A power battery core temperature rapid prediction method, device and electronic equipment

Abstract

The application relates to a power battery core temperature rapid prediction method, device and electronic equipment, wherein the method comprises the following steps: determining a temperature prediction point in a power battery; establishing a first temperature response convolution integral calculation model according to the change of a heat generation rate of the power battery with time and the change of the temperature of the temperature prediction point with time under the action of a pulse heat source; the pulse heat source is a pulse current signal acting on the power battery to cause heat generation of the power battery; predicting the temperature of the temperature prediction point under the action of an actual heat source according to the first temperature response convolution integral calculation model and the initial temperature of the temperature prediction point at an initial moment; the actual heat source is a continuous current signal acting on the power battery to cause heat generation of the power battery; the method can rapidly and accurately predict the temperature of a position in a power battery module, which is difficult to arrange a temperature measuring point but is prone to local high temperature, and can reduce the calculation resources required for temperature prediction.

Description

Technical Field

[0001] This application relates to the field of power batteries, and in particular to a method, apparatus and electronic device for rapid prediction of the core temperature of a power battery. Background Technology

[0002] The widespread adoption of electric vehicles has led to the large-scale application of power batteries. Temperature is a major factor affecting the lifespan and safety performance of power batteries; therefore, battery management systems need to be able to accurately monitor the battery's temperature state. To ensure that the power battery operates within a suitable temperature range during vehicle operation, accurate temperature prediction is also necessary to design optimal thermal management control strategies, thereby achieving the desired thermal management and regulation effects.

[0003] Existing methods for monitoring the temperature state of power batteries typically involve placing thermocouples at measurement points on the battery module surface. However, the limited number of thermocouples makes it difficult to detect localized high-temperature points within the battery in a timely manner, thus hindering effective monitoring of the battery core temperature (i.e., the actual temperature of the internal cells). Some research has proposed using a distributed lumped heat model to estimate the internal core temperature based on the battery surface temperature. However, predicting the battery core temperature using this method still requires complex calculations using finite element methods or computational fluid dynamics (CFD), which are unsuitable for vehicle battery management modules with limited computing resources. These shortcomings of existing power battery temperature state monitoring methods lead to deviations in a series of SOX estimation algorithms that depend on battery temperature state, affecting the safety performance and lifespan of the power battery. Summary of the Invention

[0004] In view of this, this application proposes a method, device and electronic device for rapid prediction of the core temperature of a power battery, which can quickly and accurately predict the temperature of locations in the power battery module where it is difficult to place temperature measurement points but where local high temperatures are likely to occur, and can reduce the computing resources required for temperature prediction.

[0005] According to one aspect of this application, a method for rapid prediction of the core temperature of a power battery is provided, comprising: determining a temperature prediction point inside the power battery; establishing a first temperature response convolutional integral calculation model based on the change of the heat generation rate of the power battery over time and the change of the temperature at the temperature prediction point over time under the action of a pulsed heat source; wherein the pulsed heat source is the self-heating of the power battery caused by a pulsed current signal acting on the power battery; and predicting the temperature of the temperature prediction point under the action of an actual heat source based on the first temperature response convolutional integral calculation model and the initial temperature of the temperature prediction point at an initial moment; wherein the actual heat source is the self-heating of the power battery caused by a continuous current signal acting on the power battery.

[0006] In one possible implementation, the first temperature response convolution integral calculation model is as follows: Wherein, δT1(t) represents the temperature change at time t of the predicted temperature point under the action of the actual heat source; q1(x) represents the heat generation rate of the power battery at time x; TR1(t-x) represents the temperature change at time (t-x) of the predicted temperature point under the action of the pulse heat source; t>0; 0≤x≤t.

[0007] In one possible implementation, the temperature at the predicted temperature point under the influence of an actual heat source is predicted based on the first temperature response convolutional integral calculation model and the initial temperature of the predicted temperature point at the initial moment. This includes: discretizing the first temperature response convolutional integral calculation model to obtain a first temperature prediction model, wherein the first temperature prediction model is: Wherein, ΔT1[n] represents the temperature change at time n of the predicted temperature point under the action of the actual heat source; q1[i] represents the heat generation rate of the power battery at time i; TR1[n-i] represents the temperature change at time (n-i) of the predicted temperature point under the action of the pulse heat source; n and i are positive integers; n>0; 0≤i≤n; According to the first temperature prediction model and the initial temperature, the temperature of the predicted temperature point under the action of the actual heat source is predicted by the following formula: T1[n]=T[0]+ΔT1[n]; Wherein, T1[n] represents the temperature of the predicted temperature point at time n under the action of the actual heat source; T[0] represents the initial temperature.

[0008] In one possible implementation, q1[i] is calculated as: q1[i] = I[i] 2 ·θ+I[i]·T·(dU / dT); where I[i] represents the operating current of the power battery at time i; θ represents the internal resistance of the power battery; T represents the temperature of the power battery; and dU / dT represents the rate of change of the voltage of the power battery with the temperature of the power battery.

[0009] In one possible implementation, when the time length of the prediction time domain is less than or equal to a preset threshold, when predicting the temperature of the temperature prediction point under the action of the actual heat source within the prediction time domain, the calculation formula for q1[i] is T=T[0]; the prediction time domain is [0,n]; when the time length of the prediction time domain is greater than the preset threshold, the method further includes: dividing the prediction time domain into N time intervals, and predicting the temperature of the temperature prediction point under the action of the actual heat source within each of the time intervals respectively; N>1; the N time intervals are {[0,n1],……,[n N-1 +1,n]};n1、……、n N-1 All are positive integers; the duration of each time interval is not greater than the preset threshold; wherein, when predicting the temperature of the predicted temperature point in the first time interval [0, n1] under the action of the actual heat source, T = T[0] in the calculation formula of q1[i]; for the temperature predicted point in the kth time interval [n] under the action of the actual heat source, k-1 +1,n k When predicting the temperature within ], the formula for calculating q1[i] includes T = T1[n]. k-1 ];1 <k≤N。

[0010] In one possible implementation, when the power battery has a thermal management structure, the method further includes: establishing a second temperature response convolutional integral calculation model based on the change in the heat dissipation rate of the power battery over time and the change in temperature at the predicted temperature point under the action of a pulsed cold source over time; the pulsed cold source is used to cool the power battery at the thermal management structure according to a pulsed cooling signal; the pulsed cooling signal is used to control the temperature and flow rate of the cooling airflow at the thermal management structure; predicting the temperature at the predicted temperature point under the combined action of the actual heat source and the actual cold source based on the first temperature response convolutional integral calculation model, the second temperature response convolutional integral calculation model, and the initial temperature; the actual cold source is used to cool the power battery at the thermal management structure according to the actual temperature and actual flow rate of the cooling airflow; wherein, the second temperature response convolutional integral calculation model is: δT2(t) represents the temperature change at time t of the predicted temperature point under the action of the actual cold source; q2(x) represents the change of the heat dissipation rate of the power battery at time x with time t; TR2(t-x) represents the temperature change at time (t-x) of the predicted temperature point under the action of the pulse cold source; t>0; 0≤x≤t.

[0011] In one possible implementation, predicting the temperature at the predicted temperature point under the combined action of the actual heat source and the actual cold source, based on the first temperature response convolutional integral calculation model, the second temperature response convolutional integral calculation model, and the initial temperature, includes: discretizing the second temperature response convolutional integral calculation model to obtain a second temperature prediction model, wherein the second temperature prediction model is: Wherein, ΔT2[n] represents the temperature change at time n of the predicted temperature point under the action of the actual cold source; q2[i] represents the heat dissipation rate of the power battery at time i; TR2[n-i] represents the temperature change at time (n-i) of the predicted temperature point under the action of the pulse cold source; n and i are positive integers; n>0; 0≤i≤n; According to the first temperature prediction model, the second temperature prediction model and the initial temperature, the temperature of the predicted temperature point under the combined action of the actual heat source and the actual cold source is predicted by the following formula: T2[n]=T[0]+ΔT1[n]+ΔT2[n]; Wherein, T2[n] represents the temperature of the predicted temperature point at time n under the combined action of the actual heat source and the actual cold source.

[0012] According to another aspect of this application, a device for rapid prediction of the core temperature of a power battery is provided, comprising: a point determination module for determining temperature prediction points inside the power battery; a first model establishment module for establishing a first temperature response convolutional integral calculation model based on the change of the heat generation rate of the power battery over time and the change of the temperature of the temperature prediction point over time under the action of a pulsed heat source; wherein the pulsed heat source is the self-heating of the power battery caused by a pulsed current signal acting on the power battery; and a first prediction module for predicting the temperature of the temperature prediction point under the action of an actual heat source based on the first temperature response convolutional integral calculation model and the initial temperature of the temperature prediction point at an initial moment; wherein the actual heat source is the self-heating of the power battery caused by a continuous current signal acting on the power battery.

[0013] According to another aspect of this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-described method for rapid prediction of the core temperature of a power battery when executing the instructions stored in the memory.

[0014] According to another aspect of this application, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions, when executed by a processor, implement the above-described method for rapid prediction of the core temperature of a power battery.

[0015] According to another aspect of this application, a computer program product is provided, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device executes the above-described method for rapid prediction of the core temperature of a power battery.

[0016] The rapid prediction method for the core temperature of the power battery proposed in this application can quickly predict the temperature of locations in the power battery module where it is difficult to place temperature measurement points but where local high temperatures are likely to occur, and can achieve high prediction accuracy. Moreover, compared with the existing power battery core temperature estimation algorithms that use lumped thermal models, the method proposed in this application can significantly reduce the computational resources required for temperature prediction using finite element and CFD methods.

[0017] Other features and aspects of this application will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0018] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this application together with the specification and serve to explain the principles of this application.

[0019] Figure 1 A flowchart is shown for a method for rapid prediction of the core temperature of a power battery according to an embodiment of this application.

[0020] Figure 2 This diagram illustrates a method for rapidly predicting the core temperature of a power battery according to an embodiment of this application.

[0021] Figure 3 A schematic diagram of a power battery module is shown.

[0022] Figure 4 The temperature response curves of a temperature prediction point to a pulsed heat source and a pulsed cold source are shown according to an embodiment of this application.

[0023] Figure 5 A schematic diagram showing the prediction results of a method for rapid prediction of the core temperature of a power battery according to an embodiment of this application is illustrated.

[0024] Figure 6 A schematic diagram illustrating the temperature prediction error of a method for rapid prediction of the core temperature of a power battery according to an embodiment of this application is shown.

[0025] Figure 7 This diagram illustrates the structure of a power battery core temperature rapid prediction device according to an embodiment of this application.

[0026] Figure 8A block diagram of an electronic device 1900 according to an embodiment of this application is shown. Detailed Implementation

[0027] Various exemplary embodiments, features, and aspects of this application will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0028] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0029] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented without certain specific details. In some instances, methods, means, components, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.

[0030] Figure 1 This diagram illustrates a flowchart of a method for rapidly predicting the core temperature of a power battery according to an embodiment of this application. This method can be executed by a device with processing capabilities, such as a processor or server. Figure 1 As shown, the method may include:

[0031] S101. Determine the predicted temperature points inside the power battery.

[0032] For example, temperature prediction points can be selected based on locations within the power battery where localized high temperatures are prone to occur. These temperature prediction points can also be referred to as nodes. The temperature at the temperature prediction point can be considered the core temperature of the power battery.

[0033] As an example, the temperature prediction point could be the location where the localized highest temperature occurs inside the power battery. The temperature field during the power battery's discharge process can be obtained through high-precision simulation or experimentation, thereby determining the location where the highest temperature occurs inside the power battery, which can then be used as the temperature prediction point. Infrared thermography can be used as an experimental method to better visualize the temperature field.

[0034] S102. Based on the change of the heat generation rate of the power battery over time and the change of the temperature at the temperature prediction point over time under the action of the pulse heat source, establish a first temperature response convolution integral calculation model.

[0035] The pulsed heat source is the self-generated heat of the power battery caused by a pulsed current signal acting on it. Under the action of the pulsed current signal, the power battery generates heat, which leads to a temperature change at the predicted temperature point inside the battery. This process can be referred to as the temperature change at the predicted temperature point caused by the pulsed heat source, and this pulsed current signal can be called a pulsed heating signal.

[0036] In one possible implementation, the first temperature response convolution integral calculation model is as follows:

[0037]

[0038] Wherein, δT1(t) represents the temperature change at the predicted temperature point at time t under the action of the actual heat source; q1(t) represents the heat generation rate of the power battery at time t; TR1(t-x) represents the temperature change at the predicted temperature point at time (t-x) under the action of the pulsed heat source; t>0; 0≤x≤t. δT1(·) represents the temperature change of the predicted temperature point over time under the action of the actual heat source; q1(·) represents the change in the heat generation rate of the power battery over time; TR1(·) represents the temperature change (i.e., the change) of the predicted temperature point over time under the action of the pulsed heat source.

[0039] The actual heat source is the heat generated by the power battery itself due to the continuous current signal acting on it. Under the action of the continuous current signal, the power battery generates heat, which leads to a temperature change at the predicted temperature point inside the battery. This process can be referred to as the temperature change at the predicted temperature point caused by the actual heat source.

[0040] The heat generation rate of a power battery refers to the rate at which heat is generated during the charging and discharging process. For lithium-ion power batteries, the heat generation rate can be expressed as a function related to the battery's temperature T, operating current I, internal resistance θ, and entropy coefficient dU / dT. The formula for calculating the heat generation rate q is as follows:

[0041] q = I 2 ·θ+I·T·(dU / dT) (2)

[0042] The operating current I of a power battery represents the current of the power battery under different operating conditions, and I varies with time. The internal resistance θ of the power battery is related to the battery temperature T and the state of charge (SOC). The entropy coefficient dU / dT of the power battery represents the rate of change of the battery voltage U with the battery temperature T. For example, hybrid power pulse characteristic (HPPC) tests can be performed at different temperatures to obtain the curves of θ versus T and SOC, as well as the curves of dU / dT versus T. The specific implementation process can be found in relevant technologies.

[0043] The curve showing the temperature change over time at a predicted temperature point under the influence of a pulsed heat source can be referred to as the temperature response curve of the predicted temperature point to the pulsed heat source. For example, the temperature response curve of the predicted temperature point to the pulsed heat source can be determined through preliminary experiments or high-precision simulations.

[0044] As an example, experimentally, a discharge current pulse with a specific waveform from the battery module can be used as a pulse heating signal. The power of the pulse heating signal can be calculated by combining the current temperature, SOC, and internal resistance of the power battery. The core temperature information inside the battery can be obtained using methods such as thermocouples implanted inside the battery, ultrasound, or optical fibers. The time interval for collecting the core temperature information is from the start of the pulse heat source to the point where the temperature rise stabilizes, thus obtaining the temperature response curve of the predicted temperature point to the pulse heat source. The specific implementation process can be found in relevant technologies. It should be noted that the power of the pulse heating signal should be appropriately selected; excessive power may cause a short circuit, while insufficient power may make it difficult to identify temperature changes. The temperature rise caused by the pulse heating signal should be much greater than the sensor's accuracy, and the actual vehicle mounting structure of the battery module should be considered during the experiment to ensure proper insulation of relevant locations.

[0045] As another example, if high-precision simulation methods are used, physical field simulation can be achieved by establishing a numerical calculation model that includes the battery HPPC experimental calibration parameters. The waveform and amplitude of the pulse heating signal are set in the simulation boundary conditions, and the temperature changes at the predicted temperature points within the numerical model are recorded, thereby obtaining the temperature response curve of the predicted temperature points to the pulse heat source. For specific implementation details, please refer to relevant technologies.

[0046] When the power of the pulse heating signal is within a suitable range, the temperature change will vary linearly with the power of the pulse heating signal. That is, under the action of pulse heating signals of different powers, the shape of the temperature response curve remains unchanged, only the amplitude changes. For the temperature response curve obtained from experiments or simulations, the amplitude of the temperature response curve needs to be normalized. The normalization method can be to multiply the amplitude of the temperature response curve by (1 / P1), where P1 represents the power of the actual pulse heating signal.

[0047] S103. Based on the first temperature response convolution integral calculation model and the initial temperature of the temperature prediction point at the initial moment, predict the temperature of the temperature prediction point under the action of the actual heat source.

[0048] The initial time represents the moment when the prediction begins (i.e., time 0). For example, the surface temperature of the power battery at the initial time can be monitored using existing power battery temperature state monitoring methods. Based on existing algorithms that estimate the internal core temperature based on the battery surface temperature, the core temperature of the power battery at the initial time can be estimated and used as the initial temperature of the temperature prediction point. The initial temperature can be denoted as T[0].

[0049] In formula (1), t and x represent continuous time. However, in actual prediction, the data obtained are discrete time series, so the first temperature response convolution integral calculation model needs to be discretized.

[0050] In one possible implementation, predicting the temperature of the predicted temperature point under the influence of an actual heat source, based on the first temperature response convolution integral calculation model and the initial temperature of the predicted temperature point at the initial moment, may include:

[0051] (1) Discretize the first temperature response convolution integral calculation model to obtain the first temperature prediction model, which is:

[0052]

[0053] Where ΔT1[n] represents the temperature change at the predicted temperature point at time n under the action of the actual heat source (i.e., the temperature change at time n relative to the initial temperature); q1[i] represents the heat generation rate of the power battery at time i; TR1[n-i] represents the temperature change at the predicted temperature point at time (n-i) under the action of the pulse heat source; n and i are positive integers; n>0; 0≤i≤n.

[0054] In formula (3), n and i represent discrete time. Therefore, q1[·] represents the time series of the change of the heat generation rate of the power battery with time (which can be called the heat generation rate time series), and TR1[·] represents the time series of the change of temperature at the temperature prediction point with time under the action of the pulse heat source (which can be called the pulse heat source temperature response time series).

[0055] For example, the formula for calculating q1[i] can be obtained from the above formula (2):

[0056] q1[i] = I[i] 2 ·θ+I[i]·T·(dU / dT) (4)

[0057] Where I[i] represents the operating current of the power battery at time i. I[·] represents the time series of the operating current of the power battery as a function of time, and I[·] can be called the operating current time series.

[0058] For example, the operating current time series I[·] can be obtained by human predefinition; or it can be calculated based on the speed distribution of the electric vehicle navigation route. The calculation method can refer to relevant technologies.

[0059] When predicting the temperature at time n, the value of T in formula (4) can be the temperature predicted at time n-1. That is, the prediction result at time n can be calculated using the prediction result at time n-1. However, this calculation method will consume a lot of computing resources. During the operation of the power battery under the action of the actual heat source, the change of T is small in a short time interval, and the influence of the change of T on the heat generation rate can be ignored. Therefore, in the actual calculation process, the prediction time domain can be divided into multiple short time intervals. It can be assumed that T is constant in each time interval, and θ and dU / dT related to T can also be assumed to be constant. In this way, the temperature of the temperature prediction point can be predicted in segments. When predicting the temperature in each time interval, it can be assumed that T, θ and dU / dT in formula (4) are constant values. As long as the operating current time series is obtained, the heat generation rate at each moment in the time interval can be calculated, thereby predicting the temperature of the temperature prediction point at each moment in the time interval.

[0060] In one possible implementation, when the time length of the prediction time domain is less than or equal to a preset threshold, when predicting the temperature of the temperature prediction point under the action of the actual heat source within the prediction time domain, the calculation formula for q1[i] is T = T[0]; the prediction time domain represents a time interval from the initial moment onwards that needs to be predicted, i.e., [0, n]; when the time length of the prediction time domain is greater than the preset threshold, the method further includes: dividing the prediction time domain into N time intervals, and predicting the temperature of the temperature prediction point under the action of the actual heat source within each of the time intervals respectively; N > 1; the N time intervals are {[0, n1], ..., [n N-1 +1,n]};n1、……、n N-1 All are positive integers; the duration of each time interval is not greater than the preset threshold; wherein, when predicting the temperature of the predicted temperature point in the first time interval [0, n1] under the action of the actual heat source, T = T[0] in the calculation formula of q1[i]; for the temperature predicted point in the kth time interval [n] under the action of the actual heat source, k-1 +1,n k When predicting the temperature within ], the formula for calculating q1[i] includes T = T1[n]. k-1 ];1 <k≤N。T1[n k-1 [] indicates that the predicted temperature point under the action of an actual heat source, obtained by the method of the embodiments of this application, is at n. k-1 The temperature of a moment.

[0061] For example, the preset threshold can be set by those skilled in the art according to actual needs, for example, it can be 200s.

[0062] In one embodiment, when the prediction time domain is short (e.g., 0-200s), T can be considered constant throughout the entire prediction time domain. T[0] can be substituted into formula (4) as T to calculate the temperature of the temperature prediction point throughout the entire prediction time domain. When the prediction time domain is long, the prediction time domain can be divided into multiple time intervals (e.g., the length of each time interval can be less than 200s). T, θ, and dU / dT in each time interval are considered constant. For the first time interval, T[0] can be substituted into formula (4) as T to calculate the temperature of the temperature prediction point in the first time interval. For the kth time interval (k>1), the temperature predicted at the end of the (k-1)th time interval can be substituted into formula (4) as T to calculate the temperature of the temperature prediction point in the kth time interval. The value of θ in each time interval can be determined based on the curves of θ changing with T and SOC, the value of T in each time interval, and the SOC of the battery. The value of dU / dT in each time interval can be determined based on the curves of dU / dT changing with T and the value of T in each time interval.

[0063] For example, the pulse heat source temperature response time series TR1[·] can be obtained from the temperature response curve of the pulse heat source at the temperature prediction point.

[0064] After obtaining q1[·] and TR1[·], the temperature change ΔT1[n] at time n under the action of the actual heat source can be calculated according to formula (3).

[0065] (2) Based on the first temperature prediction model and the initial temperature, the temperature at the predicted temperature point under the action of the actual heat source is predicted using the following formula:

[0066] T1[n]=T[0]+ΔT1[n] (5)

[0067] Where T1[n] represents the temperature at time n of the predicted temperature point under the action of the actual heat source. By predicting the temperature at each time point in the prediction time domain under the action of the actual heat source, the predicted time series of the core temperature of the power battery under the action of the actual heat source can be obtained.

[0068] The method for rapid prediction of the core temperature of a power battery in this application first defines a convolutional integral calculation model to describe the temperature change of the power battery over time. The inputs to this model include the battery heat generation rate determined by the operating current of the power battery and the temperature response curve of a specific location within the battery module (i.e., the temperature prediction point) to a pulsed heating signal. The output of the model is the predicted temperature at that specific location. In the model input, the operating current of the power battery can be predefined manually or predicted by a driving route determined by navigation, generating a time-series vector with current information. The temperature response of a specific location inside the battery to the pulsed heating signal can be calibrated through experiments or simulations, ultimately obtaining a time-series vector with temperature information. Since the actual datasets obtained are discrete time series, the integral calculation of the model designed in this application can be regarded as the summation of the inverse product of the time-series vector with current information and the time-series vector with temperature information. The method of this application embodiment can quickly predict the temperature of locations in the power battery module where it is difficult to place temperature measurement points but where local high temperatures are likely to occur, and can achieve high prediction accuracy. Moreover, compared with the existing power battery core temperature estimation algorithm that uses a lumped thermal model, the method of this application embodiment significantly reduces the computational resources required for temperature prediction using the original finite element and CFD methods.

[0069] The above method only considers the case where a heat source exists within the battery module. When a thermal management structure (such as an air-cooled channel or a liquid-cooled plate) exists within the battery module, a cold source also exists. The method in this application embodiment further includes predicting the change in the core temperature of the power battery under the combined action of heat and cold sources.

[0070] In one possible implementation, when the power battery has a thermal management structure, the method may further include:

[0071] (1) Based on the change of the heat dissipation rate of the power battery over time and the change of the temperature at the temperature prediction point over time under the action of the pulse cold source, a second temperature response convolution integral calculation model is established.

[0072] The pulsed cooling source is used to cool the power battery at the thermal management structure according to a pulsed cooling signal; the pulsed cooling signal is used to control the temperature and flow rate of the cooling airflow at the thermal management structure. For example, if the thermal management structure is a finned heat exchanger, the cooling airflow passing through the fins will have a corresponding heat transfer flux density, which multiplied by the area is the current cooling rate. By controlling the flow rate and temperature of the cooling airflow through the pulsed cooling signal, pulsed cooling can be achieved.

[0073] For example, the second temperature response convolution integral calculation model is as follows:

[0074]

[0075] Where δT2(t) represents the temperature change at the predicted temperature point at time t under the actual cold source; q2(x) represents the heat dissipation rate of the power battery at time x; TR2(t-x) represents the temperature change at the predicted temperature point at time (t-x) under the pulsed cold source; t>0; 0≤x≤t. δT2(·) represents the temperature change at the predicted temperature point over time under the actual cold source; q2(·) represents the heat dissipation rate of the power battery over time; TR2(·) represents the temperature change (i.e., the change) at the predicted temperature point over time under the pulsed cold source.

[0076] The heat dissipation rate of a power battery refers to the rate at which the heat generated by the power battery is dissipated into the surrounding environment. The change in the heat dissipation rate of a given thermal management structure over time can be determined through laboratory testing and calibration.

[0077] The curve showing the temperature change over time at a predicted temperature point under the influence of a pulsed cold source can be referred to as the temperature response curve of the predicted temperature point to the pulsed cold source. For example, the temperature response curve of the predicted temperature point to the pulsed cold source can be determined through preliminary experiments or high-precision simulations.

[0078] As an example, if an experimental approach is used, when a thermal management structure exists in the battery module, achieving the same level of cooling power as heating power during the experiment requires significantly more effort. Therefore, a pulsed heating signal of the same power can be applied instead of a pulsed cooling signal. The temperature change caused by the pulsed heating signal is then negatively represented as the temperature change caused by the pulsed cooling signal, thus obtaining the temperature response curve of the predicted temperature point to the pulsed cold source. This operation is valid only if the battery parameters do not change significantly within the temperature prediction time domain. The pulsed heating signal can be achieved by placing a positive temperature coefficient (PTC) heating film at the contact point between the battery cell and the thermal management components in the battery module.

[0079] As another example, if high-precision simulation methods are used, the pulsed cooling signal can be achieved by setting heat flux boundary conditions at the air duct or liquid cooling plate. For specific implementation details, please refer to relevant technologies.

[0080] Similar to pulsed heating signals, when the power of pulsed cooling signals is within a suitable range, the shape of the temperature response curve remains unchanged under the action of pulsed cooling signals of different powers; only the amplitude changes. Therefore, it is also necessary to normalize the amplitude of the temperature response curve under the action of the pulsed cold source. The normalization method can be to multiply the amplitude of the temperature response curve by (1 / P2), where P2 represents the power of the actual pulsed cooling signal.

[0081] (2) Based on the first temperature response convolution integral calculation model, the second temperature response convolution integral calculation model and the initial temperature, the temperature at the temperature prediction point under the combined action of the actual heat source and the actual cold source is predicted.

[0082] The actual cold source is used to cool the power battery at the thermal management structure based on the actual temperature and actual flow rate of the cooling airflow. The convective heat transfer intensity can be calculated based on the actual temperature and actual flow rate of the cooling airflow, and the power battery can be cooled accordingly.

[0083] In formula (6), t and x represent continuous time. In the actual prediction process, the convolution integral calculation model for the second temperature response also needs to be discretized to obtain the second temperature prediction model, which is shown below:

[0084]

[0085] Where ΔT2[n] represents the temperature change at time n under the action of the actual cold source; q2[i] represents the heat dissipation rate of the power battery at time i; TR2[n-i] represents the temperature change at time (n-i) under the action of the pulse cold source; n and i are positive integers; n>0; 0≤i≤n.

[0086] In formula (7), n and i represent discrete time. Therefore, q2[·] represents the time series of the heat dissipation rate of the power battery changing with time (which can be called the heat dissipation rate time series), and TR2[·] represents the time series of the temperature change of the temperature prediction point under the action of the pulse cold source (which can be called the pulse cold source temperature response time series).

[0087] For example, the heat dissipation rate time series q2[·] can be obtained by testing based on predefined operating parameters of the thermal management components (such as cooling air temperature, cooling fluid temperature, cooling flow rate, etc.). Specific implementation methods can be found in related technologies.

[0088] For example, the pulsed cold source temperature response time series TR2[·] can be obtained from the temperature response curve of the pulsed cold source at the temperature prediction point.

[0089] After obtaining q2[·] and TR2[·], the temperature change ΔT2[n] at time n under the action of the actual cold source can be calculated according to formula (7). After obtaining ΔT2[n], the temperature at time n under the combined action of the actual heat source and the actual cold source can be predicted according to ΔT1[n], ΔT2[n] and the initial temperature T[0], using the following formula:

[0090] T2[n]=T[0]+ΔT1[n]+ΔT2[n] (8)

[0091] Where T2[n] represents the temperature at time n of the predicted temperature point under the combined action of the actual heat source and the actual cold source. By predicting the temperature at each time point in the prediction time domain under the combined action of the actual heat source and the actual cold source, the predicted time series of the core temperature of the power battery under the combined action of the actual heat source and the actual cold source can be obtained.

[0092] For example, when the power battery is not discharging, the temperature at time n when the temperature prediction point is T3[n] = T[0] + ΔT2[n] can be predicted based on ΔT2[n] and T[0].

[0093] The method for rapid prediction of the core temperature of a power battery according to the embodiments of this application can predict the core temperature of the power battery under the action of an actual heat source only, under the action of an actual cold source only, and under the combined action of an actual heat source and an actual cold source, and generate a prediction time series of the core temperature inside the battery. This allows for rapid and accurate prediction of the core temperature of the power battery and reduces the computational resources required for temperature prediction.

[0094] Figure 2 This diagram illustrates a method for rapidly predicting the core temperature of a power battery according to an embodiment of this application. Figure 2 As shown, the temperature response convolution integral calculation model is first defined. Where j = 1, it represents the model for predicting the temperature change of the power battery under the action of an actual heat source (i.e., the first temperature response convolution integral calculation model); j = 2, it represents the model for predicting the temperature change of the power battery under the action of an actual cold source (i.e., the second temperature response convolution integral calculation model). Then, the temperature response curve TR of the temperature prediction point to the pulse source term is determined through preliminary experiments or high-precision simulations. j When a thermal management structure exists in the power battery module, it is necessary to determine the temperature response curves TR1(·) and TR2(·) of the temperature prediction point to the pulse heat source and the pulse cold source, respectively, so as to determine the temperature response time series TR1[·] of the pulse heat source and TR2[·] of the pulse cold source. Next, the operating current time series I[·], the heat generation rate time series q1[·], and the heat dissipation rate time series q2[·] of the power battery are determined. Then, a discretized temperature prediction model is adopted. The temperature change at the predicted temperature point within the predicted time domain interval is calculated, which is the convolution sum of the temperature response time series vector and the heat generation rate time series vector / heat dissipation rate time series vector; where j=1 indicates the model that predicts the temperature change under the action of the actual heat source (i.e., the first temperature prediction model); j=2 indicates the model that predicts the temperature change under the action of the actual cold source (i.e., the second temperature prediction model). Finally, based on the initial temperature T[0] of the power battery and the calculation result ΔT of the temperature prediction model, the temperature change is calculated. j [n] can be used to obtain the predicted value T of the core temperature inside the battery at time n in the future. j [n]=T[0]+∑ΔT j [n]. When a thermal management structure exists in the power battery module and its influence is considered, j = [1,2]; when the thermal management structure and its influence are not considered, j = 1. Thus, the method of this application embodiment can obtain the predicted temperature value at each moment in the prediction time domain and generate a predicted time series of the core temperature inside the battery. This allows for convenient and quick integration with global optimal control algorithms such as predictive control and dynamic programming to obtain the optimal battery energy management strategy, thereby achieving beneficial effects such as improving battery safety and extending battery life.

[0095] Figure 3 A schematic diagram of a power battery module is shown, which uses 51Ah square cells with dimensions of 148mm*27mm*98mm. Figure 3 This is a front view schematic diagram of the power battery module structure, as shown below. Figure 3 As shown, the power battery module is arranged in a 1P12S side-by-side configuration, with 1-12 being the cell numbers within the module. A heat dissipation plate is placed below the module, in contact with the bottom surface of the cell, and its extended portion is connected to the thermal management heat dissipation structure. It can be directly cooled by a finned heat exchanger or other methods. Figure 3 The top right corner shows a perspective isometric view of cell #6 to better illustrate the temperature prediction point (i.e., Figure 3 The specific location of the node (in the process) is such that the temperature prediction point can be located at the center of the longitudinal section of cell No. 6. Figure 3 The power battery module shown contains a heat source for cell heat generation and a cold source for cold plate heat dissipation. When using the method of the embodiment of this application to predict the core temperature of the power battery, it is necessary to consider the temperature response curves of the temperature prediction point to the pulse heat source and the pulse cold source respectively, and to predict the core temperature change under the actual heat source and the core temperature change under the actual cold source respectively.

[0096] Figure 4This paper illustrates the temperature response curves of a temperature prediction point to a pulsed heat source and a pulsed cold source according to an embodiment of this application. The embodiment employs a high-precision simulation method, using multiphysics numerical calculations to obtain the temperature response curves of the temperature prediction point to the pulsed heat source and the temperature response curves of the temperature prediction point to the pulsed cold source, respectively. In this embodiment, the pulse source is a square wave signal with a power of 100W and a duration of 1s. Figure 4 As can be seen, the temperature prediction points do not respond exactly to the temperature of the pulse heat source and the pulse cold source. This is because the boundary conditions corresponding to the heat source and cold source terms in the battery module are different. The physical meaning of the heat source is the volumetric heat generated during the discharge process of the cell, while the cold source is the heat flux density applied to the bottom surface of the cell.

[0097] Figure 5 This diagram illustrates the prediction results of a rapid prediction method for the core temperature of a power battery according to an embodiment of this application. Figure 5 As shown, Figure 5 The y-axis on the right shows the curve of the operating current changing over time when using the method of this application embodiment to predict the core temperature. This application embodiment uses the operating current of the power battery during the vertical take-off and landing of the UAV as the test input. It can be seen from the curve of the operating current change that there is a continuous discharge rate of close to 6C for about 120 seconds during the vertical take-off and landing phases of the UAV. At this time, the battery heat generation problem is severe, and it is necessary to make an accurate judgment on its internal true temperature in order to ensure the normal operation and safety performance of the UAV. Figure 5 The left y-axis shows the temperature prediction results of the power battery core temperature using the method of the embodiments of this application, the temperature prediction results of the core temperature estimation algorithm using the existing lumped heat model, the power battery surface temperature monitoring results, and the actual core temperature change curve. Figure 6 This diagram illustrates the temperature prediction error of a rapid prediction method for the core temperature of a power battery according to an embodiment of this application. Figure 6 The relative errors between the predicted core temperature of a power battery using the method of this application and the actual core temperature, the relative errors between the predicted core temperature using an existing lumped heat model core temperature estimation algorithm and the actual core temperature, and the relative errors between the power battery surface temperature monitoring results and the actual core temperature are shown respectively. Figure 5 As can be seen from this, the temperature prediction results of the method in this application embodiment can ensure good tracking of the core temperature inside the battery under dynamic operating current, combined with Figure 6 It is known that the temperature prediction accuracy of the method in this application embodiment is always kept within 5% in the prediction time domain. The temperature prediction accuracy is higher than that of the core temperature estimation algorithm of the lumped heat model. However, monitoring the surface temperature alone cannot effectively identify the state in which the internal temperature has actually reached a dangerous level.

[0098] Based on the same inventive concept in the above method embodiments, this application also proposes a device for rapid prediction of the core temperature of a power battery.

[0099] Figure 7 This diagram illustrates the structure of a rapid prediction device for the core temperature of a power battery according to an embodiment of this application. This device can be used to implement the rapid prediction method for the core temperature of a power battery according to the embodiments of this application. Figure 7 As shown, the device may include: a point determination module 701, used to determine the temperature prediction point inside the power battery; a first model establishment module 702, used to establish a first temperature response convolution integral calculation model based on the change of the heat generation rate of the power battery over time and the change of the temperature of the temperature prediction point over time under the action of a pulse heat source; the pulse heat source is the self-heating of the power battery caused by a pulse current signal acting on the power battery; and a first prediction module 703, used to predict the temperature of the temperature prediction point under the action of an actual heat source based on the first temperature response convolution integral calculation model and the initial temperature of the temperature prediction point at the initial moment; the actual heat source is the self-heating of the power battery caused by a continuous current signal acting on the power battery.

[0100] In one possible implementation, the first temperature response convolution integral calculation model is as follows: Wherein, δT1(t) represents the temperature change at time t of the predicted temperature point under the action of the actual heat source; q1(x) represents the heat generation rate of the power battery at time x; TR1(t-x) represents the temperature change at time (t-x) of the predicted temperature point under the action of the pulse heat source; t>0; 0≤x≤t.

[0101] In one possible implementation, the prediction module 703 is further configured to: discretize the first temperature response convolution integral calculation model to obtain a first temperature prediction model, wherein the first temperature prediction model is: Wherein, ΔT1[n] represents the temperature change at time n of the predicted temperature point under the action of the actual heat source; q1[i] represents the heat generation rate of the power battery at time i; TR1[n-i] represents the temperature change at time (n-i) of the predicted temperature point under the action of the pulse heat source; n and i are positive integers; n>0; 0≤i≤n; According to the first temperature prediction model and the initial temperature, the temperature of the predicted temperature point under the action of the actual heat source is predicted by the following formula: T1[n]=T[0]+ΔT1[n]; Wherein, T1[n] represents the temperature of the predicted temperature point at time n under the action of the actual heat source; T[0] represents the initial temperature.

[0102] In one possible implementation, q1[i] is calculated as: q1[i] = I[i] 2 ·θ+I[i]·T·(dU / dT); where I[i] represents the operating current of the power battery at time i; θ represents the internal resistance of the power battery; T represents the temperature of the power battery; and dU / dT represents the rate of change of the voltage of the power battery with the temperature of the power battery.

[0103] In one possible implementation, when the time length of the prediction time domain is less than or equal to a preset threshold, when predicting the temperature of the temperature prediction point under the action of the actual heat source within the prediction time domain, the calculation formula for q1[i] is T=T[0]; the prediction time domain is [0,n]; when the time length of the prediction time domain is greater than the preset threshold, the device further includes: a segmented prediction module, used to divide the prediction time domain into N time intervals, and predict the temperature of the temperature prediction point under the action of the actual heat source within each of the time intervals respectively; N>1; the N time intervals are {[0,n1],……,[n N-1 +1,n]};n1、……、n N-1 All are positive integers; the duration of each time interval is not greater than the preset threshold; wherein, when predicting the temperature of the predicted temperature point in the first time interval [0, n1] under the action of the actual heat source, T = T[0] in the calculation formula of q1[i]; for the temperature predicted point in the kth time interval [n] under the action of the actual heat source, k-1 +1,n k When predicting the temperature within ], the formula for calculating q1[i] includes T = T1[n]. k-1 ];1 <k≤N。

[0104] In one possible implementation, when the power battery has a thermal management structure, the device further includes: a second model building module, used to establish a second temperature response convolutional integral calculation model based on the change in the heat dissipation rate of the power battery over time and the change in temperature at the temperature prediction point over time under the action of a pulsed cold source; the pulsed cold source is used to cool the power battery at the thermal management structure according to a pulsed cooling signal; the pulsed cooling signal is used to control the temperature and flow rate of the cooling airflow at the thermal management structure; a second prediction module, used to predict the temperature at the temperature prediction point under the combined action of the actual heat source and the actual cold source based on the first temperature response convolutional integral calculation model, the second temperature response convolutional integral calculation model, and the initial temperature; the actual cold source is used to cool the power battery at the thermal management structure according to the actual temperature and actual flow rate of the cooling airflow; wherein, the second temperature response convolutional integral calculation model is... δT2(t) represents the temperature change at time t of the predicted temperature point under the action of the actual cold source; q2(x) represents the heat dissipation rate of the power battery at time x; TR2(t-x) represents the temperature change at time (t-x) of the predicted temperature point under the action of the pulsed cold source; t>0; 0≤x≤t.

[0105] In one possible implementation, the second prediction module is further configured to: discretize the second temperature response convolution integral calculation model to obtain a second temperature prediction model, wherein the second temperature prediction model is: Wherein, ΔT2[n] represents the temperature change at time n of the predicted temperature point under the action of the actual cold source; q2[i] represents the heat dissipation rate of the power battery at time i; TR2[n-i] represents the temperature change at time (n-i) of the predicted temperature point under the action of the pulse cold source; n and i are positive integers; n>0; 0≤i≤n; According to the first temperature prediction model, the second temperature prediction model and the initial temperature, the temperature of the predicted temperature point under the combined action of the actual heat source and the actual cold source is predicted by the following formula: T2[n]=T[0]+ΔT1[n]+ΔT2[n]; Wherein, T2[n] represents the temperature of the predicted temperature point at time n under the combined action of the actual heat source and the actual cold source.

[0106] The power battery core temperature rapid prediction device of this application embodiment can quickly and accurately predict the temperature of the power battery module in the process of power battery working under the action of actual heat source, where it is difficult to arrange temperature measurement points but local high temperature is easy to occur. It can also quickly and accurately predict the core temperature of the power battery in the process of power battery working under the combined action of actual heat source and actual cold source. Furthermore, the method of this application embodiment can reduce the computing resources required for temperature prediction.

[0107] In some embodiments, the functions or modules of the apparatus provided in this application can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0108] This application also proposes a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the aforementioned method for rapid prediction of the core temperature of a power battery. The computer-readable storage medium can be volatile or non-volatile.

[0109] This application also proposes an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-mentioned method for rapid prediction of the core temperature of a power battery when executing the instructions stored in the memory.

[0110] This application also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described method for rapid prediction of the core temperature of a power battery.

[0111] Figure 8 A block diagram of an electronic device 1900 according to an embodiment of this application is shown. For example, the electronic device 1900 may be provided as a server or a terminal device. (Refer to...) Figure 8 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the aforementioned method for rapid prediction of the core temperature of a power battery.

[0112] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). Electronic device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM Mac OS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0113] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the above-described method for rapid prediction of the core temperature of the power battery.

[0114] This application may be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this application.

[0115] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0116] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0117] The computer program instructions used to perform the operations of this application may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuits, such as programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), are personalized by utilizing state information from the computer-readable program instructions. These electronic circuits can execute the computer-readable program instructions to implement various aspects of this application.

[0118] Various aspects of this application are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0119] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0120] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, 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 an instruction containing one or more executable instructions for implementing a specified logical function. 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 the block diagrams and / or flowcharts, and combinations of blocks in the 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.

[0122] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technological improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for rapid prediction of the core temperature of a power battery, characterized in that, include: Determine the predicted temperature point inside the power battery; wherein, the predicted temperature point is the location where the local highest temperature occurs inside the power battery; the power battery has a thermal management structure; Based on the change in the heat generation rate of the power battery over time and the change in the temperature at the predicted temperature point over time under the action of a pulsed heat source, a first temperature response convolution integral calculation model is established; the pulsed heat source is the heat generated by the power battery itself caused by a pulsed current signal acting on the power battery. Based on the change in the heat dissipation rate of the power battery over time and the change in the temperature at the predicted temperature point over time under the action of the pulsed cold source, a second temperature response convolutional integral calculation model is established; the pulsed cold source is used to cool the power battery at the thermal management structure according to the pulsed cooling signal; the pulsed cooling signal is used to control the temperature and flow rate of the cooling airflow at the thermal management structure; Based on the first temperature response convolution integral calculation model, the second temperature response convolution integral calculation model, and the initial temperature of the temperature prediction point at the initial moment, the temperature of the temperature prediction point is predicted under the combined action of the actual heat source and the actual cold source; the actual heat source is the heat generated by the power battery itself due to the continuous current signal acting on the power battery; the actual cold source is used to cool the power battery at the thermal management structure according to the actual temperature and actual flow rate of the cooling airflow. The step of predicting the temperature of the predicted temperature point under the combined action of an actual heat source and an actual cold source, based on the first temperature response convolution integral calculation model, the second temperature response convolution integral calculation model, and the initial temperature of the predicted temperature point at the initial moment, includes: discretizing both the first and second temperature response convolution integral calculation models to obtain a first temperature prediction model and a second temperature prediction model; wherein, both the first and second temperature prediction models are used to calculate the summation of the reverse product of a time series vector with current information and a time series vector with temperature information; when the time length of the prediction time domain is less than or equal to a preset threshold, the temperature of the predicted temperature point under the action of the actual heat source within the prediction time domain is predicted; when the time length of the prediction time domain is greater than the preset threshold, the prediction time domain is divided into multiple time intervals, and based on the first temperature prediction model, the second temperature prediction model, and the initial temperature, the temperature of the predicted temperature point at each moment within each time interval under the action of the actual heat source is predicted; wherein, the temperature is the same within each time interval, and the time length of each time interval is not greater than the preset threshold.

2. The method according to claim 1, characterized in that, The first temperature response convolution integral calculation model is as follows: δT1(t)=∫0 t q1(x)TR1(t-x)dx Wherein, δT1(t) represents the temperature change at time t of the predicted temperature point under the action of the actual heat source; q1(x) represents the heat generation rate of the power battery at time x; TR1(t-x) represents the temperature change at time (t-x) of the predicted temperature point under the action of the pulse heat source; t>0; 0≤x≤t.

3. The method according to claim 2, characterized in that, Based on the first temperature response convolution integral calculation model and the initial temperature of the temperature prediction point at the initial moment, the temperature of the temperature prediction point under the action of the actual heat source is predicted, including: Discretize the first temperature response convolution integral calculation model to obtain the first temperature prediction model, which is as follows: Wherein, ΔT1[n] represents the temperature change at the predicted temperature point at time n under the action of the actual heat source; q1[i] represents the heat generation rate of the power battery at time i; TR1[n-i] represents the temperature change at the predicted temperature point at time (n-i) under the action of the pulse heat source; n and i are positive integers; n>0; 0≤i≤n; Based on the first temperature prediction model and the initial temperature, the temperature at the predicted temperature point under the influence of the actual heat source is predicted using the following formula: T1[n] = T[0] + ΔT1[n] Wherein, T1[n] represents the temperature at time n of the predicted temperature point under the action of the actual heat source; T[0] represents the initial temperature.

4. The method according to claim 3, characterized in that, The formula for calculating q1[i] is: q1[i]=I[i] 2 ·θ+I[i]·T·(dU / dT) Where I[i] represents the operating current of the power battery at time i; θ represents the internal resistance of the power battery; T represents the temperature of the power battery; and dU / dT represents the rate of change of the voltage of the power battery with the temperature of the power battery.

5. The method according to claim 4, characterized in that, When the time length of the prediction time domain is less than or equal to a preset threshold, when predicting the temperature of the temperature prediction point under the action of the actual heat source within the prediction time domain, the calculation formula of q1[i] is T=T[0]; the prediction time domain is [0,n]; When the time length of the predicted time domain is greater than the preset threshold, the method further includes: The prediction time domain is divided into N time intervals, and the temperature at the predicted temperature point under the action of the actual heat source is predicted in each of the time intervals; N>1; the N time intervals are {[0,n1],……,[n...} N-1 +1,n]};n1、……、n N-1 All are positive integers; the duration of each time interval is not greater than the preset threshold. Specifically, when predicting the temperature at the predicted temperature point under the actual heat source in the first time interval [0, n1], the formula for calculating q1[i] includes T = T[0]; when predicting the temperature at the predicted temperature point under the actual heat source in the kth time interval [n... k-1 +1,n k When predicting the temperature within ], the formula for calculating q1[i] includes T = T1[n]. k-1 ];1 <k≤N。 6. The method according to any one of claims 3-5, characterized in that, The second temperature response convolution integral calculation model is as follows: δT2(t)=∫0 t q2(x)TR2(t-x)dx δT2(t) represents the temperature change at time t of the predicted temperature point under the action of the actual cold source; q2(x) represents the heat dissipation rate of the power battery at time x; TR2(t-x) represents the temperature change at time (t-x) of the predicted temperature point under the action of the pulsed cold source; t>0; 0≤x≤t.

7. The method according to claim 6, characterized in that, Based on the first temperature response convolution integral calculation model, the second temperature response convolution integral calculation model, and the initial temperature, the temperature at the predicted temperature point under the combined action of the actual heat source and the actual cold source is predicted, including: Discretize the second temperature response convolution integral calculation model to obtain the second temperature prediction model, which is as follows: Wherein, ΔT2[n] represents the temperature change at time n of the predicted temperature point under the action of the actual cold source; q2[i] represents the heat dissipation rate of the power battery at time i; TR2[n-i] represents the temperature change at time (n-i) of the predicted temperature point under the action of the pulse cold source; n and i are positive integers; n>0; 0≤i≤n; Based on the first temperature prediction model, the second temperature prediction model, and the initial temperature, the temperature at the predicted temperature point under the combined action of the actual heat source and the actual cold source is predicted using the following formula: T2[n] = T[0] + ΔT1[n] + ΔT2[n] Wherein, T2[n] represents the temperature at time n of the predicted temperature point under the combined action of the actual heat source and the actual cold source.

8. A device for rapid prediction of the core temperature of a power battery, characterized in that, include: The temperature prediction module is used to determine the predicted temperature points inside the power battery; wherein, the predicted temperature points are the locations where the local highest temperature occurs inside the power battery; the power battery has a thermal management structure; The first model building module is used to establish a first temperature response convolution integral calculation model based on the change of the heat generation rate of the power battery over time and the change of the temperature at the temperature prediction point over time under the action of a pulse heat source; the pulse heat source is the heat generated by the power battery itself caused by the action of a pulse current signal on the power battery. The second model building module is used to establish a second temperature response convolutional integral calculation model based on the change in the heat dissipation rate of the power battery over time and the change in the temperature of the predicted temperature point over time under the action of a pulsed cold source. The pulsed cold source is used to cool the power battery at the thermal management structure according to a pulsed cooling signal. The pulsed cooling signal is used to control the temperature and flow rate of the cooling airflow at the thermal management structure. The first prediction module is used to predict the temperature of the predicted temperature point under the combined action of the actual heat source and the actual cold source based on the first temperature response convolutional integral calculation model, the second temperature response convolutional integral calculation model, and the initial temperature of the predicted temperature point at the initial moment. The actual heat source is the heat generated by the power battery itself due to a continuous current signal acting on the power battery. The actual cold source is used to cool the power battery at the thermal management structure according to the actual temperature and actual flow rate of the cooling airflow. The step of predicting the temperature of the predicted temperature point under the combined action of an actual heat source and an actual cold source, based on the first temperature response convolution integral calculation model, the second temperature response convolution integral calculation model, and the initial temperature of the predicted temperature point at the initial moment, includes: discretizing both the first and second temperature response convolution integral calculation models to obtain a first temperature prediction model and a second temperature prediction model; wherein, both the first and second temperature prediction models are used to calculate the summation of the reverse product of a time series vector with current information and a time series vector with temperature information; when the time length of the prediction time domain is less than or equal to a preset threshold, the temperature of the predicted temperature point under the action of the actual heat source within the prediction time domain is predicted; when the time length of the prediction time domain is greater than the preset threshold, the prediction time domain is divided into multiple time intervals, and based on the first temperature prediction model, the second temperature prediction model, and the initial temperature, the temperature of the predicted temperature point at each moment within each time interval under the action of the actual heat source is predicted; wherein, the temperature is the same within each time interval, and the time length of each time interval is not greater than the preset threshold.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1-7 when executing instructions stored in the memory.

10. A non-volatile computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1-7.

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