Battery extreme temperature estimation method and device, electronic equipment and storage medium

Abstract

The invention relates to a battery extreme temperature estimation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a historical maximum temperature, a historical minimum temperature, a historical surface temperature, a historical heat production rate parameter and a historical heat efficiency parameter of a battery at a previous moment; obtaining a weight coefficient of a battery extreme temperature estimation model, wherein the battery extreme temperature estimation model comprises a maximum temperature estimation model and a minimum temperature estimation model; based on the historical maximum temperature, the historical surface temperature, the historical heat production rate parameter, the historical heat efficiency parameter and a weight coefficient of a maximum temperature estimation model, the current maximum temperature of the battery is determined; and determining the current minimum temperature of the battery based on the historical minimum temperature, the historical surface temperature, the historical heat production rate parameter, the historical heat efficiency parameter and a weight coefficient of a minimum temperature estimation model. By adopting the technical scheme, online estimation of the maximum temperature and the minimum temperature of the battery is realized, and the engineering application scene is wide.

Description

Technical Field

[0001] The present disclosure relates to the technical field of battery management, and in particular to a method, device, electronic device and storage medium for estimating battery extreme temperature. Background Art

[0002] Battery temperature is an important indicator in the battery working process, and has a great impact on the battery working performance and safe use. If the temperature is too high and reaches its critical temperature, it will cause thermal runaway of the battery. If the temperature is too low, the internal impedance of the battery will increase, and lithium dendrites may form to pierce the diaphragm, causing a short circuit in the battery and causing safety problems.

[0003] The temperature of electric vehicle battery packs is mostly collected on the surface of single cells, but in the battery management system, the maximum and minimum temperatures of the battery are more important. During the battery operation, the difference between the maximum temperature and the surface temperature, and between the surface temperature and the minimum temperature can reach 5°C to 10°C, so the estimation of the battery extreme temperature (maximum temperature and minimum temperature) is of great significance to the battery state estimation such as the battery state of charge (SOC), the battery state of health (SOH), the battery charge and discharge control, the fault judgment, and the thermal management.

[0004] In the related technologies, most of them are based on the equivalent thermal model of the battery and use estimation algorithms to estimate the battery core temperature. However, due to the influence of the vehicle thermal management system, in cooling mode, the battery core temperature represents the maximum temperature of the battery, and in heating mode, the battery core temperature represents the minimum temperature of the battery. Therefore, the existing battery core temperature estimation algorithm cannot obtain the maximum and minimum temperatures of the battery at the same time, which has limitations in engineering applications. Summary of the invention

[0005] In order to solve the above technical problem or at least partially solve the above technical problem, at least one embodiment of the present disclosure provides a battery extreme temperature estimation method, device, electronic device and storage medium.

[0006] In a first aspect, the present disclosure provides a battery extreme temperature estimation method, comprising:

[0007] Obtain the historical maximum temperature, historical minimum temperature, historical surface temperature, historical heat generation rate parameters and historical thermal efficiency parameters of the battery at the last moment;

[0008] Obtaining a weight coefficient of a battery extreme temperature estimation model, wherein the battery extreme temperature estimation model includes a maximum temperature estimation model and a minimum temperature estimation model, and the weight coefficient includes a weight coefficient of the maximum temperature estimation model and a weight coefficient of the minimum temperature estimation model;

[0009] determining a current maximum temperature of the battery based on the historical maximum temperature, the historical surface temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter, and a weight coefficient of the maximum temperature estimation model;

[0010] The current minimum temperature of the battery is determined based on the historical minimum temperature, the historical surface temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter, and a weight coefficient of the minimum temperature estimation model.

[0011] In a second aspect, the present disclosure provides a battery extreme temperature estimation device, comprising:

[0012] A parameter acquisition module is used to obtain the historical maximum temperature, historical minimum temperature, historical surface temperature, historical heat generation rate parameter and historical thermal efficiency parameter of the battery at the previous moment;

[0013] A weight acquisition module, used to acquire a weight coefficient of a battery extreme temperature estimation model, wherein the battery extreme temperature estimation model includes a maximum temperature estimation model and a minimum temperature estimation model, and the weight coefficient includes a weight coefficient of the maximum temperature estimation model and a weight coefficient of the minimum temperature estimation model;

[0014] a maximum temperature estimation module, configured to determine a current maximum temperature of the battery based on the historical maximum temperature, the historical surface temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter, and a weight coefficient of the maximum temperature estimation model;

[0015] The minimum temperature estimation module is used to determine the current minimum temperature of the battery based on the historical minimum temperature, the historical surface temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter and the weight coefficient of the minimum temperature estimation model.

[0016] In a third aspect, the present disclosure provides an electronic device, including: a processor and a memory;

[0017] The processor is used to execute the battery extreme temperature estimation method as described in the first aspect by calling the program or instruction stored in the memory.

[0018] In a fourth aspect, the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instruction, wherein the program or instruction enables a computer to execute the battery extreme temperature estimation method as described in the first aspect.

[0019] In a fifth aspect, the present disclosure provides a computer program product, wherein the computer program product is used to execute the battery extreme temperature estimation method as described in the first aspect.

[0020] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has at least the following advantages:

[0021] In the disclosed embodiment, the historical maximum temperature, historical minimum temperature, historical surface temperature, historical heat production rate parameter and historical thermal efficiency parameter of the battery at the previous moment are obtained, and the weight coefficient of the battery extreme temperature estimation model is obtained. The battery extreme temperature estimation model includes a maximum temperature estimation model and a minimum temperature estimation model, and the weight coefficient includes a weight coefficient of the maximum temperature estimation model and a weight coefficient of the minimum temperature estimation model. Then, based on the historical maximum temperature, historical surface temperature, historical heat production rate parameter, historical thermal efficiency parameter and the weight coefficient of the maximum temperature estimation model, the current maximum temperature of the battery is determined, and based on the historical minimum temperature, historical surface temperature, historical heat production rate parameter, historical thermal efficiency parameter and the weight coefficient of the minimum temperature estimation model, the current minimum temperature of the battery is determined. By adopting the above technical solution, through the maximum temperature estimation model and the minimum temperature estimation model, combined with the corresponding weight coefficient and related parameters, the online estimation of the maximum temperature and the minimum temperature of the battery is realized, thereby providing more accurate temperature input for vehicle charging and discharging current control, SOX estimation, thermal management, etc., and the engineering application scenarios are wide. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0024] Figure 1 A flowchart of a battery extreme temperature estimation method provided by an exemplary embodiment of the present disclosure;

[0025] Figure 2 It is a schematic diagram of an OCV_SOC_T two-dimensional table of an exemplary embodiment of the present disclosure;

[0026] Figure 3 A flowchart of a battery extreme temperature estimation method provided by another exemplary embodiment of the present disclosure;

[0027] Figure 4 A block diagram of a battery extreme temperature estimation model according to an exemplary embodiment of the present disclosure is shown;

[0028] Figure 5 It is a schematic diagram of current curves at different times at 25° C. of an exemplary embodiment of the present disclosure;

[0029] Figure 6 A schematic diagram of temperature curves at different sampling points of an exemplary embodiment of the present disclosure;

[0030] Figure 7 A schematic diagram of a result of a Kalman online estimation error according to an exemplary embodiment of the present disclosure;

[0031] Figure 8 A schematic diagram of the structure of a battery extreme temperature estimation device provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] In order to more clearly understand the above-mentioned purposes, features and advantages of the present disclosure, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understandable that the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments, and the specific embodiments described herein are only used to explain the present disclosure, rather than to limit the present disclosure. In the absence of conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in the field belong to the scope of protection of the present disclosure.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0034] In response to the problem that the battery core temperature in the existing scheme cannot simultaneously characterize the maximum temperature and the minimum temperature of the battery, the present disclosure provides a battery extreme temperature estimation scheme, which can estimate the maximum temperature and the minimum temperature of the battery online without understanding the internal reaction mechanism of the battery. The algorithm is simple and the calculation amount is small. It can provide more accurate temperature input for vehicle charging and discharging current control, SOX estimation, thermal management, etc., and has a wide range of engineering application scenarios.

[0035] The specific implementation of the battery extreme temperature estimation method, device, electronic device and storage medium disclosed in the present invention is explained in detail below with reference to the accompanying drawings.

[0036] Figure 1 A flow chart of a method for estimating a battery extreme temperature provided for an exemplary embodiment of the present disclosure is provided. The method can be executed by a battery extreme temperature estimation device provided in this embodiment. The battery extreme temperature estimation device can be implemented using software and / or hardware and can be integrated in an electronic device.

[0037] like Figure 1 As shown, the battery extreme temperature estimation method may include the following steps:

[0038] Step 101, obtaining the historical maximum temperature, historical minimum temperature, historical surface temperature, historical heat generation rate parameter and historical thermal efficiency parameter of the battery at the last moment.

[0039] Among them, the previous moment is relative to the current moment, and the previous moment is the closest historical moment to the current moment. The time interval between the previous moment and the current moment is related to the period or frequency of the electronic device acquiring data such as temperature and heat generation rate parameters. For example, if the period of the electronic device acquiring the above data is 3 seconds, then the time interval between the previous moment and the current moment is 3 seconds. The historical maximum temperature and historical minimum temperature of the battery at the previous moment are the battery temperatures estimated and determined by the battery extreme temperature estimation model at the previous moment; the historical surface temperature can be the temperature collected by the temperature sensor on the battery surface at the previous moment. When the battery extreme temperature estimation model includes a battery surface temperature estimation model, the historical surface temperature can also be the temperature estimated and determined by the battery surface temperature estimation model at the previous moment; it can be understood that in the initial calculation, that is, when calculating the maximum temperature and minimum temperature at the first moment, the historical maximum temperature, historical minimum temperature, and historical surface temperature are all the temperatures collected by the temperature sensor on the battery surface.

[0040] The historical heat generation rate parameters may include but are not limited to the battery charge and discharge current at the last moment, the battery open circuit voltage at the last moment, the battery terminal voltage at the last moment, the surface collection temperature at the last moment and the entropy thermal coefficient at the last moment, wherein the battery charge and discharge current, the battery terminal voltage and the surface collection temperature may be obtained by collecting corresponding sensors; the battery open circuit voltage (OCV) may be obtained by looking up a table, for example, the battery open circuit voltage may be obtained by looking up the OCV_SOC_T two-dimensional table, wherein the OCV_SOC_T two-dimensional table uses SOC and temperature (denoted as T) as coordinate values, and the battery open circuit voltage as corresponding data (in volts (V)). According to the SOC of the battery at the last moment and the surface collection temperature at the last moment, the battery open circuit voltage at the last moment may be determined by looking up the OCV_SOC_T two-dimensional table, and the OCV_SOC_T two-dimensional table is as follows: Figure 2 For example, when the SOC is 15% and the temperature T is 0°C, by querying Figure 2 From the table shown, it can be determined that the corresponding OCV is 3.544. The entropy thermal coefficient at the last moment can be determined based on the battery open circuit voltage at the last moment and the surface acquisition temperature at the last moment.

[0041] The historical thermal efficiency parameters vary depending on the thermal management method of the battery management system. Taking liquid cooling as an example, the historical thermal efficiency parameters may include but are not limited to the coolant volume flow rate, coolant outlet temperature, and coolant inlet temperature at the previous moment. These parameters can be acquired through corresponding sensors. In some embodiments, when the thermal management method is liquid cooling, the historical thermal efficiency parameters may also include the coolant specific heat capacity and coolant density. These parameters can be configured by the manufacturer during production according to the coolant used.

[0042] Step 102, obtaining a weight coefficient of a battery extreme temperature estimation model, wherein the battery extreme temperature estimation model includes a maximum temperature estimation model and a minimum temperature estimation model, and the weight coefficient includes a weight coefficient of the maximum temperature estimation model and a weight coefficient of the minimum temperature estimation model.

[0043] In the disclosed embodiment, the battery extreme temperature estimation model includes a maximum temperature estimation model and a minimum temperature estimation model. Accordingly, the weight coefficient of the battery extreme temperature estimation model includes the weight coefficient of the maximum temperature estimation model and the weight coefficient of the minimum temperature estimation model.

[0044] Among them, the weight coefficient can be set according to needs and experience, or it can be determined by offline identification based on the battery extreme temperature estimation model. The present disclosure does not limit this. After the weight coefficient is determined by offline identification, the determined weight coefficient can be stored so that it can be directly obtained and used when performing temperature estimation.

[0045] Step 103 , determining the current maximum temperature of the battery based on the historical maximum temperature, the historical surface temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter and the weight coefficient of the maximum temperature estimation model.

[0046] In this embodiment, after obtaining the weight coefficient of the maximum temperature estimation model, the obtained weight coefficient of the maximum temperature estimation model, the historical maximum temperature, the historical surface temperature, the historical heat production rate parameter, and the historical thermal efficiency parameter can be used as input data of the maximum temperature estimation model for calculation to obtain the maximum temperature of the battery at the current moment, that is, the current maximum temperature.

[0047] Step 104 , determining the current minimum temperature of the battery based on the historical minimum temperature, the historical surface temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter and the weight coefficient of the minimum temperature estimation model.

[0048] In this embodiment, after obtaining the weight coefficient of the minimum temperature estimation model, the obtained weight coefficient of the minimum temperature estimation model, the historical minimum temperature, the historical surface temperature, the historical heat production rate parameter, and the historical thermal efficiency parameter can be used as input data of the minimum temperature estimation model for calculation to obtain the minimum temperature of the battery at the current moment, that is, the current minimum temperature.

[0049] It can be understood that in the present embodiment, the calculation process of the current maximum temperature and the current minimum temperature of the battery is ongoing, and the current maximum temperature determined at the current moment becomes the new historical maximum temperature at the next moment. Similarly, the current minimum temperature determined at the current moment becomes the new historical minimum temperature at the next moment, which are respectively used to determine the maximum temperature and minimum temperature at the next moment, thereby realizing online estimation of the extreme temperature of the battery.

[0050] It should be noted that, in this embodiment, the execution order of step 103 and step 104 is not particular, and the two can be executed simultaneously or sequentially. Figure 1 The illustrated embodiment only takes step 104 being executed after step 103 as an example to explain the present disclosure, and is not intended to limit the present disclosure.

[0051] In this embodiment, by incorporating the surface temperature, heat generation rate parameter and thermal efficiency parameter of the battery into the determination of the extreme temperature of the battery, the accuracy of the determined extreme temperature can be ensured.

[0052] The battery extreme temperature estimation method of the disclosed embodiment obtains the historical maximum temperature, historical minimum temperature, historical surface temperature, historical heat production rate parameter and historical thermal efficiency parameter of the battery at the previous moment, and obtains the weight coefficient of the battery extreme temperature estimation model, the battery extreme temperature estimation model includes a maximum temperature estimation model and a minimum temperature estimation model, and the weight coefficient includes the weight coefficient of the maximum temperature estimation model and the weight coefficient of the minimum temperature estimation model, and then based on the historical maximum temperature, historical surface temperature, historical heat production rate parameter, historical thermal efficiency parameter and the weight coefficient of the maximum temperature estimation model, the current maximum temperature of the battery is determined, and based on the historical minimum temperature, historical surface temperature, historical heat production rate parameter, historical thermal efficiency parameter and the weight coefficient of the minimum temperature estimation model, the current minimum temperature of the battery is determined. The above technical solution is adopted, through the maximum temperature estimation model and the minimum temperature estimation model, combined with the corresponding weight coefficient and related parameters, to realize the online estimation of the maximum temperature and minimum temperature of the battery, thereby providing more accurate temperature input for vehicle charging and discharging current control, SOX estimation, thermal management, etc., and has a wide range of engineering application scenarios.

[0053] In an optional embodiment of the present disclosure, the battery extreme temperature estimation model may further include a battery surface temperature estimation model, and accordingly, the weight coefficient of the battery extreme temperature estimation model also includes the weight coefficient of the battery surface temperature estimation model, and the method further includes determining the current surface temperature of the battery based on the historical maximum temperature, the historical surface temperature, the historical minimum temperature, the historical heat production rate parameter, the historical thermal efficiency parameter and the weight coefficient of the battery surface temperature estimation model, wherein the determined current surface temperature is used as the historical surface temperature at the next moment. That is to say, in this embodiment, the current surface temperature estimated by the battery surface temperature estimation model is used as the new historical surface temperature when estimating the extreme temperature at the next moment, and the online estimation of the battery surface temperature is used for the determination of the extreme temperature, which helps to improve the accuracy of the extreme temperature estimation.

[0054] In an optional embodiment of the present disclosure, the weight coefficient of the battery extreme temperature estimation model can be obtained by offline identification, so that Figure 3 As shown, based on the above embodiment, step 102 may include the following sub-steps:

[0055] Step 201 , obtaining a battery parameter set of a battery under different working conditions, wherein the battery parameter set includes a maximum temperature, a minimum temperature, a surface temperature, a heat generation rate parameter, and a thermal efficiency parameter.

[0056] As an example, the positions of the maximum temperature and the minimum temperature during the battery operation process can be obtained by thermal simulation. Specifically, experiments that meet different working conditions of the battery can be designed, including different starting temperatures, different starting SOCs, different currents, etc. The experimental data such as the battery's SOC, current, terminal voltage, maximum temperature, battery surface temperature, minimum temperature, inlet / outlet temperature, coolant volume flow rate, etc. under each battery working condition are obtained through simulation as a battery parameter set, thereby obtaining a battery parameter set under different working conditions.

[0057] As an example, multiple thermocouple temperature sensors can be distributed inside and on the surface of the battery core shaft to collect the maximum temperature and minimum temperature during the battery operation process. Specifically, multiple batteries with different working conditions can be tested to obtain the maximum temperature, minimum temperature, battery surface temperature, and water inlet / outlet temperature collected by each temperature sensor during the test, as well as the battery SOC, coolant volume flow rate, current, and terminal voltage collected by the sensor during the test, thereby obtaining a set of battery parameters under different battery working conditions.

[0058] Step 202 , based on the maximum temperature, the minimum temperature, the surface temperature, the heat generation rate parameter and the thermal efficiency parameter, the battery extreme temperature estimation model is estimated to obtain the weight coefficient of the battery extreme temperature estimation model.

[0059] In this embodiment, after obtaining a set of battery parameters under different battery operating conditions, the acquired data can be used to perform parameter estimation on the battery extreme temperature estimation model to obtain the weight coefficient of the battery surface temperature estimation model, the weight coefficient of the maximum temperature estimation model, and the weight coefficient of the minimum temperature estimation model.

[0060] Among them, the currently commonly used model parameter estimation algorithm can be used to identify the weight coefficients of the battery extreme temperature estimation model. As an example, the least square method can be used to identify all weight coefficients of the battery extreme temperature estimation model, and the identified weight coefficients can be stored and used to estimate the battery extreme temperature.

[0061] The battery extreme temperature estimation method of the present embodiment obtains a battery parameter set under different battery operating conditions, performs parameter estimation on a battery extreme temperature estimation model based on different battery parameter sets, and obtains a weight coefficient of the battery extreme temperature estimation model. Thus, a more accurate model weight coefficient can be obtained, providing data support for improving the accuracy of battery extreme temperature estimation.

[0062] In an optional embodiment of the present disclosure, the maximum temperature estimation model can be expressed as shown in formula (1), the minimum temperature estimation model can be expressed as shown in formula (2), and the battery surface temperature estimation model can be expressed as shown in formula (3):

[0063] Tmax(k)=a1*Tmax(k-1)+a2*Tntc(k-1)+a3*Qcur(k-1)+a4*Qthm(k-1)(1)

[0064] Tmin(k)=c1*Tntc(k-1)+c2*Tmin(k-1)+c3*Qcur(k-1)+c4*Qthm(k-1)(2)

[0065] Tntc(k)=b1*Tmax(k-1)+b2*Tntc(k-1)+b3*Tmin(k-1)+b4*Qcur(k-1)+b5*Qthm(k-1)(3)

[0066] Among them, Tmax(k), Tmin(k) and Tntc(k) respectively represent the maximum temperature, minimum temperature and surface temperature at the current moment k, Tmax(k-1), Tntc(k-1) and Tmin(k-1) respectively represent the maximum temperature, surface temperature and minimum temperature at the previous moment of moment k, Qcur(k-1) represents the heat generation rate of the charge and discharge current determined according to the heat generation rate parameter at the previous moment of moment k, Qthm(k-1) represents the thermal efficiency determined according to the thermal efficiency parameter at the previous moment of moment k, and a1, a2, a3, a4, b1, b2, b3, b4, b5, c1, c2, c3 and c4 are all weight coefficients. It can be understood that in the above-mentioned battery extreme temperature estimation model constructed, the above-mentioned weight coefficients are all to be identified. After identifying each weight coefficient, substituting them into the battery extreme temperature estimation model can estimate the battery extreme temperature and surface temperature online.

[0067] Further, in an optional embodiment of the present disclosure, when the historical heat generation rate parameters include the battery charge and discharge current, the battery open circuit voltage, the battery terminal voltage, the surface collection temperature and the entropy heat coefficient, according to the calculation formula of the first law of thermodynamics, the charge and discharge current heat generation rate at the previous moment determined according to the historical heat generation rate parameters at the previous moment can be expressed as shown in the following formula (4):

[0068]

[0069] Wherein, I represents the battery charge and discharge current, charging is negative and discharging is positive; OCV represents the battery open circuit voltage, U represents the battery terminal voltage, T represents the surface collection temperature, dOCV / dT represents the entropy thermal coefficient, and the acquisition of each data can refer to the relevant description of step 101 in the aforementioned embodiment, which will not be repeated here.

[0070] In an optional embodiment of the present disclosure, when the thermal management method of the battery is liquid cooling, the historical thermal efficiency parameters include the coolant volume flow rate, the coolant outlet temperature and the coolant inlet temperature. The thermal management coolant thermal efficiency Qthm(k-1) determined according to the thermal efficiency parameters at the previous moment can be expressed as shown in the following formula (5):

[0071] Qthm(k-1)=V*(Toutlet-Tinlet)(5)

[0072] Where V is the coolant volume flow rate, Toutlet is the coolant outlet temperature, and Tinlet is the coolant inlet temperature.

[0073] Typically, the thermal efficiency of the thermal management coolant is determined by the following formula (6):

[0074] Qthm=Cp*V*ρ*(Toutlet-Tinlet)(6)

[0075] In the above formula (6), Cp represents the specific heat capacity of the coolant, and ρ represents the density of the coolant.

[0076] It can be seen from formula (6) that in order to determine the thermal efficiency of the thermal management coolant, it is necessary not only to obtain the coolant volume flow rate, the coolant outlet temperature and the coolant inlet temperature, but also to obtain the two parameters of the coolant specific heat capacity and the coolant density. If these two parameters cannot be obtained, the thermal efficiency of the thermal management coolant cannot be accurately obtained. By adopting the solution disclosed in the present invention, the thermal efficiency of the thermal management coolant is simplified as shown in formula (5), and the coolant specific heat capacity and the coolant density are fitted to the coefficients of thermal efficiency, i.e., a4, b5, and c4, so that there is no need to know the specific values ​​of the coolant specific heat capacity and the coolant density, thereby reducing the amount of calculation.

[0077] In an optional embodiment of the present disclosure, when the thermal management mode of the battery is air cooling or natural cooling, the historical thermal efficiency parameters include the surface collection temperature and the ambient temperature. The thermal management thermal efficiency Qthm(k-1) determined according to the thermal efficiency parameters at the previous moment can be expressed as shown in the following formula (7):

[0078] Qthm(k-1)=Tntc-Tamb(7)

[0079] Wherein, Tntc represents the surface collection temperature, and Tamb represents the ambient temperature.

[0080] Generally, when the thermal management method of the battery management system is air cooling or natural cooling, the thermal management thermal efficiency is determined by the following formula (8):

[0081] Qthm=h*A*(Tntc-Tamb)(8)

[0082] In the above formula (8), h represents the convection heat transfer coefficient, and A represents the contact area between the battery and the environment.

[0083] It can be seen from formula (8) that when the thermal management mode of the battery management system is air cooling or natural cooling, in order to determine the thermal management thermal efficiency, it is necessary not only to obtain the surface collection temperature and ambient temperature of the battery, but also to obtain the two parameters of the convection heat transfer coefficient and the contact area between the battery and the environment. If these two parameters cannot be obtained, the thermal management thermal efficiency cannot be accurately obtained. By adopting the solution disclosed in the present invention, the thermal management thermal efficiency is simplified as shown in formula (7), and the convection heat transfer coefficient and the contact area between the battery and the environment are fitted to the coefficients of thermal efficiency, i.e., a4, b5, and c4, so that there is no need to know the specific values ​​of the convection heat transfer coefficient and the contact area between the battery and the environment, which reduces the amount of calculation and the difficulty of calculation.

[0084] Taking the thermal management method of the battery management system as liquid cooling as an example, Figure 4 A block diagram of a battery extreme temperature estimation model according to an exemplary embodiment of the present disclosure is shown. Figure 4 It can be seen that by obtaining the battery current, SOC, terminal voltage, surface collection temperature, water inlet temperature, water outlet temperature and coolant volume flow rate, these data are used as input data of the battery extreme temperature estimation model. After estimation by the battery extreme temperature estimation model, the maximum battery temperature and the minimum battery temperature at the current moment can be determined.

[0085] For example, when estimating the maximum battery temperature and the minimum battery temperature based on the battery extreme temperature estimation model, the Kalman filter algorithm can be used to estimate the maximum battery temperature and the minimum battery temperature online. The battery extreme temperature estimation model can be expressed as a state equation as shown in formula (9):

[0086]

[0087] To facilitate the description of the iterative process of the Kalman filter algorithm, let:

[0088]

[0089] The iterative process of the Kalman filter algorithm can be expressed as follows:

[0090] Status prediction:

[0091] Covariance matrix prediction:

[0092] Kalman gain:

[0093] State estimate update:

[0094] Covariance matrix estimate update:

[0095] in, represents the predicted values ​​of maximum temperature, surface temperature and minimum temperature at time k; represents the estimated values ​​of the maximum temperature, surface temperature, and minimum temperature at time k, that is, the maximum temperature and minimum temperature at the current time (time k) that are finally determined; Q, R, P k The initial value of can be determined according to the acquisition accuracy of the temperature sensor actually used; k In the initial value of , Tmax(k), Tntc(k) and Tmin(k) are all collected values ​​of the battery surface temperature sensor, and E is a known unit matrix.

[0096] Through experiments, the current (in amperes (A)) at different times at a temperature of 25°C is as follows: Figure 5 As shown in the experiment, the maximum temperature Tmax, surface temperature Tntc and minimum temperature Tmin (in °C) obtained are as follows Figure 6 In the case shown, the weight coefficient of the battery extreme temperature estimation model determined by offline identification is:

[0097] a1=0.9913, a2=0.0087, a3=0.00005, a4=0.00003;

[0098] b1=0.0010, b2=0.9991, b3=-0.00001, b4=0.00004, b5=-0.00001;

[0099] c1=0.0135, c2=0.9862, c3=0.00002, c4=-0.00002.

[0100] The Kalman online estimation error is Figure 7 As shown, it can be seen that, by using the solution disclosed in the present invention, the estimation error of most sampling points is between -0.4 and 0.4, and the battery extreme temperature estimated by the solution disclosed in the present invention has a high accuracy.

[0101] In order to implement the above embodiments, the present disclosure further provides a battery extreme temperature estimation device, which can be implemented by software and / or hardware and can be integrated in an electronic device.

[0102] Figure 8 A schematic diagram of the structure of a battery extreme temperature estimation device provided by an embodiment of the present disclosure, such as Figure 8 As shown, the battery extreme temperature estimation device 30 may include: a parameter acquisition module 310 , a weight acquisition module 320 , a maximum temperature estimation module 330 and a minimum temperature estimation module 340 .

[0103] The parameter acquisition module 310 is used to obtain the historical maximum temperature, historical minimum temperature, historical surface temperature, historical heat generation rate parameter and historical thermal efficiency parameter of the battery at the last moment;

[0104] A weight acquisition module 320, used to acquire a weight coefficient of a battery extreme temperature estimation model, the battery extreme temperature estimation model includes a maximum temperature estimation model and a minimum temperature estimation model, and the weight coefficient includes a weight coefficient of the maximum temperature estimation model and a weight coefficient of the minimum temperature estimation model;

[0105] A maximum temperature estimation module 330, for determining a current maximum temperature of the battery based on a historical maximum temperature, a historical surface temperature, a historical heat generation rate parameter, a historical thermal efficiency parameter, and a weight coefficient of a maximum temperature estimation model;

[0106] The minimum temperature estimation module 340 is used to determine the current minimum temperature of the battery based on the historical minimum temperature, the historical surface temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter and the weight coefficient of the minimum temperature estimation model.

[0107] Optionally, the battery extreme temperature estimation model further includes a battery surface temperature estimation model, and the weight coefficient of the battery extreme temperature estimation model further includes the weight coefficient of the battery surface temperature estimation model; the battery extreme temperature estimation device 30 further includes:

[0108] The surface temperature estimation module is used to determine the current surface temperature of the battery based on the historical maximum temperature, the historical surface temperature, the historical minimum temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter and the weight coefficient of the battery surface temperature estimation model; wherein the current surface temperature is used as the historical surface temperature at the next moment.

[0109] Further optionally, the weight acquisition module 320 is further configured to:

[0110] Acquire a battery parameter set of the battery under different working conditions, wherein the battery parameter set includes a maximum temperature, a minimum temperature, a surface temperature, a heat generation rate parameter, and a thermal efficiency parameter;

[0111] Based on the maximum temperature, the minimum temperature, the surface temperature, the heat generation rate parameter and the thermal efficiency parameter, parameter estimation is performed on the battery extreme temperature estimation model to obtain a weight coefficient of the battery extreme temperature estimation model.

[0112] Alternatively, the maximum temperature estimation model is expressed as:

[0113] Tmax(k)=a1*Tmax(k-1)+a2*Tntc(k-1)+a3*Qcur(k-1)+a4*Qthm(k-1);

[0114] and / or,

[0115] The minimum temperature estimation model is expressed as:

[0116] Tmin(k)=c1*Tntc(k-1)+c2*Tmin(k-1)+c3*Qcur(k-1)+c4*Qthm(k-1);

[0117] Among them, Tmax(k-1), Tntc(k-1) and Tmin(k-1) respectively represent the maximum temperature, surface temperature and minimum temperature at the previous moment of moment k, Qcur(k-1) represents the heat generation rate of the charge and discharge current determined according to the heat generation rate parameter at the previous moment, Qthm(k-1) represents the thermal efficiency determined according to the thermal efficiency parameter at the previous moment, and a1, a2, a3, a4, c1, c2, c3 and c4 are all weight coefficients.

[0118] Further optionally, the historical heat generation rate parameters include: battery charge and discharge current, battery open circuit voltage, battery terminal voltage, surface collection temperature and entropy thermal coefficient;

[0119]

[0120] Among them, I represents the battery charge and discharge current, OCV represents the battery open circuit voltage, U represents the battery terminal voltage, T represents the surface collection temperature, and dOCV / dT represents the entropy thermal coefficient.

[0121] Optionally, when the thermal management method of the battery is liquid cooling, the historical thermal efficiency parameters include: coolant volume flow rate, coolant outlet temperature and coolant inlet temperature;

[0122] Qthm(k-1)=V*(Toutlet-Tinlet);

[0123] Where V is the coolant volume flow rate, Toutlet is the coolant outlet temperature, and Tinlet is the coolant inlet temperature.

[0124] Optionally, when the thermal management mode of the battery is air cooling or natural cooling, the historical thermal efficiency parameters include: surface collection temperature and ambient temperature;

[0125] Qthm(k-1)=Tntc-Tamb;

[0126] Wherein, Tntc represents the surface collection temperature, and Tamb represents the ambient temperature.

[0127] The battery extreme temperature estimation device applicable to electronic devices provided in the embodiments of the present disclosure can execute the battery extreme temperature estimation method provided in the embodiments of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method. The contents not fully described in the embodiments of the present disclosure device can refer to the description in any method embodiment of the present disclosure.

[0128] The embodiment of the present disclosure also provides an electronic device, including a processor and a memory; the processor calls the program or instructions stored in the memory to execute the steps of the various embodiments of the battery extreme temperature estimation method as described above, which will not be repeated here to avoid repeated description.

[0129] The embodiments of the present disclosure also provide a computer-readable storage medium, which is non-transitory and stores programs or instructions. The programs or instructions enable a computer to execute the steps of the embodiments of the battery extreme temperature estimation method as described above. To avoid repeated description, they will not be repeated here.

[0130] The embodiments of the present disclosure also provide a computer program product, which is used to execute the steps of the embodiments of the battery extreme temperature estimation method as described above.

[0131] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0132] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A battery extreme temperature estimation method, characterized in that: The method comprises: Obtain the historical maximum temperature, historical minimum temperature, historical surface temperature, historical heat generation rate parameters and historical thermal efficiency parameters of the battery at the last moment; Obtaining a weight coefficient of a battery extreme temperature estimation model, wherein the battery extreme temperature estimation model includes a maximum temperature estimation model and a minimum temperature estimation model, and the weight coefficient includes a weight coefficient of the maximum temperature estimation model and a weight coefficient of the minimum temperature estimation model; determining a current maximum temperature of the battery based on the historical maximum temperature, the historical surface temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter, and a weight coefficient of the maximum temperature estimation model; The current minimum temperature of the battery is determined based on the historical minimum temperature, the historical surface temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter, and a weight coefficient of the minimum temperature estimation model.

2. The method according to claim 1, characterized in that: The battery extreme value temperature estimation model further includes a battery surface temperature estimation model, and the weight coefficient of the battery extreme value temperature estimation model further includes the weight coefficient of the battery surface temperature estimation model; The method further comprises: Determining a current surface temperature of the battery based on the historical maximum temperature, the historical surface temperature, the historical minimum temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter, and a weight coefficient of the battery surface temperature estimation model; The current surface temperature is used as the historical surface temperature at the next moment.

3. The method according to claim 2, characterized in that The step of obtaining the weight coefficient of the battery extreme temperature estimation model includes: Acquire a battery parameter set of the battery under different working conditions, wherein the battery parameter set includes a maximum temperature, a minimum temperature, a surface temperature, a heat generation rate parameter, and a thermal efficiency parameter; Based on the maximum temperature, the minimum temperature, the surface temperature, the heat generation rate parameter and the thermal efficiency parameter, parameter estimation is performed on the battery extreme temperature estimation model to obtain a weight coefficient of the battery extreme temperature estimation model.

4. The method according to any one of claims 1 to 3, characterized in that: The maximum temperature estimation model is expressed as: Tmax(k)=a1*Tmax(k-1)+a2*Tntc(k-1)+a3*Qcur(k-1)+a4*Qthm(k-1); and / or, The minimum temperature estimation model is expressed as: Tmin(k)=c1*Tntc(k-1)+c2*Tmin(k-1)+c3*Qcur(k-1)+c4*Qthm(k-1); Among them, Tmax(k-1), Tntc(k-1) and Tmin(k-1) respectively represent the maximum temperature, surface temperature and minimum temperature at the previous moment of moment k, Qcur(k-1) represents the heat generation rate of the charge and discharge current determined according to the heat generation rate parameter at the previous moment, Qthm(k-1) represents the thermal efficiency determined according to the thermal efficiency parameter at the previous moment, and a1, a2, a3, a4, c1, c2, c3 and c4 are all weight coefficients.

5. The method according to claim 4, characterized in that The historical heat generation rate parameters include: battery charge and discharge current, battery open circuit voltage, battery terminal voltage, surface collection temperature and entropy thermal coefficient; Wherein, I represents the battery charge and discharge current, OCV represents the battery open circuit voltage, U represents the battery terminal voltage, T represents the surface collection temperature, and dOCV / dT represents the entropy thermal coefficient.

6. The method according to claim 4, characterized in that When the thermal management method of the battery is liquid cooling, the historical thermal efficiency parameters include: coolant volume flow rate, coolant outlet temperature and coolant inlet temperature; Qthm(k-1)=V*(Toutlet-Tinlet); Wherein, V represents the volume flow rate of the coolant, Toutlet represents the coolant outlet temperature, and Tinlet represents the coolant inlet temperature.

7. The method according to claim 4, characterized in that When the thermal management mode of the battery is air cooling or natural cooling, the historical thermal efficiency parameters include: surface collection temperature and ambient temperature; Qthm(k-1)=Tntc-Tamb; Wherein, Tntc represents the surface collection temperature, and Tamb represents the ambient temperature.

8. A battery extreme temperature estimation device, characterized in that: include: A parameter acquisition module is used to obtain the historical maximum temperature, historical minimum temperature, historical surface temperature, historical heat generation rate parameter and historical thermal efficiency parameter of the battery at the previous moment; A weight acquisition module, used to acquire a weight coefficient of a battery extreme temperature estimation model, wherein the battery extreme temperature estimation model includes a maximum temperature estimation model and a minimum temperature estimation model, and the weight coefficient includes a weight coefficient of the maximum temperature estimation model and a weight coefficient of the minimum temperature estimation model; a maximum temperature estimation module, configured to determine a current maximum temperature of the battery based on the historical maximum temperature, the historical surface temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter, and a weight coefficient of the maximum temperature estimation model; The minimum temperature estimation module is used to determine the current minimum temperature of the battery based on the historical minimum temperature, the historical surface temperature, the historical heat generation rate parameter, the historical thermal efficiency parameter and the weight coefficient of the minimum temperature estimation model.

9. An electronic device, characterized in that: include: Processor and memory; The processor is used to execute the battery extreme temperature estimation method as described in any one of claims 1 to 7 by calling the program or instruction stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or an instruction, and the program or the instruction enables a computer to execute the battery extreme temperature estimation method according to any one of claims 1 to 7.

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