Battery temperature estimation method and system and battery system
By constructing a battery node thermal model and using a real-time correction method, the problem of difficulty in estimating battery core temperature was solved, enabling real-time estimation of battery core temperature and accurate acquisition of heat generation power, thereby reducing the risk of lithium plating and hardware costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-04-14
AI Technical Summary
Existing technologies make it difficult to accurately estimate the temperature of the battery core, which leads to the risk of local lithium plating in the core during fast charging. Furthermore, the placement of sensors inside the core presents technical challenges and high costs.
By constructing an initial lumped parameter thermal model for each node of the battery, and using the measured data from the top cover temperature sensor and preset correction coefficients, the thermal model is adjusted to estimate the battery core temperature in real time, thus avoiding the direct placement of the sensor inside the core.
It enables real-time estimation of battery core temperature, reduces the risk of lithium plating, provides accurate real-time battery heat generation power, identifies battery aging trends, and reduces hardware costs.
Smart Images
Figure CN121856809A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of battery technology, and in particular to battery temperature estimation methods, systems, and battery systems. Background Technology
[0002] As battery fast charging rates increase, the temperature difference along the height (Z-axis) of batteries (such as prismatic batteries) becomes significant due to bottom cooling. Typically, a temperature sensor is placed on the top cover of the battery to detect its temperature. However, the temperature measured in this way may differ significantly from the actual core temperature under certain operating conditions. For example, the temperature near the liquid cooling plate at the bottom of the core may be lower, potentially leading to localized lithium plating risks when determining the charging current based on the measured top cover temperature during fast charging. Furthermore, the core temperature itself is difficult to measure using sensors. Therefore, it is necessary to provide a solution that can estimate the battery core temperature in real time. Summary of the Invention
[0003] A battery temperature estimation method, system, and battery system are provided to estimate the battery core temperature in real time.
[0004] In a first aspect, a battery temperature estimation method is provided, wherein the battery is equipped with multiple nodes, and the multiple nodes include at least a first node, a second node, and a third node that are sequentially adjacent; the first node is equipped with a temperature detection module; the method includes: Determine the initial lumped parameter thermal model for each node of the battery; The calculated temperature of the first node is determined based on the initial lumped parameter thermal model; Obtain the measured temperature of the first node detected by the temperature detection module; The calculated temperatures of the second and third nodes are determined based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model. The temperature at the bottom of the battery core is estimated based on the calculated temperatures of the second and third nodes.
[0005] In some embodiments, the first node is the top cover of the battery, the second node is the center of the large surface of the battery, and the third node is the bottom of the large surface of the battery.
[0006] In some embodiments, a method for determining the initial lumped parameter thermal model for each node of the battery includes: Determine the battery's heat generation power, heat dissipation power, and heat flow between each node and adjacent nodes; The initial lumped parameter thermal model of each node of the battery is determined based on the heat generation power, heat dissipation power, heat flow between each node and adjacent nodes, and pre-calibrated battery parameters.
[0007] In some embodiments, the method for determining heat generation power includes: Obtain the battery's current current, DC internal resistance, and Kelvin temperature; The irreversible heat of the battery is determined based on the current and DC internal resistance; The reversible heat of the battery is determined based on the current, Kelvin temperature, and preset entropy thermal coefficient. The heat generation power is determined based on the irreversible heat, the reversible heat, and the preset correction coefficient.
[0008] In some embodiments, determining the calculated temperature of the second node and the calculated temperature of the third node based on the calculated temperature, the measured temperature, a preset correction factor, and the initial lumped parameter thermal model includes: The target lumped parameter thermal model is determined based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model. The calculation temperatures of the second and third nodes are determined based on the target lumped parameter thermal model.
[0009] In some embodiments, determining the target lumped parameter thermal model based on the calculated temperature, the measured temperature, a preset correction factor, and the initial lumped parameter thermal model includes: The model bias of the initial lumped parameter thermal model is determined based on the calculated temperature and the measured temperature. The model deviation is adjusted according to the preset filtering algorithm, preset correction coefficient, and the correspondence between the preset correction coefficient and the model deviation to obtain the target ensemble parameter thermal model.
[0010] In some embodiments, the model deviation is adjusted according to a preset filtering algorithm, preset correction coefficients, and the correspondence between the preset correction coefficients and the model deviation to obtain a target lumped parameter thermal model, including: The target correction coefficient is obtained by correcting the preset correction coefficient according to the preset filtering algorithm; The model deviation is adjusted according to the correspondence between the target correction coefficient, the preset correction coefficient and the model deviation, so as to obtain the target lumped parameter thermal model.
[0011] In some embodiments, estimating the core bottom temperature of the battery based on the calculated temperatures of the second node and the third node includes: The first item is determined based on the calculated temperature of the second node and the first preset weight; The second item is determined based on the calculated temperature of the third node and the second preset weight; Estimate the temperature at the bottom of the battery core based on the sum of the first and second items.
[0012] Secondly, embodiments of this application also provide a battery temperature estimation system, wherein the battery is provided with multiple nodes, the multiple nodes including a first node, a second node and a third node that are sequentially adjacent; the first node is provided with a temperature detection module; the system includes: The first determining module is used to determine the initial lumped parameter thermal model for each node of the battery; The second determining module is used to determine the calculation temperature of the first node based on the initial lumped parameter thermal model; The acquisition module is used to acquire the measured temperature of the first node detected by the temperature detection module; The third determination module is used to determine the calculation temperature of the second node and the calculation temperature of the third node based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model. The estimation module is used to estimate the core bottom temperature of the battery based on the calculated temperatures of the second and third nodes.
[0013] Thirdly, this application also provides a battery system including the battery temperature estimation system as described in the second aspect.
[0014] Beneficial Effects: This application provides a battery temperature estimation method, system, and battery system. The battery temperature estimation method includes: determining an initial lumped parameter thermal model for each node of the battery; determining the calculated temperature of a first node based on the initial lumped parameter thermal model; obtaining the measured temperature of the first node detected by a temperature detection module; determining the calculated temperatures of a second node and a third node based on the calculated temperature, the measured temperature, a preset correction coefficient, and the initial lumped parameter thermal model; and estimating the bottom temperature of the battery core based on the calculated temperatures of the second and third nodes. The battery temperature estimation method provided in this application obtains the calculated temperature of the first node by constructing an initial lumped parameter thermal model for each node of the battery, adjusts the initial lumped parameter thermal model based on the calculated temperature, the measured temperature, and a preset correction coefficient to obtain a target lumped parameter thermal model, and then obtains the calculated temperatures of other nodes based on the target lumped parameter thermal model, thereby estimating the battery core temperature in real time based on the calculated temperatures of other nodes. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings, wherein the same reference numerals in the following description denote the same parts.
[0017] Figure 1 This is a schematic diagram of the square battery structure provided in the embodiments of this application; Figure 2 This is a flowchart of a battery temperature estimation method provided in the embodiments of this application; Figure 3 This is an equivalent thermal path diagram of the lumped parameter thermal model provided in the embodiments of this application; Figure 4 This is a schematic diagram of battery temperature estimation under fast charging conditions provided in the embodiments of this application; Figure 5 This is a schematic diagram of the technical route of the battery temperature estimation method provided in the embodiments of this application; Figure 6 This is a schematic diagram of the principle structure of a battery temperature estimation system provided in the embodiments of this application. Detailed Implementation
[0018] The technical solutions of the embodiments of this application 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 application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application.
[0019] In the embodiments of this application, "at least one" refers to one or more; "multiple" refers to two or more. In the description of this application, the terms "first," "second," "third," etc., are used only for the purpose of distinguishing descriptions and should not be construed as indicating or implying relative importance, nor should they be construed as indicating or implying order.
[0020] References such as “one embodiment” or “some embodiments” as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the terms “comprising,” “including,” “having,” and variations thereof, as used in this specification, mean “including, but not limited to,” unless otherwise specifically emphasized.
[0021] It should be noted that in the embodiments of this application, "and / or" describes the relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. In addition, the character " / ", unless otherwise specified, generally indicates that the associated objects before and after it are in an "or" relationship.
[0022] It should be noted that in the embodiments of this application, "connection" can be understood as electrical connection. The connection between two electrical components can be a direct or indirect connection between the two electrical components. For example, the connection between A and B can be a direct connection between A and B, or an indirect connection between A and B through one or more other electrical components.
[0023] The applicant's research revealed that as battery fast-charging rates increase, the temperature difference along the height (Z-axis) of the battery (e.g., prismatic batteries) becomes significant due to cooling of the bottom surface. A temperature sensor is typically placed on the battery top cover to detect the battery temperature. However, the temperature measured in this way may differ significantly from the actual core temperature under certain operating conditions. For example, the temperature near the liquid cooling plate at the bottom of the core may be lower, potentially leading to localized lithium plating risks when determining the charging current based on the measured top cover temperature during fast charging. Furthermore, the battery core temperature itself is difficult to measure using sensors.
[0024] One of the mainstream cooling solutions for square lithium-ion batteries in the industry is bottom cooling, where the bottom of the square battery contacts a liquid cooling plate. During high-rate fast charging, the heat generated is significant, and heat dissipation through the bottom results in a large temperature difference along the battery's height (Z-axis). The industry typically uses a temperature sensor placed at the top cover of the battery. However, the measured temperature may be inaccurate, and it needs to be used to determine the fast charging current from a table. In reality, the temperature at the bottom of the core near the liquid cooling plate may be lower than expected. Due to the characteristics of the battery's electrochemical system, lithium plating on the negative electrode is more likely to occur at low temperatures, posing a safety risk to the battery and needing to be avoided. Therefore, determining the fast charging current from a table based on the measured top cover temperature during fast charging may lead to the risk of localized lithium plating at the bottom of the core. If the core temperature could be obtained, it could be used as the battery bottleneck temperature and then used to determine the charging current from a table to reduce the risk of localized lithium plating. Currently, similar solutions to avoid localized lithium plating in the core often involve leaving redundancy when determining the fast charging current from the top cover temperature table; the approach is relatively simple, for example, slightly reducing the current obtained from the table lookup.
[0025] The temperature of the battery's internal core can differ significantly from the temperature measured by conventional surface temperature sensors under different operating conditions and thermal management conditions. The core temperature is closer to the battery's true bottleneck temperature. Measuring this temperature by placing a temperature sensor inside the battery presents significant technical challenges and could negatively impact the battery's electrochemical system. For example, internal temperature sensors can affect battery aging lifespan, and the technology is also costly. Furthermore, the temperature sensor leads are a major issue for battery pack engineering applications. For instance, even large-area center temperature measurement is rarely used in power battery packs. The difficulty lies in the fact that measuring the center temperature of a large area requires long temperature sensor leads with bends, which can lead to long-term durability and reliability issues. Therefore, the current mainstream solution is to place the temperature sensor at the top cover of the battery. A simpler approach to avoid localized lithium plating in the core, such as slightly reducing the current during fast charging by referring to a table based on the top cover temperature, has the main drawback of being difficult to adapt to the changing operating conditions of the battery. The redundancy may be too large or too small, lacking sufficient theoretical basis.
[0026] In view of this, embodiments of this application provide a battery temperature estimation method, system, and battery system. Embodiments of this application obtain the calculated temperature of the first node by constructing an initial lumped parameter thermal model of each node of the battery. The initial lumped parameter thermal model is adjusted according to the calculated temperature, measured temperature, and preset correction coefficient of the first node to obtain a target lumped parameter thermal model. The calculated temperatures of other nodes are obtained according to the target lumped parameter thermal model, and the battery core temperature is estimated in real time according to the calculated temperatures of other nodes.
[0027] It should be noted that the battery in this application embodiment can be a prismatic battery, a cylindrical battery, etc. For example, a prismatic battery is used as an example in this application embodiment, and will not be repeated below. The battery temperature estimation method provided in this application embodiment is applicable to the real-time estimation of battery core temperature during battery charging (e.g., fast charging and slow charging) and discharging (e.g., vehicle battery discharging scenarios). For example, a fast charging condition is used as an example in this application embodiment.
[0028] For example, this application embodiment uses the first node, the second node, and the third node (i.e., the first node is adjacent to the second node, the second node is adjacent to both the first node and the third node, and the third node is adjacent to the second node) as the top cover, the center of the large surface, and the bottom of the large surface of the battery in sequence, which will not be repeated below.
[0029] Figure 1 This is a schematic diagram of a square battery structure provided in an embodiment of this application. Exemplarily, the relationships between the top cover, the top of the large surface, the center of the large surface, the bottom of the large surface, the bottom of the core, and the bottom liquid cooler are as follows: Figure 1 As shown.
[0030] Figure 2 This is a flowchart illustrating a battery temperature estimation method provided in an embodiment of this application. This method is applicable to the real-time estimation of battery cell temperature within a battery management system. The method can be executed by a battery temperature estimation system, which can be implemented in software and / or hardware and can be configured within the processor or controller of the battery management system. Please refer to... Figure 2 The method includes the following steps: Step 110: Determine the initial lumped parameter thermal model for each node of the battery.
[0031] Figure 3 This is an equivalent thermal path diagram of the lumped-parameter thermal model provided in the embodiments of this application. For example, regarding... Figure 1 A simplified Z-axis lumped-parameter thermal model of a bottom-cooled square battery is established based on heat transfer principles, such as... Figure 3 As shown. The model simplifications mainly include: Actual measurements and simulations show that the temperature difference between the top cover and the top of the main surface is within 1℃ under all operating conditions, so they are considered equivalent. The battery heat generation Q is divided into three equal parts using the principle of lumped parameter modeling; the virtual division transfers the differences in physical components to the thermal resistance R and heat capacity C that need to be calibrated. Heat dissipation can be calculated directly using the temperature difference between the coolant inlet and outlet combined with the flow rate, or it can be calculated based on the average temperature T of the coolant inlet and outlet under conditions without a flow sensor. 冷却液 and the third thermal resistance Calculation (using this as an example below).
[0032] For example, see Figure 3 The equivalent thermal path includes a first node, a second node, a third node, a fourth node, and a first heat capacity. Second heat capacity Third heat capacity First thermal resistance Second thermal resistance and the third thermal resistance Among them, the first node, the second node, the third node, and the fourth node are, in order, the top cover of the battery, the center of the large surface, the bottom of the large surface, and the coolant.
[0033] In some embodiments, a method for determining the initial lumped parameter thermal model of each node of a battery includes: determining the heat generation power, heat dissipation power, and heat flow between each node and adjacent nodes of the battery; and determining the initial lumped parameter thermal model of each node of the battery based on the heat generation power, heat dissipation power, heat flow between each node and adjacent nodes, and pre-calibrated battery parameters.
[0034] Among them, the pre-calibrated battery parameters include the battery's thermal capacity C and thermal resistance R.
[0035] See Figure 3 For a temperature node in a lumped parameter thermal model The expression for the lumped-parameter thermal model is: ; in, Heat capacity, measured in J / K; It is time, and its unit is seconds (s). Thermal resistance, measured in K / W; The heat output power is expressed in W. Heat dissipation power, measured in W; Represents temperature nodes Heat flow to its surrounding nodes (or adjacent nodes); Indicates temperature node Adjacent nodes, temperature unit is K.
[0036] It should be noted that the heat generation / dissipation power part in the expression of the above lumped parameter thermal model may or may not be involved. The specific details depend on the actual node conditions of the equivalent thermal path in the actual lumped parameter thermal model, and no specific limitations are made here.
[0037] For example, see Figure 3 Taking the first, second, and third adjacent nodes as the top cover, center of the large surface, and bottom of the large surface of the battery, respectively, as an example, the first node has some exposed areas that dissipate heat. However, due to the effect of the battery pack's insulation layer, the heat leakage to the environment (i.e., heat dissipation) is minimal, so this part of the heat dissipation power can be ignored. Therefore, the expression for the initial lumped parameter thermal model of the first node is: ; See Figure 3 The expressions for the initial lumped parameter thermal models of the second and third nodes are as follows: ; ; In some embodiments, the method for determining the heat generation power includes: acquiring the current current, DC internal resistance, and Kelvin temperature of the battery; determining the irreversible heat of the battery based on the current current and DC internal resistance; determining the reversible heat of the battery based on the current current, Kelvin temperature, and a preset entropy coefficient; and determining the heat generation power based on the irreversible heat, the reversible heat, and a preset correction coefficient.
[0038] The heat generated by a battery consists of irreversible heat and reversible heat. The battery's heat generation power... The calculation formula is: ; in, Indicates irreversible heat; Indicates reversible heat; This indicates a preset correction factor, which is a reserved correction factor. On the one hand, it is considered that the DC internal resistance data provided by the battery supplier may have some deviation. On the other hand, it is also considered that the battery will age after long-term operation, and the DC internal resistance may change, thus affecting heat generation.
[0039] Among them, irreversible heat For ternary lithium batteries, the terminal voltage U and open-circuit voltage can be used as a basis. For the differential pressure calculation, due to the potential for significant errors in the State of Charge (SOC) during actual applications of lithium iron phosphate batteries, it is recommended to calculate the irreversible thermal resistance based on the DC internal resistance (i.e., thermal resistance R). Among them, open-circuit voltage Obtained from a table based on SOC. Irreversible heat. The calculation formula is: ; Where I represents the current current, which is the real-time current of the battery during operation (such as fast charging).
[0040] Among them, reversible heat It is based on the pre-determined entropy-heat coefficient measured experimentally. Calculations show that reversible heat... The calculation formula is: ; in, Calculations are performed using Kelvin temperature values. If the entropy-heat coefficient parameters are incomplete and high-rate scenarios are considered, reversible heat can be ignored. The preset entropy-heat coefficient... Related to SOC.
[0041] Among them, the pre-calibrated battery parameters include the battery's thermal capacity (e.g., the first thermal capacity). Second heat capacity and the third heat capacity ) and thermal resistance (e.g., first thermal resistance) Second thermal resistance and the third thermal resistance ).
[0042] For example, taking the battery fast charging condition as an example, the first heat capacity... Second heat capacity Third heat capacity First thermal resistance Second thermal resistance and the third thermal resistance The calibration process is as follows: Steady-state heat generation of the battery is achieved through pulsed operating conditions (where reversible heat generation cancels out each other due to the same magnitude but opposite sign of the calculated heat generation power during charging and discharging, and alternating charging and discharging maintains a stable SOC; irreversible heat generation also maintains stability). Calibration is performed by calibrating the above six parameters separately for prismatic batteries of different capacities and sizes. Specific testing methods include: discharging a fully charged battery to 50% SOC; setting the high-low temperature chamber to 25℃ and placing the battery pack with a windproof shield inside; activating the battery liquid cooling function, for example, circulating 15℃ coolant, and continuing for 3 hours to achieve complete thermal equilibrium; applying a bidirectional square wave pulse current with a frequency of 1Hz and a rate of 1C to the battery, i.e., alternating 1C constant current charging and 1C constant current discharging within a 1s time period; and continuing the pulse current for more than 3 hours to achieve a stable thermal equilibrium state. Each cell in the battery pack has three thermocouples: one on the top cover, one at the center of the large surface area, and one at the bottom of the large surface area (approximately 5mm from the bottom edge). Three stable temperature values were collected after thermal equilibrium. , , These are the temperatures of the top cover, the center of the large surface area, and the bottom of the large surface area, respectively. Based on the voltage difference between the open-circuit voltage and the terminal voltage at 50% SOC and the current data, the battery heat generation power Q at thermal equilibrium is calculated. Since the heat generation power equals the heat dissipation power, the system reaches thermal equilibrium steady state, and the first thermal resistance can then be calculated. Second thermal resistance and the third thermal resistance First thermal resistance Second thermal resistance and the third thermal resistance The calculation formulas are as follows: ; ; ; in, This indicates the temperature of the coolant.
[0043] After cutting off the pulse current, cooling continued and data was collected for another 3 hours to obtain three cooling curves. These curves were then combined with the known thermal resistance value, specifically the first thermal resistance. Second thermal resistance and the third thermal resistance and the first heat capacity Second heat capacity and third heat capacity The relationship is used to calibrate the first heat capacity. Second heat capacity and third heat capacity The heat capacity value. First heat capacity. Second heat capacity and third heat capacity The relationships are as follows: ; ; ; Step 120: Determine the calculation temperature of the first node based on the initial lumped parameter thermal model.
[0044] Specifically, based on the expressions for the initial lumped parameter thermal model of the first node, the initial lumped parameter thermal model of the second node, and the initial lumped parameter thermal model of the third node, the calculation temperature of the first node can be calculated. .
[0045] Step 130: Obtain the measured temperature of the first node detected by the temperature detection module.
[0046] The temperature detection module includes temperature sensors and other temperature detection components.
[0047] The temperature detection module is located at the top cover and is used to detect the actual temperature value of the top cover.
[0048] Step 140: Determine the calculated temperature of the second node and the calculated temperature of the third node based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model.
[0049] In some embodiments, determining the calculation temperature of the second node and the calculation temperature of the third node based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model includes: determining the target lumped parameter thermal model based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model; and determining the calculation temperature of the second node and the calculation temperature of the third node based on the target lumped parameter thermal model.
[0050] Specifically, the initial lumped parameter thermal model is modified based on the calculated temperature of the first node, the measured temperature of the first node, and a preset correction coefficient to obtain the modified lumped parameter thermal model, i.e., the target lumped parameter thermal model. Then, the calculated temperatures of the second and third nodes can be calculated based on the target lumped parameter thermal model. Therefore, by modifying the initial lumped parameter thermal model, the calculation accuracy of the target lumped parameter thermal model can be improved, thereby improving the estimation accuracy of the calculated temperatures of the second and third nodes. This, in turn, helps to improve the accuracy of subsequent estimation of the bottom temperature of the battery core and facilitates real-time estimation of the battery core temperature.
[0051] In some embodiments, determining the target lumped parameter thermal model based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model includes: determining the model deviation of the initial lumped parameter thermal model based on the calculated temperature and the measured temperature; adjusting the model deviation based on the preset filtering algorithm, the preset correction coefficient, and the correspondence between the preset correction coefficient and the model deviation to obtain the target lumped parameter thermal model.
[0052] The correspondence between the preset correction coefficient and the model deviation is achieved by using a filtering algorithm (for example, the preset filtering algorithm is Kalman filtering), that is, the preset correction coefficient k is automatically adjusted by the filtering algorithm to minimize the model deviation.
[0053] Specifically, the deviation ΔT between the calculated and measured temperatures of the first node can be obtained. This deviation ΔT can be used to characterize the model bias of the initial lumped parameter thermal model (such as the model's calculation error and cumulative bias). Then, the lumped parameter thermal model is corrected according to a preset filtering algorithm and correction coefficients. The accuracy of the calculated temperatures of the second and third nodes obtained from the corrected lumped parameter thermal model will be improved, which will help improve the accuracy of subsequent battery core bottom temperature estimation and facilitate real-time estimation of battery core temperature.
[0054] In some embodiments, adjusting the model deviation according to a preset filtering algorithm, a preset correction coefficient, and the correspondence between the preset correction coefficient and the model deviation to obtain a target lumped parameter thermal model includes: correcting the preset correction coefficient according to the preset filtering algorithm to obtain a target correction coefficient; and adjusting the model deviation according to the target correction coefficient, the correspondence between the preset correction coefficient and the model deviation to obtain a target lumped parameter thermal model.
[0055] Specifically, a preset filtering algorithm is introduced to correct the preset correction coefficient k in the battery heat generation power calculation formula in real time, obtaining the target correction coefficient. Then, based on the correspondence between the preset correction coefficient and the model deviation, and the corrected preset correction coefficient k (i.e., the target correction coefficient), the model deviation ΔT of the initial lumped parameter thermal model can be minimized, achieving real-time correction of the model error of the initial lumped parameter thermal model, and obtaining the target lumped parameter thermal model. Since the target lumped parameter thermal model is obtained by implementing error correction, the calculated temperatures of the second and third nodes can be calculated in real time based on the real-time corrected target lumped parameter thermal model, which helps improve the accuracy of subsequent battery core bottom temperature estimation and facilitates real-time estimation of battery core temperature. Furthermore, the real-time estimated battery core temperature is closer to the actual bottleneck temperature of the battery, which can be used to look up tables to determine the charging current, reducing the risk of lithium plating. Moreover, the corrected preset correction coefficient can obtain a more accurate real-time battery heat generation power, which can be used for battery thermal management control. In addition, the change trend of DC internal resistance after battery aging can be identified based on the change in the correction magnitude of heat generation power throughout the battery's entire life cycle (i.e., the change trend of the preset correction coefficient k).
[0056] Step 150: Estimate the bottom temperature of the battery core based on the calculated temperature of the second node and the calculated temperature of the third node.
[0057] Since the calculated temperatures of the second and third nodes are obtained from the target lumped parameter thermal model that is corrected in real time, the temperature at the bottom of the battery core can be estimated in real time based on the calculated temperatures of the second and third nodes.
[0058] In some embodiments, estimating the core bottom temperature of the battery based on the calculated temperature of the second node and the calculated temperature of the third node includes: determining a first term based on the calculated temperature of the second node and a first preset weight; determining a second term based on the calculated temperature of the third node and a second preset weight; and estimating the core bottom temperature of the battery based on the sum of the first term and the second term.
[0059] The first preset weight is a pre-calibrated correlation weight coefficient. Specifically, the correlation weight coefficient is a pre-calibrated correlation weight coefficient relating to the calculated temperature at the center of the large surface (i.e., the calculated temperature of the second node) and the calculated temperature at the bottom of the large surface (i.e., the calculated temperature of the third node). For example, the calibration process for the first preset weight is as follows: The correlation relationship between the bottom temperature of the battery core and the temperatures at the center and bottom of the large surface is pre-calibrated using a high-order thermal model, i.e., the correlation weight coefficient b in the empirical formula is calibrated.
[0060] Among them, the high-order thermal model is a detailed three-dimensional thermal model that can reflect the specific structure of the battery (including the core, casing, etc.) and the thermally conductive adhesive layer between the battery and the liquid cooling plate.
[0061] The sum of the first preset weight and the second preset weight is 1.
[0062] Specifically, based on the calculated temperatures at the center and bottom of the large surface area, combined with the pre-calibrated bottom temperature of the battery core using a high-order thermal model and the calculated temperature T at the center of the large surface area,... 大面中心 and the calculated temperature T at the bottom of the large surface 大面底部 Based on the correlation, the bottom temperature of the battery core is estimated. The formula for calculating the estimated bottom temperature of the battery core is as follows: Estimated temperature at the bottom of the core = b * T 大面中心 +(1-b)*T 大面底部 ; In some embodiments, the first node is the top cover of the battery, the second node is the center of the large surface of the battery, and the third node is the bottom of the large surface of the battery.
[0063] It is understood that the battery temperature estimation method provided in this application obtains the calculated temperature of the first node by constructing an initial lumped parameter thermal model of each node of the battery, and adjusts the initial lumped parameter thermal model according to the calculated temperature of the first node, the measured temperature and the preset correction coefficient to obtain the target lumped parameter thermal model, thereby obtaining the calculated temperature of other nodes according to the target lumped parameter thermal model, and thus estimating the battery core temperature in real time according to the calculated temperature of other nodes.
[0064] Figure 4 This is a schematic diagram illustrating battery temperature estimation under fast charging conditions provided in this application embodiment. For example, a battery pack experiment was conducted using the battery temperature estimation method provided in this application embodiment. The battery cell of interest was modified in the early stages by embedding a temperature sensor inside, which can measure the temperature at the bottom of the core. The results of the fast charging process are as follows... Figure 4 As shown. Figure 4 In the diagram, curve L1 represents the change in the measured temperature of the top cover, curve L2 represents the change in the estimated temperature of the center of the large surface, curve L3 represents the change in the estimated temperature of the bottom of the core, curve L4 represents the change in the measured temperature of the bottom of the core, and curve L5 represents the change in the estimated temperature of the bottom of the large surface. The comparison shows that the battery core temperature estimation method provided in this application embodiment performs well, with high estimation accuracy. For example, the maximum temperature difference between the top cover and the bottom of the core reaches 8.5℃, and the maximum deviation between the estimated and measured bottom temperature of the core is 1.5℃.
[0065] The battery temperature estimation provided in this application embodiment is used to perform real-time temperature estimation for all batteries with temperature sensors arranged in the battery pack. That is, in addition to the measured top cover temperature data, the estimated bottom temperature data of the core is also added. The two temperature data are used to look up the fast charging current table and take the smaller value to reduce the risk of local lithium plating in the battery.
[0066] Meanwhile, the model can also use the output real-time battery heat generation power Q for battery thermal management control, and can also identify the changing trend of DC internal resistance after battery aging by the changing trend of the preset correction coefficient k in the heat generation power calculation formula (for example, by observing that the preset correction coefficient k gradually increases during battery aging, it can be determined that the DC internal resistance of the battery gradually increases after aging).
[0067] Figure 5 This is a schematic diagram of the technical route of the battery temperature estimation method provided in the embodiments of this application. For example, see [link to relevant documentation]. Figure 5 First, a Z-axis lumped-parameter thermal model of the battery is established, including heat generation, heat transfer (the model's heat capacity and thermal resistance parameters are calibrated through pulsed thermal balance testing), and heat dissipation (measured inlet and outlet water temperatures of the liquid cooling plate). Then, the temperature at each location on the battery is calculated in real time. The temperature of the battery top cover is measured using a temperature sensor, and the difference between the measured and calculated values, i.e., the model deviation ΔT, is calculated. A filtering algorithm is used to correct the initial lumped-parameter thermal model in real time, outputting the calculated temperature values at each location on the battery and the battery's heat generation power. The relationship between the temperature at each location on the battery and the core temperature is obtained through high-dimensional thermal model simulation. Based on the relationship between the temperature at each location on the battery and the core temperature, and the temperature values at each location on the battery, the core temperature is estimated.
[0068] In summary, this application establishes a temperature estimation model based on the Z-axis lumped parameter thermal model of a square battery (i.e., the lumped parameter thermal model of each node of the battery). Model parameters such as thermal resistance and thermal capacity are calibrated through a designed battery pulse thermal balance test. During battery operation, heat generation, heat dissipation, and heat transfer can all be calculated in real time within the Battery Management System (BMS). (The lumped parameter thermal model meets the low computational requirements; higher-order thermal models, such as three-dimensional thermal models, while accurate, are difficult to use for online real-time calculation due to their high computational cost.) Simultaneously, data collected by a temperature sensor at the top of the battery is used to correct the initial lumped parameter thermal model in real time, avoiding computational errors such as cumulative deviations caused by long-term model operation. After real-time correction of the initial lumped parameter thermal model, a relatively accurate battery heat generation power can be output, and based on the relationship between the temperature at various locations of the battery and the core temperature, the estimated value of the battery core temperature can be output in real time.
[0069] The purpose of this application is to achieve real-time estimation of battery core temperature, that is, to enable the core temperature of batteries with temperature sensors on the top cover to be calculated in real time during operation. Furthermore, the battery core temperature estimation in this application is mainly used for: 1. Real-time estimated core temperature is closer to the actual bottleneck temperature of the battery, and using it to determine the charging current can reduce the risk of lithium plating. 2. Obtaining more accurate real-time battery heat generation power can be used for battery thermal management control. 3. Identifying the changing trend of DC internal resistance after battery aging based on the changes in the correction range of heat generation power throughout the battery's life cycle.
[0070] The battery temperature estimation method provided in this application has the following advantages: First, it can obtain temperature values even without placing temperature sensors inside the battery core. The core temperature is closer to the actual bottleneck temperature of the battery, and using it to determine the charging current can reduce the risk of lithium plating. It can also obtain more accurate real-time heat generation power of the battery and identify the changing trend of DC internal resistance after battery aging. Second, it is low-cost and easy to implement, and can be implemented entirely by software algorithms without additional hardware. Third, it has good adaptability. This real-time temperature estimation method is applicable to low-temperature, room-temperature, and high-temperature fast charging, as well as thermal management heating or cooling. Moreover, it is not limited to electric vehicles; it can also be used to estimate the battery core temperature for bottom-cooled battery packs in other scenarios such as energy storage.
[0071] Figure 6 This is a schematic diagram of the battery temperature estimation system provided in the embodiments of this application. On the other hand, the embodiments of this application also provide a battery temperature estimation system, see below. Figure 6 The battery temperature estimation system 100 includes: a first determining module 101, used to determine the initial lumped parameter thermal model of each node of the battery; a second determining module 102, used to determine the calculated temperature of the first node based on the initial lumped parameter thermal model; an acquiring module 103, used to acquire the measured temperature of the first node detected by the temperature detection module; a third determining module 104, used to determine the calculated temperature of the second node and the calculated temperature of the third node based on the calculated temperature, the measured temperature, a preset correction coefficient, and the initial lumped parameter thermal model; and an estimation module 105, used to estimate the temperature of the bottom of the battery core based on the calculated temperature of the second node and the calculated temperature of the third node.
[0072] The technical solution of this application embodiment provides a battery temperature estimation system. It obtains the calculated temperature of the first node by constructing an initial lumped parameter thermal model of each node of the battery. The initial lumped parameter thermal model is adjusted according to the calculated temperature of the first node, the measured temperature and the preset correction coefficient to obtain a target lumped parameter thermal model. The calculated temperatures of other nodes are obtained according to the target lumped parameter thermal model. The battery core temperature is then estimated in real time based on the calculated temperatures of other nodes.
[0073] In some embodiments, the first node is the top cover of the battery, the second node is the center of the large surface of the battery, and the third node is the bottom of the large surface of the battery.
[0074] In some embodiments, the first determining module 101 is configured to: Determine the battery's heat generation power, heat dissipation power, and heat flow between each node and adjacent nodes; The initial lumped parameter thermal model of each node of the battery is determined based on the heat generation power, heat dissipation power, heat flow between each node and adjacent nodes, and pre-calibrated battery parameters.
[0075] In some embodiments, the first determining module 101 is further configured to: Obtain the battery's current current, DC internal resistance, and Kelvin temperature; The irreversible heat of the battery is determined based on the current and DC internal resistance; The reversible heat of the battery is determined based on the current, Kelvin temperature, and preset entropy thermal coefficient. The heat generation power is determined based on irreversible heat, reversible heat, and a preset correction factor.
[0076] In some embodiments, the third determining module 104 is further configured to: The target lumped parameter thermal model is determined based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model. The calculation temperatures of the second and third nodes are determined based on the target lumped parameter thermal model.
[0077] In some embodiments, the third determining module 104 is further configured to: The model bias of the initial lumped parameter thermal model is determined based on the calculated temperature and the measured temperature. The model deviation is adjusted according to the preset filtering algorithm, preset correction coefficient, and the correspondence between the preset correction coefficient and the model deviation to obtain the target set total parameter thermal model.
[0078] In some embodiments, the third determining module 104 is further configured to: The target correction coefficient is obtained by correcting the preset correction coefficient according to the preset filtering algorithm; The model deviation is adjusted according to the correspondence between the target correction coefficient, the preset correction coefficient and the model deviation, so as to obtain the target lumped parameter thermal model.
[0079] In some embodiments, the estimation module 105 is further configured to: The first item is determined based on the calculated temperature of the second node and the first preset weight; The second item is determined based on the calculated temperature of the third node and the second preset weight; Estimate the temperature at the bottom of the battery core based on the sum of the first and second items.
[0080] This application also provides a battery system, which includes the battery temperature estimation system provided in any embodiment of this application.
[0081] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.
[0082] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although this application has disclosed preferred embodiments as above, it is not intended to limit this application. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the technical solution of this application. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of this application without departing from the scope of the technical solution of this application shall still fall within the scope of the technical solution of this application.
Claims
1. A method for estimating battery temperature, characterized in that, The battery is provided with multiple nodes, the multiple nodes including at least a first node, a second node, and a third node that are sequentially adjacent to each other; the first node is provided with a temperature detection module; the method includes: Determine the initial lumped parameter thermal model for each node of the battery; The calculated temperature of the first node is determined based on the initial lumped parameter thermal model; Obtain the measured temperature of the first node detected by the temperature detection module; The calculated temperature of the second node and the calculated temperature of the third node are determined based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model. The core bottom temperature of the battery is estimated based on the calculated temperature of the second node and the calculated temperature of the third node.
2. The battery temperature estimation method according to claim 1, characterized in that, The first node is the top cover of the battery, the second node is the center of the large surface of the battery, and the third node is the bottom of the large surface of the battery.
3. The battery temperature estimation method according to claim 1, characterized in that, A method for determining the initial lumped parameter thermal model for each node of the battery includes: Determine the heat generation power, heat dissipation power, and heat flow between each node and adjacent nodes of the battery; The initial lumped parameter thermal model of each node of the battery is determined based on the heat generation power, the heat dissipation power, the heat flow between each node and adjacent nodes, and the pre-calibrated battery parameters.
4. The battery temperature estimation method according to claim 3, characterized in that, The method for determining the heat generation power includes: Obtain the current current, DC internal resistance, and Kelvin temperature of the battery; The irreversible heat of the battery is determined based on the current and the DC internal resistance; The reversible heat of the battery is determined based on the current, the Kelvin temperature, and the preset entropy coefficient. The heat generation power is determined based on the irreversible heat, the reversible heat, and the preset correction coefficient.
5. The battery temperature estimation method according to claim 1, characterized in that, The step of determining the calculated temperature of the second node and the calculated temperature of the third node based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model includes: The target lumped parameter thermal model is determined based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model. The calculation temperatures of the second node and the third node are determined based on the target lumped parameter thermal model.
6. The battery temperature estimation method according to claim 5, characterized in that, The step of determining the target lumped parameter thermal model based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model includes: The model deviation of the initial lumped parameter thermal model is determined based on the calculated temperature and the measured temperature. The model deviation is adjusted according to the preset filtering algorithm, the preset correction coefficient, and the correspondence between the preset correction coefficient and the model deviation to obtain the target ensemble parameter thermal model.
7. The battery temperature estimation method according to claim 6, characterized in that, The step of adjusting the model deviation according to the preset filtering algorithm, the preset correction coefficient, and the correspondence between the preset correction coefficient and the model deviation to obtain the target lumped parameter thermal model includes: The preset correction coefficient is corrected according to the preset filtering algorithm to obtain the target correction coefficient; The model deviation is adjusted according to the correspondence between the target correction coefficient, the preset correction coefficient and the model deviation, so as to obtain the target lumped parameter thermal model.
8. The battery temperature estimation method according to claim 1, characterized in that, The step of estimating the core bottom temperature of the battery based on the calculated temperature of the second node and the calculated temperature of the third node includes: The first item is determined based on the calculated temperature of the second node and the first preset weight; The second item is determined based on the calculated temperature of the third node and the second preset weight; The temperature at the bottom of the battery core is estimated based on the sum of the first and second items.
9. A battery temperature estimation system, characterized in that, The battery is provided with multiple nodes, the multiple nodes including a first node, a second node, and a third node that are sequentially adjacent to each other; the first node is provided with a temperature detection module; the system includes: The first determining module is used to determine the initial lumped parameter thermal model of each node of the battery; The second determining module is used to determine the calculated temperature of the first node based on the initial lumped parameter thermal model; The acquisition module is used to acquire the measured temperature of the first node detected by the temperature detection module; The third determining module is used to determine the calculated temperature of the second node and the calculated temperature of the third node based on the calculated temperature, the measured temperature, the preset correction coefficient, and the initial lumped parameter thermal model. An estimation module is used to estimate the core bottom temperature of the battery based on the calculated temperature of the second node and the calculated temperature of the third node.
10. A battery system, characterized in that, Includes the battery temperature estimation system as described in claim 9.