Battery internal temperature estimation method and device, storage medium and equipment

Abstract

The invention provides a battery internal temperature estimation method and device, a storage medium and equipment, and the method comprises the steps: determining a reduced-order model of a battery pack thermal model, which is established according to a target working condition, as a target reduced-order model when a battery pack is in the target working condition; and performing online identification on model parameters of the target reduced-order model by using target battery parameters collected in real time to obtain a corrected target reduced-order model, and estimating the internal temperature of the battery based on the corrected target reduced-order model. Thus, due to the fact that the algorithm calculation amount of the reduced-order model is moderate, real-time operation can be achieved in the vehicle-mounted working condition, real-time prediction of the internal temperature of the battery is achieved, the actual working state of the model and the actual working state of the battery are kept consistent through on-line identification of the internal resistance, the heat exchange coefficient, the equivalent specific heat capacity and the equivalent thermal resistance of the battery, and the real-time prediction of the internal temperature of the battery is achieved. Therefore, high-precision estimation of the internal temperature of the battery in the whole life cycle and the whole temperature domain is ensured.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and more specifically, to a method, apparatus, storage medium, and device for estimating the internal temperature of a battery. Background Technology

[0002] The internal temperature of a battery significantly impacts its safety, lifespan, and performance. Therefore, estimating the internal temperature of new energy vehicle batteries is a crucial and essential task in power battery management systems. Related technologies primarily employ methods based on physical models or equivalent circuit thermal models to estimate battery internal temperature. However, methods based on physical models are computationally complex, require large amounts of data, and rely on precise characteristic parameters, making it difficult to estimate the battery's internal temperature in real-time under actual vehicle operating conditions. Furthermore, simplified equivalent circuit thermal models cannot fully reflect the complex internal conditions of a real battery, especially the parameter changes that occur after battery aging. These changes are difficult for the model to track, leading to inaccurate full-lifecycle estimations. Additionally, model simplification ignores spatial temperature distribution, resulting in lower estimation accuracy for methods based on equivalent circuit thermal models. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, storage medium and device for estimating the internal temperature of a battery, in order to solve the problem that the battery internal temperature estimation methods in the related art cannot estimate the battery internal temperature in real time under actual vehicle operating conditions, and it is difficult to guarantee accurate estimation of the battery internal temperature throughout the entire battery life cycle.

[0004] In a first aspect, this application provides a method for estimating the internal temperature of a battery, comprising: when the battery pack is under a target operating condition, obtaining a target reduced-order model; the target reduced-order model is a reduced-order model of the battery pack thermal model established based on the target operating condition; using real-time collected target battery parameters to perform online identification of the model parameters of the target reduced-order model, and obtaining a corrected target reduced-order model based on the parameter values ​​obtained from the online identification; the model parameters include battery internal resistance, heat transfer coefficient, equivalent specific heat capacity, and equivalent thermal resistance; and estimating the internal temperature of the battery based on the corrected target reduced-order model.

[0005] In the above implementation process, when the battery pack is under the target operating condition, the reduced-order model of the battery pack thermal model established based on the target operating condition is determined as the target reduced-order model. The model parameters of the target reduced-order model are identified online using real-time collected target battery parameters to obtain the corrected target reduced-order model. Then, the battery's internal temperature is estimated based on the corrected target reduced-order model. Thus, because the algorithm of the reduced-order model has a moderate computational load, it can run in real-time under vehicle operating conditions, achieving real-time prediction of the battery's internal temperature. Furthermore, by identifying the battery's internal resistance, heat transfer coefficient, equivalent specific heat capacity, and equivalent thermal resistance online, the model is kept consistent with the battery's actual operating state, thereby ensuring high-accuracy estimation of the battery's internal temperature throughout its entire lifespan and temperature range.

[0006] Furthermore, in some examples, the target order reduction model includes a first state equation, a second state equation, and an observation equation; the first state equation describes the change of the state variables of the target order reduction model over time; the state variables include multiple internal battery temperatures and multiple battery surface temperatures; the second state equation describes the relationship between the internal battery temperatures, the battery surface temperatures, and the model parameters; the observation equation describes the mapping relationship between the state variables and multiple battery surface temperature measurements; the battery surface temperature measurements are battery surface temperatures collected by temperature sensors.

[0007] In the above implementation process, the target reduced-order model describes the dynamic changes of battery temperature through state equations and establishes a mathematical mapping between state variables and sensor measurements through observation equations, thereby enabling real-time estimation of the battery's internal temperature under vehicle operating conditions.

[0008] Furthermore, in some examples, the second equation of state includes a heat accumulation term, a heat generation term, a heat dissipation term, and an internal heat conduction term; the heat accumulation term is calculated using the battery pack mass, the equivalent specific heat capacity, and the battery internal temperature; the heat generation term is calculated using the battery operating current and the battery internal resistance; the heat dissipation term is calculated using the heat transfer coefficient, the heat transfer area, the battery surface temperature, and the coolant temperature; and the internal heat conduction term is calculated using the battery internal temperature and the equivalent thermal resistance.

[0009] In the above implementation process, by quantifying the heat generation inside the battery, the heat loss between the surface and the coolant, and the dynamic process of internal heat conduction, a mathematical relationship between the internal temperature and measurable parameters is established, providing theoretical support for accurately predicting the internal temperature of the battery.

[0010] Furthermore, in some examples, the target battery parameters include the individual cell voltage and the battery operating current; the online identification of the model parameters of the target reduced-order model using the real-time acquired target battery parameters includes: calculating the real-time terminal voltage of the battery pack using the individual cell voltage, calculating the current SOC using the operating current, and obtaining the open-circuit voltage of the battery pack based on the current SOC; and using the real-time terminal voltage and the open-circuit voltage, performing online identification of the battery internal resistance using the recursive least squares method.

[0011] In the above implementation process, a specific method for online identification of battery internal resistance is provided, namely, using the real-time terminal voltage and open-circuit voltage of the battery, combined with the recursive least squares method, to identify the battery internal resistance under the current operating conditions online.

[0012] Furthermore, in some examples, the target battery parameters also include a measured value of the battery surface temperature; the measured value of the battery surface temperature is the battery surface temperature collected by a temperature sensor; the online identification of the model parameters of the target reduction model using the real-time collected target battery parameters further includes: calculating the deviation between the measured value of the battery surface temperature and the predicted value of the battery surface temperature, inputting the deviation into a PI controller to obtain the corrected heat transfer coefficient; the predicted value of the battery surface temperature is the battery surface temperature predicted by the target reduction model.

[0013] In the above implementation process, a specific method for online identification of the heat transfer coefficient is provided, namely, calculating the deviation between the battery surface temperature collected by the temperature sensor and the surface temperature predicted by the model, and dynamically adjusting the heat transfer coefficient in the model through a PI controller.

[0014] Furthermore, in some examples, the target battery parameters also include coolant temperature; the online identification of the model parameters of the target reduced-order model using the real-time acquired target battery parameters further includes: obtaining an objective function based on a lumped-parameter thermal model; the objective function describes the relationship between the equivalent specific heat capacity and the equivalent thermal resistance; and solving the objective function using a recursive least squares method based on the measured values ​​of the coolant temperature and the battery surface temperature to obtain the online identified equivalent specific heat capacity and equivalent thermal resistance.

[0015] In the above implementation process, a specific method for online identification of equivalent specific heat capacity and equivalent thermal resistance is provided, namely, based on the lumped parameter thermal model, an objective function describing its thermal dynamics is constructed, and the objective function is solved based on the recursive least squares method to identify the equivalent specific heat capacity and equivalent thermal resistance of the battery pack online.

[0016] Furthermore, in some examples, estimating the battery internal temperature based on the corrected target reduction model includes: obtaining a predicted value for the battery internal temperature and a predicted value for the battery surface temperature based on the corrected target reduction model; calculating a Kalman gain using the corrected target reduction model; correcting the predicted values ​​for the battery internal temperature and the battery surface temperature based on the Kalman gain and the new battery surface temperature measurement; and outputting the finally estimated battery internal temperature.

[0017] In the above implementation process, the corrected target reduction model can be used to predict the battery internal temperature and battery surface temperature at the next moment. When the new battery surface temperature measurement value arrives, the Kalman gain is calculated using the corrected target reduction model. Based on the Kalman gain, the state estimate is updated by combining the predicted value and the measured value, and finally a high-precision estimation result is output.

[0018] Secondly, this application provides a battery internal temperature estimation device, comprising: an acquisition module, used to acquire a target reduced-order model when the battery pack is under a target operating condition; the target reduced-order model is a reduced-order model of the battery pack thermal model established based on the target operating condition; an identification module, used to identify the model parameters of the target reduced-order model online using real-time collected target battery parameters, and obtain a corrected target reduced-order model based on the parameter values ​​obtained from the online identification; the model parameters include battery internal resistance, heat transfer coefficient, equivalent specific heat capacity, and equivalent thermal resistance; and an estimation module, used to estimate the battery internal temperature based on the corrected target reduced-order model.

[0019] Thirdly, this application provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method described in any of the first aspects.

[0020] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described in any of the first aspects.

[0021] Fifthly, this application provides a computer program product that, when run on a computer, causes the computer to perform the method described in any of the first aspects.

[0022] Other features and advantages disclosed in this application will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the above-described technology disclosed in this application.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a method for estimating the internal temperature of a battery, as provided in this application embodiment; Figure 2 A schematic diagram illustrating the workflow of a battery lifecycle temperature estimation scheme based on model fusion, provided in an embodiment of this application; Figure 3 A block diagram of a battery internal temperature estimation device provided in an embodiment of this application; Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0026] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0027] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] With the rapid development of new energy vehicles and energy storage industries, the safety and lifespan of power batteries have become a key focus. Battery thermal state is a core factor affecting its performance; overheating can lead to thermal runaway, while uneven temperature accelerates battery degradation, resulting in reduced charging efficiency, slow heating response, and a series of other problems. Therefore, precise thermal management is crucial, which requires comprehensive sensing of the battery's internal temperature. Accurate prediction of the battery's internal temperature directly impacts its safety, lifespan, and performance; thus, estimating the internal temperature of new energy vehicle batteries has become an important and indispensable task in power battery management systems. However, under actual vehicle operating conditions, it is difficult to obtain the battery's internal temperature in real time, and related technologies struggle to achieve online prediction and ensure accuracy across all temperatures and throughout the battery pack's entire lifespan. Specifically, current methods mainly use physical models or equivalent circuit thermal models to estimate the internal temperature of batteries. However, physical model-based methods suffer from computational complexity, high computational load, and reliance on precise characteristic parameters, making them difficult to apply to real-time battery temperature estimation scenarios in BMS (Battery Management System). On the other hand, simplified equivalent circuit thermal models cannot fully reflect the real and complex internal conditions of batteries, especially the parameter changes after battery aging. The model struggles to track these changes, leading to inaccurate estimations over the entire battery lifecycle. Furthermore, the model simplification ignores spatial temperature distribution. Therefore, methods based on equivalent circuit thermal models cannot guarantee accurate estimation of the internal battery temperature throughout its entire lifecycle.

[0029] To address the aforementioned issues, this application provides a battery internal temperature estimation scheme. This scheme employs a reduced-order model to estimate the battery internal temperature. Since the computational load of the reduced-order model algorithm is moderate, it can run in real-time under vehicle operating conditions, enabling real-time prediction of the battery internal temperature. Furthermore, by identifying the battery internal resistance, heat transfer coefficient, equivalent specific heat capacity, and equivalent thermal resistance online, this scheme ensures that the model remains consistent with the actual operating state of the battery, thereby guaranteeing high-precision estimation of the battery internal temperature throughout its entire life cycle and temperature range.

[0030] The embodiments of this application will be described below: like Figure 1 As shown, Figure 1 This is a flowchart illustrating a method for estimating the internal temperature of a battery, as provided in an embodiment of this application. The method can be applied to a battery management system (BMS).

[0031] The method includes: Step 101: When the battery pack is under the target operating condition, obtain the target reduced-order model; the target reduced-order model is a reduced-order model of the battery pack thermal model established based on the target operating condition; The battery pack thermal model mentioned in this step can be a mathematical model used to describe the heat generation, transfer, and dissipation processes inside the battery pack. It can include a heat generation thermal model and a heat dissipation thermal model. In this embodiment, the battery pack thermal model has different model parameters for different operating conditions. The target operating conditions mentioned in this step can include charging conditions, water cooling flow request conditions, etc. When the battery pack is under the target operating condition, the model parameters of the battery pack thermal model are initialized according to the target operating condition.

[0032] In this embodiment, a reduced-order model (ROM) is used to estimate the internal temperature of the battery, thereby reducing the computational load. A reduced-order model refers to a lower-order model obtained by reducing the order of a state-space model using model aggregation, or by ignoring higher-order terms of a model established using differential equations, difference equations, or time series analysis. Because the computational load of the reduced-order model algorithm is moderate, it can be run and calculated in real time within the BMS microcontroller.

[0033] In some embodiments, the target order reduction model mentioned in this step may include a first state equation, a second state equation, and an observation equation; the first state equation describes the change of the state variables of the target order reduction model over time; the state variables include multiple internal battery temperatures and multiple battery surface temperatures; the second state equation describes the relationship between the internal battery temperatures, the battery surface temperatures, and the model parameters; the observation equation describes the mapping relationship between the state variables and the measured values ​​of multiple battery surface temperatures; the measured values ​​of the battery surface temperatures are battery surface temperatures collected by temperature sensors. That is, the target order reduction model can be a state-space model that uses multiple internal battery temperatures and multiple battery surface temperatures as state variables. Here, the multiple internal battery temperatures can refer to the core temperatures of multiple battery cells, and the multiple battery surface temperatures can refer to the measured values ​​of multiple temperature sensors arranged on the surface of each cell, which can be expressed as... , among them express The internal temperature of the battery express The model uses a sample of surface temperature points on the battery pack to describe how the battery temperature dynamically evolves with time, input, and environmental disturbances. This can be expressed as: ,in, This represents the state transition matrix, which describes the natural evolution of the state from the previous moment to the current moment according to the law of heat conduction. This represents the input matrix, describing the effect of heat generated by current on temperature. This represents the input vector, which can be the battery pack charging / discharging current and the external coolant temperature of the battery pack. This represents process noise, such as inaccuracies in the heat generation formula or variations in the heat dissipation coefficient. The second state equation establishes the mathematical relationship between the battery's internal temperature to be estimated and measurable parameters, such as the battery surface temperature and operating current. The model's observational model establishes a mathematical mapping between state variables and sensor measurements, which can be expressed as... ,in This indicates the surface temperature of the battery as measured by a temperature sensor. , Represents the observation matrix. This represents observation noise. Thus, using this target-reduced model, the internal temperature of the battery can be estimated in real time under vehicle operating conditions.

[0034] Furthermore, in some embodiments, the aforementioned second equation of state may include a heat accumulation term, a heat generation term, a heat dissipation term, and an internal heat conduction term; the heat accumulation term is calculated using the battery pack mass, the equivalent specific heat capacity, and the battery internal temperature; the heat generation term is calculated using the battery's operating current and the battery's internal resistance; the heat dissipation term is calculated using the heat transfer coefficient, heat transfer area, the battery surface temperature, and the coolant temperature; and the internal heat conduction term is calculated using the battery's internal temperature and the equivalent thermal resistance. In other words, the second equation of state for the target order reduction model can be expressed as the following formula:

[0035] In the formula, the left side of the equation represents the heat accumulation term, reflecting the rate of temperature change inside the battery. For battery pack quality, The equivalent specific heat capacity of the battery pack. The internal temperature of the battery to be estimated. For time; the first term on the right side of the equals sign, i.e. The term that generates heat is Joule heat, in which... This is the battery's operating current. This is the battery's internal resistance; the second term on the right side of the equation is... This is the heat loss term, representing the heat transferred away by the convection between the electric current and the surface and the coolant. The heat transfer coefficient, For heat exchange area, For the measurable battery surface temperature, The third term on the right side of the equation is the coolant temperature. For internal heat conduction, where This refers to the temperature difference between the internal temperature of the battery and the surface temperature of the battery. The equivalent thermal resistance of the thermally conductive silicone, heat insulation pad, and liquid cooling plate is calculated. Thus, by quantifying the dynamic processes of heat generation inside the battery, heat loss between the surface and the coolant, and internal heat conduction, a mathematical relationship between internal temperature and measurable parameters is established, providing theoretical support for accurately predicting the internal temperature of the battery.

[0036] Step 102: Use the real-time collected target battery parameters to perform online identification of the model parameters of the target reduced-order model, and obtain the corrected target reduced-order model based on the parameter values ​​obtained from the online identification; the model parameters include battery internal resistance, heat transfer coefficient, equivalent specific heat capacity and equivalent thermal resistance; In practical applications, the battery's internal resistance is affected by its state of charge (SOC), temperature, and state of health (SOH). The heat transfer coefficient changes with operating conditions, aging, and coolant properties. Furthermore, the battery's specific heat capacity and the equivalent thermal resistance of thermally conductive silicone, heat insulation pads, and liquid cooling plates also vary in real time under different operating conditions. These changes impact the model's estimation accuracy. Therefore, this embodiment identifies the battery's internal resistance, heat transfer coefficient, equivalent specific heat capacity, and equivalent thermal resistance online, ensuring the model closely matches the battery's actual operating state and thus improving the model's estimation accuracy.

[0037] In some embodiments, the target battery parameters mentioned in this step may include the individual cell voltage and the battery operating current; correspondingly, the online identification of the model parameters of the target reduced-order model using the real-time acquired target battery parameters mentioned in this step may include calculating the real-time terminal voltage of the battery pack using the individual cell voltage, calculating the current SOC using the operating current, obtaining the open-circuit voltage of the battery pack based on the current SOC, and using the real-time terminal voltage and the open-circuit voltage to perform online identification of the battery internal resistance using the recursive least squares method. In other words, the BMS acquires the individual cell voltage. and battery operating current Then, based on the individual battery cell voltage Based on the connection method of each cell in the battery pack, the real-time terminal voltage of the battery pack is calculated. According to the battery's operating current The current state of charge (SOC) is estimated using the ampere-hour integration method, and the open-circuit voltage is obtained by looking up a table based on the current SOC. Applying the Recursive Least Squares (RLS) method, to For the model, the battery internal resistance Perform online identification throughout the entire lifecycle.

[0038] Furthermore, in some embodiments, the target battery parameters mentioned in this step may also include the measured value of the battery surface temperature; the measured value of the battery surface temperature is the battery surface temperature collected by a temperature sensor; accordingly, the online identification of the model parameters of the target reduced-order model using the real-time collected target battery parameters mentioned in this step may also include: calculating the deviation between the measured value of the battery surface temperature and the predicted value of the battery surface temperature, inputting the deviation into a PI controller to obtain the corrected heat transfer coefficient; the predicted value of the battery surface temperature is the battery surface temperature predicted by the target reduced-order model. That is, when performing online identification of the heat transfer coefficient, the BMS collects the measured value of the battery surface temperature through a temperature sensor arranged on the surface of the battery cell. The battery surface temperature is estimated by combining the model, i.e., the predicted value of the battery surface temperature. The deviation was obtained. Then, a PI controller is used to adjust the current heat transfer coefficient. The corrected heat transfer coefficient is obtained by performing correction. ,in and These represent the proportional and integral coefficients of the PI controller, respectively. This allows for dynamic adjustment of the heat transfer coefficient in the target reduced-order model, ensuring the model output matches the actual measured value and improving the accuracy of the thermal model and the effectiveness of thermal management. Furthermore, considering that the heat transfer coefficient is usually related to the coolant flow rate, the coolant flow rate can also be collected and correlated with the deviation. The data is also input into the PI controller to further improve the estimation accuracy of the model.

[0039] Furthermore, in some embodiments, the target battery parameters mentioned in this step may also include coolant temperature; accordingly, the online identification of the model parameters of the target reduced-order model using the real-time acquired target battery parameters mentioned in this step may also include: obtaining an objective function based on a lumped-parameter thermal model; the objective function describing the relationship between the equivalent specific heat capacity and the equivalent thermal resistance; and solving the objective function using a recursive least squares method based on the measured values ​​of the coolant temperature and the battery surface temperature to obtain the online identified equivalent specific heat capacity and equivalent thermal resistance. That is, when performing online identification of the equivalent specific heat capacity and equivalent thermal resistance, the BMS acquires the coolant temperature. Based on the lumped-parameter thermal model, its thermal dynamics can be described by an objective function, which can be expressed as: , among them To generate heat, the objective function is then solved using the recursive least squares method, thereby realizing the equivalent specific heat capacity of the battery pack throughout its entire life cycle. and equivalent thermal resistance Online identification.

[0040] Step 103: Estimate the internal temperature of the battery based on the corrected target reduced-order model.

[0041] In this embodiment, the parameter values ​​obtained from online identification are used to update the model parameters of the target reduction model, thereby obtaining the corrected target reduction model. This ensures that the corrected target reduction model is highly consistent with the actual working state of the battery. Thus, based on the corrected target reduction model, the internal temperature of the battery can be accurately estimated.

[0042] In some embodiments, this step may include obtaining predicted values ​​for the battery's internal temperature and battery surface temperature based on the corrected target reduction model; calculating the Kalman gain using the corrected target reduction model; correcting the predicted values ​​for the battery's internal temperature and battery surface temperature based on the Kalman gain and the new battery surface temperature measurement; and outputting the finally estimated battery internal temperature. In other words, the corrected target reduction model can be used to predict the battery's internal temperature at the next moment. and battery surface temperature When the new battery surface temperature measurement value Upon arrival, the Kalman gain is calculated using the corrected target reduced-order model. Based on the Kalman gain, the state estimate is updated by combining the predicted and measured values, and finally, a high-precision estimation result is output, namely the accurate internal temperature of the battery.

[0043] In this embodiment, when the battery pack is under target operating conditions, a reduced-order model of the battery pack thermal model established based on the target operating conditions is determined as the target reduced-order model. The model parameters of the target reduced-order model are identified online using real-time collected target battery parameters to obtain a corrected target reduced-order model. The battery's internal temperature is then estimated based on this corrected target reduced-order model. Thus, because the algorithm for the reduced-order model has a moderate computational load, it can run in real-time under vehicle operating conditions, achieving real-time prediction of the battery's internal temperature. Furthermore, by identifying the battery's internal resistance, heat transfer coefficient, equivalent specific heat capacity, and equivalent thermal resistance online, the model is kept consistent with the battery's actual operating state, thereby ensuring high-precision estimation of the battery's internal temperature throughout its entire lifespan and temperature range.

[0044] To provide a more detailed explanation of the solution in this application, a specific embodiment is described below: This embodiment provides a battery lifecycle temperature estimation scheme based on model fusion. Taking a ternary lithium battery pack of a certain vehicle as an example, the scheme uses simulated cloud maps of the battery pack under normal temperature (25℃), high temperature (40℃), and low temperature (-15℃) fast charging and discharging conditions as observation benchmarks. Temperature sensors are placed at the geometric centers of the lowest and highest temperatures on the battery surface under each simulated condition as observation references. The workflow of this scheme is as follows: Figure 2 As shown, it includes: S201. A reduced-order model of the battery thermal model is established based on different battery operating conditions. Specifically, this reduced-order model is a state-space model, whose state vectors are multiple internal battery temperatures and multiple battery surface temperatures. Its state equations describe how the battery temperature dynamically evolves with time, input, and environmental disturbances. Its observation equations establish a mathematical mapping between state variables and temperature sensor measurements. This reduced-order model also includes a state equation for estimating the battery pack temperature, which can be expressed as:

[0045] In the formula, For battery pack quality, The equivalent specific heat capacity of the battery pack. The internal temperature of the battery to be estimated. For time; This is the battery's operating current. This refers to the battery's internal resistance. The heat transfer coefficient, For heat exchange area, For the measurable battery surface temperature, This refers to the coolant temperature. This refers to the temperature difference between the internal temperature of the battery and the surface temperature of the battery. The equivalent thermal resistance of thermally conductive silicone, heat insulation pad, and liquid cooling plate; S202. Identify the operating conditions and initialize the reduced-order model. Specifically, by identifying whether a vehicle's ternary lithium battery pack has charging or cooling requirements, determine whether to enter the temperature estimation process. If the determination result is yes, load the basic parameters of the battery pack, including battery pack mass, heat transfer area, and battery specific heat capacity, initialize the state vector and covariance matrix of the reduced-order model, and initialize the battery internal resistance, heat transfer coefficient, equivalent specific heat capacity, and equivalent thermal resistance. S203, Data Acquisition; Specifically, the BMS acquires the battery cell voltage, battery operating current, coolant temperature, coolant flow rate, and battery surface temperature; S204. Online identification of battery internal resistance; Specifically, using the real-time terminal voltage and open-circuit voltage of the battery, combined with the recursive least squares method, the battery internal resistance under the current operating conditions is identified online. S205. The heat transfer coefficient is corrected online by a PI controller. Specifically, the deviation between the battery surface temperature collected by the temperature sensor and the surface temperature predicted by the model is calculated, and the heat transfer coefficient in the model is dynamically adjusted by the PI controller. S206. Online identification of the equivalent specific heat capacity and equivalent thermal resistance of the battery pack; specifically, based on the lumped parameter thermal model, the thermal dynamics are described by an objective function, and the objective function is solved by the recursive least squares method to identify the equivalent specific heat capacity and equivalent thermal resistance of the battery pack online. S207. Estimate the battery internal temperature based on the corrected reduced-order model. Specifically, the reduced-order model uses the online identified model parameters combined with the collected battery parameters to predict the battery internal temperature and battery surface temperature at the next moment. Then, the Kalman gain is calculated, and the state estimate is updated by combining the predicted value and the measured value, thereby estimating the accurate battery internal temperature and battery surface temperature.

[0046] This embodiment has at least the following advantages: First, by identifying key heat generation parameters online, combining offline test conditions with online identification, and adaptively correcting key heat dissipation coefficients, the model maintains a high degree of consistency with the actual working state of the battery. Through the fusion of different models and test conditions, high-precision estimation of battery temperature across the entire temperature range throughout the battery's lifespan is ensured. Second, by adopting a reduced-order model, the computational load of the algorithm is reduced, allowing it to run and calculate in real time within the BMS microcontroller, making it highly practical. Third, through online identification, the internal resistance differences caused by battery aging, as well as performance changes in heat transfer coefficient, heat capacity, and thermal resistance, can be automatically tracked, ensuring that the estimation accuracy does not decay throughout the battery's lifespan.

[0047] Corresponding to the embodiments of the aforementioned methods, this application also provides embodiments of a battery internal temperature estimation device and a terminal application thereof: like Figure 3 As shown, Figure 3 This is a block diagram of a battery internal temperature estimation device provided in an embodiment of this application. The device includes: The acquisition module 31 is used to acquire a target reduced-order model when the battery pack is under the target operating condition; the target reduced-order model is a reduced-order model of the battery pack thermal model established based on the target operating condition. The identification module 32 is used to identify the model parameters of the target reduced-order model online using the target battery parameters collected in real time, and to obtain the corrected target reduced-order model based on the parameter values ​​obtained from the online identification; the model parameters include battery internal resistance, heat transfer coefficient, equivalent specific heat capacity and equivalent thermal resistance; The estimation module 33 is used to estimate the internal temperature of the battery based on the corrected target reduced-order model.

[0048] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0049] This application also provides an electronic device, please refer to [link to application]. Figure 4 , Figure 4This is a structural block diagram of an electronic device provided in an embodiment of this application. The electronic device may include a processor 410, a communication interface 420, a memory 430, and at least one communication bus 440. The communication bus 440 is used to enable direct communication between these components. In this embodiment, the communication interface 420 of the electronic device is used for signaling or data communication with other node devices. The processor 410 may be an integrated circuit chip with signal processing capabilities.

[0050] The processor 410 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor, or the processor 410 can be any conventional processor.

[0051] The memory 430 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc. The memory 430 stores computer-readable instructions. When these computer-readable instructions are executed by the processor 410, the electronic device can perform the aforementioned operations. Figure 1 The various steps involved in the method implementation examples.

[0052] Alternatively, the electronic device may also include a storage controller and an input / output unit.

[0053] The memory 430, storage controller, processor 410, peripheral interface, and input / output unit are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses 440. The processor 410 is used to execute executable modules stored in the memory 430, such as software function modules or computer programs included in electronic devices.

[0054] The input / output unit is used to provide users with the ability to create tasks and to set optional start periods or preset execution times for those tasks, thereby enabling user-server interaction. The input / output unit may be, but is not limited to, a mouse and keyboard.

[0055] Understandable. Figure 4 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 4 The more or fewer components shown, or having the same Figure 4 The different configurations shown. Figure 4 The components shown can be implemented using hardware, software, or a combination thereof.

[0056] This application also provides a storage medium storing instructions. When the instructions are run on a computer, the computer program is executed by a processor to implement the method described in the method embodiment. To avoid repetition, the method will not be described again here.

[0057] This application also provides a computer program product that, when run on a computer, causes the computer to perform the method described in the method embodiment.

[0058] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0059] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0060] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0061] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0062] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0063] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for estimating the internal temperature of a battery, characterized in that, include: When the battery pack is under the target operating condition, obtain the target reduced-order model; The target reduced-order model is a reduced-order model of the battery pack thermal model established based on the target operating conditions; The model parameters of the target reduced-order model are identified online using the target battery parameters collected in real time. Based on the parameter values ​​obtained from the online identification, the corrected target reduced-order model is obtained. The model parameters include battery internal resistance, heat transfer coefficient, equivalent specific heat capacity, and equivalent thermal resistance. The internal temperature of the battery is estimated based on the corrected target reduced-order model.

2. The method according to claim 1, characterized in that, The target order reduction model includes a first state equation, a second state equation, and an observation equation. The first state equation describes the change of the state variables of the target order reduction model over time. The state variables include multiple internal battery temperatures and multiple battery surface temperatures. The second state equation describes the relationship between the internal battery temperatures, the battery surface temperatures, and the model parameters. The observation equation describes the mapping relationship between the state variables and multiple battery surface temperature measurements. The battery surface temperature measurements are battery surface temperatures collected by temperature sensors.

3. The method according to claim 2, characterized in that, The second equation of state includes a heat accumulation term, a heat generation term, a heat loss term, and an internal heat conduction term. The heat accumulation term is calculated using the battery pack mass, the equivalent specific heat capacity, and the battery internal temperature. The heat generation term is calculated using the battery operating current and the battery internal resistance. The heat loss term is calculated using the heat transfer coefficient, heat transfer area, battery surface temperature, and coolant temperature. The internal heat conduction term is calculated using the battery internal temperature and the equivalent thermal resistance.

4. The method according to claim 1, characterized in that, The target battery parameters include the individual cell voltage and the battery operating current; The online identification of model parameters for the target reduction model using real-time acquired target battery parameters includes: The real-time terminal voltage of the battery pack is calculated using the voltage of the individual battery cells, the current state of charge (SOC) is calculated using the operating current, and the open-circuit voltage of the battery pack is obtained based on the current SOC. The battery internal resistance is identified online using the real-time terminal voltage and the open-circuit voltage, employing a recursive least squares method.

5. The method according to claim 4, characterized in that, The target battery parameters also include the battery surface temperature measurement value; the battery surface temperature measurement value is the battery surface temperature collected by a temperature sensor; The online identification of model parameters of the target reduction model using real-time acquired target battery parameters also includes: Calculate the deviation between the measured value of the battery surface temperature and the predicted value of the battery surface temperature, input the deviation into the PI controller, and obtain the corrected heat transfer coefficient; The predicted battery surface temperature is the battery surface temperature predicted by the target reduced-order model.

6. The method according to claim 5, characterized in that, The target battery parameters also include coolant temperature; The online identification of model parameters of the target reduction model using real-time acquired target battery parameters also includes: The objective function is obtained based on the lumped parameter thermal model; the objective function describes the relationship between the equivalent specific heat capacity and the equivalent thermal resistance. Based on the measured values ​​of the coolant temperature and the battery surface temperature, the objective function is solved using the recursive least squares method to obtain the online identified equivalent specific heat capacity and equivalent thermal resistance.

7. The method according to claim 2, characterized in that, The estimation of the battery's internal temperature based on the corrected target reduced-order model includes: Based on the corrected target reduced-order model, the predicted values ​​of the battery internal temperature and battery surface temperature are obtained. The Kalman gain is calculated using the corrected target reduced-order model. Based on the Kalman gain and the new battery surface temperature measurement, the predicted battery internal temperature and the predicted battery surface temperature are corrected, and the final estimated battery internal temperature is output.

8. A battery internal temperature estimation device, characterized in that, include: The acquisition module is used to acquire the target reduced-order model when the battery pack is under the target operating condition; The target reduced-order model is a reduced-order model of the battery pack thermal model established based on the target operating conditions; The identification module is used to identify the model parameters of the target reduced-order model online using the target battery parameters collected in real time, and to obtain the corrected target reduced-order model based on the parameter values ​​obtained from the online identification; the model parameters include battery internal resistance, heat transfer coefficient, equivalent specific heat capacity and equivalent thermal resistance; The estimation module is used to estimate the internal temperature of the battery based on the corrected target reduced-order model.

9. A computer-readable storage medium, characterized in that, It stores a computer program thereon, which, when executed by a processor, implements the method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any one of claims 1 to 7.

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