Battery cell temperature determination method and device, vehicle, equipment and storage medium

Abstract

The invention provides a battery cell temperature determination method and device, a vehicle, equipment and a storage medium, and relates to the technical field of vehicles. The method comprises the following steps: acquiring working condition data of a battery cell and actually measured temperature data measured at a first position of the battery cell; determining an estimated temperature of a second position of the battery cell based on a heat effect relationship according to the actually measured temperature data and the battery cell working condition data; and compensating the estimated temperature according to the battery cell working condition data to obtain a target temperature corresponding to the second position. According to the method, the heat transfer rule in the battery cell and various factors in actual working conditions can be fully considered, the detection precision and the implementation cost are considered, and the target temperature of the second position on the battery cell is quickly and accurately obtained, so that the temperature distribution condition of the battery cell can be accurately mastered, and a key safety decision basis is provided for battery management.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle technology, and in particular to a method, apparatus, vehicle, equipment, and storage medium for determining a battery cell temperature. Background Art

[0002] With the advancement of computing and vehicle intelligence, the requirements for battery management systems are increasing. Accurate temperature estimation of battery cells is crucial in vehicle battery systems. Accurate temperature estimation effectively ensures safe battery operation, preventing overheating or overcooling that can lead to performance degradation, shortened battery life, and even accidents. Furthermore, precise temperature information helps optimize the battery's thermal management system, allowing for appropriate adjustments to cooling and heating strategies and improving battery energy efficiency.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0004] The present disclosure aims to provide a method, device, vehicle, equipment and storage medium for determining battery cell temperature.

[0005] According to a first aspect of an embodiment of the present disclosure, a method for determining a battery cell temperature is provided, comprising: obtaining battery cell operating condition data and actual temperature data measured at a first position of the battery cell; determining an estimated temperature at a second position of the battery cell based on a thermal effect relationship according to the actual temperature data and the battery cell operating condition data; and compensating the estimated temperature according to the battery cell operating condition data to obtain a target temperature corresponding to the second position.

[0006] In some embodiments, the thermal effect relationship includes a heat conduction term and a battery cell heat generation term; wherein the heat conduction term is constructed based on the heat conduction path between the first position and the second position, and is used to characterize the dynamic temperature relationship between the first position and the second position; the battery cell heat generation term is constructed based on the battery cell self-heating data and the correction coefficient corresponding to the second position; wherein, according to the measured temperature data and the battery cell operating condition data, the estimated temperature of the second position of the battery cell is determined based on the thermal effect relationship, including: determining the heat conduction temperature contribution value of the first position to the second position based on the measured temperature data and the heat conduction term; determining the heat generation temperature contribution value of the battery cell to the second position based on the battery cell operating condition data and the battery cell heat generation term; and determining the estimated temperature of the second position of the battery cell based on the heat conduction temperature contribution value and the heat generation temperature contribution value.

[0007] In some embodiments, the estimated temperature is compensated according to the battery cell operating condition data to obtain a target temperature corresponding to the second position, including: determining the compensation temperature corresponding to the second position according to the battery cell operating condition data; and performing weighted summation of the estimated temperature and the compensation temperature to obtain the target temperature corresponding to the second position.

[0008] In some embodiments, determining the compensation temperature corresponding to the second position according to the battery cell operating condition data includes: inputting the battery cell operating condition data into a trained neural network model, and outputting the compensation temperature through the neural network model.

[0009] In some embodiments, the heat conduction item includes an equivalent thermal resistance parameter and an equivalent thermal capacity parameter corresponding to the heat conduction path; the battery cell temperature determination method also includes: optimizing the equivalent thermal resistance parameter and the equivalent thermal capacity parameter based on the battery cell multi-point sampling temperature data under pure heating conditions and / or pure cooling conditions; optimizing the correction coefficient corresponding to the second position based on the battery cell multi-point sampling temperature data and the battery cell operation sampling data under charging conditions; or, jointly optimizing the equivalent thermal resistance parameter, the equivalent thermal capacity parameter and the correction coefficient corresponding to the second position based on the battery cell multi-point sampling temperature data and the battery cell operation sampling data under charging conditions.

[0010] In some embodiments, the compensation is achieved through a trained neural network model; the battery cell temperature determination method also includes: training an initial neural network model based on multi-point sampling temperature data of the battery cell under discharge conditions and battery cell operating condition sampling data, optimizing model parameters of the neural network model, and obtaining the trained neural network model.

[0011] In some embodiments, the method for determining the battery cell temperature also includes: during the training process of the neural network model, based on the multi-point sampling temperature data of the battery cell under discharge conditions and the battery cell working condition sampling data, jointly optimizing at least one parameter in the thermal effect relationship within a preset range; wherein the parameters in the thermal effect relationship include equivalent thermal resistance parameters, equivalent heat capacity parameters and correction coefficients.

[0012] In some embodiments, the method for determining the battery cell temperature further includes: deploying the thermal effect relationship and the trained neural network model to the vehicle side.

[0013] In some embodiments, the battery cell operating condition data includes battery cell operation data and environmental data; wherein, the battery cell operation data includes at least: current, terminal voltage, state of charge; the environmental data includes at least: ambient temperature, coolant flow, coolant inlet temperature and coolant outlet temperature.

[0014] In some embodiments, the first position is the installation position of the battery cell surface temperature sensor; the second position is the battery cell temperature extreme position, including at least one of the following: the physical center point of the battery cell, the surface position of the battery cell close to the cooling system.

[0015] In some embodiments, the measured temperature data is obtained by performing median filtering on the measured temperature data corresponding to a plurality of historical continuous sampling points.

[0016] According to a second aspect of an embodiment of the present disclosure, a battery cell temperature determination device is provided, including: an acquisition unit for acquiring battery cell operating condition data and actual temperature data measured at a first position of the battery cell; a temperature estimation unit for determining an estimated temperature of a second position of the battery cell based on a thermal effect relationship according to the actual temperature data and the battery cell operating condition data; the temperature estimation unit is further used to compensate the estimated temperature according to the battery cell operating condition data to obtain a target temperature corresponding to the second position.

[0017] In some embodiments, the thermal effect relationship includes a heat conduction term and a battery cell heat generation term; wherein the heat conduction term is constructed based on the heat conduction path between the first position and the second position, and is used to characterize the dynamic temperature relationship between the first position and the second position; the battery cell heat generation term is constructed based on the battery cell self-heating data and the correction coefficient corresponding to the second position; wherein the temperature estimation unit determines the estimated temperature of the second position of the battery cell based on the thermal effect relationship according to the measured temperature data and the battery cell operating condition data, including: determining the heat conduction temperature contribution value of the first position to the second position based on the measured temperature data and the heat conduction term; determining the heat generation temperature contribution value of the battery cell to the heat generation temperature of the second position based on the battery cell operating condition data and the battery cell heat generation term; and determining the estimated temperature of the second position of the battery cell based on the heat conduction temperature contribution value and the heat generation temperature contribution value.

[0018] In some embodiments, the temperature estimation unit compensates the estimated temperature according to the battery cell operating condition data to obtain a target temperature corresponding to the second position, including: determining the compensated temperature corresponding to the second position according to the battery cell operating condition data; and performing a weighted summation of the estimated temperature and the compensated temperature to obtain the target temperature corresponding to the second position.

[0019] In some embodiments, the temperature estimation unit determines the compensation temperature corresponding to the second position based on the battery cell operating condition data, including: inputting the battery cell operating condition data into a trained neural network model, and outputting the compensation temperature through the neural network model.

[0020] In some embodiments, the heat conduction item includes an equivalent thermal resistance parameter and an equivalent thermal capacity parameter corresponding to the heat conduction path; the battery cell temperature determination device also includes a parameter optimization unit, which is used to: optimize the equivalent thermal resistance parameter and the equivalent thermal capacity parameter based on the multi-point sampling temperature data of the battery cell under pure heating conditions and / or pure cooling conditions; optimize the correction coefficient corresponding to the second position based on the multi-point sampling temperature data of the battery cell under charging conditions and the battery cell operation sampling data; or jointly optimize the equivalent thermal resistance parameter, the equivalent thermal capacity parameter and the correction coefficient corresponding to the second position based on the multi-point sampling temperature data of the battery cell under charging conditions and the battery cell operation sampling data.

[0021] In some embodiments, the compensation is achieved through a trained neural network model; the parameter optimization unit is also used to: train the initial neural network model based on the multi-point sampling temperature data of the battery cell under discharge conditions and the battery cell working condition sampling data, optimize the model parameters of the neural network model, and obtain the trained neural network model.

[0022] In some embodiments, the parameter optimization unit is also used to: during the training process of the neural network model, based on the multi-point sampling temperature data of the battery cell under discharge conditions and the battery cell working condition sampling data, jointly optimize at least one parameter in the thermal effect relationship within a preset range; wherein the parameters in the thermal effect relationship include equivalent thermal resistance parameters, equivalent heat capacity parameters and correction coefficients.

[0023] In some embodiments, the battery cell temperature determination device further includes a deployment unit for deploying the thermal effect relationship and the trained neural network model to a vehicle end.

[0024] In some embodiments, the battery cell operating condition data includes battery cell operation data and environmental data; wherein, the battery cell operation data includes at least: current, terminal voltage, state of charge; the environmental data includes at least: ambient temperature, coolant flow, coolant inlet temperature and coolant outlet temperature.

[0025] In some embodiments, the first position is the installation position of the battery cell surface temperature sensor; the second position is the battery cell temperature extreme position, including at least one of the following: the physical center point of the battery cell, the surface position of the battery cell close to the cooling system.

[0026] In some embodiments, the measured temperature data is obtained by performing median filtering on the measured temperature data corresponding to a plurality of historical continuous sampling points.

[0027] According to a third aspect of an embodiment of the present disclosure, a vehicle is provided, characterized in that it includes: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above-mentioned method for determining the battery cell temperature.

[0028] According to a fourth aspect of an embodiment of the present disclosure, an electronic device is provided, characterized in that it includes: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above-mentioned method for determining the battery cell temperature.

[0029] According to the fifth aspect of the embodiment of the present disclosure, a non-temporary computer-readable storage medium is provided. When the instructions in the storage medium are executed by the processor of the mobile terminal, the mobile terminal is enabled to execute a method for determining the battery cell temperature. The method includes: obtaining battery cell operating condition data and actual temperature data measured at a first position of the battery cell; determining an estimated temperature of a second position of the battery cell based on a thermal effect relationship according to the actual temperature data and the battery cell operating condition data; compensating the estimated temperature according to the battery cell operating condition data to obtain a target temperature corresponding to the second position.

[0030] According to a sixth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, which implements the above-mentioned method for determining the battery cell temperature when executed by a processor.

[0031] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects:

[0032] The present disclosure can fully consider the heat transfer laws inside the battery cell and various factors in actual working conditions, take into account both detection accuracy and implementation cost, and quickly and accurately obtain the target temperature of the second position on the battery cell, thereby helping to accurately grasp the temperature distribution of the battery cell and provide a key safety decision-making basis for battery management.

[0033] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0035] Figure 1 This is a flow chart of a method for determining a battery cell temperature according to some embodiments of the present disclosure.

[0036] Figure 2 is a schematic diagram of a first position and a second position in a method for determining a battery cell temperature according to some embodiments of the present disclosure.

[0037] Figure 3 is a flow chart illustrating another method for determining a battery cell temperature according to some embodiments of the present disclosure.

[0038] Figure 4 is a flow chart illustrating another method for determining a battery cell temperature according to some embodiments of the present disclosure.

[0039] Figure 5 is a flow chart illustrating another method for determining a battery cell temperature according to some embodiments of the present disclosure.

[0040] Figure 6 is a schematic diagram of a neural network model in a method for determining a battery cell temperature according to some embodiments of the present disclosure.

[0041] Figure 7 1 is a schematic diagram of a battery cell for providing multi-point sampling temperature data of a battery cell in a method for determining the battery cell temperature according to some embodiments of the present disclosure.

[0042] Figure 8 This is a flowchart of optimizing parameters in a method for determining a battery cell temperature according to some embodiments of the present disclosure.

[0043] Figure 9 4 is a block diagram of a device for determining a battery cell temperature according to some embodiments of the present disclosure.

[0044] Figure 10 is a block diagram illustrating an apparatus 1000 for determining a battery cell temperature according to some embodiments of the present disclosure. DETAILED DESCRIPTION

[0045] Some exemplary embodiments of the present disclosure will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. Various changes, modifications and equivalents of the methods, devices and / or systems described herein will become apparent after understanding the present disclosure. For example, the order of operations described herein is merely an example and is not limited to those orders set forth herein, but may be changed as becomes apparent after understanding the present disclosure, except for operations that must be performed in a specific order. In addition, descriptions of features known in the art may be omitted for clarity and brevity.

[0046] The following exemplary embodiments of the present disclosure do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0047] The specific implementation of the embodiment of the present disclosure is described in detail below with reference to the accompanying drawings.

[0048] Figure 1 is a flow chart showing a method for determining a battery cell temperature according to some embodiments of the present disclosure. Figure 1 As shown, the method for determining battery cell temperature can be applied to vehicles and electronic devices associated with the vehicle. The electronic devices include, but are not limited to, terminal devices such as vehicle-mounted systems, vehicle-mounted terminals, smartphones, smart tablets, wearable devices, desktop computers, laptops, and smart speakers. They can also include server-side devices such as local servers and cloud servers. The server-side can be deployed in a single computer or a computer cluster consisting of multiple computers. The method for determining battery cell temperature can include the following steps.

[0049] In step S110 , the battery cell operating condition data and the actual temperature data measured at the first position of the battery cell are obtained.

[0050] In the disclosed embodiments, the battery cell is the core component of the battery and the basic unit for storing and releasing electrical energy. The battery cell may be a battery cell used in a vehicle. The battery cell operating condition data may include various parameters reflecting the battery cell's operating state or operating environment in the vehicle environment or during vehicle use. This data can affect changes in the battery cell's temperature and provide a data foundation for subsequent temperature analysis and calculations.

[0051] The measured temperature data measured at the first position of the battery cell is data actually measured at a specific position (i.e., the first position) of the battery cell. For example, it can be measured by thermocouple measurement, thermistor measurement, integrated sensor measurement, or infrared temperature measurement. The measured temperature data can reflect the temperature at a fixed position on the battery cell.

[0052] In some embodiments of the present disclosure, the battery cell operating condition data includes battery cell operation data and environmental data; wherein, the battery cell operation data includes at least: current, terminal voltage, state of charge; the environmental data includes at least: ambient temperature, coolant flow, coolant inlet temperature and coolant outlet temperature.

[0053] In the embodiment of the present disclosure, the battery cell operation data may at least include the current, terminal voltage, and state of charge of the battery cell. Among them, the current can reflect the charge and discharge intensity of the battery cell. The magnitude of the current when the battery cell is charged and discharged can affect the electrochemical reaction rate inside the battery cell, thereby affecting the heat generation. The terminal voltage can reflect the working state and energy level of the battery cell. The change in terminal voltage can reflect the energy conversion and loss inside the battery cell, affecting the battery cell temperature. The state of charge (SOC) can indicate the remaining power of the battery cell. The internal resistance, heat capacity, open circuit voltage value and other parameters of the battery cell will be different under different SOCs, thereby affecting the temperature characteristics. The battery cell operation data can be used as input condition data for the thermal effect relationship to calculate the estimated temperature of the second position.

[0054] In an exemplary embodiment, the open circuit voltage corresponding to the state of charge may be found according to a pre-stored mapping table, and the open circuit voltage may also be used to determine the estimated temperature of the second location.

[0055] In the disclosed embodiment, the environmental data may at least include the ambient temperature, the coolant flow rate, the coolant inlet temperature and the coolant outlet temperature. Among them, the ambient temperature is the basic parameter of the external thermal environment in which the battery cell is located, and the coolant flow rate, inlet and outlet temperatures are closely related to the heat dissipation system (i.e., the cooling system) of the battery cell, and can jointly affect the heat exchange process between the battery cell and the surrounding environment. Among them, the coolant flow rate reflects the cooling capacity of the cooling system on the battery cell. The greater the flow rate, the more heat is taken away, and the stronger the regulating effect on the battery cell temperature. Coolant inlet temperature and outlet temperature: the inlet temperature is the initial temperature of the coolant when it enters the battery cell, and the outlet temperature reflects the temperature change of the coolant after absorbing the heat of the battery cell. These two temperatures can reflect the temperature change of the coolant, and then reflect the cooling effect of the cooling system on the battery cell. The battery cell operation data and environmental data can be used to comprehensively determine the impact of the environment on the temperature of the battery cell, so as to compensate and adjust the estimated temperature.

[0056] Through the disclosed embodiments, comprehensive and detailed battery cell operating condition data provides rich information for determining estimated temperatures and compensating for them. Operating data such as current, terminal voltage, and state of charge can accurately describe the heat generation mechanism within the battery cell, while environmental data such as ambient temperature and cooling system-related data can accurately reflect the heat exchange between the battery cell and the external environment. Taking these factors into consideration, the final target temperature can be closer to the actual temperature, improving the accuracy of temperature estimation.

[0057] In step S120 , an estimated temperature of a second position of the battery cell is determined based on a thermal effect relationship according to the measured temperature data and the battery cell operating condition data.

[0058] In the disclosed embodiments, the thermal effect relationship can describe the physical laws of heat generation, heat transfer, and distribution within the battery cell, and can reflect the intrinsic connection between the battery cell operating condition data and the battery cell temperature distribution. The thermal effect relationship can be constructed based on a battery cell heat transfer mechanism model. For example, based on known principles of heat conduction, heat convection, and heat radiation, combined with the material properties and structure of the battery cell, the relationship between temperatures at different locations can be established, as well as the intrinsic connection between the heat generated by factors such as current during battery cell operation, which affects the temperature distribution.

[0059] Thermal effect relationships physically connect temperatures at different locations and provide the theoretical foundation for temperature prediction. Based on these relationships, the temperature and operating conditions at one location (i.e., the first location) can be used to infer the estimated temperature at another location (i.e., the second location).

[0060] In step S130 , the estimated temperature is compensated according to the battery cell operating condition data to obtain a target temperature corresponding to the second position.

[0061] In the disclosed embodiments, compensation calculations can be performed based on the estimated temperature obtained above, based on the cell operating condition data. This can compensate for calculation deviations caused by simplified assumptions in the thermal effect relationship model or other factors, such as the ambient temperature of the cell's operation and the operating status of the cooling system near the cell. By using compensation based on the cell operating condition data, the estimated temperature can be adjusted and corrected, improving the accuracy of the temperature estimation and thereby accurately determining the target temperature corresponding to the second position.

[0062] The target temperature of the second position obtained after compensation can be closer to the actual temperature of the position and can more accurately reflect the temperature state of the battery cell at the position.

[0063] As can be seen from the above steps, the battery cell temperature determination method provided by the present disclosure can quickly estimate the temperature at the second position based on the measured temperature and operating condition data, combined with the thermal effect relationship, and then dynamically compensate and adjust the estimated temperature using the battery cell operating condition data, so that the target temperature at the second position obtained is closer to the actual temperature value. It can be seen that this solution can fully consider the heat transfer laws inside the battery cell and various factors in actual operating conditions, taking into account both detection accuracy and implementation costs, and quickly and accurately obtain the target temperature of the second position on the battery cell, which in turn helps to accurately grasp the temperature distribution of the battery cell and provide a key safety decision basis for battery management.

[0064] In some embodiments of the present disclosure, the first position is the installation position of the battery cell surface temperature sensor; the second position is the battery cell temperature extreme position, including at least one of the following: the physical center point of the battery cell, the surface position of the battery cell close to the cooling system.

[0065] In the disclosed embodiments, a device for collecting temperature may be pre-installed at a specific location on the battery cell. For example, an NTC temperature sensor may be pre-installed at a specific location between the two tabs of the battery cell to directly collect the temperature at that specific location. In this case, the first location is the specific location between the two tabs of the battery cell.

[0066] In the embodiment of the present disclosure, the second position may be the position of the extreme temperature of the battery cell, and the extreme temperature may include the maximum temperature and the minimum temperature. Among them, the physical center point of the battery cell is the heat accumulation position determined by the battery cell structure and heat generation characteristics. The temperature of the center point will be higher than the temperature of other positions and can be used as the position of the maximum temperature of the battery cell. The surface position of the battery cell is close to the cooling system. Compared with other positions, it is closer to the cooling system. The temperature of this position will be more significantly affected by the cooling effect of the cooling system, so that the temperature of this position will be lower than the temperature of other positions. The surface position of the battery cell close to the cooling system can be used as the position of the minimum temperature of the battery cell.

[0067] The cell temperature extreme value position can be used as a second position to determine the target temperature, and then used for analysis and management of the cell temperature.

[0068] In an exemplary embodiment, the second position may be dynamically selected according to the geometric shape of the battery cell. For example, the second position may be the geometric center of a square battery cell or the radial center of a cylindrical battery cell.

[0069] Through the disclosed embodiments, the first position can be clearly defined as the starting point of the measured temperature, and the second position as the key temperature extreme point, so that the thermal effect relationship can be constructed more accurately and specifically to evaluate the overall temperature distribution of the battery cell, making the temperature measurement and analysis more targeted. Among them, when using the thermal effect relationship to estimate the temperature of the second position, a clear position definition can make the establishment and application of the thermal effect relationship more accurate, and a clear position definition can help to more accurately describe the thermal effect law, thereby improving the accuracy of the estimated temperature. In addition, paying attention to the temperature of the temperature extreme position of the battery cell can clarify the temperature range of the battery cell under the working state, help monitor thermal anomalies within the telecommunications system, prevent safety accidents caused by overheating, and help with battery health management.

[0070] Figure 2 FIG. 1 is a schematic diagram of a first position and a second position in a method for determining a battery cell temperature according to some embodiments of the present disclosure. Figure 2 As shown, it includes a battery cell 200 and a coolant 204 of a cooling system.

[0071] Among them, a temperature sensor NTC is set between the two tabs of the battery cell 200, and the position 201 where the temperature sensor NTC is set can be determined as the first position, and the measured temperature data T at the position 201 (i.e., the first position) can be collected by the temperature sensor NTC. ntc .like Figure 2 As shown, the coolant 204 is close to the left side of the battery cell 200 , so the center point 202 of the left side can be determined as a second position, and the physical center point 203 of the battery cell 200 can be determined as another second position.

[0072] In such Figure 2 In the embodiment shown, the measured temperature data T ntc and the cell operating condition data of the cell 200, determine the estimated temperature of the position 202 and / or the position 203 based on the thermal effect relationship, and compensate the estimated temperature of the position 202 and / or the position 203 according to the cell operating condition data of the cell 200 to obtain the target temperature of the position 202 and / or the position 203.

[0073] In some embodiments of the present disclosure, the thermal effect relationship includes a heat conduction term and a battery cell heat generation term; wherein, the heat conduction term is constructed based on the heat conduction path between the first position and the second position, and is used to characterize the dynamic temperature relationship between the first position and the second position; the battery cell heat generation term is constructed based on the battery cell self-heating data and the correction coefficient corresponding to the second position.

[0074] In the disclosed embodiments, the thermal effect relationship can be constructed based on a non-steady-state one-dimensional heat transfer model, which can include the temperature effects generated by both heat conduction and heat generation. A heat conduction path can refer to a heat energy transfer channel from a first location to a second location within the battery cell, and its physical properties can be comprehensively characterized by equivalent thermal resistance, heat capacitance, and / or geometric parameters.

[0075] A heat conduction term can be constructed based on the heat conduction path between the first and second locations. The specific characteristics of the heat conduction path, such as the path length and the thermal conductivity of the material, can be determined. Then, based on these characteristics or parameters that reflect these characteristics, a heat conduction term can be established, allowing the heat conduction term to characterize the dynamic temperature relationship between the first and second locations. This allows the estimated temperature at the second location to be derived given the measured temperature data at the first location.

[0076] The cell heat generation item can also be constructed based on the cell self-heating data and the correction coefficient corresponding to the second position. The cell self-heating data reflects the situation in which the cell generates heat by itself during operation, while the correction coefficient corresponding to the second position is based on the special positional attributes of the second position (such as being close to the cooling system, being in the physical center, etc.). The degree to which the cell heat generation affects the temperature of the position is adjusted so that the heat generation item can better fit the actual temperature of the second position affected by heat generation. That is, the correction coefficient can be used to adjust the spatial distribution of the cell self-heating item to reflect the local thermal characteristics at the second position. Among them, the cell self-heating data can be related to the operating conditions of the cell (such as current, voltage, etc.).

[0077] In an exemplary embodiment, the heat exchange power P between the cell surface and the environment can affect the cell center temperature with a lag, and the lag effect is a first-order inertia link. Based on this characteristic, the following expression relationship can be determined:

[0078]

[0079] Among them, the L position to the 0 position can form a heat conduction path; is the self-heating data of the battery cell; k0 is the correction coefficient multiplied by the self-heating data; R is the thermal resistance between the L position and the 0 position, and the thermal resistance can be directional; C is the thermal capacitance of the battery cell; dt is the simulation sampling period.

[0080] refer to Figure 2 In this embodiment, heat conduction paths can be considered to exist between position 201 (i.e., the first position) and position 203 (i.e., the second position), between position 201 (i.e., the first position) and position 202 (i.e., the second position), and between position 203 and position 202. Based on Equation 1, a thermal effect relationship based on the temperature at position 201 can be constructed for positions 203 and 202.

[0081] In some embodiments of the present disclosure, the measured temperature data is obtained by performing median filtering on the measured temperature data corresponding to a plurality of historical continuous sampling points.

[0082] In the disclosed embodiments, the temperature sensor positioned at the first position of the battery cell can continuously collect temperature data at multiple historical moments, generating a series of measured temperature data corresponding to these sampling points. This measured temperature data can capture temperature changes over time. Median filtering can be performed on the measured temperature data corresponding to these multiple consecutive historical sampling points to remove noise and outliers.

[0083] In an exemplary embodiment, the measured temperature data may be arranged in order of size, and then the value in the middle position is taken as the filtered result. If the number of data is even, the average of the two middle values ​​is taken as the median.

[0084] Through the disclosed embodiments, noise and outliers in the measured temperature data can be effectively removed by median filtering, making the obtained temperature data more accurate, thereby improving the reliability of subsequent temperature estimation and target temperature determination.

[0085] In an exemplary embodiment, when constructing a thermal effect relationship, multiple (such as 3, 5, 10, etc.; 5 sampling points are used as an example to construct a thermal effect relationship below, but the present disclosure is not limited to this) sampling points can be taken forward to form a time window when calculating the internal temperature of the battery cell at the current sampling point. The median of the temperature correction value in the time window is taken to filter out noise and burrs, and the internal heat transfer model of the battery cell is rewritten in an iterative form. Figure 2 , the temperature of the cell surface (position 203) can be regarded as being affected by the heat transfer path "position 201 to position 203", and the temperature of the cell center (position 202) can be regarded as being affected by the heat transfer paths "position 201 to position 202" and "position 203 to position 202".

[0086] The temperature at position 203 (the surface temperature of the battery cell close to the water cooling plate) can be recorded as T surface , the temperature at position 202 (cell center temperature) is recorded as T center Based on formula 1, a thermal effect relationship based on the temperature at position 201 is constructed for position 203, and a thermal effect relationship based on the temperatures at position 201 and position 203 is constructed for position 202, including:

[0087] T surface (t) = T surface (t-5)+dt*(K2*P(SOC(t),I(t))+median(((T ntc (t)-T surface (t-1))+(T ntc (k-1)-T surface (k-2))+(T ntc (t-2)-T surface (t-3))+(T ntc (t-3)-T surface (t-4))+(T ntc (t-4)-T surface (t-5)))) / R3)*5 / C cell ...Formula (2)

[0088] T center (t) = Tcenter (t-5)+dt*(K1*P(SOC(t),I(t))+(median((T ntc (t)-T center (t-1))+(T ntc (t-1)-T center (t-2))+(T ntc (t-2)-T center (t-3))+(T ntc (t-3)-T center (t-4))+(T nt c(t-4)-T center (t-5)))) / R1+(median((T surface (t)-T center (t-1))+(T surface (t-1)-T center (t-2))+(T surface (t-2)-T center (t-3))+(T surface (t-3)-T center (t-4))+(T surface (t-4)-T center (t-5)))) / R2)*5 / C cell ;...Formula (3)

[0089] Formula (2) may be a thermal effect relationship of the surface position of the battery cell close to the cooling system (i.e., position 203, the second position) relative to the first position, and formula (3) may be a thermal effect relationship of the physical center point of the battery cell (i.e., position 202, the second position) relative to the first position.

[0090] Among them, the self-heating power of the battery cell is P(SOC(t), I(t)) = |(U(t)-OCV(t))*I(t)|-I*T(t)*dOCV / dT, "|(U(t)-OCV(t))*I(t)|" is the irreversible heat generation of the battery cell, and "-I*T(t)*dOCV / dT" is the reversible heat generation of the battery cell. U(t) is the current terminal voltage of the battery cell, OCV(t) is the current open circuit voltage, which can be obtained by looking up the table based on the current battery cell SOC, I(t) is the current current, dOCV / dT is the entropy coefficient, T(t) is the current temperature, median(·) means taking the median, and dt is the simulation period. In formula (2) and formula (3), the part containing "P(SOC(t), I(t))" is the battery cell heat generation term, and the remaining part is the heat conduction term.

[0091] In formula (2) and formula (3), there may be six parameters as shown in Table 1. These parameters can be fitted or optimized based on sample data to be used in the method for determining the battery cell temperature provided in the embodiment of the present disclosure.

[0092] Table 1

[0093] Parameter name unit Correction coefficient k1 for self-heating at the center of the cell / Cell surface self-heating correction factor k2 / <![CDATA[Cell heat capacity C cell (Equivalent heat capacity)]]> J / (kg*K) The equivalent thermal resistance R1 from the NTC position to the center of the battery cell K / W The equivalent thermal resistance R2 from the cell surface to the center of the cell K / W The equivalent thermal resistance R3 of the NTC position from the battery cell to the surface of the water-cooled plate K / W

[0094] Figure 3 FIG. 1 is a flow chart of another method for determining the temperature of a battery cell according to some embodiments of the present disclosure. In some embodiments of the present disclosure, Figure 3 The method for determining the battery core temperature includes any one of step S310, step S320, step S330, step S340, and step S350, or any combination of multiple steps, wherein steps S310 and S350 are respectively Figure 1 Steps S110 and S130 in the cell temperature determination method shown correspond to each other and are not repeated here.

[0095] In the embodiment of the present disclosure, Figure 1 Based on the method of determining the cell temperature shown, Figure 3 The method for determining the battery cell temperature may further include the following steps.

[0096] In step S320, a heat conduction temperature contribution value of the first position to the second position is determined based on the measured temperature data and the heat conduction term.

[0097] In the embodiment of the present disclosure, the measured temperature data of the first position can be used as input information, and the heat conduction temperature contribution value of the first position to the second position can be calculated in combination with the heat conduction relationship between the first position and the second position described by the heat conduction term.

[0098] In step S330 , a contribution value of the heat generated by the battery cell to the heat generation temperature of the second position is determined based on the battery cell operating condition data and the battery cell heat generation item.

[0099] In the embodiment of the present disclosure, the battery cell operating condition data (such as current, voltage, etc.) can be used as input information, combined with the battery cell heat generation item (including the battery cell self-heating data and the second position correction coefficient) to calculate the contribution value of the battery cell heat generation to the heat generation temperature of the second position.

[0100] In step S340 , an estimated temperature of a second position of the battery cell is determined according to the heat conduction temperature contribution value and the heat generation temperature contribution value.

[0101] In the embodiment of the present disclosure, the heat conduction temperature contribution value and the heat generation temperature contribution value may be added together to obtain the estimated temperature of the second position of the battery cell.

[0102] By disclosing the thermal effect relationship into the heat conduction term and the battery cell heat generation term, the manner and degree of influence of different factors on the battery cell temperature can be clearly demonstrated. Accurate calculations based on the measured temperature data and battery cell operating condition data can then more comprehensively reflect the temperature formed by the combined effects of the heat transfer and heat generation processes inside the battery cell on the second position, thereby improving the accuracy of the temperature estimation at the second position.

[0103] Figure 4 FIG. 1 is a flow chart of another method for determining the temperature of a battery cell according to some embodiments of the present disclosure. In some embodiments of the present disclosure, Figure 4 The method for determining the battery core temperature includes any one of step S410, step S420, step S430, and step S440, or any combination of multiple steps, wherein step S410 and step S420 are respectively Figure 1 Steps S110 and S120 in the cell temperature determination method shown correspond to each other and are not repeated here.

[0104] In the embodiment of the present disclosure, Figure 1 Based on the method of determining the cell temperature shown, Figure 4 The method for determining the battery cell temperature may further include the following steps.

[0105] In step S430 , a compensation temperature corresponding to the second position is determined according to the battery cell operating condition data.

[0106] In the embodiment of the present disclosure, multiple factors such as current, voltage, and ambient temperature can be comprehensively considered based on the battery cell operating condition data. These factors can reflect the current working status of the battery cell and the environmental conditions in which it is located. By analyzing these data, factors that have an impact on the temperature at the second position but have not been fully considered when estimating the temperature can be found, thereby determining a compensation temperature to correct the estimated temperature, or directly deriving the temperature impact of these factors on the temperature at the second position (i.e., the compensation temperature) to correct the estimated temperature.

[0107] In step S440, a weighted sum is performed on the estimated temperature and the compensated temperature to obtain a target temperature corresponding to the second position.

[0108] In an embodiment of the present disclosure, the estimated temperature can be compensated and adjusted based on the compensation temperature. Weights can be assigned to the estimated temperature and the compensation temperature, and then the two can be added together according to the weights to ultimately obtain the target temperature corresponding to the second position. This approach can comprehensively consider the impact of the estimated temperature and the compensation temperature on the final temperature result. The weight of the estimated temperature can be set to 1, and the weight of the compensation temperature can be obtained by fitting and / or optimizing based on sample data.

[0109] The disclosed embodiments can take into account the various factors contained in the battery cell operating condition data, thereby accounting for the battery cell's conditions under different operating states and external environments. By performing compensation based on the operating condition data, the temperature determination method can be adapted to complex and variable operating conditions, such as high and low temperature environments, as well as special operating conditions such as high current charging and discharging, allowing for more accurate determination of the battery cell temperature, thereby enhancing the method's versatility, practicality, and accuracy.

[0110] Figure 5 FIG. 1 is a flow chart of another method for determining the temperature of a battery cell according to some embodiments of the present disclosure. In some embodiments of the present disclosure, Figure 5 The method for determining the battery core temperature includes any one of step S510, step S520, step S530, and step S540, or any combination of multiple steps, wherein steps S510, S520, and S540 are respectively Figure 4 Steps S410 , S420 , and S440 in the cell temperature determination method shown correspond to each other and are not repeated here.

[0111] In the embodiment of the present disclosure, Figure 4 Based on the method of determining the cell temperature shown, Figure 5 The method for determining the battery cell temperature may further include the following steps.

[0112] In step S530, the battery cell operating condition data is input into the trained neural network model, and the compensated temperature is output through the neural network model.

[0113] In the disclosed embodiments, a neural network model, such as a multilayer perceptron (MLP), is constructed to process the relationship between cell operating condition data and compensation temperature. The model is trained using a large amount of historical sample data, and its parameters are continuously adjusted during the training process, enabling the model to accurately output the corresponding compensation temperature based on the input cell operating condition data. In other words, the neural network model can learn, through training, the nonlinear effect of the output cell operating condition data on temperature error.

[0114] After obtaining the trained neural network model, the battery cell operating condition data collected in real time can be input into the trained neural network model. After calculation, the model can output the compensated temperature corresponding to the second position.

[0115] The disclosed embodiments leverage the powerful nonlinear fitting capabilities of neural network models to process the complex nonlinear relationship between cell operating condition data and compensation temperature. Through extensive data training, the model can learn the underlying patterns and characteristics within the data, enabling more accurate prediction of compensation temperature and improving the accuracy of temperature compensation.

[0116] In addition, the thermal effect relationship constructed in the present disclosure can provide more known heat transfer mechanism information, so a smaller neural network model can also meet the determination of the compensation temperature in this solution, and can also reduce the size of the data set required for training the neural network model, thereby increasing the cost of model construction and model training, and improving the efficiency of solution execution.

[0117] Figure 6 FIG. 1 is a schematic diagram of a neural network model in a method for determining a battery cell temperature according to some embodiments of the present disclosure. Figure 6 As shown in the figure, a neural network model is shown, including a multi-layer perceptron. The neural network can contain three layers: input layer, hidden layer, and output layer. The input layer can be denoted as X and can contain 7 elements, denoted as:

[0118] X(I, U, T_amb, Q, T_in, T_out, SOC), the input variables are the cell current I, the cell terminal voltage U, the ambient temperature T_amb, the coolant flow rate through the battery pack Q, the coolant battery pack inlet temperature T_in, the coolant battery pack outlet temperature T_out, and the cell charge SOC.

[0119] The hidden layer H can contain 64 units. A nonlinear activation function sigmoid is added to the hidden layer for nonlinear mapping, and then linearly mapped to the output layer O. The calculation relationship is as follows:

[0120]

[0121] H=W1X+b1;

[0122] H′=sigmoid(H);

[0123] O=W2H'+b2;

[0124] The output value O can be regarded as the compensation temperature corresponding to the second position (including the core center temperature T surface Or the surface temperature of the battery cell close to the water cooling plate T center ) is the corrected value.

[0125] In some embodiments of the present disclosure, the heat conduction item includes an equivalent thermal resistance parameter and an equivalent thermal capacitance parameter corresponding to the heat conduction path.

[0126] In the disclosed embodiments, the heat conduction term incorporates equivalent thermal resistance and equivalent thermal capacity parameters, corresponding to the heat transfer resistance and heat storage capacity of the heat conduction path, respectively. These parameters are key parameters in constructing the heat conduction term. Furthermore, the cell heat generation term includes a correction coefficient corresponding to the second location, which adjusts for the effect of the cell's self-heating on the temperature at the second location.

[0127] The method for determining the battery cell temperature also includes: optimizing the equivalent thermal resistance parameter and the equivalent thermal capacity parameter based on the battery cell multi-point sampling temperature data under pure heating conditions and / or pure cooling conditions; and optimizing the correction coefficient corresponding to the second position based on the battery cell multi-point sampling temperature data and battery cell operation sampling data under charging conditions.

[0128] In the embodiment of the present disclosure, multi-point sampling temperature data of the battery cell can be collected under pure heating conditions and / or pure cooling conditions. Based on these data, the equivalent thermal resistance parameters and equivalent heat capacity parameters are optimized. Pure heating or cooling conditions are relatively simple, with fewer interference factors, and can more clearly reflect the heat conduction characteristics. By analyzing the temperature change data under this condition, the parameters can be adjusted to make it more consistent with the actual heat conduction process. Among them, the pure heating condition can be a scenario where only the heating system is turned on and there is no charging and discharging, and the pure cooling condition can be a scenario where only the cooling system is turned on and the SOC remains constant or there is no charging and discharging.

[0129] Then, under charging conditions, collect multi-point temperature data and operational data from the battery cells. You can choose to optimize the correction factor corresponding to the second location separately. Based on the temperature data and operational data under charging conditions, adjust the correction factor to more accurately reflect the impact of battery cell heat generation on the temperature at the second location. The charging condition can be DC fast charging.

[0130] In an exemplary embodiment, a temperature sensor can be installed in a custom battery cell, and a training temperature sensor NTC can be installed on the custom battery cell using the same setup as an actual NTC temperature sensor. Multi-point temperature data of the battery cell can be collected using these temperature sensors and the training temperature sensor NTC.

[0131] The extreme values ​​in the multi-point temperature data can be used as label data for training. For example, the maximum value in the multi-point temperature data can be used as the temperature of the physical center of the battery cell, or the maximum value in the multi-point temperature data can be used as the temperature of the battery cell surface near the cooling system.

[0132] In some embodiments of the present disclosure, the equivalent thermal resistance parameter, the equivalent thermal capacity parameter and the correction coefficient corresponding to the second position may be jointly optimized based on multi-point sampling temperature data of the battery cell under charging conditions and battery cell operation sampling data.

[0133] In the disclosed embodiment, the equivalent thermal resistance parameters, the equivalent heat capacity parameters and the correction coefficient corresponding to the second position can also be jointly optimized under charging conditions, comprehensively considering the heat conduction and heat generation factors, and through comprehensive analysis of the charging condition data, these parameters can be coordinated with each other to jointly improve the accuracy of temperature calculation.

[0134] In the embodiment of the present disclosure, in the process of optimizing the equivalent thermal resistance parameters, the equivalent thermal capacity parameters and the correction coefficient corresponding to the second position, the training data used for input may include: the SOC of each special battery cell, the current, the terminal voltage, the battery cell NTC collection temperature, and the maximum and minimum temperatures of the internal temperature sensing line.

[0135] The maximum simulation temperature T of each cell can be optimized by parameter fitting (such as particle swarm optimization, etc.) maxSim and the minimum simulation temperature T minSim The maximum temperature T collected by the actual temperature sensor inside the special battery cell maxReal and minimum temperature T minReal The root mean square error is calculated to optimize the parameters.

[0136] Through the disclosed embodiments, the equivalent thermal resistance parameters, equivalent thermal capacity parameters and correction coefficients can be targetedly optimized or jointly optimized under different working conditions, so that the parameters of the heat conduction item and the battery cell heat generation item are more in line with the actual situation, thereby more accurately describing the heat conduction and heat generation process inside the battery cell, improving the accuracy of the second position estimated temperature determined based on the thermal effect relationship and the final target temperature, and providing more reliable data for battery cell thermal management.

[0137] Figure 7 FIG. 1 is a schematic diagram of a battery cell for providing multi-point sampling temperature data of a battery cell in a battery cell temperature determination method according to some embodiments of the present disclosure. Figure 7 As shown, a special battery cell 700 is included. Multiple temperature sensing lines can be set on the special battery cell 700, including temperature sensing line 701, temperature sensing line 702, and temperature sensing line 703, and a temperature sensor NTC can be set at position 704. Multi-point sampling temperature data of the battery cell can be collected by temperature sensing lines 701, temperature sensing line 702, temperature sensing line 703, and the temperature sensor NTC set at position 704.

[0138] In some embodiments of the present disclosure, the compensation is achieved through a trained neural network model; the method for determining the battery cell temperature also includes: training an initial neural network model based on multi-point sampling temperature data of the battery cell under discharge conditions and battery cell operating condition sampling data, optimizing model parameters of the neural network model, and obtaining the trained neural network model.

[0139] In the embodiment of the present disclosure, multi-point sampling temperature data of the battery cell under the discharge condition and the battery cell working condition sampling data can be collected. The multi-point sampling temperature data reflects the temperature conditions of different positions of the battery cell under the discharge condition, and the battery cell working condition sampling data includes the battery cell operation data (such as current, terminal voltage, state of charge) and environmental data (such as ambient temperature, coolant flow, etc.). These data will be used as the input basis for training the neural network model to obtain the simulated temperature after compensation by the neural network model. Among them, the discharge condition can be a simulation of vehicle driving, which can include a variety of vehicle driving scenarios, such as steady low-speed driving, steady high-speed driving, sudden braking, sharp turns, sudden acceleration, etc.

[0140] In an exemplary embodiment, the discharge condition may be simulated based on the WLTC (World Light Vehicle Test Cycle) method or the US06 (part of the Supplemental Federal Test Procedure developed by the U.S. Environmental Protection Agency for testing vehicle emissions and fuel economy) method.

[0141] Then according to the maximum simulation temperature T of each battery cell maxSim and the minimum simulation temperature T minSim The maximum temperature T collected by the actual temperature sensor inside the special battery cell maxReal and minimum temperature T minReal Construct a loss function (such as root mean square error) to train the neural network model, that is, to optimize the model parameters of the neural network model.

[0142] In an exemplary embodiment, reference Figure 6 , can be optimized as Figure 6 The weight parameters and bias parameters of the MLP model shown.

[0143] The disclosed embodiments demonstrate that discharge conditions are a critical operating condition for battery cells. Training a neural network model based on data from this condition allows the model to better adapt to temperature variations during discharge, providing support for accurate estimation of battery cell temperature under these conditions. Furthermore, using the neural network model to calculate compensation temperature allows for rapid, automated, and intelligent calculations, improving both efficiency and accuracy.

[0144] In some embodiments of the present disclosure, the method for determining the battery cell temperature also includes: during the training process of the neural network model, based on the multi-point sampling temperature data of the battery cell under discharge conditions and the battery cell operating condition sampling data, at least one parameter in the thermal effect relationship is jointly optimized within a preset range; wherein the parameters in the thermal effect relationship include equivalent thermal resistance parameters, equivalent heat capacity parameters and correction coefficients.

[0145] In the embodiment of the present disclosure, during the training process of the neural network model, the data under the discharge condition is used as input, and not only the parameters of the neural network model are optimized, but also at least one parameter in the thermal effect relationship can be jointly optimized within a preset range. This means that during the training process, the neural network model parameters and the thermal effect relationship parameters can be adjusted simultaneously so that the final output (including the estimated temperature output by the thermal effect relationship and the compensated temperature output by the model) is more consistent with the actual temperature data. Among them, the preset range can be set based on demand, such as floating up and down by 10%, 20%, etc.

[0146] By jointly optimizing the neural network model parameters and the thermal effect relationship parameters through the disclosed embodiments, it is possible to comprehensively consider multiple factors such as heat conduction, cell heat generation, and compensation temperature prediction, so that the model can more accurately reflect the thermal behavior of the cell under discharge conditions, and further optimize the parameters in the thermal effect relationship, thereby improving the estimation accuracy of the target temperature at the second position.

[0147] Figure 8 FIG. 1 is a flow chart showing the optimization parameters in a method for determining a battery core temperature according to some embodiments of the present disclosure. Figure 8 As shown, the process of optimizing parameters may include the following steps.

[0148] Step S810: Optimizing equivalent thermal resistance parameters and equivalent thermal capacity parameters based on multi-point sampling temperature data of the battery cells under pure heating conditions and / or pure cooling conditions.

[0149] Step S820 , optimizing the correction coefficient corresponding to the second position based on the multi-point sampling temperature data of the battery cell under the charging condition and the battery cell operation sampling data.

[0150] Step S830: training an initial neural network model based on the multi-point sampling temperature data of the battery cell under the discharge condition and the battery cell working condition sampling data, optimizing the model parameters of the neural network model, and jointly optimizing at least one parameter in the thermal effect relationship within a preset range.

[0151] The parameters in the thermal effect relationship include an equivalent thermal resistance parameter, an equivalent heat capacity parameter and a correction coefficient.

[0152] Figure 8 For other contents of the embodiment, reference can be made to the other embodiments mentioned above.

[0153] In some embodiments of the present disclosure, the method for determining the battery cell temperature further includes: deploying the thermal effect relationship and the trained neural network model to the vehicle side.

[0154] In the disclosed embodiment, the construction and optimization of the thermal effect relationship can be completed first to clarify the heat conduction term, the cell heat generation term and the corresponding parameters; at the same time, the neural network model can be trained so that it can accurately output the compensation temperature according to the cell operating condition data, providing a mature theoretical model and algorithm tool for subsequent deployment. Then, the thermal effect relationship that has been determined and optimized and the trained neural network model can be configured to the relevant systems or equipment on the vehicle side. For example, it can be configured to the vehicle's battery management system (BMS) so that the thermal effect relationship and model can play a role in the actual operating environment of the vehicle.

[0155] Through the disclosed embodiments, after the thermal effect relationship and neural network model are deployed on the vehicle side, the battery cell operating condition data collected in real time by the vehicle can be directly used to quickly calculate the target temperature at different positions of the battery cell. Compared with traditional offline calculations or simple estimates, it can reflect the current temperature status of the battery cell more timely and accurately, making it easier for the vehicle to grasp the working status of the battery cell in real time.

[0156] Accurate temperature determination provides a reliable basis for the vehicle's thermal management system. Based on accurate cell temperature, the thermal management system can more effectively control the cooling system's start and stop, adjust coolant flow, and other functions. This prevents excessive temperatures from affecting cell performance and lifespan, or energy waste from excessive cooling, thereby improving the efficiency and effectiveness of the entire thermal management system.

[0157] It should be noted that the above figures are merely illustrative of the processes included in the methods according to some embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0158] The following are embodiments of the apparatus disclosed herein, which can be used to implement the method embodiments disclosed herein. For details not disclosed in the apparatus embodiments disclosed herein, please refer to the method embodiments disclosed herein.

[0159] Figure 9 FIG1 is a block diagram of a device for determining a battery core temperature according to some embodiments of the present disclosure. Figure 9 The device includes: an acquisition unit 901, a temperature estimation unit 902, a parameter optimization unit 903 and a deployment unit 904.

[0160] The acquisition unit 901 is used to obtain the battery cell operating condition data and the actual temperature data measured at the first position of the battery cell; the temperature estimation unit 902 is used to determine the estimated temperature of the second position of the battery cell based on the thermal effect relationship according to the measured temperature data and the battery cell operating condition data; the temperature estimation unit 902 is also used to compensate the estimated temperature according to the battery cell operating condition data to obtain the target temperature corresponding to the second position.

[0161] In some embodiments of the present disclosure, the thermal effect relationship includes a heat conduction term and a battery cell heat generation term; wherein the heat conduction term is constructed based on the heat conduction path between the first position and the second position, and is used to characterize the dynamic temperature relationship between the first position and the second position; the battery cell heat generation term is constructed based on the battery cell self-heating data and the correction coefficient corresponding to the second position; wherein the temperature estimation unit 902 determines the estimated temperature of the second position of the battery cell based on the thermal effect relationship according to the measured temperature data and the battery cell operating condition data, including: determining the heat conduction temperature contribution value of the first position to the second position based on the measured temperature data and the heat conduction term; determining the heat generation temperature contribution value of the battery cell to the heat generation temperature of the second position based on the battery cell operating condition data and the battery cell heat generation term; and determining the estimated temperature of the second position of the battery cell based on the heat conduction temperature contribution value and the heat generation temperature contribution value.

[0162] In some embodiments of the present disclosure, the temperature estimation unit 902 compensates the estimated temperature according to the battery cell operating condition data to obtain a target temperature corresponding to the second position, including: determining the compensation temperature corresponding to the second position according to the battery cell operating condition data; and performing a weighted summation of the estimated temperature and the compensation temperature to obtain the target temperature corresponding to the second position.

[0163] In some embodiments of the present disclosure, the temperature estimation unit 902 determines the compensation temperature corresponding to the second position based on the battery cell operating condition data, including: inputting the battery cell operating condition data into a trained neural network model, and outputting the compensation temperature through the neural network model.

[0164] In some embodiments of the present disclosure, the heat conduction item includes an equivalent thermal resistance parameter and an equivalent thermal capacity parameter corresponding to the heat conduction path; the parameter optimization unit 903 is used to: optimize the equivalent thermal resistance parameter and the equivalent thermal capacity parameter based on the multi-point sampling temperature data of the battery cell under pure heating conditions and / or pure cooling conditions; optimize the correction coefficient corresponding to the second position based on the multi-point sampling temperature data of the battery cell under charging conditions and the battery cell operation sampling data; or jointly optimize the equivalent thermal resistance parameter, the equivalent thermal capacity parameter and the correction coefficient corresponding to the second position based on the multi-point sampling temperature data of the battery cell under charging conditions and the battery cell operation sampling data.

[0165] In some embodiments of the present disclosure, the compensation is achieved through a trained neural network model; the parameter optimization unit 903 is also used to: train the initial neural network model based on the multi-point sampling temperature data of the battery cell under discharge conditions and the battery cell working condition sampling data, optimize the model parameters of the neural network model, and obtain the trained neural network model.

[0166] In some embodiments of the present disclosure, the parameter optimization unit 903 is also used to: during the training process of the neural network model, based on the multi-point sampling temperature data of the battery cell under discharge conditions and the battery cell working condition sampling data, jointly optimize at least one parameter in the thermal effect relationship within a preset range; wherein the parameters in the thermal effect relationship include equivalent thermal resistance parameters, equivalent heat capacity parameters and correction coefficients.

[0167] In some embodiments of the present disclosure, the deployment unit 904 is used to deploy the thermal effect relationship and the trained neural network model to the vehicle side.

[0168] In some embodiments of the present disclosure, the battery cell operating condition data includes battery cell operation data and environmental data; wherein, the battery cell operation data includes at least: current, terminal voltage, state of charge; the environmental data includes at least: ambient temperature, coolant flow, coolant inlet temperature and coolant outlet temperature.

[0169] In some embodiments of the present disclosure, the first position is the installation position of the battery cell surface temperature sensor; the second position is the battery cell temperature extreme position, including at least one of the following: the physical center point of the battery cell, the surface position of the battery cell close to the cooling system.

[0170] In some embodiments of the present disclosure, the measured temperature data is obtained by performing median filtering on the measured temperature data corresponding to a plurality of historical continuous sampling points.

[0171] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0172] Figure 10 FIG1 is a block diagram illustrating an apparatus 1000 for determining a battery cell temperature according to some embodiments of the present disclosure. For example, the apparatus 1000 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0173] Reference Figure 10 , the device 1000 may include one or more of the following components: a processing component 1002 , a memory 1004 , a power component 1006 , a multimedia component 1008 , an audio component 1010 , an input / output (I / O) interface 1012 , a sensor component 1014 , and a communication component 1016 .

[0174] The processing component 1002 generally controls the overall operation of the device 1000, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 1002 may include one or more processors 1020 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 1002 may include one or more modules to facilitate interaction between the processing component 1002 and other components. For example, the processing component 1002 may include a multimedia module to facilitate interaction between the multimedia component 1008 and the processing component 1002.

[0175] The memory 1004 is configured to store various types of data to support the operations of the device 1000. Examples of such data include instructions for any application or method operating on the device 1000, contact data, phone book data, messages, pictures, videos, etc. The memory 1004 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0176] The power component 1006 provides power to the various components of the device 1000. The power component 1006 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 1000.

[0177] The multimedia component 1008 includes a screen that provides an output interface between the device 1000 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1008 includes a front camera and / or a rear camera. When the device 1000 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0178] The audio component 1010 is configured to output and / or input audio signals. For example, the audio component 1010 includes a microphone (MIC) that is configured to receive external audio signals when the device 1000 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 1004 or transmitted via the communication component 1016. In some embodiments, the audio component 1010 also includes a speaker for outputting audio signals.

[0179] I / O interface 1012 provides an interface between processing component 1002 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0180] The sensor assembly 1014 includes one or more sensors for providing various aspects of the status assessment of the device 1000. For example, the sensor assembly 1014 can detect the open / closed state of the device 1000, the relative positioning of components, such as the display and keypad of the device 1000. The sensor assembly 1014 can also detect changes in the position of the device 1000 or a component of the device 1000, the presence or absence of user contact with the device 1000, the orientation or acceleration / deceleration of the device 1000, and changes in the temperature of the device 1000. The sensor assembly 1014 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 1014 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1014 can also include an accelerometer, a gyroscope, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0181] The communication component 1016 is configured to facilitate wired or wireless communication between the device 1000 and other devices. The device 1000 can access a wireless network based on a communication standard, such as WiFi, 3G, 4G, 5G, other communication standards, or a combination thereof. In some embodiments of the present disclosure, the communication component 1016 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In some embodiments of the present disclosure, the communication component 1016 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0182] In some embodiments of the present disclosure, the apparatus 1000 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-mentioned methods.

[0183] In some embodiments of the present disclosure, a non-transitory computer-readable storage medium including instructions is further provided, such as a memory 1004 including instructions, and the instructions can be executed by the processor 1020 of the apparatus 1000 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0184] A non-temporary computer-readable storage medium, when instructions in the storage medium are executed by a processor of a mobile terminal, enables the mobile terminal to execute a method for determining the temperature of a battery cell, the method comprising: obtaining battery cell operating condition data and actual temperature data measured at a first position of the battery cell; determining an estimated temperature at a second position of the battery cell based on a thermal effect relationship according to the actual temperature data and the battery cell operating condition data; and compensating the estimated temperature according to the battery cell operating condition data to obtain a target temperature corresponding to the second position.

[0185] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0186] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for determining a battery cell temperature, characterized in that: include: Obtaining battery cell operating condition data and actual temperature data measured at a first position of the battery cell; Determining an estimated temperature of a second position of the battery cell based on a thermal effect relationship according to the measured temperature data and the battery cell operating condition data; The estimated temperature is compensated according to the battery cell operating condition data to obtain a target temperature corresponding to the second position.

2. The method according to claim 1, characterized in that The thermal effect relationship includes a heat conduction term and a cell heat generation term; wherein the heat conduction term is constructed based on the heat conduction path between the first position and the second position, and is used to characterize the temperature dynamic relationship between the first position and the second position; the cell heat generation term is constructed based on the cell self-heating data and the correction coefficient corresponding to the second position; Wherein, determining the estimated temperature of the second position of the battery cell based on the measured temperature data and the battery cell operating condition data and a thermal effect relationship includes: determining a heat conduction temperature contribution value of the first position to the second position based on the measured temperature data and the heat conduction term; Determining a contribution value of the heat generated by the battery cell to the heat generation temperature of the second position based on the battery cell operating condition data and the battery cell heat generation item; An estimated temperature of a second position of the battery cell is determined according to the heat conduction temperature contribution value and the heat generation temperature contribution value.

3. The method according to claim 1, characterized in that Compensating the estimated temperature according to the battery cell operating condition data to obtain a target temperature corresponding to the second position includes: determining a compensation temperature corresponding to the second position according to the battery cell operating condition data; A weighted sum is performed on the estimated temperature and the compensated temperature to obtain a target temperature corresponding to the second position.

4. The method according to claim 3, characterized in that Determining a compensation temperature corresponding to the second position according to the battery cell operating condition data includes: The battery cell operating condition data is input into a trained neural network model, and the compensated temperature is output through the neural network model.

5. The method according to claim 2, characterized in that The heat conduction term includes an equivalent thermal resistance parameter and an equivalent thermal capacity parameter corresponding to the heat conduction path; the method further includes: Based on the multi-point sampling temperature data of the battery cell under pure heating conditions and / or pure cooling conditions, the equivalent thermal resistance parameter and the equivalent thermal capacity parameter are optimized; based on the multi-point sampling temperature data of the battery cell under charging conditions and the battery cell operation sampling data, the correction coefficient corresponding to the second position is optimized; or Based on the multi-point sampling temperature data of the battery cell under the charging condition and the battery cell operation sampling data, the equivalent thermal resistance parameter, the equivalent thermal capacity parameter and the correction coefficient corresponding to the second position are jointly optimized.

6. The method according to claim 4 or 5, characterized in that The compensation is achieved through a trained neural network model; the method further comprises: An initial neural network model is trained based on the multi-point sampling temperature data of the battery cell under discharge conditions and the battery cell working condition sampling data, and the model parameters of the neural network model are optimized to obtain the trained neural network model.

7. The method according to claim 1, characterized in that The battery cell operating condition data includes battery cell operation data and environmental data; wherein, the battery cell operation data includes at least: current, terminal voltage, state of charge; the environmental data includes at least: ambient temperature, coolant flow, coolant inlet temperature and coolant outlet temperature.

8. The method according to claim 1, characterized in that The first position is the installation position of the battery cell surface temperature sensor; the second position is the battery cell temperature extreme position, including at least one of the following: the physical center point of the battery cell, the surface position of the battery cell close to the cooling system.

9. The method according to claim 1, characterized in that The measured temperature data is obtained by median filtering the measured temperature data corresponding to multiple historical continuous sampling points.

10. A device for determining the temperature of a battery cell, characterized in that: include: an acquisition unit, configured to acquire the battery cell operating condition data and actual temperature data measured at a first position of the battery cell; a temperature estimation unit, configured to determine an estimated temperature of a second position of the battery cell based on a thermal effect relationship according to the measured temperature data and the battery cell operating condition data; The temperature estimation unit is further configured to compensate the estimated temperature according to the battery cell operating condition data to obtain a target temperature corresponding to the second position.

11. The device according to claim 10, characterized in that The temperature estimation unit compensates the estimated temperature according to the battery cell operating condition data to obtain a target temperature corresponding to the second position, including: Determining a compensation temperature corresponding to the second position according to the battery cell operating condition data; wherein the compensation temperature is output by processing the battery cell operating condition data through a trained neural network model; A weighted sum is performed on the estimated temperature and the compensated temperature to obtain a target temperature corresponding to the second position.

12. The device according to claim 11, characterized in that The battery cell temperature determination device further includes a deployment unit configured to: The thermal effect relationship and the trained neural network model are deployed to the vehicle side.

13. The device according to claim 10, characterized in that The first position is the installation position of the battery cell surface temperature sensor; the second position is the battery cell temperature extreme position, including at least one of the following: the physical center point of the battery cell, the surface position of the battery cell close to the cooling system.

14. A vehicle, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the steps of the method according to any one of claims 1 to 9.

15. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the steps of the method according to any one of claims 1 to 9.

16. A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of a mobile terminal, enables the mobile terminal to perform the steps of the method according to any one of claims 1 to 9.

17. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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