Thermal management system of energy storage battery

Abstract

The invention relates to the technical field of battery thermal management, and discloses an energy storage battery thermal management system which comprises temperature sensors, a controller, a heat dissipation device, a heating device and a signal display screen, the temperature sensors are distributed at key positions, and the controller can accurately judge the temperature condition of a battery module according to collected data; a heat dissipation instruction is generated when the temperature is too high, and a heating instruction is generated when the temperature is too low, thereby ensuring that the battery works in a suitable temperature environment, facilitating performance maintenance and prolonging the service life; when nonuniformity occurs, an evaluation value is calculated through a series of operations in combination with related data of each monitoring area and compared with a threshold value, the abnormity of the sensors in the monitoring areas can be accurately judged, abnormal signals are generated and displayed in time, and faults can be quickly positioned and solved; regional evaluation and control are carried out according to temperature distribution uniformity, monitoring regions are divided when the temperature is not uniform, and regional battery bodies or abnormal connection can be accurately positioned by calculating data such as the temperature of each monitoring region and comparing the data with a threshold value, so that a regional control instruction is generated, and accurate regulation and control are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of battery thermal management, and in particular to a thermal management system for energy storage batteries. Background Art

[0002] With the rapid development of energy storage technology, energy storage batteries have been widely used in many fields, such as renewable energy storage, distributed power generation systems, and electric vehicles. However, energy storage batteries generate heat during the charging and discharging process. Failure to effectively manage battery temperature can lead to a series of serious problems.

[0003] On the one hand, excessively high temperatures accelerate the chemical reactions within the battery, leading to performance degradation such as capacity decay and increased internal resistance, severely impacting the battery's service life and charge / discharge efficiency. On the other hand, uneven temperature distribution within the battery module can cause batteries in different locations to age at different rates, further reducing the reliability and stability of the entire energy storage system and potentially triggering safety incidents such as thermal runaway, posing a significant threat to personnel and equipment.

[0004] Traditional thermal management systems for energy storage batteries often suffer from numerous shortcomings. Some systems lack accurate and comprehensive temperature monitoring, with temperature sensors installed in only a few locations. This makes it difficult to accurately reflect the actual temperature conditions within the battery module as a whole and within each region. Furthermore, the ability to detect temperature sensor failures is limited. Any sensor anomaly can lead to erroneous control instructions, compromising thermal management effectiveness. Furthermore, there is a lack of refined regional temperature control, making it impossible to effectively adjust heat dissipation or heating to address temperature differences within different regions of the battery module, making it difficult to meet the increasingly demanding performance and safety requirements of energy storage batteries.

[0005] Therefore, in order to overcome the defects of existing technologies and ensure the efficient, safe and stable operation of energy storage batteries, it is particularly urgent and important to develop a new energy storage battery thermal management system that can accurately monitor temperature, effectively detect sensor failures, and perform regionalized precise evaluation and control of battery modules. Summary of the Invention

[0006] The purpose of the present invention is to provide a thermal management system for an energy storage battery, which solves the technical problems raised in the background technology.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A thermal management system for an energy storage battery, comprising:

[0009] Temperature sensors are distributed at various key locations of the energy storage battery module to collect battery temperature data in real time;

[0010] The controller is used to receive temperature data from the temperature sensor, perform temperature control analysis on the temperature data, and generate corresponding control instructions. The corresponding control instructions are used to drive the operation of the heat dissipation device and the heating device. It is also used to divide the energy storage battery module into multiple monitoring areas, and perform sensor fault analysis on the temperature data based on the temperature data from the temperature sensor. Based on the analysis results, it is determined whether the temperature sensor is abnormal, and regional assessment analysis and regional adjustment control of the energy storage battery module are performed based on the sensor fault analysis results.

[0011] a heat dissipation device, configured to activate a heat dissipation function in conjunction with corresponding control instructions and adopt air cooling and / or liquid cooling to reduce the battery temperature to a specified temperature range;

[0012] The heating device is used to combine corresponding control instructions, use electric heating elements to start the heating function, and increase the battery temperature to a specified temperature range.

[0013] The signal display screen is used to display abnormal temperature sensor signals and battery abnormality signals to relevant management personnel.

[0014] As a further solution of the present invention: the temperature sensors are specifically and evenly distributed on the surface of each single battery of the energy storage battery pack and at key heat conduction path positions inside the battery pack.

[0015] As a further solution of the present invention: the temperature control analysis method is as follows:

[0016] Step A1: Mark the temperature values ​​collected in real time by multiple temperature sensors at multiple relevant key locations as T i , i=1, 2, ... n, n represents the number of temperature sensors, that is, the number of key locations;

[0017] Step A2, by:

[0018] ;

[0019] Calculate the average temperature TP of the energy storage battery module;

[0020] Step A3, then compare the average temperature value TP of the energy storage battery module with the corresponding preset maximum allowable temperature T max and the minimum allowable temperature T min For comparison:

[0021] If TP>T max , it means that the temperature of the energy storage battery module is too high and a heat dissipation control instruction is generated;

[0022] Also through:

[0023] ;

[0024] Calculate the heat dissipation power P of the heat dissipation device cool ;

[0025] Where k1 is the preset heat dissipation coefficient, which is determined according to the type and performance of the heat dissipation device;

[0026] If TP<T min , it means that the temperature of the energy storage battery module is too low, and a heating control instruction is generated;

[0027] Also through:

[0028] ;

[0029] Calculate the heat dissipation power P of the heat dissipation device heat ;

[0030] Where k2 is the preset heating coefficient, which is determined according to the type and performance of the heating device;

[0031] If T min ≤TP≤T max , it means that the temperature of the energy storage battery module is moderate, and no heat dissipation control instruction or heating control instruction is generated.

[0032] As a further solution of the present invention: the sensor failure analysis method is as follows:

[0033] Step B1, by:

[0034] ;

[0035] Calculate the temperature value T collected by each temperature sensor i The difference from the average temperature TC i ;

[0036] Step B2, by:

[0037] ;

[0038] Calculate the temperature standard deviation TB of the energy storage battery module;

[0039] Step B3, extracting the corresponding preset distribution threshold FBy;

[0040] Then, the temperature distribution uniformity within the energy storage battery module is evaluated by the temperature standard deviation TB and the distribution threshold FBy:

[0041] If TB>FBy, it indicates uneven temperature distribution, and a fault assessment analysis is then performed;

[0042] If TB≤FBy, it indicates that the temperature distribution is uniform.

[0043] As a further solution of the present invention: wherein, the fault assessment and analysis method is as follows:

[0044] Step B3.1. Divide the energy storage battery module into m monitoring areas, and each detection area is equipped with a heat dissipation device and a heating device;

[0045] Among them, m monitoring areas contain corresponding number of temperature sensors;

[0046] Step B3.2: When TB>FBy, the |TC used in the standard deviation is i |Sort in descending order, and then delete the largest|TC i | The Ti value corresponding to the value;

[0047] Then calculate the standard deviation of the remaining Ti;

[0048] Then compare the standard deviation of the remaining Ti with the distribution threshold FBy. If the standard deviation of the remaining Ti is still greater than the distribution threshold FBy, continue to delete the |TC used in the standard deviation of the remaining Ti. i | Medium, Maximum | TC i | The Ti value corresponding to the value;

[0049] Among them, || is the absolute value symbol;

[0050] And so on, until the standard deviation of all undeleted Ti is less than or equal to the distribution threshold FBy;

[0051] Step B3.3, in each monitoring area, obtain the number of corresponding deleted temperature values ​​in each monitoring area and mark it as SS j , j = 1, 2, ... m;

[0052] And by obtaining the number of all temperature sensors corresponding to each monitoring area and marking it as N0 j ;

[0053] Then pass:

[0054] BN j =N0 j -SS j ;

[0055] Calculate the first evaluation value BN of each monitoring area j ;

[0056] Step B3.4. Calculate the average value of the corresponding deleted temperature values ​​in each monitoring area and record it as TSP j ;

[0057] And calculate the average value of all temperature values ​​in each monitoring area and record it as T0P j ;

[0058] Then pass

[0059] BT j =|T0P j -TSP j |;

[0060] Calculate the second evaluation value BT of each monitoring area j ;

[0061] Among them, || is the absolute value symbol;

[0062] Step B3.5, the first evaluation value BN of each monitoring area j and the second evaluation value BT j Compare with the preset first evaluation threshold BNy and second evaluation threshold BTy respectively:

[0063] Select a monitoring area:

[0064] When BN j <BNy and BT j >BTy, it means that there is a temperature sensor monitoring anomaly in the monitoring area. Then, the temperature sensor corresponding to the deleted temperature value in the monitoring area is extracted, and the temperature sensor anomaly signal of the monitoring area is generated according to the preset sensor number corresponding to the corresponding temperature sensor;

[0065] When BN j <BNy and BT j >If at least one item of BTy is not true, it means that the corresponding temperature sensor abnormal signal is not generated in the monitoring area.

[0066] As a further solution of the present invention: Based on the temperature distribution uniformity within the energy storage battery module, regional evaluation analysis and regional adjustment control are performed on the energy storage battery module:

[0067] When the temperature distribution is uniform, regional assessment analysis and regional adjustment control are not performed;

[0068] When the temperature distribution is uneven, regional assessment analysis and regional adjustment control are carried out as follows:

[0069] Step C1, the energy storage battery module is divided into m monitoring areas, and each detection area is equipped with a heat dissipation device and a heating device;

[0070] Among them, m monitoring areas contain corresponding number of temperature sensors;

[0071] Step C2: Calculate the average temperature of each monitoring area according to the method of Step A1-Step A2 and mark it as TP1 j , j = 1, 2, ... m;

[0072] Step C3, obtain the average temperature value corresponding to each adjacent monitoring area;

[0073] Select a group of two adjacent monitoring areas;

[0074] The corresponding average temperature values ​​are marked as TP1 a and TP1 b ;

[0075] Among them, TP1 a ∈TP1 j TP1 b ∈TP1 j , and TP1 a ≠TP1 b ;

[0076] Step C4, through:

[0077] TPC ab =TP1 a -TP1 b ;

[0078] Calculate the average temperature difference TPC corresponding to each adjacent monitoring area ab ;

[0079] Step C5: calculate the average temperature difference TPC corresponding to each of the two adjacent monitoring areas. ab Compare with the preset regional temperature difference threshold TPCy:

[0080] If TPC ab >TPCy, it means that there are battery body or connection abnormalities in the two adjacent monitoring areas of the energy storage battery module, and a battery abnormality signal is generated;

[0081] If TPC ab <TPCy, no battery abnormality signal is generated;

[0082] Step C6, follow the method of Step A3 to calculate the average temperature value TP1 of each monitoring area. j Respectively correspond to the preset maximum allowable temperature T max and the minimum allowable temperature T min A comparison is performed, and corresponding regional heat dissipation control instructions and heating control instructions are generated based on the comparison results.

[0083] As a further solution of the present invention: wherein, the regional heat dissipation control instruction and heating control instruction refer to the heat dissipation function and heating function that only control the corresponding monitoring area.

[0084] Beneficial effects of the present invention:

[0085] Real-time monitoring and accurate control: By evenly distributing temperature sensors at key locations throughout the energy storage battery module, real-time battery temperature data can be collected. The controller uses this data to perform temperature control analysis and accurately determine the overall temperature of the energy storage battery module. If the temperature is too high, a heat dissipation control instruction is generated, driving the heat dissipation device to reduce the battery temperature to a specified range using air cooling and / or liquid cooling. If the temperature is too low, a heating control instruction is generated, using the heating device's electric heating element or heat exchanger to raise the battery temperature to a specified range. This ensures that the battery always operates at an appropriate temperature, which helps maintain battery performance and extend battery life.

[0086] Timely fault detection: It has a complete sensor fault analysis mechanism. By calculating the difference between the temperature value collected by each temperature sensor and the average temperature value, as well as the temperature standard deviation of the energy storage battery module, the uniformity of the temperature distribution can be evaluated. When the temperature distribution is uneven, a series of operations such as sorting and deleting abnormal values ​​are further performed. Combined with data such as the number of deleted temperature values ​​in each monitoring area, the number of all temperature sensors, and the corresponding temperature average value, the first evaluation value and the second evaluation value of each monitoring area are calculated and compared with the preset threshold value. It can accurately determine which monitoring area has abnormal temperature sensor monitoring, generate abnormal temperature sensor signals in time and display them on the signal display screen, so that relevant management personnel can know them, help to quickly locate and solve sensor failure problems, and ensure the accuracy of temperature monitoring.

[0087] Precise positioning of regional anomalies: Based on the uniformity of temperature distribution within the energy storage battery module, regional assessment analysis and regional adjustment control of the energy storage battery module can be performed when the temperature distribution is uneven. By dividing the energy storage battery module into multiple monitoring areas, calculating the average temperature value of each monitoring area, obtaining the average temperature difference between two adjacent monitoring areas and comparing it with the preset regional temperature difference threshold, it can accurately determine whether there are battery body or connection abnormalities in adjacent monitoring areas, and generate a battery abnormality signal once an abnormality is found. At the same time, based on the comparison results of the average temperature value of each monitoring area with the preset maximum and minimum allowable temperatures, regional heat dissipation control instructions and heating control instructions only for the corresponding monitoring area can be generated to achieve precise regulation of different areas, further enhance the thermal management system's control capabilities over the energy storage battery module, and ensure the stable operation of the entire energy storage battery system. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] The present invention will be further described below with reference to the accompanying drawings.

[0089] Figure 1 It is a system block diagram of an energy storage battery thermal management system of the present invention.

[0090] Figure 2 It is a schematic diagram of a module in a thermal management system of an energy storage battery of the present invention. DETAILED DESCRIPTION

[0091] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0092] Example 1

[0093] See also Figure 1 and Figure 2 As shown, the present invention is a thermal management system for an energy storage battery, comprising:

[0094] Temperature sensors are distributed at various key locations of the energy storage battery module to collect battery temperature data in real time;

[0095] The temperature sensors are evenly distributed on the surface of each single cell of the energy storage battery pack and at key heat conduction path locations inside the battery pack;

[0096] The controller is used to receive temperature data from the temperature sensor, perform temperature control analysis on the temperature data, and generate corresponding control instructions, which are used to drive the operation of the heat dissipation device and the heating device;

[0097] The temperature control analysis method is as follows:

[0098] Step A1: Mark the temperature values ​​collected in real time by multiple temperature sensors at multiple relevant key locations as T i , i=1, 2, ... n, n represents the number of temperature sensors, that is, the number of key locations;

[0099] Step A2, by:

[0100] ;

[0101] Calculate the average temperature TP of the energy storage battery module;

[0102] Step A3, then compare the average temperature value TP of the energy storage battery module with the corresponding preset maximum allowable temperature T max and the minimum allowable temperature T min For comparison:

[0103] If TP>T max , it means that the temperature of the energy storage battery module is too high and a heat dissipation control instruction is generated;

[0104] Also through:

[0105] ;

[0106] Calculate the heat dissipation power P of the heat dissipation device cool ;

[0107] Where k1 is the preset heat dissipation coefficient, which is determined according to the type and performance of the heat dissipation device;

[0108] If TP<T min , it means that the temperature of the energy storage battery module is too low, and a heating control instruction is generated;

[0109] Also through:

[0110] ;

[0111] Calculate the heat dissipation power P of the heat dissipation device heat ;

[0112] Where k2 is the preset heating coefficient, which is determined according to the type and performance of the heating device;

[0113] If T min ≤TP≤T max , it means that the temperature of the energy storage battery module is moderate, and no heat dissipation control instruction or heating control instruction is generated;

[0114] a heat dissipation device, configured to activate a heat dissipation function in conjunction with corresponding control instructions and adopt air cooling and / or liquid cooling to reduce the battery temperature to a specified temperature range;

[0115] A heating device, configured to activate a heating function by utilizing an electric heating element in conjunction with corresponding control instructions to raise the battery temperature to a specified temperature range;

[0116] In this embodiment, the heat dissipation device and the heating device are both existing technologies, so they are not described in detail.

[0117] This embodiment evenly distributes temperature sensors at key locations throughout the energy storage battery module, enabling real-time battery temperature data collection. Based on this data, the controller performs temperature control analysis and generates corresponding control instructions to drive the cooling or heating device, effectively maintaining the battery module within an appropriate temperature range and ensuring battery performance and lifespan. By comparing the calculated average temperature of the energy storage battery module with the preset maximum and minimum allowable temperatures, the controller automatically generates cooling or heating control instructions and accurately calculates the required heat dissipation power of the cooling device, achieving intelligent and precise regulation of battery temperature.

[0118] Example 2

[0119] See also Figure 1 and Figure 2 As shown, as the second embodiment of the present invention, when the present application is specifically implemented, compared with the first embodiment, the technical solution of this embodiment is different from that of the first embodiment only in that:

[0120] The controller is also used to divide the energy storage battery module into multiple monitoring areas and perform sensor fault analysis based on the temperature data transmitted by the temperature sensor;

[0121] Sensor failure analysis methods are as follows:

[0122] Step B1, by:

[0123] ;

[0124] Calculate the temperature value T collected by each temperature sensor i The difference from the average temperature TC i ;

[0125] Step B2, by:

[0126] ;

[0127] Calculate the temperature standard deviation TB of the energy storage battery module;

[0128] Step B3, extracting the corresponding preset distribution threshold FBy;

[0129] Then, the temperature distribution uniformity within the energy storage battery module is evaluated by the temperature standard deviation TB and the distribution threshold FBy:

[0130] If TB>FBy, it indicates uneven temperature distribution, and a fault assessment analysis is then performed;

[0131] If TB≤FBy, it indicates that the temperature distribution is uniform;

[0132] The fault assessment and analysis methods are as follows:

[0133] Step B3.1. Divide the energy storage battery module into m monitoring areas, and each detection area is equipped with a heat dissipation device and a heating device;

[0134] Among them, m monitoring areas contain corresponding number of temperature sensors;

[0135] Step B3.2: When TB>FBy, the |TC used in the standard deviation is i |Sort in descending order, and then delete the largest|TC i | The Ti value corresponding to the value;

[0136] Then calculate the standard deviation of the remaining Ti;

[0137] Then compare the standard deviation of the remaining Ti with the distribution threshold FBy. If the standard deviation of the remaining Ti is still greater than the distribution threshold FBy, continue to delete the |TC used in the standard deviation of the remaining Ti. i | Medium, Maximum | TC i | Ti value corresponding to the value;

[0138] Among them, || is the absolute value symbol;

[0139] And so on, until the standard deviation of all undeleted Ti is less than or equal to the distribution threshold FBy;

[0140] Step B3.3, in each monitoring area, obtain the number of corresponding deleted temperature values ​​in each monitoring area and mark it as SS j , j = 1, 2, ... m;

[0141] And by obtaining the number of all temperature sensors corresponding to each monitoring area and marking it as N0 j ;

[0142] Then pass:

[0143] BN j =N0 j -SS j ;

[0144] Calculate the first evaluation value BN of each monitoring area j ;

[0145] Step B3.4. Calculate the average value of the corresponding deleted temperature values ​​in each monitoring area and record it as TSP j ;

[0146] And calculate the average value of all temperature values ​​in each monitoring area and record it as T0P j ;

[0147] Then pass

[0148] BT j =|T0P j -TSP j |;

[0149] Calculate the second evaluation value BT of each monitoring area j ;

[0150] Among them, || is the absolute value symbol;

[0151] Step B3.5, the first evaluation value BN of each monitoring area j and the second evaluation value BT j Compare with the preset first evaluation threshold BNy and second evaluation threshold BTy respectively:

[0152] Select a monitoring area:

[0153] When BN j <BNy and BT j >BTy, it means that there is a temperature sensor monitoring anomaly in the monitoring area. Then, the temperature sensor corresponding to the deleted temperature value in the monitoring area is extracted, and the temperature sensor anomaly signal of the monitoring area is generated according to the preset sensor number corresponding to the corresponding temperature sensor;

[0154] When BN j <BNy and BT j > If at least one item of BTy is not true, it means that the corresponding temperature sensor abnormal signal is not generated in the monitoring area;

[0155] This embodiment also includes:

[0156] Signal display screen, used to display abnormal temperature sensor signals to relevant management personnel;

[0157] In this embodiment, based on the first embodiment, the controller divides the energy storage battery module into multiple monitoring zones and performs sensor fault analysis based on temperature sensor data. By calculating the difference between the temperature value and the average temperature value, the temperature standard deviation, and other factors, the controller can assess the uniformity of the temperature distribution and detect potentially faulty temperature sensors. Through a series of calculation and comparison steps, such as processing data related to deleted temperature values ​​within the monitoring zone, the controller can ultimately accurately determine whether there are temperature sensor monitoring anomalies in each monitoring zone. The controller then generates an anomaly signal based on a preset number, making it easier for relevant management personnel to quickly locate and resolve faulty sensors.

[0158] Example 3

[0159] See also Figure 1 and Figure 2As shown, as the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments. The technical solution of this embodiment differs from the first and second embodiments only in that:

[0160] This embodiment performs regional assessment and analysis and regional adjustment control on the energy storage battery module based on the sensor failure analysis and the temperature distribution uniformity within the energy storage battery module:

[0161] When the temperature distribution is uniform, regional assessment analysis and regional adjustment control are not performed;

[0162] When the temperature distribution is uneven, regional assessment analysis and regional adjustment control are carried out:

[0163] The specific method is as follows:

[0164] Step C1, the energy storage battery module is divided into m monitoring areas, and each detection area is equipped with a heat dissipation device and a heating device;

[0165] Among them, m monitoring areas contain corresponding number of temperature sensors;

[0166] Step C2: Calculate the average temperature of each monitoring area according to the method of Step A1-Step A2 and mark it as TP1 j , j = 1, 2, ... m;

[0167] Step C3, obtain the average temperature value corresponding to each adjacent monitoring area;

[0168] Select a group of two adjacent monitoring areas;

[0169] The corresponding average temperature values ​​are marked as TP1 a and TP1 b ;

[0170] Among them, TP1 a ∈TP1 j TP1 b ∈TP1 j , and TP1 a ≠TP1 b ;

[0171] Step C4, through:

[0172] TPC ab =TP1 a -TP1 b ;

[0173] Calculate the average temperature difference TPC corresponding to each adjacent monitoring areaab ;

[0174] Step C5: calculate the average temperature difference TPC corresponding to each of the two adjacent monitoring areas. ab Compare with the preset regional temperature difference threshold TPCy:

[0175] If TPC ab >TPCy, it means that there are battery body or connection abnormalities in the two adjacent monitoring areas of the energy storage battery module, and a battery abnormality signal is generated;

[0176] If TPC ab <TPCy, no battery abnormality signal is generated;

[0177] Step C6, follow the method of Step A3 to calculate the average temperature value TP1 of each monitoring area. j Respectively correspond to the preset maximum allowable temperature T max and the minimum allowable temperature T min Perform comparisons and generate corresponding regional heat dissipation control instructions and heating control instructions based on the comparison results;

[0178] Among them, the regional heat dissipation control instructions and heating control instructions refer to the heat dissipation function and heating function that only control the corresponding monitoring area;

[0179] For example:

[0180] Assume that the energy storage battery module is divided into two monitoring areas, left and right, that is, m=2.

[0181] Step C1: Install a heat sink, a heating device, and temperature sensors in the left and right monitoring areas. There are three temperature sensors in the left monitoring area and three temperature sensors in the right monitoring area.

[0182] Step C2: The average temperature value TP11 of the left monitoring area is calculated to be 35°C, and the average temperature value TP12 of the right monitoring area is calculated to be 25°C.

[0183] Step C3-Step C4, select this group of adjacent monitoring areas, TP1 a =TP11=35℃,TP1 b =TP12=25℃, calculate the average temperature difference TPC12=TP11-TP12=35℃-25℃=10℃.

[0184] Step C5: Assume that the preset regional temperature difference threshold TPCy = 5°C. Since TPC12 = 10°C > TPCy = 5°C, a battery abnormality signal is generated, indicating that there may be problems with the battery body or connection on the left and right sides.

[0185] But we can still try to adjust and control the temperature balance.

[0186] Step C6, because TP11>TP12, and TP11=35℃ is close to the preset maximum allowable temperature T max , assuming T max =40℃, while TP12=25℃ is much lower than T max , so the heat dissipation control instruction for the left monitoring area is generated, the left heat dissipation device is started, and the left temperature is reduced; at the same time, since the right temperature is lower and is far from the minimum allowable temperature T min , assuming T min =15℃, there is still a certain distance, so the right heating control command is temporarily not generated. Continuously monitor temperature changes. When the temperature difference is less than the threshold or within a reasonable range, adjust or stop the control command according to the specific situation.

[0187] For example, after a period of heat dissipation, the temperature on the left side drops to 30°C, while the temperature on the right side rises to 28°C due to factors such as heat conduction. At this time, the temperature difference is 2°C < TPCy = 5°C. Stop the heat dissipation control command and continue monitoring.

[0188] The signal display screen is also used to display battery abnormality signals to relevant management personnel.

[0189] This embodiment combines the solutions of embodiment one and embodiment two, and can not only perform sensor fault analysis, but also perform regional module assessment analysis and regional adjustment control based on the temperature distribution uniformity within the energy storage battery module. When the temperature distribution is uneven, by calculating the average temperature difference between adjacent monitoring areas and comparing it with a preset threshold, it can quickly determine whether there is an abnormality in the battery body or connection, and generate a battery abnormality signal for timely processing to ensure the normal operation of the battery system. Based on the comparison results of the average temperature value of each monitoring area with the preset maximum and minimum allowable temperatures, regional heat dissipation control instructions and heating control instructions can be generated to achieve targeted adjustment of battery temperatures in different areas, improving the accuracy and effectiveness of temperature control.

[0190] Example 4

[0191] As the fourth embodiment of the present invention, when the present application is implemented, compared with the first, second and third embodiments, the only difference between this embodiment and the first, second and third embodiments is that in this embodiment: the energy storage battery is wrapped with a heat-insulating material to reduce heat exchange between the battery and the external environment, thereby reducing the energy consumption of the thermal management system;

[0192] The thermal insulation material of the energy storage battery is any one of aerogel, foam material and glass fiber;

[0193] Aerogel is a high-performance thermal insulation material with extremely low thermal conductivity. Its nanoporous structure can effectively inhibit heat transfer and achieve good thermal insulation effect at a very thin thickness.

[0194] Foam materials include polystyrene foam and polyurethane foam. These foam materials have a large number of closed pores, which hinder the conduction of heat through the pores, thus providing thermal insulation. For example, polyurethane foam has a low thermal conductivity, which provides good thermal insulation for batteries and can be customized according to the shape of the battery module.

[0195] Glass fiber is made by drawing glass into filaments and then weaving or filling it into thermal insulation materials; it can effectively reduce heat radiation and conduction, and also has certain fire resistance, which can improve the safety of battery systems;

[0196] This embodiment wraps the energy storage battery with insulation material, which can effectively reduce heat exchange between the battery and the external environment, reduce the energy consumption of the thermal management system, and improve energy utilization efficiency. It also provides a variety of insulation materials such as aerogel, foam material, and glass fiber. These materials have their own characteristics, such as the high-performance thermal insulation of aerogel, the customizable processing of foam material, and the fire-resistant properties of glass fiber. They can be flexibly selected according to actual needs to further optimize the performance of the battery thermal management system.

[0197] Example 5

[0198] As the fifth embodiment of the present invention, when this application is specifically implemented, compared with the first, second, third and fourth embodiments, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned first, second, third and fourth embodiments.

[0199] This embodiment combines and implements the solutions of the above-mentioned embodiments 1, 2, 3, and 4, integrating the advantages of each embodiment to achieve comprehensive optimization of the energy storage battery thermal management system in multiple aspects, including temperature monitoring, sensor fault detection, regional assessment and adjustment, and energy consumption reduction. It can maximize the stable operation of the energy storage battery system, improve performance, extend life, and reduce energy consumption.

[0200] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0201] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A thermal management system for an energy storage battery, characterized in that: include: Temperature sensors are distributed at various key locations of the energy storage battery module to collect battery temperature data in real time; The controller is used to receive temperature data from the temperature sensor, perform temperature control analysis on the temperature data, and generate corresponding control instructions. The corresponding control instructions are used to drive the operation of the heat dissipation device and the heating device. It is also used to divide the energy storage battery module into multiple monitoring areas, and perform sensor fault analysis on the temperature data based on the temperature data from the temperature sensor. Based on the analysis results, it is determined whether the temperature sensor is abnormal, and regional assessment analysis and regional adjustment control of the energy storage battery module are performed based on the sensor fault analysis results. a heat dissipation device, configured to activate a heat dissipation function in conjunction with corresponding control instructions and adopt air cooling and / or liquid cooling to reduce the battery temperature to a specified temperature range; A heating device, configured to activate a heating function in conjunction with corresponding control instructions and utilize an electric heating element to raise the battery temperature to a specified temperature range; The signal display screen is used to display abnormal temperature sensor signals and battery abnormality signals to relevant management personnel.

2. The energy storage battery thermal management system according to claim 1, characterized in that: The temperature control analysis method is as follows: Step A1: Mark the temperature values ​​collected in real time by multiple temperature sensors at multiple relevant key locations as T i , i=1, 2, ... n, n represents the number of temperature sensors, that is, the number of key locations; Step A2: Calculate all temperature values ​​T in the energy storage battery module i The average value of is recorded as the average temperature value TP; Step A3, then compare the average temperature value TP of the energy storage battery module with the corresponding preset maximum allowable temperature T max and the minimum allowable temperature T min A comparison is performed, and based on the comparison result, it is determined whether a heating control instruction and a heat dissipation control instruction are generated.

3. The energy storage battery thermal management system according to claim 2, characterized in that: The comparison method in Step A3 is as follows: If TP>T max , then generate heat dissipation control instructions; Also through: ； Calculate the heat dissipation power P of the heat dissipation device cool ; Where k1 is the preset heat dissipation coefficient; If TP<T min , then generate heating control instructions; Also through: ； Calculate the heat dissipation power P of the heat dissipation device heat ; Where k2 is the preset heating coefficient; If T min ≤TP≤T max , no cooling control instruction and heating control instruction are generated.

4. The energy storage battery thermal management system according to claim 1, characterized in that: Sensor failure analysis methods are as follows: Step B1: Temperature values ​​T collected by various temperature sensors i Subtract the average temperature value TP to calculate the temperature value T collected by each temperature sensor i The difference from the average temperature is recorded as TC i ; Step B2: Calculate all temperature values ​​T in the energy storage battery module i The standard deviation of , and record it as the temperature standard deviation TB; Step B3, extracting the corresponding preset distribution threshold FBy; Then, the temperature distribution uniformity within the energy storage battery module is evaluated by the temperature standard deviation TB and the distribution threshold FBy: If TB>FBy, it indicates that the temperature distribution is uneven, and then a fault assessment analysis is performed; If TB≤FBy, it indicates that the temperature distribution is uniform.

5. The energy storage battery thermal management system according to claim 4, characterized in that: in, The fault assessment and analysis method is as follows: Step B3.

1. Divide the energy storage battery module into m monitoring areas, and each detection area is equipped with a heat dissipation device and a heating device; Among them, m monitoring areas contain corresponding numbers of temperature sensors, j = 1, 2, ... m; Step B3.2: When TB>FBy, the |TC used in the standard deviation is i |Sort in descending order, and then delete the largest|TC i | The Ti value corresponding to the value; Then calculate the standard deviation of the remaining Ti; Then compare the standard deviation of the remaining Ti with the distribution threshold FBy. If the standard deviation of the remaining Ti is still greater than the distribution threshold FBy, continue to delete the |TC used in the standard deviation of the remaining Ti. i | Medium, Maximum | TC i | The Ti value corresponding to the value; Among them, || is the absolute value symbol; And so on, until the standard deviation of all undeleted Ti is less than or equal to the distribution threshold FBy; Step B3.3, in each monitoring area, obtain the number of corresponding deleted temperature values ​​in each monitoring area, and obtain the number of all corresponding temperature sensors in each monitoring area; Then, the number of all temperature sensors in each monitoring area is subtracted from the number of deleted temperature values ​​in the corresponding monitoring area, and the result is recorded as the first evaluation value BN of the corresponding monitoring area. j ; Step B3.4, simultaneously calculate the average value of the deleted temperature values ​​in each monitoring area, and calculate the average value of all temperature values ​​in each monitoring area; Then calculate the absolute value of the difference between the average value of all temperature values ​​in each monitoring area and the average value of the deleted temperature values ​​in the corresponding monitoring area, and record it as the second evaluation value BT of the corresponding monitoring area j ; Step B3.5, the first evaluation value BN of each monitoring area j and the second evaluation value BT j The temperature sensor is compared with the preset first evaluation threshold BNy and second evaluation threshold BTy respectively, and a corresponding temperature sensor abnormality signal is generated according to the comparison result.

6. The energy storage battery thermal management system according to claim 5, characterized in that: The comparison method in Step B3.5 is as follows: Select a monitoring area: When BN j <BNy and BT j >BTy, it means that there is a temperature sensor monitoring anomaly in the monitoring area. Then, the temperature sensor corresponding to the deleted temperature value in the monitoring area is extracted, and the temperature sensor anomaly signal of the monitoring area is generated according to the preset sensor number corresponding to the corresponding temperature sensor; When BN j <BNy and BT j >If at least one item of BTy is not true, it means that the corresponding temperature sensor abnormal signal is not generated in the monitoring area.

7. The energy storage battery thermal management system according to claim 4, characterized in that: The controller also performs regional assessment analysis and regional adjustment control on the energy storage battery module based on the temperature distribution uniformity within the energy storage battery module: When the temperature distribution is uneven, regional assessment analysis and regional adjustment control are carried out; When the temperature distribution is uniform, regional assessment analysis and regional adjustment control are not performed.

8. The energy storage battery thermal management system according to claim 7, characterized in that: Regional assessment analysis and regional adjustment control methods are as follows: Step C1, the energy storage battery module is divided into m monitoring areas, and each detection area is equipped with a heat dissipation device and a heating device; Among them, m monitoring areas contain corresponding number of temperature sensors; Step C2: Calculate the average temperature of each monitoring area according to the method of Step A1-Step A2 and mark it as TP1 j , j = 1, 2, ... m; Step C3, obtain the average temperature value corresponding to each adjacent monitoring area; Select a group of two adjacent monitoring areas; The corresponding average temperature values ​​are marked as TP1 a and TP1 b ; Among them, TP1 a ∈TP1 j TP1 b ∈TP1 j , and TP1 a ≠TP1 b ; Step C4, through: TPC ab =TP1 a -TP1 b ; Calculate the average temperature difference TPC corresponding to each adjacent monitoring area ab ; Step C5: calculate the average temperature difference TPC corresponding to each of the two adjacent monitoring areas. ab Compare the temperature with the preset regional temperature difference threshold TPCy and generate a corresponding battery abnormality signal based on the comparison result; Step C6, follow the method of Step A3 to calculate the average temperature value TP1 of each monitoring area. j Respectively correspond to the preset maximum allowable temperature T max and the minimum allowable temperature T min A comparison is performed, and corresponding regional heat dissipation control instructions and heating control instructions are generated based on the comparison results.

9. The energy storage battery thermal management system according to claim 8, characterized in that: The comparison method in Step C5 is as follows: If TPC ab >TPCy, it means that there are battery body or connection abnormalities in the two adjacent monitoring areas of the energy storage battery module, and a battery abnormality signal is generated; If TPC ab <TPCy, no battery abnormality signal is generated.

10. The energy storage battery thermal management system according to claim 8, characterized in that: in, Regional heat dissipation control instructions and heating control instructions refer to the heat dissipation function and heating function that only control the corresponding monitoring area.

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