time are respectively input into the temperature rise prediction model to determine multiple temperature prediction results;

[0025] The average of the temperature prediction results is calculated to determine the target temperature prediction result.

[0026] In one optional implementation, the target refrigerant temperature is determined based on the relationship between the predicted temperature and the energy storage system's limiting temperature, as well as a preset cooling regulation strategy. Specifically, this includes:

[0027] If the temperature prediction result is not less than the energy storage system's limiting temperature, then the target refrigerant temperature is determined to be the difference between the current refrigerant temperature and the preset first temperature.

[0028] If the temperature prediction result is not greater than the difference between the energy storage system limit temperature and the preset second temperature, then the target refrigerant temperature is determined to be the sum of the current refrigerant temperature and the preset first temperature.

[0029] If the temperature prediction result is between the energy storage system's limiting temperature and the difference between the energy storage system's limiting temperature and the preset second temperature, then it is determined that the current refrigerant temperature remains unchanged.

[0030] This disclosure also provides a thermal management device for an energy storage system based on temperature rise prediction, comprising:

[0031] The data acquisition module is used to collect the heat generation power of the battery cell under different operating parameters;

[0032] The model building module is used to build a temperature rise prediction model that uses the current highest battery temperature and the heat generation power as variable parameters, corresponding to the cell temperature after a preset time of temperature rise prediction.

[0033] The temperature prediction module is used to obtain the real-time maximum temperature of the battery and the real-time operating parameters of the cell, determine the real-time heat generation power that matches the real-time operating parameters, and input the real-time maximum temperature of the battery and the real-time heat generation power into the temperature rise prediction model to determine the temperature prediction result.

[0034] The refrigerant temperature adjustment module is used to determine the target refrigerant temperature based on the relationship between the temperature prediction result and the energy storage system limit temperature and a preset refrigerant adjustment strategy. The preset refrigerant adjustment strategy pre-sets a correspondence between the target refrigerant temperature and the current refrigerant temperature for different relationships.

[0035] In an optional implementation, the device is further configured to:

[0036] Determine the cooling start-up and cooling stop times for the refrigeration equipment to activate or deactivate the refrigeration function based on different cell temperature thresholds at different charge / discharge rates.

[0037] Based on the refrigeration performance of the refrigeration equipment, determine the time required for the refrigeration equipment to reach the target refrigeration medium temperature from the start of refrigeration;

[0038] The sum of the cooling start time and the cooling time required is determined as the predicted start time for the start temperature prediction.

[0039] This disclosure also provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the above-described thermal management method for energy storage systems based on temperature rise prediction, or any possible implementation of the above-described thermal management method for energy storage systems based on temperature rise prediction.

[0040] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the above-described thermal management method for energy storage systems based on temperature rise prediction, or any possible implementation of the above-described thermal management method for energy storage systems based on temperature rise prediction.

[0041] This disclosure also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the above-described thermal management method for energy storage systems based on temperature rise prediction, or the steps in any possible implementation of the above-described thermal management method for energy storage systems based on temperature rise prediction.

[0042] This disclosure provides a method and apparatus for thermal management of an energy storage system based on temperature rise prediction. The method collects the heat generation power of the battery cell under different operating parameters; constructs a temperature rise prediction model with the current maximum battery temperature and the heat generation power as variable parameters, corresponding to the battery cell temperature after a preset temperature rise prediction time; obtains the real-time maximum battery temperature and the real-time operating parameters of the battery cell, determines the real-time heat generation power matching the real-time operating parameters, and inputs the real-time maximum battery temperature and the real-time heat generation power into the temperature rise prediction model to determine the temperature prediction result; and determines the target cooling medium temperature based on the relationship between the temperature prediction result and the energy storage system's limiting temperature, as well as a preset cooling adjustment strategy. The preset cooling adjustment strategy pre-sets a correspondence between the target cooling medium temperature and the current cooling medium temperature for different relationships. This method can accurately predict the future maximum temperature of the battery cell, improving the reliability of temperature regulation. Furthermore, by dynamically adjusting the target cooling medium temperature based on the prediction result, the system can adapt to different operating states and external environments, avoiding insufficient or excessive cooling. Simultaneously, while meeting the temperature control requirements of the energy storage system, it optimizes the cooling system's energy consumption and reduces operating costs.

[0043] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0045] Figure 1 A flowchart of a thermal management method for an energy storage system based on temperature rise prediction, provided by an embodiment of this disclosure, is shown.

[0046] Figure 2 A flowchart is shown for another thermal management method for energy storage systems based on temperature rise prediction, provided by an embodiment of this disclosure.

[0047] Figure 3 A schematic diagram of a thermal management device for an energy storage system based on temperature rise prediction, provided in an embodiment of this disclosure, is shown.

[0048] Figure 4 A schematic diagram of an electronic device provided in an embodiment of the present disclosure is shown. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0050] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0051] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0052] Research has revealed that traditional thermal management systems primarily rely on a single variable—cell temperature—for control. For example, cooling is activated when the cell temperature exceeds a set threshold and deactivated when the temperature falls below that threshold. However, traditional thermal management strategies, designed solely based on cell temperature, can lead to several problems. At high temperatures, the system may over-rely on cooling equipment, resulting in excessive energy consumption; conversely, at low temperatures, insufficient cooling capacity may prevent timely reduction of cell temperature, potentially leading to overheating or undercooling. This not only reduces the overall efficiency of the energy storage system but also poses safety risks such as cell overheating and shortened lifespan.

[0053] Based on the above research, this disclosure provides a thermal management method and device for energy storage systems based on temperature rise prediction. The method involves collecting the heat generation power of the battery cell under different operating parameters; constructing a temperature rise prediction model with the current maximum battery temperature and the heat generation power as variable parameters, corresponding to the battery cell temperature after a preset temperature rise prediction time; acquiring the real-time maximum battery temperature and the real-time operating parameters of the battery cell, determining the real-time heat generation power matching the real-time operating parameters, and inputting the real-time maximum battery temperature and the real-time heat generation power into the temperature rise prediction model to determine the temperature prediction result; and determining the target cooling medium temperature based on the relationship between the temperature prediction result and the energy storage system's limiting temperature, as well as a preset cooling adjustment strategy. The preset cooling adjustment strategy pre-sets a correspondence between the target cooling medium temperature and the current cooling medium temperature for different temperature relationships. This method can accurately predict the future maximum temperature of the battery cell, improving the reliability of temperature regulation. Furthermore, by dynamically adjusting the target cooling medium temperature based on the prediction result, the system can adapt to different operating states and external environments, avoiding insufficient or excessive cooling. Simultaneously, while meeting the temperature control requirements of the energy storage system, it optimizes the cooling system's energy consumption and reduces operating costs.

[0054] To facilitate understanding of this embodiment, a detailed description of the thermal management method for energy storage systems based on temperature rise prediction disclosed in this disclosure embodiment will be provided first. The executing entity of the thermal management method for energy storage systems based on temperature rise prediction provided in this disclosure embodiment is generally a computer device with a certain computing capability. This computer device may include, for example, a terminal device, a server, or other processing devices. The terminal device may be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, this thermal management method for energy storage systems based on temperature rise prediction can be implemented by a processor calling computer-readable instructions stored in memory.

[0055] See Figure 1 The diagram shows a flowchart of a thermal management method for an energy storage system based on temperature rise prediction, according to an embodiment of this disclosure. The method includes steps S101 to S104, wherein:

[0056] S101. Collect the heat generation power of the battery cell under different operating parameters.

[0057] In practice, the heat generation power of the battery cell under different cell temperatures, SOC, and charge / discharge currents is measured in an experimental environment. For the charging state, a charging interpolation table is constructed to represent the heat generation power under different cell temperatures, SOC, and charging currents. For the discharging state, a discharging interpolation table is constructed to represent the heat generation power under different cell temperatures, SOC, and discharging currents.

[0058] Here, a high-precision isothermal calorimeter is selected to ensure accurate measurement of heat release from the battery cells at different temperatures, states of charge (SOC), and charge / discharge currents. The experimental environment must simulate the operating conditions of an actual energy storage system, including battery cell testing equipment, a temperature control system, and data recording devices. Furthermore, the battery cell specifications and models must be consistent with those of the actual energy storage system to ensure the representativeness of the experimental data.

[0059] Among them, the heat generation power under different combinations of variables was collected, and multiple experimental temperature points were set for the cell temperature, such as 25℃, 35℃ and 45℃, covering the actual working temperature range of the cell; the SOC ranged from 0% to 100%, set at certain intervals (such as every 5%), and a total of 21 state points were preferably selected; the charging and discharging current was selected from 0C to 1C, grouped at 0.2C intervals, and a total of 6 current rate points were set.

[0060] Specifically, under each group of cell temperature and SOC conditions, the cells were charged according to the set charging current rate, and the heat generation power of the cells under each group of conditions was recorded to obtain the corresponding experimental data; under each group of cell temperature and SOC conditions, the cells were discharged according to the set discharge current rate, and the heat generation power of the cells under each group of conditions was recorded to obtain the corresponding experimental data.

[0061] Furthermore, based on the experimental data, interpolation tables were constructed for charging and discharging conditions respectively. The charging interpolation table used cell temperature, SOC, and charging current as variables to record the interpolated value of heat generation power; the discharging interpolation table used cell temperature, SOC, and discharging current as variables to record the interpolated value of heat generation power.

[0062] Here, the interpolation table provides an accurate mapping of heat generation power under different operating parameters. The interpolation algorithm is used to extrapolate intermediate values ​​not covered by the experiment, thereby improving the continuity of data and the applicability of the model.

[0063] In this way, accurate data on the heat generation power of the battery cell can be obtained through isothermal calorimetry, ensuring the reliability of the prediction model. The experimental data of different combinations of temperature, SOC and current ratio can reflect the thermal performance of the battery cell under various operating conditions. The experimental results are stored in the form of interpolation tables, which simplifies the data query process in subsequent model calculations and saves computing resources.

[0064] S102. Construct a temperature rise prediction model that uses the current battery maximum temperature and the heat generation power as variable parameters, and corresponds to the cell temperature after a preset time for temperature rise prediction is enabled.

[0065] In practice, the input variables for the temperature rise prediction model include the battery's maximum temperature and the cell's heat generation power. The battery's maximum temperature reflects the current thermal state of the battery and is monitored in real time by the Battery Management System (BMS). The cell's heat generation power is provided by interpolation tables, which are obtained by looking up the charging and discharging interpolation tables in combination with the cell temperature, SOC, and charging / discharging current.

[0066] Here, a temperature rise prediction model is constructed based on the following formula:

[0067]

[0068] Among them, T qrt This represents the cell temperature after a preset time for temperature rise prediction has been activated; T max Represents the highest battery temperature; Cellnum represents the number of cells; V represents the heat generation power; h represents the heat dissipation coefficient; S represents the cooling contact area; T liq M represents the temperature of the cooling medium in the cooling equipment; C represents the battery mass. p T represents the specific heat of the battery. s This indicates a long preset time.

[0069] It should be noted that the heat dissipation coefficient is determined by the performance of the energy storage and liquid cooling system. In practical applications, the cooling contact area is the contact area between the cold plate and the bottom of the battery pack, and the preset time is preferably 1 hour.

[0070] S103. Obtain the real-time maximum temperature of the battery and the real-time operating parameters of the cell, determine the real-time heat generation power that matches the real-time operating parameters, and input the real-time maximum temperature of the battery and the real-time heat generation power into the temperature rise prediction model to determine the temperature prediction result.

[0071] In practice, the temperature prediction results are determined based on the following steps 1-4:

[0072] Step 1: Acquire the real-time maximum temperature, real-time cell temperature, real-time SOC, and real-time charging / discharging current of the battery collected by the battery management system.

[0073] Step 2: Based on the real-time cell temperature, the real-time SOC, and the real-time charge / discharge current, look up the corresponding real-time heat generation power in the charging interpolation table or the discharging interpolation table;

[0074] Step 3: According to the preset data acquisition frequency, the real-time maximum temperature of the battery and the real-time heat generation power collected at the corresponding time are input into the temperature rise prediction model to determine multiple temperature prediction results.

[0075] Step 4: Calculate the average of the temperature prediction results to determine the target temperature prediction result.

[0076] In practical implementation, the battery management system (BMS) collects data from the highest temperature sensor in the battery pack to calculate the current thermal state of the battery. Among the real-time operating parameters of the cells, the real-time State of Charge (SOC) reflects the current remaining capacity of the battery, and the BMS updates the SOC value in real time; the real-time charge / discharge current reflects the current charging or discharging rate of the battery, directly affecting the heat generation power of the cell; and the real-time cell temperature reflects the temperature distribution of the cell under the current operating conditions, providing a reference for interpolation matching.

[0077] Here, based on the real-time collected parameters (cell temperature, SOC, charging and discharging current), it is determined whether the current operating condition is charging or discharging. The heat generation power value that is closest to the real-time parameters is found in the corresponding interpolation table, and the real-time heat generation power under the current operating condition is calculated by using linear interpolation or multidimensional interpolation methods.

[0078] Once the temperature rise prediction function is enabled, the initial target cooling water temperature is set, and a prediction is performed every preset data acquisition frequency (e.g., 2 minutes). All real-time parameters are input into the temperature rise prediction formula for numerical calculation, and the result represents the highest temperature that the battery may reach after the prediction time.

[0079] Furthermore, the average value of the temperature prediction results obtained from multiple temperature predictions is used as the target temperature prediction result to improve the stability and accuracy of the prediction.

[0080] Here, the predicted temperature results will serve as a key input for subsequent thermal management strategies, used to adjust the target temperature of the refrigerant, dynamically control the start and stop times of the cooling system, and assess the thermal safety of the battery pack operation.

[0081] In this way, by matching real-time data with interpolation, the calculation of heat generation power is ensured to be accurate. Dynamic temperature rise prediction is adopted to provide advance for the cooling strategy and avoid failure or damage caused by excessive battery temperature.

[0082] S104. Based on the relationship between the predicted temperature and the energy storage system's limiting temperature, and a preset cooling regulation strategy, determine the target cooling medium temperature. The preset cooling regulation strategy pre-sets a correspondence between the target cooling medium temperature and the current cooling medium temperature for different relationships.

[0083] In practice, the temperature prediction result is the highest temperature of the battery at a certain future moment calculated by the temperature rise prediction model; the energy storage system limit temperature is the highest allowable temperature for safe operation of the system, exceeding this temperature may lead to a decrease in battery performance or safety hazards; the current refrigerant temperature is the temperature of the refrigerant provided by the current cooling equipment (such as the temperature of coolant or air); the target refrigerant temperature is the refrigerant temperature that needs to be set to adjust the cooling effect in response to the predicted temperature.

[0084] Here, the adjustment rules between the target refrigerant temperature and the current refrigerant temperature are defined based on the different temperature relationships between the degree prediction results and the energy storage system limit temperature.

[0085] As one possible implementation, if the temperature prediction result is not less than the energy storage system's limiting temperature, then the target refrigerant temperature is determined to be the difference between the current refrigerant temperature and a preset first temperature; if the temperature prediction result is not greater than the difference between the energy storage system's limiting temperature and a preset second temperature, then the target refrigerant temperature is determined to be the sum of the current refrigerant temperature and the preset first temperature; if the temperature prediction result is between the energy storage system's limiting temperature and the difference between the energy storage system's limiting temperature and the preset second temperature, then the current refrigerant temperature is determined to remain unchanged.

[0086] Here, when the predicted temperature exceeds the limit, the battery's future temperature may exceed the safe range, requiring immediate enhanced cooling. In this case, the current refrigerant temperature needs to be lowered to quickly reduce the system temperature and prevent it from exceeding the limit. When the predicted temperature is significantly lower than the limit, the system temperature is too low, and the cooling effect may be wasted. In this case, the current refrigerant temperature needs to be increased to reduce the cooling system's energy consumption and optimize operating efficiency. When the predicted temperature is close to the limit, the current refrigerant temperature should be kept constant to maintain system stability and avoid frequent adjustments.

[0087] Preferably, the preset first temperature can be 1℃, the preset second temperature can be 2℃, and the target cooling water temperature adjustment range is set to [18,26]℃.

[0088] This approach avoids safety hazards caused by excessive temperature, reduces cooling system energy consumption while ensuring safety, and minimizes system fluctuations caused by frequent temperature adjustments through strategy optimization. It achieves intelligent regulation of the energy storage system's thermal management, providing technical assurance for the system's operational safety and energy efficiency.

[0089] This disclosure provides a thermal management method for energy storage systems based on temperature rise prediction. The method collects the heat generation power of battery cells under different operating parameters; constructs a temperature rise prediction model with the current battery maximum temperature and the heat generation power as variable parameters, corresponding to the battery cell temperature after a preset temperature rise prediction time; obtains the real-time maximum battery temperature and the real-time operating parameters of the battery cells, determines the real-time heat generation power matching the real-time operating parameters, and inputs the real-time maximum battery temperature and the real-time heat generation power into the temperature rise prediction model to determine the temperature prediction result; and determines the target cooling medium temperature based on the relationship between the temperature prediction result and the energy storage system's limiting temperature, as well as a preset cooling adjustment strategy. The preset cooling adjustment strategy pre-sets a correspondence between the target cooling medium temperature and the current cooling medium temperature for different relationships. This method can accurately predict the future maximum temperature of the battery cells, improving the reliability of temperature regulation. Furthermore, by dynamically adjusting the target cooling medium temperature based on the prediction results, the system can adapt to different operating states and external environments, avoiding insufficient or excessive cooling. Simultaneously, while meeting the temperature control requirements of the energy storage system, it optimizes the cooling system's energy consumption and reduces operating costs.

[0090] See Figure 2 The diagram shows a flowchart of another thermal management method for an energy storage system based on temperature rise prediction, provided in an embodiment of this disclosure. The method includes steps S201 to S205, wherein:

[0091] S201. Collect the heat generation power of the battery cell under different operating parameters.

[0092] S202. Determine the cooling start-up time and cooling stop-down time of the refrigeration equipment according to different cell temperature thresholds under different charge / discharge rates; determine the time required for the refrigeration equipment to reach the target refrigeration medium temperature from the start of refrigeration based on the corresponding refrigeration performance of the refrigeration equipment; and determine the predicted start-up time of the refrigeration start-up time and the time required for refrigeration as the predicted start-up time of the start-up temperature prediction.

[0093] S203. Construct a temperature rise prediction model that uses the current battery maximum temperature and the heat generation power as variable parameters, and corresponds to the cell temperature after a preset time for temperature rise prediction is enabled.

[0094] S204. Obtain the real-time maximum temperature of the battery and the real-time operating parameters of the battery cell, determine the real-time heat generation power that matches the real-time operating parameters, and input the real-time maximum temperature of the battery and the real-time heat generation power into the temperature rise prediction model to determine the temperature prediction result.

[0095] S205. Based on the relationship between the predicted temperature and the energy storage system's limiting temperature, and a preset cooling regulation strategy, determine the target cooling medium temperature. The preset cooling regulation strategy pre-sets a correspondence between the target cooling medium temperature and the current cooling medium temperature for different relationships.

[0096] It should be noted that steps S201, S203-S205 are essentially the same as steps S101-S104, have the same implementation method and can achieve the same technical effect, and will not be described in detail here.

[0097] In practical implementation, under experimental conditions, the temperature change curves of the battery cell at different charge / discharge rates (such as 0.5C, 1C, 2C, etc.) are measured, and the time required for the temperature to reach the opening threshold and decrease to the closing threshold at each rate is recorded. The battery charge / discharge rate represents the charging and discharging speed of the battery capacity, expressed in multiples, such as 1C, which means fully charging or discharging within 1 hour.

[0098] Here, based on the real-time monitored cell temperature, when the temperature reaches the activation threshold, the time point at this moment is recorded as the cooling activation time point. After the cooling equipment starts working, the cell temperature gradually decreases to the shutdown threshold, and the time point at this moment is recorded as the cooling shutdown time point.

[0099] The time required for the refrigeration equipment to reach the target value from the start of refrigeration needs to be determined based on the actual liquid chiller calibration. The time varies depending on the performance of the refrigeration equipment (generally 5-10 minutes).

[0100] For example, when the charge / discharge rate is higher than 0.4C for 2 minutes, the cooling can be turned on when the battery's highest temperature is greater than 30°C, and the cooling can be turned off when the battery's highest temperature is not greater than 27°C; when the charge / discharge rate is between 0.2 and 0.4C for 2 minutes, the cooling can be turned on when the battery's highest temperature is greater than 36°C, and the cooling can be turned off when the battery's highest temperature is not greater than 33°C; when the charge / discharge rate is lower than 0.2C for 2 minutes, the cooling can be turned on when the battery's highest temperature is greater than 38°C, and the cooling can be turned off when the battery's highest temperature is not greater than 35°C.

[0101] This disclosure provides a thermal management method for an energy storage system based on temperature rise prediction. The method involves collecting the heat generation power of the battery cells under different operating parameters; determining the cooling start-up and cooling stop-down times of the cooling equipment based on different battery cell temperature thresholds at different charge / discharge rates; determining the cooling time required for the cooling equipment to reach the target cooling medium temperature from the start of cooling based on the corresponding cooling performance; and determining the sum of the cooling start-up time and the required cooling time as the predicted start-up time for temperature prediction. A temperature rise prediction model is constructed, using the current battery maximum temperature and the heat generation power as variable parameters, corresponding to the cell temperature after a preset temperature rise prediction time. The real-time maximum battery temperature and the real-time operating parameters of the cell are obtained, and the real-time heat generation power matching the real-time operating parameters is determined. The real-time maximum battery temperature and the real-time heat generation power are input into the temperature rise prediction model to determine the temperature prediction result. Based on the relationship between the temperature prediction result and the energy storage system's limiting temperature, and a preset cooling adjustment strategy, the target cooling medium temperature is determined. The preset cooling adjustment strategy pre-sets the correspondence between the target cooling medium temperature and the current cooling medium temperature for different temperature ranges. This method can accurately predict the future maximum temperature of the cell, improving the reliability of temperature regulation. Furthermore, by dynamically adjusting the target cooling medium temperature based on the prediction results, the system can adapt to different operating states and external environments, avoiding insufficient or excessive cooling. Simultaneously, while meeting the temperature control requirements of the energy storage system, it optimizes the cooling system's energy consumption and reduces operating costs.

[0102] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0103] Based on the same inventive concept, this disclosure also provides a thermal management device for an energy storage system based on temperature rise prediction, which corresponds to the thermal management method for an energy storage system based on temperature rise prediction. Since the principle of the device in this disclosure for solving the problem is similar to the thermal management method for an energy storage system based on temperature rise prediction described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0104] Please see Figure 3 , Figure 3 This is a schematic diagram of a thermal management device for an energy storage system based on temperature rise prediction, provided as an embodiment of this disclosure. Figure 3 As shown in the figure, the thermal management device 300 for an energy storage system based on temperature rise prediction provided in this embodiment includes:

[0105] The data acquisition module 310 is used to collect the heat generation power of the battery cell under different operating parameters.

[0106] The model building module 320 is used to build a temperature rise prediction model that uses the current battery maximum temperature and the heat generation power as variable parameters, corresponding to the cell temperature after a preset time for temperature rise prediction is enabled.

[0107] The temperature prediction module 330 is used to acquire the real-time maximum temperature of the battery and the real-time operating parameters of the battery cell, determine the real-time heat generation power that matches the real-time operating parameters, and input the real-time maximum temperature of the battery and the real-time heat generation power into the temperature rise prediction model to determine the temperature prediction result.

[0108] The refrigerant temperature adjustment module 340 is used to determine the target refrigerant temperature based on the relationship between the temperature prediction result and the energy storage system limit temperature and a preset refrigerant adjustment strategy. The preset refrigerant adjustment strategy pre-sets a correspondence between the target refrigerant temperature and the current refrigerant temperature for different relationships.

[0109] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0110] This disclosure provides a thermal management device for an energy storage system based on temperature rise prediction. It collects the heat generation power of the battery cell under different operating parameters; constructs a temperature rise prediction model with the current battery maximum temperature and the heat generation power as variable parameters, corresponding to the battery cell temperature after a preset temperature rise prediction time; obtains the real-time maximum battery temperature and the real-time operating parameters of the battery cell, determines the real-time heat generation power matching the real-time operating parameters, and inputs the real-time maximum battery temperature and the real-time heat generation power into the temperature rise prediction model to determine the temperature prediction result; and determines the target cooling medium temperature based on the relationship between the temperature prediction result and the energy storage system's limiting temperature, as well as a preset cooling adjustment strategy. The preset cooling adjustment strategy pre-sets a correspondence between the target cooling medium temperature and the current cooling medium temperature for different relationships. This device can accurately predict the future maximum temperature of the battery cell, improving the reliability of temperature regulation. Furthermore, by dynamically adjusting the target cooling medium temperature based on the prediction result, the system can adapt to different operating states and external environments, avoiding insufficient or excessive cooling. Simultaneously, while meeting the temperature control requirements of the energy storage system, it optimizes the cooling system's energy consumption and reduces operating costs.

[0111] Corresponding to Figure 1 and Figure 2The present disclosure also provides an electronic device 400, such as a thermal management method for energy storage systems based on temperature rise prediction. Figure 4 The diagram shown is a structural schematic of an electronic device 400 provided in an embodiment of this disclosure, including:

[0112] Processor 41, memory 42, and bus 43; memory 42 is used to store execution instructions, including main memory 421 and external memory 422; the main memory 421, also called internal memory, is used to temporarily store the computational data in processor 41, as well as the data exchanged with external memory 422 such as hard disk. Processor 41 exchanges data with external memory 422 through main memory 421. When the electronic device 400 is running, processor 41 and memory 42 communicate through bus 43, enabling processor 41 to execute... Figure 1 and Figure 2 The steps of the thermal management method for energy storage systems based on temperature rise prediction.

[0113] This disclosure also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs the steps of the energy storage system thermal management method based on temperature rise prediction described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0114] This disclosure also provides a computer program product, which includes computer instructions. When the computer instructions are executed by a processor, they can perform the steps of the thermal management method for energy storage systems based on temperature rise prediction as described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0115] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0116] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0117] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0119] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A thermal management method for energy storage systems based on temperature rise prediction, characterized in that, include: Collect the heat generation power of the battery cell under different operating parameters; A temperature rise prediction model is constructed, which uses the current highest battery temperature and the heat generation power as variable parameters, and corresponds to the cell temperature after a preset time for temperature rise prediction is activated. The real-time maximum temperature of the battery and the real-time operating parameters of the battery cell are obtained, the real-time heat generation power that matches the real-time operating parameters is determined, and the real-time maximum temperature of the battery and the real-time heat generation power are input into the temperature rise prediction model to determine the temperature prediction result. Based on the relationship between the predicted temperature and the energy storage system's limiting temperature, and a preset cooling regulation strategy, the target cooling medium temperature is determined. The preset cooling regulation strategy pre-sets a correspondence between the target cooling medium temperature and the current cooling medium temperature for different relationships.

2. The method according to claim 1, characterized in that, The heat generation power of the battery cell under different operating parameters is collected, specifically including: The heat generation power of the battery cell was measured under experimental conditions at different cell temperatures, SOC, and charge / discharge currents. For different charging states, a charging interpolation table is constructed to represent the heat generation power corresponding to different cell temperatures, SOC, and charging current states. For different discharge states, a discharge interpolation table is constructed to represent the heat generation power corresponding to different cell temperatures, SOC, and discharge current states.

3. The method according to claim 1, characterized in that, After collecting the heat generation power of the battery cell under different operating parameters, the method further includes: Determine the cooling start-up and cooling stop times for the refrigeration equipment to activate or deactivate the refrigeration function based on different cell temperature thresholds at different charge / discharge rates. Based on the refrigeration performance of the refrigeration equipment, determine the time required for the refrigeration equipment to reach the target refrigeration medium temperature from the start of refrigeration; The sum of the cooling start time and the cooling time required is determined as the predicted start time for the start temperature prediction.

4. The method according to claim 1, characterized in that, The temperature rise prediction model is constructed based on the following formula: Among them, T qrt This represents the cell temperature after the preset time for temperature rise prediction is activated; T max The highest temperature of the battery is represented by: Cellnum; V represents the number of cells; h represents the heat generation power; S represents the heat dissipation coefficient; and T represents the cooling contact area. liq M represents the temperature of the cooling medium in the cooling equipment; C represents the battery mass. p T represents the specific heat of the battery. s This indicates that the preset time is long.

5. The method according to claim 2, characterized in that, The system acquires the battery's real-time maximum temperature and the cell's real-time operating parameters, determines the real-time heat generation power matching the real-time operating parameters, and inputs the battery's real-time maximum temperature and the real-time heat generation power into the temperature rise prediction model to determine the temperature prediction result. Specifically, this includes: The battery's real-time maximum temperature, real-time cell temperature, real-time SOC, and real-time charging and discharging current are acquired in real time from the battery management system. Based on the real-time cell temperature, the real-time SOC, and the real-time charging and discharging current, look up the corresponding real-time heat generation power in the charging interpolation table or the discharging interpolation table; According to the preset data acquisition frequency, the real-time maximum temperature of the battery and the real-time heat generation power collected at the corresponding time are respectively input into the temperature rise prediction model to determine multiple temperature prediction results; The average of the temperature prediction results is calculated to determine the target temperature prediction result.

6. The method according to claim 1, characterized in that, Based on the relationship between the predicted temperature and the energy storage system's limiting temperature, and the preset cooling regulation strategy, the target refrigerant temperature is determined, specifically including: If the temperature prediction result is not less than the energy storage system's limiting temperature, then the target refrigerant temperature is determined to be the difference between the current refrigerant temperature and the preset first temperature. If the temperature prediction result is not greater than the difference between the energy storage system limit temperature and the preset second temperature, then the target refrigerant temperature is determined to be the sum of the current refrigerant temperature and the preset first temperature. If the temperature prediction result is between the energy storage system's limiting temperature and the difference between the energy storage system's limiting temperature and the preset second temperature, then it is determined that the current refrigerant temperature remains unchanged.

7. A thermal management device for an energy storage system based on temperature rise prediction, characterized in that, include: The data acquisition module is used to collect the heat generation power of the battery cell under different operating parameters; The model building module is used to build a temperature rise prediction model that uses the current highest battery temperature and the heat generation power as variable parameters, corresponding to the cell temperature after a preset time of temperature rise prediction. The temperature prediction module is used to obtain the real-time maximum temperature of the battery and the real-time operating parameters of the cell, determine the real-time heat generation power that matches the real-time operating parameters, and input the real-time maximum temperature of the battery and the real-time heat generation power into the temperature rise prediction model to determine the temperature prediction result. The refrigerant temperature adjustment module is used to determine the target refrigerant temperature based on the relationship between the temperature prediction result and the energy storage system limit temperature and a preset refrigerant adjustment strategy. The preset refrigerant adjustment strategy pre-sets a correspondence between the target refrigerant temperature and the current refrigerant temperature for different relationships.

8. The apparatus according to claim 7, characterized in that, The device is also used for: Determine the cooling start-up and cooling stop times for the refrigeration equipment to activate or deactivate the refrigeration function based on different cell temperature thresholds at different charge / discharge rates. Based on the refrigeration performance of the refrigeration equipment, determine the time required for the refrigeration equipment to reach the target refrigeration medium temperature from the start of refrigeration; The sum of the cooling start time and the cooling time required is determined as the predicted start time for the start temperature prediction.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the thermal management method for an energy storage system based on temperature rise prediction as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the thermal management method for an energy storage system based on temperature rise prediction as described in any one of claims 1 to 6.

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