Battery temperature prediction method, device and equipment, medium and vehicle

Abstract

The invention relates to a battery temperature prediction method and device, equipment, a medium and a vehicle, and the method comprises the steps: predicting the heat exchange amount of a fluid in a battery cooling loop and a battery at a current moment based on an environment temperature, the current water temperature of the battery cooling loop in the vehicle, the target water temperature of the battery cooling loop, and the current temperature of the battery; and the power consumed by battery thermal management from the current moment to the future moment; predicting heat generated by the battery based on the current temperature of the battery, the power consumed by battery thermal management and the pre-obtained driving demand power of the vehicle; based on the heat generated by the battery, the heat exchange amount and the current temperature of the battery, the temperature of the battery at the future moment is predicted, and the accuracy of battery temperature prediction is improved.

Description

Technical Field

[0001] This disclosure relates to the field of battery temperature prediction technology, and in particular to a battery temperature prediction method, apparatus, equipment, medium, and vehicle. Background Technology

[0002] The initial charging temperature of a battery has a significant impact on vehicle charging time. A lower initial charging temperature not only results in longer charging times but also increases the risk of lithium plating. Therefore, precise control of the initial charging temperature requires accurate prediction of the battery temperature.

[0003] Related technologies generally predict the average heat generation power and heat exchange power of the battery through a preset model, and then predict the battery temperature in the future based on the average heat generation power, heat exchange power and the current temperature of the battery. However, existing technologies do not consider the impact of factors such as drive demand and thermal management on battery temperature, resulting in low accuracy of battery temperature prediction. Therefore, how to achieve accurate prediction of battery temperature is an urgent technical problem to be solved. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a battery temperature prediction method, apparatus, device, medium, and vehicle.

[0005] A first aspect of this disclosure provides a battery temperature prediction method, the method comprising:

[0006] Based on the ambient temperature, the current water temperature of the battery cooling circuit in the vehicle, the target water temperature of the battery cooling circuit, and the current battery temperature, predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, as well as the power consumed by battery thermal management from the current moment to the future moment.

[0007] Based on the current temperature of the battery, the power consumed by the battery thermal management, and the pre-obtained driving power demand of the vehicle, the heat generated by the battery is predicted;

[0008] Based on the heat generated by the battery, the heat exchange, and the current temperature of the battery, the temperature of the battery at a future time is predicted.

[0009] In one implementation, the prediction of the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, and the power consumed by battery thermal management from the current moment to a future moment, based on the ambient temperature, the current water temperature of the battery cooling circuit in the vehicle, the target water temperature of the battery cooling circuit, and the current battery temperature, includes:

[0010] Based on the current battery temperature, the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature, predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, as well as the water temperature of the battery cooling circuit at a future moment.

[0011] Based on the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit, the power consumed by the battery thermal management is predicted.

[0012] In one implementation, predicting the power consumed by the battery thermal management based on the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit includes:

[0013] Based on the physical relationship between the energy consumed by battery thermal management and the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit, the energy consumed by battery thermal management from the current time to the future time is predicted.

[0014] Based on the physical relationship between the energy consumed by the battery thermal management and the power consumed by the battery thermal management, the power consumed by the battery thermal management is predicted.

[0015] In one implementation, predicting the energy consumed by battery thermal management from the current moment to a future moment based on the physical relationship between the energy consumed by battery thermal management and the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit includes:

[0016] Based on the current water temperature and the future water temperature of the battery cooling circuit, determine the temperature change of the battery cooling circuit from the current time to the future time;

[0017] Based on the temperature change, the energy consumed by the battery thermal management is calculated.

[0018] In one implementation, predicting the power consumed by the battery thermal management based on the physical relationship between the energy consumed by the battery thermal management and the power consumed by the battery thermal management includes:

[0019] The power consumed by the battery thermal management is calculated based on the energy consumed by the battery thermal management, the preset time step, and the efficiency of the thermal management system in transferring heat to the battery cooling circuit.

[0020] In one embodiment, predicting the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, and the water temperature of the battery cooling circuit at a future moment, based on the current battery temperature, the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature, includes:

[0021] Based on the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature, the water temperature of the battery cooling circuit at future times is predicted.

[0022] Based on the current water temperature of the battery cooling circuit and the current battery temperature, predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment.

[0023] In one embodiment, predicting the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, based on the current water temperature of the battery cooling circuit and the current battery temperature, includes:

[0024] Based on the physical relationship between the battery heat exchange, the current water temperature of the battery cooling circuit, and the current battery temperature, the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment is predicted.

[0025] In one embodiment, predicting the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, based on the physical relationship between the battery heat exchange, the current water temperature of the battery cooling circuit, and the current battery temperature, includes:

[0026] Calculate the difference between the current water temperature of the battery cooling circuit and the current temperature of the battery;

[0027] Based on the difference, the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment is calculated.

[0028] In one implementation,

[0029] The method of predicting the heat generated by the battery based on the current temperature of the battery, the power consumed by the battery thermal management, and the pre-obtained driving power demand of the vehicle includes:

[0030] Based on the driving power demand and the power consumed by the battery thermal management, the discharge current of the battery is predicted;

[0031] The heat generated by the battery is predicted based on the battery's discharge current and current temperature.

[0032] In one implementation, the driving power demand is predicted based on the physical relationship between vehicle speed and driving power demand.

[0033] In one implementation, predicting the battery discharge current based on the drive power demand and the power consumed by battery thermal management includes:

[0034] Based on the physical relationship between driving power demand and driving power consumption, the driving power consumption of the vehicle is predicted.

[0035] The battery discharge power is determined based on the physical relationship between the battery discharge power, the drive power consumption, and the power consumed by the battery thermal management.

[0036] The discharge current of the battery is determined based on the physical relationship between the battery discharge power and the battery discharge current.

[0037] In one implementation, predicting the vehicle's drive power consumption based on the physical relationship between drive power demand and drive power consumption includes:

[0038] Based on the driving power demand, transmission system efficiency, and motor efficiency, the driving power consumption of the vehicle is calculated.

[0039] In one embodiment, determining the battery discharge power based on the physical relationship between the battery discharge power, the drive power consumption, and the power consumed by the battery thermal management includes:

[0040] Calculate the sum of the power consumed by the drive and the power consumed by the battery thermal management;

[0041] Based on the sum and the battery discharge efficiency, the battery discharge power is calculated.

[0042] In one embodiment, determining the battery discharge current based on the physical relationship between the battery discharge power and the battery discharge current includes:

[0043] The discharge current of the battery is calculated based on the battery discharge power and the battery voltage.

[0044] A second aspect of this disclosure provides a battery temperature prediction device, the device comprising:

[0045] The first prediction module is used to predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, and the power consumed by battery thermal management from the current moment to a future moment, based on the ambient temperature, the current water temperature of the battery cooling circuit in the vehicle, the target water temperature of the battery cooling circuit, and the current temperature of the battery.

[0046] The second prediction module is used to predict the heat generated by the battery based on the current temperature of the battery, the power consumed by the battery thermal management, and the pre-obtained driving power demand of the vehicle.

[0047] The third prediction module is used to predict the temperature of the battery at a future time based on the heat generated by the battery, the heat exchange, and the current temperature of the battery.

[0048] In one implementation, the first prediction module is configured to:

[0049] Based on the current battery temperature, the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature, predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, as well as the water temperature of the battery cooling circuit at a future moment.

[0050] Based on the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit, the power consumed by the battery thermal management is predicted.

[0051] In one implementation, the first prediction module is configured to:

[0052] Based on the physical relationship between the energy consumed by battery thermal management and the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit, the energy consumed by battery thermal management from the current time to the future time is predicted.

[0053] Based on the physical relationship between the energy consumed by the battery thermal management and the power consumed by the battery thermal management, the power consumed by the battery thermal management is predicted.

[0054] In one implementation, the first prediction module is configured to:

[0055] Based on the current water temperature and the future water temperature of the battery cooling circuit, determine the temperature change of the battery cooling circuit from the current time to the future time;

[0056] Based on the temperature change, the energy consumed by the battery thermal management is calculated.

[0057] In one implementation, the first prediction module is configured to:

[0058] The power consumed by the battery thermal management is calculated based on the energy consumed by the battery thermal management, the preset time step, and the efficiency of the thermal management system in transferring heat to the battery cooling circuit.

[0059] In one implementation, the first prediction module is configured to:

[0060] Based on the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature, the water temperature of the battery cooling circuit at future times is predicted.

[0061] Based on the current water temperature of the battery cooling circuit and the current battery temperature, predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment.

[0062] In one implementation, the first prediction module is configured to:

[0063] Based on the physical relationship between the battery heat exchange, the current water temperature of the battery cooling circuit, and the current battery temperature, the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment is predicted.

[0064] In one implementation, the first prediction module is configured to:

[0065] Calculate the difference between the current water temperature of the battery cooling circuit and the current temperature of the battery;

[0066] Based on the difference, the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment is calculated.

[0067] In one implementation, the second prediction module is configured to:

[0068] Based on the driving power demand and the power consumed by the battery thermal management, the discharge current of the battery is predicted;

[0069] The heat generated by the battery is predicted based on the battery's discharge current and current temperature.

[0070] In one implementation, the driving power demand is predicted based on the physical relationship between vehicle speed and driving power demand.

[0071] In one implementation, the second prediction module is configured to:

[0072] Based on the physical relationship between driving power demand and driving power consumption, the driving power consumption of the vehicle is predicted.

[0073] The battery discharge power is determined based on the physical relationship between the battery discharge power, the drive power consumption, and the power consumed by the battery thermal management.

[0074] The discharge current of the battery is determined based on the physical relationship between the battery discharge power and the battery discharge current.

[0075] In one implementation, the second prediction module is configured to:

[0076] Based on the driving power demand, transmission system efficiency, and motor efficiency, the driving power consumption of the vehicle is calculated.

[0077] In one implementation, the second prediction module is configured to:

[0078] Calculate the sum of the power consumed by the drive and the power consumed by the battery thermal management;

[0079] Based on the sum and the battery discharge efficiency, the battery discharge power is calculated.

[0080] In one implementation, the second prediction module is configured to:

[0081] The discharge current of the battery is calculated based on the battery discharge power and the battery voltage.

[0082] A third aspect of this disclosure provides a computer device, the device comprising:

[0083] Memory;

[0084] Processor; and

[0085] A computer program, wherein the computer program is stored in memory and configured to be executed by a processor to implement the method described in the first aspect above.

[0086] A fourth aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the first aspect above.

[0087] A fifth aspect of this disclosure provides a vehicle including the aforementioned computer equipment.

[0088] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0089] The battery temperature prediction method, apparatus, device, medium, and vehicle provided in this disclosure, based on ambient temperature, the current water temperature of the battery cooling circuit in the vehicle, the target water temperature of the battery cooling circuit, and the current battery temperature, can accurately predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, as well as the power consumed by battery thermal management from the current moment to a future moment; based on the current battery temperature, the power consumed by battery thermal management, and the pre-obtained driving power demand of the vehicle, it can accurately predict the heat generated by the battery; based on the heat generated by the battery, the heat exchange, and the current battery temperature, it can predict the battery temperature at a future moment, fully considering the influence of factors such as the heat exchange between the fluid in the battery cooling circuit and the battery, the power consumed by battery thermal management, and the battery's self-generated heat on the battery temperature, thus improving the accuracy of battery temperature prediction. Attached Figure Description

[0090] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0091] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0092] Figure 1 This is a schematic diagram of a battery temperature control scenario provided in an embodiment of this disclosure;

[0093] Figure 2 This is a flowchart of a battery temperature prediction method provided in an embodiment of this disclosure;

[0094] Figure 3 This is a flowchart of a method for predicting heat exchange capacity and power consumption for battery thermal management, provided in an embodiment of this disclosure.

[0095] Figure 4 This is a flowchart of a model training method provided in an embodiment of this disclosure;

[0096] Figure 5 This is a schematic diagram of a battery equivalent circuit based on internal resistance provided in an embodiment of this disclosure;

[0097] Figure 6 This is a schematic diagram of a battery temperature prediction architecture provided in an embodiment of this disclosure;

[0098] Figure 7 This is a schematic diagram of the structure of a battery temperature prediction device provided in an embodiment of this disclosure;

[0099] Figure 8 A schematic diagram of the structure of a computer device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0100] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0101] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0102] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0103] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0104] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0105] In practice, the vehicle's thermal management system's control strategies and the battery's self-generated heat during driving both affect battery temperature. Therefore, predicting battery temperature requires considering both the vehicle's thermal management system's influence on battery temperature (i.e., simulating the thermal management system's control strategies) and the battery's self-generated heat during driving (i.e., simulating the battery's thermal response). However, simultaneously modeling both "strategy + response" is challenging due to the difficulty in designing the model structure and modeling. Consequently, existing technologies for predicting vehicle battery temperature typically set the influence of the vehicle's thermal management system on battery temperature and the battery's self-generated heat as fixed constants, only considering future vehicle operating conditions such as distance to charging stations, vehicle speed, and estimated arrival time as variables. However, in reality, the influence of the vehicle's thermal management system on battery temperature and the battery's self-generated heat vary under different vehicle operating conditions. Therefore, setting these constants leads to inaccurate temperature predictions.

[0106] To address the problems existing in related technologies, this disclosure provides a battery temperature prediction scheme. This scheme can accurately predict the impact of the vehicle thermal management system and the battery's self-generated heat on the battery temperature, avoiding the problem of inaccurate battery temperature prediction caused by setting the impact of the vehicle thermal management system on the battery temperature and the battery's self-generated heat as fixed constants, thus improving the accuracy of battery temperature prediction.

[0107] Example, Figure 1 This is a schematic diagram of a battery temperature control scenario provided by an embodiment of this disclosure. Figure 1 As shown, the target battery temperature yr This can be understood as the target battery temperature to be reached at a future moment (e.g., the next moment). The target battery temperature can be obtained based on a preset battery temperature control strategy. For example, based on the preset control strategy, the expected time it takes for the vehicle to reach the charging station can be used to determine the temperature the battery should reach at the future moment, thus ensuring that the vehicle reaches the corresponding temperature when it arrives at the charging station. Of course, this is only an example of a method for determining the target battery temperature and not the only one.

[0108] Figure 1 The controller in this context can be understood as a component on the vehicle equipped with computing and processing capabilities. The controller includes an optimization solver, a battery temperature prediction model, an objective function, and constraints. The constraints are used to constrain the optimization solver, such as limiting the maximum and / or minimum output heating power. The optimization solver outputs multiple heating power values ​​based on the constraints and the target battery temperature. The battery temperature prediction model implements the battery temperature prediction method provided in this embodiment, predicting the battery temperature at future times based on the water temperature of the battery cooling circuit (or battery circuit) corresponding to the heating power values ​​output by the optimization solver. The objective function calculates the predicted future battery temperature and the target battery temperature y. r The deviation value is used to optimize the output heating power value of the solver based on the deviation value, until the temperature value predicted by the battery temperature prediction model and the target battery temperature y are obtained. r If the deviation value is less than a preset threshold, the optimal heating power value is obtained. Then, based on the preset correspondence between heating power and battery cooling circuit water temperature, the controller determines the battery cooling circuit water temperature corresponding to the optimal heating power value as the target battery cooling circuit water temperature u. Figure 1 The target water temperature u in the battery cooling circuit is simply referred to as the battery circuit target water temperature and is output. The battery cooling circuit can be understood as a circuit containing fluid (such as coolant), which controls the battery temperature through heat exchange with the battery. The battery cooling circuit water temperature can be understood as the temperature of the fluid in the battery cooling circuit.

[0109] exist Figure 1 In this process, the controlled objects include the thermal management system and the power battery (hereinafter referred to as the battery). After obtaining the target water temperature u of the battery cooling circuit, the controlled objects control the water temperature of the battery cooling circuit through the thermal management system to ensure that the water temperature of the battery cooling circuit reaches the target water temperature u. This allows the battery to reach a corresponding battery temperature y, such as the target battery temperature y, through heat exchange between the fluid in the battery cooling circuit and the battery. r , or related to the battery target temperature y r The temperature deviation is less than the preset threshold.

[0110] pass Figure 1The control scheme shown can control the battery to reach the appropriate temperature at the appropriate time, ensuring that the vehicle reaches the optimal temperature when it arrives at the charging station or designated charging location, thereby shortening the charging time and reducing the risk of lithium plating in the battery.

[0111] The following describes the battery temperature prediction method provided in this disclosure, using exemplary embodiments as examples. Figure 1 The method employed by the battery temperature prediction model mentioned herein shall be explained.

[0112] Example, Figure 2 This is a flowchart illustrating a battery temperature prediction method provided in an embodiment of this disclosure. The method can be executed by a computer device, such as... Figure 1 The controller or other device with computing and processing capabilities (such as a cloud server) mentioned above. For example, if the navigation destination is a charging station, or if the destination can be predicted to be a charging station based on the current direction of travel and the user's historical driving habits, the computer device can perform the following steps 201-203 to predict the battery temperature.

[0113] Step 201: Based on the ambient temperature, the current water temperature of the battery cooling circuit in the vehicle, the target water temperature of the battery cooling circuit, and the current battery temperature, predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, as well as the power consumed by battery thermal management from the current moment to the future moment.

[0114] In this embodiment of the disclosure, the ambient temperature can be understood as the ambient temperature of the road segment or location where the vehicle is located. This temperature can be collected by a temperature sensor mounted on the vehicle or obtained from a network, such as from a weather website via a vehicle-to-everything (V2X) network. In this embodiment of the disclosure, the ambient temperature can be real-time or fixed by default. For example, it can be understood that the ambient temperature of the vehicle remains constant on the same road segment, that is, the temperature collected when entering the road segment is taken as the ambient temperature of the vehicle on the entire road segment.

[0115] The current water temperature of the battery cooling circuit (or simply the battery circuit) can be obtained by a preset sensor or by a preset prediction model. No specific limitation is made in this embodiment.

[0116] For the target water temperature of the battery cooling circuit, please refer to [reference needed]. Figure 1 The optimal heating power value obtained from the optimization solver and the corresponding relationship between the heating power and the water temperature of the battery cooling circuit are determined.

[0117] The current battery temperature can be obtained by a preset battery temperature sensor, or it can be predicted from a previous time using the battery temperature prediction method provided in this embodiment.

[0118] For example, in one embodiment of this disclosure, the ambient temperature, the current water temperature of the battery cooling circuit in the vehicle, the target water temperature of the battery cooling circuit, and the current battery temperature can be input into a preset first prediction model. The first prediction model can then predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, as well as the power consumed by battery thermal management from the current moment to a future moment.

[0119] The first prediction model can consist of one or more models. For example, in one implementation, a data-driven model, such as a machine learning model or a neural network model, can be pre-trained. By inputting the ambient temperature, the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the current battery temperature into the data-driven model, the model simultaneously outputs the heat exchange between the fluid in the battery cooling circuit and the battery, as well as the power consumed by battery thermal management. Alternatively, in another implementation, the heat exchange between the fluid in the battery cooling circuit and the battery, as well as the power consumed by battery thermal management, can also be predicted separately by different model groups included in the first prediction model. Each model group can consist of one or more models, and the types of models within the same model group can be the same or different. The types of models included in the model group include data-driven models and / or physical models. For example, Figure 3 This is a flowchart illustrating a method for predicting heat exchange capacity and power consumption in battery thermal management, as provided in an embodiment of this disclosure. Figure 3 As shown, in an exemplary embodiment, the first prediction model referred to in this disclosure may include a first model group and a second model group, and the method for predicting heat exchange capacity and power consumption of battery thermal management based on the first model group and the second model group may include steps 301 and 302.

[0120] Step 301: Input the current battery temperature, the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature into the first model group. The heat exchange between the fluid in the battery cooling circuit and the battery, as well as the water temperature of the battery cooling circuit at future times, are predicted by the first model group.

[0121] For example, in this embodiment of the disclosure, the first model group may consist of one or more models. For instance, in one embodiment, the first model group may consist of a pre-trained neural network model. The inputs of this neural network are the current battery temperature, the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature. The outputs are the heat exchange between the fluid in the battery cooling circuit and the battery, and the water temperature of the battery cooling circuit at a future time. Alternatively, in another embodiment, the first model group may include a battery cooling circuit water temperature prediction model and a battery heat exchange prediction model. The battery cooling circuit water temperature prediction model can be understood as a data-driven model, such as a deep neural network model (DNN) or a limit gradient boosting model (XGBoost). The inputs of the battery cooling circuit water temperature prediction model include: the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature; the output is the water temperature of the battery cooling circuit at a future time (e.g., the next time). The discrete-time form of the battery cooling circuit water temperature prediction model can be expressed as:

[0122] T batt,loop (t+1)=BLT(T tar (t),T batt,loop (t),T amb (t))

[0123] Among them, T batt,loop (t+1) represents the water temperature at time t+1 in the battery cooling circuit; BLT(·) represents the water temperature prediction model for the battery cooling circuit; T tar (t) is the target water temperature of the battery cooling circuit; T batt,loop (t) represents the current water temperature in the battery cooling circuit; T amb (t) represents the ambient temperature. For example, Figure 4 This disclosure provides a flowchart of a model training method. In one embodiment of this disclosure, a battery cooling circuit water temperature prediction model can be trained as follows: Figure 4 The model is trained using the methods shown in steps S31-S34. For specific training methods, please refer to relevant techniques; they will not be elaborated upon here. It should be noted that when using… Figure 4 When training the battery cooling circuit water temperature prediction model using the method shown, large datasets of tens of millions or more can be used to ensure the model's accuracy.

[0124] In other words, in one embodiment of this disclosure, the water temperature of the battery cooling circuit at a future time can be predicted based on the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature.

[0125] In one feasible implementation, the battery heat exchange prediction model can be specifically a physical model that predicts the heat exchange between the fluid in the battery cooling circuit and the battery based on the physical relationship between the battery's heat exchange capacity, the current water temperature of the battery cooling circuit, and the current battery temperature. The current water temperature of the battery cooling circuit and the current battery temperature can be input into the battery heat exchange prediction model to predict the heat exchange between the fluid in the battery cooling circuit and the battery.

[0126] For example, in one instance, the battery heat transfer prediction model described in this disclosure embodiment can be specifically a battery heat transfer model based on Newton's law of cooling, and the physical relationship on which this battery heat transfer model is based can be expressed as:

[0127] Q c (t)=h·A·[T batt,loop (t)-T batt (t)]

[0128] Among them, Q c (t) represents the heat exchange between the fluid and the battery in the battery cooling circuit at the current moment; h is the convective heat transfer coefficient; A is the heat exchange area between the battery and the fluid; T batt,loop (t) represents the current water temperature in the battery cooling circuit, T batt,loop (t) can be the prediction obtained from the battery cooling circuit water temperature prediction model at the previous moment; T batt (t) represents the current battery temperature. In other words, in one embodiment, the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment can be predicted based on the current water temperature of the battery cooling circuit and the current battery temperature. Specifically, the prediction method may include calculating the difference between the current water temperature of the battery cooling circuit and the current battery temperature, and calculating the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment based on the difference.

[0129] By using a battery cooling circuit water temperature prediction model to predict the future water temperature of the battery cooling circuit, dynamic prediction of the battery cooling circuit water temperature can be achieved, providing accurate data for battery temperature prediction. Similarly, by using a battery heat exchange prediction model to predict the heat exchange between the fluid and the battery in the battery cooling circuit, and specifically as a physical model, dynamic prediction of both the battery cooling circuit water temperature and battery heat exchange can be achieved, thus providing accurate data for battery temperature prediction. Furthermore, the physical model offers better interpretability and facilitates backtracking. When the predicted battery temperature value deviates significantly or an error occurs, the physical model can be used to trace back to the cause and location of the error or the large deviation in the predicted value. Moreover, the physical model can address the problems of overfitting and outliers that data-driven models are prone to.

[0130] Step 302: Input the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit into the second model group, and use the second model group to predict the power consumed by battery thermal management.

[0131] In one embodiment, the second model group referred to in this disclosure can be a data-driven model, such as a neural network model. Alternatively, in another embodiment, the second model group can be specifically defined as a physical model. This physical model is used to predict the energy consumed by battery thermal management based on the physical relationship between the energy consumed by battery thermal management and the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit. It also predicts the power consumed by battery thermal management based on the physical relationship between the energy consumed by battery thermal management and the power consumed by battery thermal management. In other words, in one embodiment, the energy consumed by battery thermal management from the current time to a future time can be predicted based on the physical relationship between the energy consumed by battery thermal management and the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit, and the power consumed by battery thermal management can be predicted based on the physical relationship between the energy consumed by battery thermal management and the power consumed by battery thermal management.

[0132] For example, since the temperature changes in the battery cooling circuit are all for preheating the battery, the heat absorbed by the fluid in the battery cooling circuit can be considered as the energy consumed by battery thermal management. Therefore, according to the heat balance equation, the energy consumed by battery thermal management can be obtained. That is, based on the current water temperature and the future water temperature of the battery cooling circuit, the temperature change of the battery cooling circuit from the current time to the future time can be determined. Based on the temperature change, the energy consumed by battery thermal management can be calculated, and its expression can be expressed as:

[0133] E cons (t)=C coolant ·m batt,loop ·(T batt,loop (t+1)-T batt,loop (t))

[0134] Among them, E cons (t) represents the energy consumed by battery thermal management; C coolant It is the specific heat capacity of the fluid in the battery cooling circuit; m batt,loop It is the total mass of the fluid in the battery cooling circuit; T batt,loop (t+1) is the water temperature at any given moment in the battery cooling circuit; T batt,loop (t) represents the current water temperature in the battery cooling circuit.

[0135] The physical relationship between the energy consumed by battery thermal management and the power consumed by battery thermal management can be expressed as:

[0136]

[0137] Among them, P TMS (t) is the power consumed by battery thermal management; E cons (t) represents the energy consumed by battery thermal management; step is the time step, which is a constant; η trans The efficiency of the thermal management system in transferring heat to the battery cooling circuit is constant. That is, in one implementation, the power consumed by the battery thermal management can be calculated based on the energy consumed by the battery thermal management, a preset time step, and the efficiency of the thermal management system in transferring heat to the battery cooling circuit.

[0138] By specifying the second model group as a physical model, battery thermal management consumption becomes more interpretable and adaptable. Furthermore, by inputting the future water temperature of the battery cooling circuit and its current water temperature into the second model group, the power consumption for battery thermal management can be predicted, improving the accuracy of thermal management consumption prediction. This provides dynamic and accurate data for battery temperature prediction, thus enhancing the accuracy of battery temperature forecasting.

[0139] See Figure 3 In one embodiment of this disclosure, the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, and the future water temperature of the battery cooling circuit, can be predicted based on the current battery temperature, the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature. Then, based on the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit, the power consumed by battery thermal management can be predicted. This provides dynamic and accurate data for battery temperature prediction, improving the accuracy of battery temperature prediction.

[0140] Step 202: Based on the current battery temperature, the power consumed by battery thermal management, and the pre-obtained driving power demand of the vehicle, predict the heat generated by the battery.

[0141] For example, in one embodiment of this disclosure, the heat generated by the battery can be predicted by a preset second prediction model. That is, the current battery temperature, the power consumed by battery thermal management, and the pre-obtained driving power demand of the vehicle are input into the second prediction model, and the heat generated by the battery is predicted by the second prediction model.

[0142] For example, in one implementation, the second prediction model may consist of one or more models. For instance, in one implementation, the second prediction model may consist of a data-driven model (such as a neural network model), whose inputs are the current battery temperature, the power consumed by battery thermal management, and the pre-obtained driving power demand of the vehicle, and whose output is the heat generated by the battery. Alternatively, the driving power demand of the vehicle may also be predicted by the second prediction model based on the vehicle speed. In this case, the inputs of the second prediction model may be the vehicle speed, the current battery temperature, and the power consumed by battery thermal management, and the output is the heat generated by the battery. In another feasible implementation, the second prediction model may also consist of multiple models, which may be of the same or different types. For example, in one example, the second prediction model may include: a driving power demand prediction model, a battery discharge current estimation model, and a battery heat generation model. At least some of the driving power demand prediction model, battery discharge current estimation model, and battery heat generation model may be physical models. This is exemplified by the driving power demand prediction model, battery discharge current estimation model, and battery heat generation model all being physical models.

[0143] The driving demand power prediction model is used to predict the driving power demand of a vehicle based on the input vehicle speed and the physical relationship between vehicle speed and driving power demand. The vehicle speed can be understood as the vehicle's average speed, which can be calculated based on the length of the road segment traveled and the estimated travel time. It is assumed that the vehicle maintains a constant speed, and the total driving force is equal to the total wheel-side resistance. The total wheel-side resistance F... r Strongly correlated with vehicle speed, the total wheel-side resistance during vehicle operation can be predicted using a speed-based driving resistance model in this embodiment of the disclosure. The physical relationship is as follows:

[0144] F r (t)=DRM(v(t))=k0+k1v(t)+k2v(t) 2

[0145] Where F r This represents the total wheel-side resistance during vehicle operation; DRM(·) represents the preset driving resistance model; v is the vehicle speed; k0, k1, and k2 are the resistance coefficients of the model constant term, the first-order vehicle speed term, and the second-order vehicle speed term, respectively. The resistance coefficient vector {k0, k1, k2} can be obtained from experimental data using a numerical method based on optimization calculations. The numerical method based on optimization calculations can be found in relevant technologies and will not be elaborated here. Therefore, the overall vehicle driving force F... t It can be represented as:

[0146] F t (t)=F r(t)=k0+k1v(t)+k2v(t) 2

[0147] Therefore, the driving power required by the vehicle in the subsequent journey can be expressed as:

[0148] P req,drv (t)=F t (t)·v(t)

[0149] Right now

[0150] P req,drv (t)=k0v(t)+k1v(t) 2 +k2v(t) 3

[0151] In the formula P req,drv This refers to the driving power required; it should be noted that the embodiments of this disclosure assume that the actual driving power in the subsequent stroke can reach the driving power required.

[0152] The battery discharge current estimation model is used to estimate the battery's discharge power based on the drive demand power and the power consumed by battery thermal management, and then to estimate the discharge current based on the battery's discharge power. The battery's discharge power can be calculated using the following expression:

[0153]

[0154] Among them, P batt,dis It is the battery discharge power; P batt,drv It is the power consumed by the drive; P TMS This is the power consumed by battery thermal management; P req,drv It is the driving power demand; η dis η i η motor These are the battery discharge efficiency, transmission system efficiency, and motor efficiency, all of which are constants. In one implementation, the vehicle's drive power consumption can be calculated based on the drive demand power, transmission system efficiency, and motor efficiency. Then, the sum of the drive power consumption and the power consumed by battery thermal management is calculated. Based on this sum and the battery discharge efficiency, the battery discharge power is calculated. Furthermore, the battery discharge current can be calculated from the battery discharge power.

[0155]

[0156] Among them, I batt It is the battery's discharge current; P batt,dis It is the battery's discharge power; V batt It is the battery voltage.

[0157] In other words, in this embodiment of the disclosure, the battery discharge current can be predicted based on the driving power demand and the power consumed by battery thermal management. Specifically, the driving power consumption of the vehicle can be determined based on the physical relationship between the driving power demand and the driving power consumption, the battery discharge power can be determined based on the physical relationship between the battery discharge power, the driving power consumption, and the power consumed by battery thermal management, and the battery discharge current can be determined based on the physical relationship between the battery discharge power and the battery discharge current.

[0158] In this embodiment of the disclosure, a battery heat generation model is used to predict the heat generated by the battery based on the battery's discharge current and current battery temperature. For example, Figure 5 This is a schematic diagram of a battery equivalent circuit based on internal resistance provided in an embodiment of this disclosure. In one implementation, this embodiment of the disclosure can be based on... Figure 5 The equivalent circuit shown, based on the Bernardi heat generation rate model of the power battery, yields the battery's heat generation rate:

[0159]

[0160] In the formula Q p It is the heat generated by the battery; I batt It is the battery's discharge current; R p R0 is the polarization resistance, representing the battery's polarization heat; T is the ohmic resistance, representing the battery's Joule heat; batt This is the current battery temperature; U oc It is the open-circuit voltage, which is related to the battery's state of charge (SOC). It represents the temperature entropy coefficient, indicating the heat of a chemical reaction.

[0161] However, based on relevant technologies, if the temperature difference along the battery thickness direction is small or the battery is very thin, the polarization reaction heat of the battery can be ignored, and the temperature entropy coefficient can be considered constant for batteries of the same type. Therefore, based on the Bernardi heat generation rate model mentioned above, the battery heat generation model referred to in the embodiments of this disclosure can be simplified as follows:

[0162] Q p =I batt 2 R0+a1I batt T batt

[0163] In the formula Q p It is the heat generated by the battery; I batt R0 is the battery's discharge current; R0 is the ohmic internal resistance, a constant; a1 is a constant representing the temperature entropy coefficient, determined by the battery type; T0 batt This is the current temperature of the battery. The discrete-time form of the battery heat generation model can be expressed as:

[0164] Q p (t)=I batt (t) 2 R0+a1I batt (t)T batt (t)

[0165] This embodiment of the invention inputs the vehicle speed, the power consumed by battery thermal management, and the current battery temperature into a second prediction model. Based on the second prediction model, the heat generated by the battery can be accurately predicted, thereby providing accurate data for battery temperature prediction.

[0166] Step 203: Based on the heat generated by the battery, the heat exchange, and the current temperature of the battery, predict the temperature of the battery at future times.

[0167] For example, in one exemplary implementation, the battery temperature at a future time can be predicted using a third prediction model. That is, the heat generated by the battery, the heat exchanged, and the current battery temperature are used as inputs to the third prediction model, which then outputs the battery temperature at the future time.

[0168] In one implementation, the third prediction model can consist of a data-driven model (such as a neural network model). The inputs to this data-driven model are the heat generated by the battery, the heat exchange between the fluid in the battery cooling circuit and the battery, and the current battery temperature. The output is the battery temperature at a future time. In another implementation, the third prediction model can also be specifically a physical model, with the following discrete-time form:

[0169]

[0170] In the formula ΔT batt (t) is the temperature change of the battery; Q c (t) represents the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment; Q p It is the heat generated by the power battery itself; C batt It is the specific heat capacity of the battery; m batt It refers to the quality of the battery; T K T is the coefficient for converting Kelvin to Celsius, and it is a constant; batt (t+1) is the future temperature of the battery; T batt (t) represents the current battery temperature. Where T is the current battery temperature. batt(t) can be the battery temperature prediction method based on the embodiments of this disclosure, predicted at a previous moment. That is, the battery temperature prediction method provided in the embodiments of this disclosure applies a rolling prediction mechanism, using the battery temperature predicted at the previous moment as the input for the battery temperature prediction at the next moment, gradually realizing long-term prediction of the battery temperature (for example, the prediction step size can be selected as 1 second), for precise and optimal control of the battery temperature. The models involved in the embodiments of this disclosure can all be identified using large datasets of tens of millions or more to ensure the accuracy of battery temperature prediction and reduce accumulated errors.

[0171] This embodiment of the disclosure, based on ambient temperature, the current water temperature of the battery cooling circuit in the vehicle, the target water temperature of the battery cooling circuit, and the current battery temperature, can accurately predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, as well as the power consumed by battery thermal management from the current moment to the future moment; based on the current battery temperature, the power consumed by battery thermal management, and the pre-obtained driving power demand of the vehicle, it can accurately predict the heat generated by the battery; based on the heat generated by the battery, the heat exchange, and the current battery temperature, it can predict the battery temperature at the future moment, fully considering the influence of factors such as the heat exchange between the fluid in the battery cooling circuit and the battery, the power consumed by battery thermal management, and the battery's self-generated heat on the battery temperature, thus improving the accuracy of battery temperature prediction.

[0172] Figure 6 This is a schematic diagram of a battery temperature prediction architecture provided in an embodiment of this disclosure, as shown below. Figure 6 As shown, the battery cooling circuit ( Figure 6 The current water temperature of the battery cooling circuit (referred to as the battery circuit in Chinese), the target water temperature of the battery circuit, and the ambient temperature are used as the basis for the battery cooling circuit water temperature prediction model. Figure 6The system takes the following inputs (referred to as the battery circuit water temperature prediction model) and outputs a prediction of the battery circuit water temperature at a future time. The current water temperature of the battery circuit and the current battery temperature are input into the battery heat exchange prediction model, which predicts the heat exchange between the fluid in the battery circuit and the battery. This heat exchange is then used as input to the third prediction model. Simultaneously, the future water temperature of the battery circuit and the current water temperature of the battery circuit, output by the battery circuit water temperature prediction model, are used as inputs to the second model group. The second model group outputs the power consumed by battery thermal management and uses this power as input to the battery discharge current estimation model. Vehicle speed is used as input to the drive demand power prediction model to predict the drive demand power. The drive demand power and the power consumed by battery management are input to the battery discharge current estimation model to obtain the battery discharge current. The battery discharge current and the current battery temperature are input to the battery heat generation model to obtain the heat generated by the battery. The heat generated by the battery, the aforementioned heat exchange, and the current battery temperature are input to the third prediction model, which outputs the battery temperature at a future time.

[0173] Figure 6 The specific implementation method and beneficial effects of the architecture shown can be found in the above method implementation examples, and will not be repeated here.

[0174] Figure 7 This is a schematic diagram of a battery temperature prediction device provided in an embodiment of this disclosure. This device can be exemplarily understood as the computer device or a functional module within a computer device as described in the above embodiments. Figure 7 As shown, the battery temperature prediction device 70 includes:

[0175] The first prediction module 71 is used to predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, and the power consumed by battery thermal management from the current moment to the future moment, based on the ambient temperature, the current water temperature of the battery cooling circuit in the vehicle, the target water temperature of the battery cooling circuit, and the current temperature of the battery.

[0176] The second prediction module 72 is used to predict the heat generated by the battery based on the current temperature of the battery, the power consumed by the battery thermal management, and the pre-obtained driving power demand of the vehicle.

[0177] The third prediction module 73 is used to predict the temperature of the battery at a future time based on the heat generated by the battery, the heat exchange, and the current temperature of the battery.

[0178] In one implementation, the first prediction module is configured to:

[0179] Based on the current battery temperature, the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature, predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, as well as the water temperature of the battery cooling circuit at a future moment.

[0180] Based on the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit, the power consumed by the battery thermal management is predicted.

[0181] In one implementation, the first prediction module is configured to:

[0182] Based on the physical relationship between the energy consumed by battery thermal management and the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit, the energy consumed by battery thermal management from the current time to the future time is predicted.

[0183] Based on the physical relationship between the energy consumed by the battery thermal management and the power consumed by the battery thermal management, the power consumed by the battery thermal management is predicted.

[0184] In one implementation, the first prediction module is configured to:

[0185] Based on the current water temperature and the future water temperature of the battery cooling circuit, determine the temperature change of the battery cooling circuit from the current time to the future time;

[0186] Based on the temperature change, the energy consumed by the battery thermal management is calculated.

[0187] In one implementation, the first prediction module is configured to:

[0188] The power consumed by the battery thermal management is calculated based on the energy consumed by the battery thermal management, the preset time step, and the efficiency of the thermal management system in transferring heat to the battery cooling circuit.

[0189] In one implementation, the first prediction module is configured to:

[0190] Based on the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature, the water temperature of the battery cooling circuit at future times is predicted.

[0191] Based on the current water temperature of the battery cooling circuit and the current battery temperature, predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment.

[0192] In one implementation, the first prediction module is configured to:

[0193] Based on the physical relationship between the battery heat exchange, the current water temperature of the battery cooling circuit, and the current battery temperature, the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment is predicted.

[0194] In one implementation, the first prediction module is configured to:

[0195] Calculate the difference between the current water temperature of the battery cooling circuit and the current temperature of the battery;

[0196] Based on the difference, the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment is calculated.

[0197] In one implementation, the second prediction module is configured to:

[0198] Based on the driving power demand and the power consumed by the battery thermal management, the discharge current of the battery is predicted;

[0199] The heat generated by the battery is predicted based on the battery's discharge current and current temperature.

[0200] In one implementation, the driving power demand is predicted based on the physical relationship between vehicle speed and driving power demand.

[0201] In one implementation, the second prediction module is configured to:

[0202] Based on the physical relationship between driving power demand and driving power consumption, the driving power consumption of the vehicle is predicted.

[0203] The battery discharge power is determined based on the physical relationship between the battery discharge power, the drive power consumption, and the power consumed by the battery thermal management.

[0204] The discharge current of the battery is determined based on the physical relationship between the battery discharge power and the battery discharge current.

[0205] In one implementation, the second prediction module is configured to:

[0206] Based on the driving power demand, transmission system efficiency, and motor efficiency, the driving power consumption of the vehicle is calculated.

[0207] In one implementation, the second prediction module is configured to:

[0208] Calculate the sum of the power consumed by the drive and the power consumed by the battery thermal management;

[0209] Based on the sum and the battery discharge efficiency, the battery discharge power is calculated.

[0210] In one implementation, the second prediction module is configured to:

[0211] The discharge current of the battery is calculated based on the battery discharge power and the battery voltage.

[0212] The battery temperature prediction device provided in this embodiment is similar in execution and beneficial effects to the method embodiments described above, and will not be repeated here.

[0213] Figure 8 A schematic diagram of the structure of a computer device provided in an embodiment of this disclosure is shown.

[0214] In this embodiment of the disclosure, Figure 8 The computer equipment shown can be a server, in-vehicle system, terminal, or server cluster. Specifically, the terminal can include a mobile phone, computer, tablet computer, in-vehicle terminal, or any device capable of being used for... Figures 1-6 Any device, etc., of any embodiment of the method is not limited herein.

[0215] like Figure 8 As shown, the computer device 170 may include a memory 171 storing computer program instructions and a processor 172.

[0216] Specifically, the processor 172 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0217] Memory 171 may include a large-capacity storage for information or instructions. For example, and not limitingly, memory 171 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 171 may include removable or non-removable (or fixed) media. Where appropriate, memory 171 may be internal or external to the integrated gateway device. In a particular embodiment, memory 171 is a non-volatile solid-state memory. In a particular embodiment, memory 171 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable programmable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0218] The processor 172 performs the steps of the method embodiments provided in this disclosure by reading and executing computer program instructions stored in the memory 171.

[0219] In one example, the electronic device may also include a communication interface 173. Wherein, such as Figure 8 As shown, the processor 172, memory 171 and communication interface 173 are connected via a bus and communicate with each other.

[0220] A bus can be hardware, software, or both. For example, and not limited to, a bus can include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, a bus can include one or more buses.

[0221] This disclosure also provides a computer-readable storage medium that can store a computer program, which, when executed by a processor, causes the processor to implement the embodiments of this disclosure. Figures 1-6 The method of any of the embodiments.

[0222] The aforementioned storage medium may, for example, include a memory 171 containing computer program instructions, which can be executed by a processor 172 of an electronic device to perform the methods described in the embodiments of this disclosure. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0223] This disclosure also provides a vehicle that includes the computer equipment described in the above embodiments, which can implement the various processes and effects described in the above embodiments of this disclosure, and will not be elaborated here.

[0224] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0225] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily 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 this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting battery temperature, characterized in that, The method includes: Based on the ambient temperature, the current water temperature of the battery cooling circuit in the vehicle, the target water temperature of the battery cooling circuit, and the current battery temperature, predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, as well as the power consumed by battery thermal management from the current moment to the future moment. Based on the current temperature of the battery, the power consumed by the battery thermal management, and the pre-obtained driving power demand of the vehicle, the heat generated by the battery is predicted; Based on the heat generated by the battery, the heat exchange, and the current temperature of the battery, the temperature of the battery at a future time is predicted.

2. The method according to claim 1, characterized in that, The method, based on ambient temperature, the current water temperature of the battery cooling circuit in the vehicle, the target water temperature of the battery cooling circuit, and the current battery temperature, predicts the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, as well as the power consumed by battery thermal management from the current moment to a future moment, including: Based on the current battery temperature, the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature, predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, as well as the water temperature of the battery cooling circuit at a future moment. Based on the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit, the power consumed by the battery thermal management is predicted.

3. The method according to claim 2, characterized in that, The prediction of the power consumed by the battery thermal management based on the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit includes: Based on the physical relationship between the energy consumed by battery thermal management and the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit, the energy consumed by battery thermal management from the current time to the future time is predicted. Based on the physical relationship between the energy consumed by the battery thermal management and the power consumed by the battery thermal management, the power consumed by the battery thermal management is predicted.

4. The method according to claim 3, characterized in that, The prediction of battery thermal management energy consumption from the current moment to a future moment, based on the physical relationship between the energy consumed by battery thermal management and the future water temperature of the battery cooling circuit and the current water temperature of the battery cooling circuit, includes: Based on the current water temperature and the future water temperature of the battery cooling circuit, determine the temperature change of the battery cooling circuit from the current time to the future time; Based on the temperature change, the energy consumed by the battery thermal management is calculated.

5. The method according to claim 3, characterized in that, The method of predicting the power consumed by battery thermal management based on the physical relationship between the energy consumed by battery thermal management and the power consumed by battery thermal management includes: The power consumed by the battery thermal management is calculated based on the energy consumed by the battery thermal management, the preset time step, and the efficiency of the thermal management system in transferring heat to the battery cooling circuit.

6. The method according to claim 2, characterized in that, The method of predicting the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, and the water temperature of the battery cooling circuit at a future moment, based on the current battery temperature, the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature, includes: Based on the current water temperature of the battery cooling circuit, the target water temperature of the battery cooling circuit, and the ambient temperature, the water temperature of the battery cooling circuit at future times is predicted. Based on the current water temperature of the battery cooling circuit and the current battery temperature, predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment.

7. The method according to claim 6, characterized in that, The method of predicting the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, based on the current water temperature of the battery cooling circuit and the current battery temperature, includes: Based on the physical relationship between the battery heat exchange, the current water temperature of the battery cooling circuit, and the current battery temperature, the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment is predicted.

8. The method according to claim 7, characterized in that, The method of predicting the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, based on the physical relationship between the battery heat exchange, the current water temperature of the battery cooling circuit, and the current battery temperature, includes: Calculate the difference between the current water temperature of the battery cooling circuit and the current temperature of the battery; Based on the difference, the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment is calculated.

9. The method according to claim 1, characterized in that, The method of predicting the heat generated by the battery based on the current temperature of the battery, the power consumed by the battery thermal management, and the pre-obtained driving power demand of the vehicle includes: Based on the driving power demand and the power consumed by the battery thermal management, the discharge current of the battery is predicted; The heat generated by the battery is predicted based on the battery's discharge current and current temperature.

10. The method according to claim 9, characterized in that, The driving power demand is predicted based on the physical relationship between vehicle speed and driving power demand.

11. The method according to claim 9, characterized in that, The method of predicting the battery discharge current based on the drive power demand and the power consumed by battery thermal management includes: Based on the physical relationship between driving power demand and driving power consumption, the driving power consumption of the vehicle is predicted. The battery discharge power is determined based on the physical relationship between the battery discharge power, the drive power consumption, and the power consumed by the battery thermal management. The discharge current of the battery is determined based on the physical relationship between the battery discharge power and the battery discharge current.

12. The method according to claim 11, characterized in that, The prediction of the vehicle's drive power consumption based on the physical relationship between drive power demand and drive power consumption includes: Based on the driving power demand, transmission system efficiency, and motor efficiency, the driving power consumption of the vehicle is calculated.

13. The method according to claim 11, characterized in that, Determining the battery discharge power based on the physical relationship between the battery discharge power, the drive power consumption, and the power consumed by battery thermal management includes: Calculate the sum of the power consumed by the drive and the power consumed by the battery thermal management; Based on the sum and the battery discharge efficiency, the battery discharge power is calculated.

14. The method according to claim 11, characterized in that, Determining the battery discharge current based on the physical relationship between the battery discharge power and the battery discharge current includes: The discharge current of the battery is calculated based on the battery discharge power and the battery voltage.

15. A battery temperature prediction device, characterized in that, include: The first prediction module is used to predict the heat exchange between the fluid in the battery cooling circuit and the battery at the current moment, and the power consumed by battery thermal management from the current moment to a future moment, based on the ambient temperature, the current water temperature of the battery cooling circuit in the vehicle, the target water temperature of the battery cooling circuit, and the current temperature of the battery. The second prediction module is used to predict the heat generated by the battery based on the current temperature of the battery, the power consumed by the battery thermal management, and the pre-obtained driving power demand of the vehicle. The third prediction module is used to predict the temperature of the battery at a future time based on the heat generated by the battery, the heat exchange, and the current temperature of the battery.

16. A computer device, characterized in that, include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in any one of claims 1-14.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-14.

18. A vehicle, characterized in that, Includes the computer device as described in claim 16.

Images