Method and device for rapidly predicting core temperature of power battery and electronic equipment

Abstract

The invention relates to a power battery core temperature rapid prediction method and device and electronic equipment, and the method comprises the steps: determining a temperature prediction point location in a power battery; establishing a first temperature response convolution integral calculation model according to the change condition of the heat production rate of the power battery along with the time and the change condition of the temperature of the temperature prediction point location along with the time under the action of the pulse heat source; the pulse heat source acts on the power battery through a pulse current signal to cause heat production of the power battery; according to the first temperature response convolution integral calculation model and the initial temperature of the temperature prediction point location at the initial moment, predicting the temperature of the temperature prediction point location under the action of the actual heat source; the actual heat source is a continuous current signal which acts on the power battery to cause the power battery to generate heat; according to the method provided by the invention, the temperature of the position where the temperature measuring point is difficult to arrange and local high temperature is easy to occur in the power battery module can be quickly and accurately predicted, and the computing resources required by temperature prediction can be reduced.

Description

Technical Field

[0001] The present application relates to the field of power batteries, and in particular to a method, device and electronic equipment for quickly predicting the core temperature of a power battery. Background Art

[0002] The popularity of electric vehicles has led to the large-scale application of power batteries. Temperature is the main factor affecting the life and safety performance of power batteries. Therefore, the battery management system needs to be able to accurately monitor the temperature status of the battery. In order to ensure that the power battery is in a suitable temperature range when the vehicle is running, it is also necessary to accurately predict the battery temperature in order to design the optimal thermal management control strategy to achieve the desired thermal management regulation effect.

[0003] The existing power battery temperature status monitoring measures generally place thermocouples at the measuring points on the battery module surface. The number of thermocouples placed is limited, and it is impossible to detect the local high temperature points inside the battery in time, making it difficult to effectively monitor the battery core temperature (i.e., the actual temperature of the battery cells inside the battery). Some studies have proposed that a distributed lumped thermal model can be used to estimate the internal core temperature based on the battery surface temperature, but this method still requires the use of complex finite element methods or computational fluid dynamics (CFD) methods to predict the battery core temperature, which is not suitable for vehicle battery management modules with limited computing resources. The above defects of the existing power battery temperature status monitoring methods will lead to deviations in a series of SOX estimation algorithms that rely on the battery temperature status, affecting the safety performance and life of the power battery. Summary of the invention

[0004] In view of this, the present application proposes a method, device and electronic device for quickly predicting 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 arrange temperature measurement points but where local high temperatures are prone to occur, and can reduce the computing resources required for temperature prediction.

[0005] According to one aspect of the present application, a method for quickly predicting the core temperature of a power battery is provided, comprising: determining a temperature prediction point inside the power battery; establishing a first temperature response convolution integral calculation model according to how the heat generation rate of the power battery changes over time and how the temperature of the temperature prediction point changes 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; based on the first temperature response convolution integral calculation model and the initial temperature of the temperature prediction point at an initial moment, predicting the temperature of the temperature prediction point under the action of an actual heat source; 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 a possible implementation, the first temperature response convolution integral calculation model is: Among them, δT1(t) represents the temperature change of the temperature prediction point at time t 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 of the temperature prediction point at time (t-x) under the action of the pulse heat source; t>0; 0≤x≤t.

[0007] In a possible implementation, according to 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 actual heat source is predicted, including: discretizing 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 of the temperature prediction 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 of the temperature prediction point at time (n-i) 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 temperature prediction 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 temperature prediction point at time n under the action of the actual heat source; T[0] represents the initial temperature.

[0008] In a possible implementation, the calculation formula of q1[i] is: q1[i]=I[i] 2 ·θ+I[i]·T·(dU / dT); wherein, 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; dU / dT represents the rate of change of the voltage of the power battery with the temperature of the power battery.

[0009] In a 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 in the prediction time domain under the action of the actual heat source, T=T[0] in the calculation formula of q1[i]; 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 in each time interval under the action of the actual heat source; N>1; the N time intervals are {[0,n1],……,[n N-1 +1,n]}; n1, ..., n N-1 are all positive integers; the time length of each time interval is not greater than the preset threshold; wherein, when predicting the temperature of the temperature prediction 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]; when predicting the temperature of the temperature prediction point in the kth time interval [n k-1 +1,n k ], the calculation formula of q1[i] is T=T1[n k-1 ]; 1 <k≤N。

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

[0011] In a possible implementation, according to the first temperature response convolution integral calculation model, the second temperature response convolution integral calculation model and the initial temperature, the temperature of the temperature prediction point under the combined action of the actual heat source and the actual cold source is predicted, including: discretizing 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 of the temperature prediction point 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 of the temperature prediction point at time (n-i) 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 temperature prediction point under the joint 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 temperature prediction point at time n under the joint action of the actual heat source and the actual cold source.

[0012] According to another aspect of the present application, a device for quickly predicting the core temperature of a power battery is provided, comprising: a point determination module, used to determine the temperature prediction point inside the power battery; a first model establishment module, used to establish a first temperature response convolution integral calculation model according to 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 the pulse current signal acting on the power battery; a first prediction module, used to predict 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 the self-heating of the power battery caused by the continuous current signal acting on the power battery.

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

[0014] According to another aspect of the present 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-mentioned method for quickly predicting the core temperature of a power battery.

[0015] According to another aspect of the present application, a computer program product is provided, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned method for quickly predicting the core temperature of a power battery.

[0016] The method for rapid prediction of the core temperature of a power battery of the present application can realize rapid prediction of the temperature at locations in the power battery module where it is difficult to arrange temperature measurement points but where local high temperatures are prone to occur, and can achieve a high prediction accuracy; and, compared with the existing power battery core temperature estimation algorithm using a lumped thermal model, the method of the present application can significantly reduce the computing resources originally required for temperature prediction using finite element and CFD methods.

[0017] Other features and aspects of the present application will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the present application and, together with the description, serve to explain the principles of the present application.

[0019] Figure 1 A flow chart of a method for quickly predicting the core temperature of a power battery according to an embodiment of the present application is shown.

[0020] Figure 2 A schematic diagram showing a method for quickly predicting the core temperature of a power battery according to an embodiment of the present application is shown.

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

[0022] Figure 4 The temperature response curve of the temperature prediction point to the pulse heat source and the pulse cold source according to one embodiment of the present application is shown.

[0023] Figure 5 A schematic diagram showing prediction results of a method for quickly predicting core temperature of a power battery according to an embodiment of the present application is shown.

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

[0025] Figure 7 A schematic structural diagram of a power battery core temperature rapid prediction device according to an embodiment of the present application is shown.

[0026] Figure 8A block diagram of an electronic device 1900 according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] Various exemplary embodiments, features and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0028] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0029] In addition, in order to better illustrate the present application, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present application can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present application.

[0030] Figure 1 A flowchart of a method for quickly predicting the core temperature of a power battery according to an embodiment of the present application is shown. The method can be executed by a device with processing capabilities such as a processor and a server. Figure 1 As shown, the method may include:

[0031] S101. Determine a temperature prediction point inside a power battery.

[0032] For example, the temperature prediction point can be selected according to the point where the power battery is prone to local high temperature. The temperature prediction point can also be called a node. The temperature at the temperature prediction point can be considered as the core temperature of the power battery.

[0033] As an example, the temperature prediction point can be the location where the highest temperature occurs locally inside the power battery. The temperature field of the power battery discharge process can be obtained through high-precision simulation or experiment, so as to determine the location where the highest temperature occurs inside the power battery as the temperature prediction point. The experimental means can use infrared temperature measurement to better show the temperature field.

[0034] S102: establishing a first temperature response convolution integral calculation model according to the change of the heat generation rate of the power battery over time and the change of the temperature of the temperature prediction point under the action of the pulse heat source over time.

[0035] The pulse heat source is the self-heating of the power battery caused by the pulse current signal acting on the power battery. Under the action of the pulse current signal, the power battery generates heat by itself, which will cause the temperature change of the temperature prediction point inside the battery. This process can be called the temperature change of the temperature prediction point caused by the pulse heat source, and this pulse current signal can be called a pulse heating signal.

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

[0037]

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

[0039] The actual heat source is the self-heating of the power battery caused by the continuous current signal acting on the power battery. Under the action of the continuous current signal, the power battery generates heat itself, which will cause the temperature change of the temperature prediction point inside the battery. This process can be called the temperature change of the temperature prediction 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 charge and discharge process of the power battery. For lithium-ion power batteries, the heat generation rate of the battery can be expressed as a function related to the temperature T, operating current I, internal resistance θ, and entropy coefficient dU / dT of the power battery. The calculation formula for the heat generation rate q can be shown as follows:

[0041] q=I 2 ·θ+I·T·(dU / dT) (2)

[0042] The operating current I of the power battery represents the current of the power battery under different operating conditions, and I changes with time. The internal resistance θ of the power battery is related to the temperature T of the power battery and the state of charge (SOC) of the battery. The entropy coefficient dU / dT of the power battery represents the rate of change of the voltage U of the power battery with the temperature T of the power battery. Exemplarily, a hybrid power pulse characteristic (HPPC) test can be performed at different temperatures to obtain the change curves of θ with T and SOC and the change curves of dU / dT with T. The specific implementation process can refer to the relevant technology.

[0043] The temperature response curve of the temperature prediction point to the pulse heat source can be determined by preliminary experiments or high-precision simulation.

[0044] As an example, if experimental means are used, a discharge current pulse with a specific waveform of a battery module can be given by a charger and discharge machine as a pulse heating signal, and the power of the pulse heating signal can be calculated in combination with the current temperature, SOC and internal resistance of the power battery. The core temperature information inside the battery can be obtained by implanting thermocouples inside the battery, ultrasound, optical fiber, etc. The time interval for collecting core temperature information is from the start of the pulse heat source to the temperature rise stabilization time, so as to obtain the temperature response curve of the temperature prediction point to the pulse heat source. The specific implementation process can refer to the relevant technology. It should be noted that the power of the pulse heating signal should be appropriately selected. Excessive power may cause a short circuit in the circuit, and too little power may make it difficult to identify the temperature change. The temperature rise caused by the pulse heating signal should be much greater than the sensor accuracy, and the actual vehicle loading structure of the battery module should be considered during the experiment to do a good job of insulation treatment for the relevant positions.

[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 calibration parameters of the battery HPPC experiment, setting the waveform and amplitude of the pulse heating signal in the simulation boundary conditions, and recording the temperature changes of the temperature prediction points in the numerical model, thereby obtaining the temperature response curve of the temperature prediction points to the pulse heat source. The specific implementation process can refer to the relevant technology.

[0046] When the power of the pulse heating signal is within an appropriate range, the temperature change will change 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 by experiment or simulation, 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. 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.

[0048] The initial time refers to the time when the prediction starts (i.e., time 0). For example, the surface temperature of the power battery at the initial time can be monitored by an existing power battery temperature state monitoring method, and the core temperature of the power battery at the initial time can be estimated according to an existing algorithm for estimating the internal core temperature based on the battery surface temperature, as the initial temperature of the temperature prediction point. The initial temperature can be recorded as T[0].

[0049] In formula (1), t and x represent continuous time. However, in the actual prediction process, the actual data obtained are all discrete time series, so it is necessary to discretize the first temperature response convolution integral calculation model.

[0050] In a possible implementation, predicting the temperature of the temperature prediction point under the actual heat source according to the first temperature response convolution integral calculation model and the initial temperature of the temperature prediction point at the initial moment may include:

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

[0052]

[0053] Among them, ΔT1[n] represents the temperature change of the temperature prediction point at time n under the action of the actual heat source (that is, the change of the temperature 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 of the temperature prediction 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, so q1[·] represents the time series of the heat generation rate of the power battery changing with time (which can be called the heat generation rate time series), and TR1[·] represents the time series of the temperature change of the temperature prediction point under the action of the pulse heat source with time (which can be called the pulse heat source temperature response time series).

[0055] For example, the calculation formula of q1[i] can be obtained according to the above formula (2):

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

[0057] Wherein, 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 changing with time, and I[·] can be called the operating current time series.

[0058] Exemplarily, the operating current time series I[·] may be obtained by artificial predefinition; or may be calculated based on the speed distribution of the navigation route of the electric vehicle, and the calculation method may refer to the relevant technology.

[0059] When predicting the temperature at time n, the value of T in formula (4) can be taken as the temperature predicted at time n-1, that is, the prediction result at time n can be used to calculate the prediction result at time n, but this calculation method consumes a lot of computing resources. In the process of the power battery working under the actual heat source, the change of T is small in a short time interval, and the effect 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 considered that T in each time interval is constant, and θ and dU / dT related to T can also be considered constant. In this way, the temperature of the temperature prediction point can be predicted in sections. When predicting the temperature in each time interval, T, θ and dU / dT in formula (4) can be considered as constants. As long as the working current time series is obtained, the heat generation rate at each moment in the time interval can be calculated, so as to predict the temperature of the temperature prediction point at each moment in the time interval.

[0060] In a 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 in the prediction time domain under the action of the actual heat source, T=T[0] in the calculation formula of q1[i]; the prediction time domain represents a time interval that needs to be predicted from the initial moment, that 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 in each time interval under the action of the actual heat source; N>1; the N time intervals are {[0,n1],……,[n N-1 +1,n]}; n1, ..., n N-1 are all positive integers; the time length of each time interval is not greater than the preset threshold; wherein, when predicting the temperature of the temperature prediction 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]; when predicting the temperature of the temperature prediction point in the kth time interval [n k-1 +1,n k ], the calculation formula of q1[i] is T=T1[n k-1 ]; 1 <k≤N。T1[n k-1 ] indicates that the temperature prediction point under the actual heat source is predicted by the method of the embodiment of the present application at n k-1 The temperature of the moment.

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

[0062] In one embodiment, when the prediction time domain is short (for example, 0 to 200 seconds), it can be considered that T is constant in the entire prediction time domain, and T[0] can be substituted as T into formula (4) for calculation to predict the temperature of the temperature prediction point in the entire prediction time domain. When the prediction time domain is long, the prediction time domain can be divided into multiple time intervals (for example, the length of each time interval can be within 200 seconds), and T, θ, and dU / dT in each time interval are considered to be constants; for the first time interval, T[0] can be substituted as T into formula (4) for calculation to predict the temperature of the temperature prediction point in the first time interval; for the kth time interval (k>1), the temperature at the end of the prediction of the k-1th time interval can be substituted as T into formula (4) for calculation to predict 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 curve 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 curve of dU / dT changing with T and the value of T in each time interval.

[0063] Exemplarily, the pulse heat source temperature response time series TR1[·] can be obtained according to the temperature response curve of the temperature prediction point to the pulse heat source.

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

[0065] (2) According to the first temperature prediction model and the initial temperature, the temperature of the temperature prediction point under the action of the actual heat source is predicted by the following formula:

[0066] T1[n]=T[0]+ΔT1[n] (5)

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

[0068] The method for quickly predicting the core temperature of a power battery in an embodiment of the present application first defines a convolution integral calculation model for describing the change of the power battery temperature over time. The input of the model includes the battery heat generation rate determined by the operating current of the power battery and the temperature response curve of a specific position in the battery module (i.e., the temperature prediction point) to a pulse heating signal. The output of the model is the predicted temperature at the specific position. In the model input, the operating current of the power battery can be artificially predefined or predicted by the driving route determined by the navigation to generate a time series vector with current information; the temperature response of a specific position inside the battery to the pulse heating signal can be calibrated through experiments or simulations, and finally a time series vector with temperature information is obtained. Since the data sets actually obtained are all discrete time series, the integral calculation of the model designed by the method of the embodiment of the present application can be regarded as the sum calculation of the inverse product of the time series vector with current information and the time series vector with temperature information. The method of the embodiment of the present application can realize the rapid prediction of the temperature at the positions in the power battery module where it is difficult to arrange temperature measurement points but where local high temperatures are prone to occur, and can achieve a high prediction accuracy; and compared with the existing power battery core temperature estimation algorithm using a lumped thermal model, the method of the embodiment of the present application greatly reduces the computing resources originally required for temperature prediction using finite element and CFD methods.

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

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

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

[0072] The pulse cooling source is used to cool the power battery at the thermal management structure according to the pulse cooling signal; the pulse 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 fin heat exchanger, the cooling airflow flowing through the fin will have a corresponding heat exchange heat flux density, which is multiplied by the area to obtain the current cooling rate. By controlling the flow rate and temperature of the cooling airflow through the pulse cooling signal, pulse cooling can be achieved.

[0073] Exemplarily, the second temperature response convolution integral calculation model is:

[0074]

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

[0076] The heat dissipation rate of a power battery refers to the rate at which the power battery dissipates the heat generated into the surrounding environment. The heat dissipation rate of a given thermal management structure can be calibrated over time by testing in the laboratory.

[0077] The temperature response curve of the temperature prediction point to the pulse cold source can be determined by preliminary experiments or high-precision simulation.

[0078] As an example, if experimental means are used, when there is a thermal management structure in the battery module, since it takes more time for the cooling power to reach the same level of heating power during the experiment, a pulse heating signal of the same power can be applied instead of a pulse cooling signal, and the battery temperature change caused by the pulse heating signal is taken as a negative value as the battery temperature change caused by the pulse cooling signal, thereby obtaining a temperature response curve of the temperature prediction point to the pulse cold source. The premise for this operation is that the various battery parameters do not change much within the temperature prediction time domain. The pulse heating signal can be achieved by arranging a positive temperature coefficient (PTC) heating film at the contact point between the battery cell and the thermal management component in the battery module.

[0079] As another example, if a high-precision simulation method is used, the pulse cooling signal can be realized by setting a heat flow boundary condition at the air duct or liquid cooling plate. The specific implementation process can refer to the relevant technology.

[0080] Similar to the pulse heating signal, when the power of the pulse cooling signal is within an appropriate range, under the action of pulse cooling signals of different powers, the shape of the temperature response curve remains unchanged, and only the amplitude changes. Therefore, it is also necessary to normalize the amplitude of the temperature response curve under the action of the pulse cooling 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 pulse cooling signal.

[0081] (2) predicting the temperature of the temperature prediction point under the combined action of the actual heat source and the actual cold source according to the first temperature response convolution integral calculation model, the second temperature response convolution integral calculation model and the initial temperature.

[0082] 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 convective heat transfer intensity can be calculated according to the actual temperature and actual flow rate of the cooling airflow, and the power battery can be cooled according to the convective heat transfer intensity.

[0083] In formula (6), t and x represent continuous time. In the actual prediction process, it is also necessary to discretize the second temperature response convolution integral calculation model to obtain the second temperature prediction model. The second temperature prediction model is as follows:

[0084]

[0085] Among them, ΔT2[n] represents the temperature change of the temperature prediction point 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 of the temperature prediction point 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, so 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 with time (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 according to predefined operating parameters of the thermal management component (such as cooling air temperature, cooling fluid temperature, cooling flow rate, etc.). The specific implementation method can refer to the relevant technology.

[0088] Exemplarily, the pulse cold source temperature response time series TR2[·] can be obtained according to the temperature response curve of the temperature prediction point to the pulse cold source.

[0089] After obtaining q2[·] and TR2[·], the temperature change ΔT2[n] of the temperature prediction point at time n under the action of the actual cold source can be calculated according to formula (7). After obtaining ΔT2[n], the temperature of the temperature prediction point at time n under the 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] Among them, T2[n] represents the temperature of the temperature prediction point at time n under the combined action of the actual heat source and the actual cold source. By predicting the temperature of the temperature prediction point at each moment 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 power battery core temperature under the combined action of the actual heat source and the actual cold source can be obtained.

[0092] Exemplarily, when the power battery is not discharged, the temperature T3[n]=T[0]+ΔT2[n] of the temperature prediction point at time n can be predicted based on ΔT2[n] and T[0] when only the actual cold source acts.

[0093] The method for quickly predicting the core temperature of a power battery in an embodiment of the present application can respectively predict the core temperature of the power battery under the action of an actual heat source only, the core temperature under the action of an actual cold source only, and the core temperature under the action of an actual heat source and an actual cold source, and generate a predicted time series of the core temperature inside the battery, thereby being able to quickly and accurately predict the core temperature of the power battery and to reduce the computing resources required for temperature prediction.

[0094] Figure 2 A schematic diagram showing a method for quickly predicting the core temperature of a power battery according to an embodiment of the present application is shown as follows: Figure 2 As shown, first define the temperature response convolution integral calculation model Among them, when j is 1, it represents the model for predicting the temperature change of the power battery under the actual heat source (i.e., the first temperature response convolution integral calculation model); when j is 2, it represents the model for predicting the temperature change of the power battery under the 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 there is a thermal management structure in the power battery module, it is necessary to determine the temperature response curve TR1(·) of the temperature prediction point to the pulse heat source and the temperature response curve TR2(·) of the temperature prediction point to the pulse cold source, so as to determine the pulse heat source temperature response time series TR1[·] and the pulse cold source temperature response time series TR2[·]. Next, determine the power battery's operating current time series I[·], heat generation rate time series q1[·], and heat dissipation rate time series q2[·]. Then, a discretized temperature prediction model is used. The temperature change of the temperature prediction point within the prediction time domain interval is calculated, that 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 is 1, it represents the model for predicting the temperature change under the actual heat source (i.e., the first temperature prediction model); j is 2, it represents the model for predicting the temperature change under the actual cold source (i.e., the second temperature prediction model). Finally, according to the temperature T[0] of the power battery at the initial moment and the calculation result ΔT of the temperature prediction model j [n], we can get the predicted value T of the battery's internal core temperature at the next n moments j [n] = T[0] + ∑ΔT j [n]. When there is a thermal management structure 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. In this way, the method of the embodiment of the present application can obtain the temperature prediction value at each moment in the prediction time domain, and generate a prediction time series of the core temperature inside the battery, which can then be conveniently and quickly combined with global optimal control algorithms such as predictive control and dynamic programming to obtain the optimal strategy for battery energy management, thereby achieving beneficial effects such as improving battery safety and improving battery life.

[0095] Figure 3 A schematic structural diagram of a power battery module is shown, which uses a 51Ah square battery cell of 148mm*27mm*98mm. Figure 3 A schematic front view of the power battery module structure is shown, as shown in Figure 3 As shown, the arrangement method of the power battery module is a lateral parallel arrangement of 1P12S, where 1-12 are the battery cell numbers in the module; a heat dissipation cold plate is arranged under the module, in contact with the bottom surface of the battery cell, and the protruding part is connected to the thermal management heat dissipation structure, and direct air cooling with a fin heat exchanger can be adopted. Figure 3 The upper right corner is a perspective isometric diagram of the No. 6 battery cell to better show the temperature prediction point (i.e. Figure 3 The specific location of the node in the figure is as follows: the temperature prediction point can be located at the center of the middle longitudinal section of battery cell No. 6. Figure 3 The power battery module shown has a heat source for generating heat from the battery cells and a cold source for dissipating heat from the cold plate. When predicting the core temperature inside the power battery using the method of an embodiment of the present application, 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 predict the core temperature change under the actual heat source and the core temperature change under the actual cold source respectively.

[0096] Figure 4The temperature response curves of the temperature prediction points to the pulse heat source and the pulse cold source according to an embodiment of the present application are shown. The embodiment of the present application adopts a high-precision simulation method, and obtains the temperature response curves of the temperature prediction points to the pulse heat source and the temperature response curves of the temperature prediction points to the pulse cold source through multi-physical field numerical calculation. The pulse source term in the embodiment of the present application is a square wave signal with a power of 100W and a duration of 1s. Figure 4 It can be seen that the temperature prediction points have different temperature responses to pulse heat sources and pulse cold sources. This is because the boundary conditions corresponding to the heat source term and the cold source term in the battery module are different. The physical meaning of the heat source is the volume heat generated during the discharge process of the battery cell, while the cold source is the heat flux density applied to the bottom surface of the battery cell.

[0097] Figure 5 A schematic diagram showing the prediction results of a method for quickly predicting the core temperature of a power battery according to an embodiment of the present application is shown as follows: Figure 5 As shown, Figure 5 The y-axis on the right side shows the curve of the operating current over time when the method of the embodiment of the present application is used to predict the core temperature. The embodiment of the present application uses the operating current of the power battery during the vertical take-off and landing of the drone as the test input. It can be seen from the operating current change curve that there is a continuous discharge rate of about 6C for about 120s during the vertical take-off and landing stages of the drone. At this time, the battery heat generation problem is serious, and it is necessary to accurately judge the actual internal temperature to ensure the normal operation and safety performance of the drone; Figure 5 The y-axis on the left in the middle shows the temperature prediction results of predicting the core temperature of the power battery using the method of an embodiment of the present application, the temperature prediction results using the existing lumped thermal model core temperature estimation algorithm, the power battery surface temperature monitoring results and the actual core temperature change curve. Figure 6 A schematic diagram showing temperature prediction error of a method for quickly predicting core temperature of a power battery according to an embodiment of the present application is shown. Figure 6 The relative error between the predicted result of the power battery core temperature predicted by the method of the embodiment of the present application and the actual core temperature, the relative error between the predicted result of the existing lumped thermal model core temperature estimation algorithm and the actual core temperature, and the relative error between the power battery surface temperature monitoring result and the actual core temperature are respectively shown. Figure 5 It can be seen that the temperature prediction result of the method in the embodiment of the present application can ensure good tracking of the core temperature inside the battery under dynamic working current. Figure 6 It can be seen that the temperature prediction accuracy of the method in the embodiment of the present application is always maintained within 5% in the prediction time domain, and the temperature prediction accuracy is higher than the lumped thermal model core temperature estimation algorithm. However, monitoring the surface temperature alone cannot effectively identify the state where the internal temperature has actually reached a dangerous temperature.

[0098] Based on the same inventive concept of the above method embodiment, the present application also proposes a device for quickly predicting the core temperature of a power battery.

[0099] Figure 7 The schematic diagram of the structure of a device for rapid prediction of the core temperature of a power battery according to an embodiment of the present application is shown, and the device can be used to implement a method for rapid prediction of the core temperature of a power battery according to an embodiment of the present 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 according to 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 the pulse current signal acting on the power battery; a first prediction module 703, used to predict 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 the initial moment; the actual heat source is the self-heating of the power battery caused by the continuous current signal acting on the power battery.

[0100] In a possible implementation, the first temperature response convolution integral calculation model is: Among them, δT1(t) represents the temperature change of the temperature prediction point at time t 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 of the temperature prediction point at time (t-x) under the action of the pulse heat source; t>0; 0≤x≤t.

[0101] In a possible implementation, the prediction module 703 is further used to: discretize the first temperature response convolution integral calculation model to obtain a first temperature prediction model, where the first temperature prediction model is: Wherein, ΔT1[n] represents the temperature change of the temperature prediction 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 of the temperature prediction point at time (n-i) 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 temperature prediction 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 temperature prediction point at time n under the action of the actual heat source; T[0] represents the initial temperature.

[0102] In a possible implementation, the calculation formula of q1[i] is: q1[i]=I[i] 2 ·θ+I[i]·T·(dU / dT); wherein, 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; dU / dT represents the rate of change of the voltage of the power battery with the temperature of the power battery.

[0103] In a 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 in the prediction time domain under the action of the actual heat source, T=T[0] in the calculation formula of q1[i]; 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, which is used to divide the prediction time domain into N time intervals, and predict the temperature of the temperature prediction point in each time interval under the action of the actual heat source; N>1; the N time intervals are {[0,n1],……,[n N-1 +1,n]}; n1, ..., n N-1 are all positive integers; the time length of each time interval is not greater than the preset threshold; wherein, when predicting the temperature of the temperature prediction 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]; when predicting the temperature of the temperature prediction point in the kth time interval [n k-1 +1,n k ], the calculation formula of q1[i] is T=T1[n k-1 ]; 1 <k≤N。

[0104] In a possible implementation, when the power battery has a thermal management structure, the device further includes: a second model establishment module, which is used to establish a second temperature response convolution integral calculation model according to the change of the heat dissipation rate of the power battery over time and the change of the temperature of the temperature prediction point under the action of a pulse cold source; the pulse cold source is used to cool the power battery at the thermal management structure according to a pulse cooling signal; the pulse cooling signal is used to control the temperature and flow rate of the cooling airflow at the thermal management structure; a second prediction module is used to predict the temperature of the temperature prediction point under the joint action of the actual heat source and the actual cold source according to the first temperature response convolution integral calculation model, the second temperature response convolution 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 convolution integral calculation model is δT2(t) represents the temperature change of the temperature prediction point at time t 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 of the temperature prediction point at time (t-x) under the action of the pulse cold source; t>0; 0≤x≤t.

[0105] In a possible implementation, the second prediction module is further used to: discretize the second temperature response convolution integral calculation model to obtain a second temperature prediction model, where the second temperature prediction model is: Wherein, ΔT2[n] represents the temperature change of the temperature prediction point 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 of the temperature prediction point at time (n-i) 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 temperature prediction point under the joint 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 temperature prediction point at time n under the joint action of the actual heat source and the actual cold source.

[0106] The device for quickly predicting the core temperature of a power battery in an embodiment of the present application can quickly and accurately predict the temperature of a location in a power battery module where it is difficult to arrange temperature measurement points but where local high temperatures are likely to occur while the power battery is working under the action of an actual heat source. It can also quickly and accurately predict the core temperature of a power battery in a process where the power battery is working under the action of an actual heat source and an actual cold source. In addition, the method in an embodiment of the present application can reduce the computing resources required for temperature prediction.

[0107] In some embodiments, the functions or modules included in the device provided in the embodiments of the present application can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0108] The present application also provides a computer-readable storage medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above-mentioned power battery core temperature rapid prediction method is implemented. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0109] An embodiment of the present application also proposes an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above-mentioned method for quickly predicting the core temperature of a power battery when executing the instructions stored in the memory.

[0110] An embodiment of the present application also provides a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned method for quickly predicting the core temperature of a power battery.

[0111] Figure 8 1 shows a block diagram of an electronic device 1900 according to an embodiment of the present application. For example, the electronic device 1900 can be provided as a server or a terminal device. Figure 8 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above-mentioned power battery core temperature rapid prediction method.

[0112] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server 2003. 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 a processing component 1922 of an electronic device 1900 to complete the above-mentioned method for quickly predicting the core temperature of a power battery.

[0114] The present application may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present application.

[0115] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

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

[0117] The computer program instructions for performing the operation of the present application can be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related 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 "C" language or similar programming languages. Computer-readable program instructions can be executed completely on a user's computer, partially on a user's computer, executed as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of a computer-readable program instruction to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA), the electronic circuit can execute a computer-readable program instruction, thereby realizing various aspects of the present application.

[0118] Various aspects of the present application are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram 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 device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in 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 so that a series of operating steps are 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 implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0121] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can also be executed in the opposite order sometimes, depending on the function involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be realized by a dedicated hardware-based system that performs the function or action of the specification, or can be realized by a combination of special-purpose hardware and computer instructions.

[0122] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for quickly predicting the core temperature of a power battery, characterized in that: include: Determine the temperature prediction point inside the power battery; A first temperature response convolution integral calculation model is established according to 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-heat generation of the power battery caused by the pulse current signal acting on the power battery; According to 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 an actual heat source is predicted; the actual heat source is the self-heating of the power battery caused by a continuous current signal acting on the power battery.

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

3. The method according to claim 2, characterized in that 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 the initial moment includes: The first temperature response convolution integral calculation model is discretized to obtain a first temperature prediction model, where the first temperature prediction model is: Wherein, ΔT1[n] represents the temperature change of the temperature prediction 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 of the temperature prediction point at time (n-i) 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 temperature prediction 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 temperature prediction point at time n 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 calculation formula of q1[i] is: q1[i]=I[i] 2 ·θ+I[i]·T·(dU / dT) Wherein, 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 the preset threshold, when predicting the temperature of the temperature prediction point in the prediction time domain under the action of the actual heat source, T=T[0] in the calculation formula of q1[i]; 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 of the temperature prediction point in each time interval under the action of the actual heat source is predicted respectively; N>1; the N time intervals are {[0,n1],...,[n N-1 +1,n]}; n1, ..., n N-1 are all positive integers; the time length of each time interval is not greater than the preset threshold; When predicting the temperature of the temperature prediction 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]; when predicting the temperature of the temperature prediction point in the kth time interval [n k-1 +1,n k ], the calculation formula of q1[i] is T=T1[n k-1 ]; 1 <k≤N。 6. The method according to any one of claims 3 to 5, characterized in that: When the power battery has a thermal management structure, the method further includes: A second temperature response convolution integral calculation model is established according to the change of the heat dissipation rate of the power battery over time and the change of the temperature of the temperature prediction point under the action of the pulse cold source over time; the pulse cold source is used to cool the power battery at the thermal management structure according to the pulse cooling signal; the pulse cooling signal is used to control the temperature and flow rate of the cooling airflow at the thermal management structure; According to the first temperature response convolution integral calculation model, the second temperature response convolution integral calculation model and the initial temperature, the temperature of the temperature prediction point under the joint action of the actual heat source and the actual cold source is predicted; 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 convolution integral calculation model is: δT2(t)=∫0 t q2(x)TR2(t-x)dx δT2(t) represents the temperature change of the temperature prediction point at time t 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 of the temperature prediction point at time (t-x) under the action of the pulse cold source; t>0; 0≤x≤t.

7. The method according to claim 6, characterized in that Predicting the temperature of the temperature prediction point under the combined action of the actual heat source and the actual cold source according to the first temperature response convolution integral calculation model, the second temperature response convolution integral calculation model and the initial temperature, including: The second temperature response convolution integral calculation model is discretized to obtain a second temperature prediction model, where the second temperature prediction model is: Wherein, ΔT2[n] represents the temperature change of the temperature prediction point 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 of the temperature prediction point at time (n-i) 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 temperature prediction 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 temperature prediction point at time n under the combined action of the actual heat source and the actual cold source.

8. A device for rapid prediction of core temperature of a power battery, characterized in that: include: A point determination module is used to determine the temperature prediction point inside the power battery; a first model building module, for building a first temperature response convolution integral calculation model according to 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-heat generation of the power battery caused by the pulse current signal acting on the power battery; The first prediction module is 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 an initial moment; the actual heat source is the self-heating of the power battery caused by the continuous current signal acting on the power battery.

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

10. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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