Battery electrothermal control method and battery electrothermal control device

Abstract

The invention provides an electric heating control method of a battery and an electric heating control device of the battery. The method comprises the steps that an electrothermal coupling model is determined, the electrothermal coupling model comprises an electric model and a thermal model which are coupled with each other, the thermal model is used for predicting at least one temperature parameter, and the at least one temperature parameter is in one-to-one correspondence with at least one temperature partition of the battery; the thermal model predicts a temperature parameter corresponding to a target temperature zone based on a battery cell in the target temperature zone, the target temperature zone is any one of at least one temperature zone, and the at least one temperature zone is obtained through division according to the difference of the heat exchange effect between the battery and the external environment; a battery state change path of the battery under the electric heating control path is predicted based on the parameters of the electric heating control path by using an electric heating coupling model, and the battery state change path comprises a change path of at least one temperature parameter; and performing electric heating control on the battery based on the battery state change path of the battery under the electric heating control path. And the calculation amount is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, and in particular to a battery electro-thermal control method and a battery electro-thermal control device. BACKGROUND

[0002] The capability and safety of a power battery are significantly affected by temperature during charging and discharging. The temperature sensitivity of the power battery and the hysteresis of the actual thermal management effect make it important to predict the battery state during charging and discharging of the battery.

[0003] In the related art, with the advent of the AI wave, a large number of engineers hope to use AI algorithms online to predict the state change path of the battery under at least one electro-thermal control path (battery charging and discharging current and thermal management mode) so as to control the electro-thermal of the battery by using the predicted battery state change path. In the related art, the prediction of the battery state is mostly performed by constructing a three-dimensional battery model, and the calculation amount of the three-dimensional battery model prediction is far beyond the hardware computing power of the vehicle, and real-time prediction calculation cannot be performed on the real vehicle. SUMMARY

[0004] Therefore, the embodiments of the present application are dedicated to providing a battery electro-thermal control method and a battery electro-thermal control device. The aspects related to the present application are introduced as follows.

[0005] In a first aspect, the present application provides a battery electro-thermal control method, comprising: determining an electro-thermal coupling model, wherein the electro-thermal coupling model comprises an electric model and a thermal model coupled with each other, the thermal model is used to predict at least one temperature parameter, the at least one temperature parameter corresponds to at least one temperature partition of a battery in a one-to-one manner, the thermal model predicts the temperature parameter corresponding to a target temperature partition based on a battery cell in the target temperature partition, the target temperature partition is any one of the at least one temperature partition, and the at least one temperature partition is obtained by dividing according to the difference in heat exchange effect between the battery and the external environment; predicting a battery state change path of the battery under an electro-thermal control path by using the electro-thermal coupling model based on parameters of the electro-thermal control path, wherein the battery state change path comprises a change path of the at least one temperature parameter; and performing electro-thermal control on the battery based on the battery state change path of the battery under the electro-thermal control path.

[0006] In one embodiment, the electrothermal coupling model is constructed as follows: determining at least one temperature parameter corresponding to at least one temperature zone of the battery at the current moment; constructing a target model based on the at least one temperature parameter corresponding to at least one temperature zone of the battery at the current moment to predict at least one temperature parameter corresponding to at least one temperature zone of the battery at the next moment, and determining a thermal model based on the target model; constructing an electrical model based on battery charge and discharge data at multiple temperatures and multiple states of charge; and coupling the thermal model and the electrical model to generate the electrothermal coupling model.

[0007] In one embodiment, the at least one temperature zone includes at least one of the following: a first temperature zone, a second temperature zone, and a third temperature zone, wherein the heat exchange effect between the first temperature zone and the external environment is better than the heat exchange effect between the second temperature zone and the third temperature zone and the external environment, and the heat exchange effect between the second temperature zone and the external environment is better than the heat exchange effect between the third temperature zone and the external environment; the step of constructing a target model based on at least one temperature parameter corresponding to at least one temperature zone of the battery at the current moment, and predicting at least one temperature parameter corresponding to at least one temperature zone of the battery at the next moment, includes: constructing a first target sub-model based on the temperature parameter corresponding to the first temperature zone of the battery at the current moment, and calculating the temperature parameter corresponding to the first temperature zone of the battery at the next moment; constructing a second target sub-model based on the temperature parameter corresponding to the second temperature zone of the battery at the current moment, and calculating the temperature parameter corresponding to the second temperature zone of the battery at the next moment; constructing a third target sub-model based on the temperature parameter corresponding to the third temperature zone of the battery at the current moment, and calculating the temperature parameter corresponding to the third temperature zone of the battery at the next moment; and determining a target model based on at least one of the first target sub-model, the second target sub-model, and the third target sub-model.

[0008] In one embodiment, the battery includes a plurality of cells, each cell including at least one region with inconsistent temperatures; determining at least one temperature parameter corresponding to at least one temperature zone of the battery at the current moment includes: determining a first cell located in at least one temperature zone of the battery, and determining at least one temperature parameter corresponding to at least one region of the first cell at the current moment; constructing a target model based on at least one temperature parameter corresponding to at least one temperature zone of the battery at the current moment to predict at least one temperature parameter corresponding to at least one temperature zone of the battery at the next moment includes: constructing a target model based on at least one temperature parameter corresponding to at least one region of the first cell at the current moment to calculate at least one temperature parameter corresponding to at least one region of the first cell at the next moment.

[0009] In one embodiment, the mutual coupling of the thermal model and the electrical model to generate an electrothermal coupled model includes: determining the model output of the electrothermal coupled model; and combining the thermal model and the electrical model to generate the electrothermal coupled model based on the model output, the signal transmission process and model interaction process between the thermal model and the electrical model, wherein the output of the thermal model includes the input of the electrical model, and the output of the electrical model includes the input of the thermal model.

[0010] In one embodiment, the mutual coupling of the thermal model and the electrical model to generate an electrothermal coupled model includes: mutually coupling the thermal model and the electrical model to generate an initial electrothermal coupled model, wherein the initial electrothermal coupled model is invoked to achieve prediction of a single sub-time domain in the target time domain; and building an electrothermal coupled model based on the initial electrothermal coupled model through an inner loop mode, wherein the electrothermal coupled model is invoked to achieve prediction of the target time domain.

[0011] In one embodiment, the step of building an electrothermal coupling model based on the initial electrothermal coupling model through an inner loop mode includes: identifying time-domain related variables in the initial electrothermal coupling model; modifying the time-domain related variables based on the inner loop mode to generate a reference electrothermal coupling model; identifying input parameters in the reference electrothermal coupling model; and modifying the input parameters to target derived parameters based on the inner loop mode to generate an electrothermal coupling model.

[0012] In one embodiment, the time-domain dependent variables include clock parameters and an integration module; the step of changing the time-domain dependent variables based on the inner loop mode to generate a reference electrothermal coupling model includes: determining the number of inner loop iterations and the duration of a single iteration; replacing the clock parameters based on the product of the number of inner loop iterations and the duration of a single iteration; determining the single iteration calculation increment; and replacing the integration module based on the single iteration calculation increment to generate the reference electrothermal coupling model.

[0013] In one embodiment, the step of building an electrothermal coupling model based on the initial electrothermal coupling model using an internal loop mode includes: constructing an internal loop system within the initial electrothermal coupling model to generate an electrothermal coupling model; the step of using the electrothermal coupling model to predict the battery state change path under the electrothermal control path based on the parameters of the electrothermal control path includes: using the parameters of the electrothermal control path as the input of the current iteration of the electrothermal coupling model, using the electrothermal coupling model to generate the first battery state under the electrothermal control path; and using the internal loop system to accumulate the input and output of the electrothermal coupling model in the current iteration to generate the first battery state under the current iteration. The battery state under the electrothermal control path is accumulated; the accumulated battery state is used as the input for the next iteration, and the second battery state under the electrothermal coupling model is generated using the electrothermal coupling model; the accumulated battery state and the second battery state are accumulated using the internal circulation system to generate a reference battery state, the input of the current iteration of the electrothermal coupling model is updated using the reference battery state, and the step of generating the first battery state under the electrothermal control path using the electrothermal coupling model is returned to be executed until the iteration stop condition is reached, and the battery state change path under the electrothermal control path is determined based on the accumulated battery state generated in each iteration.

[0014] In one embodiment, the electrothermal control path includes multiple paths; the step of performing electrothermal control on the battery based on the battery state change path under the electrothermal control path includes: determining a dimensional score for each battery state change path in the dimensions of battery temperature and / or battery state of charge based on the battery state change path under each electrothermal control path; determining a target electrothermal control path from the multiple electrothermal control paths based on the dimensional score of each battery state change path; and performing electrothermal control on the battery based on the target electrothermal control path.

[0015] Secondly, embodiments of this application provide an electrothermal control device for a battery. The device includes: a determining module configured to determine an electrothermal coupling model, wherein the electrothermal coupling model includes a coupled electrical model and a thermal model, the thermal model being used to predict at least one temperature parameter, the at least one temperature parameter corresponding one-to-one with at least one temperature zone of the battery, the thermal model predicting the temperature parameter corresponding to the target temperature zone based on the cells in the target temperature zone, the target temperature zone being any one of the at least one temperature zone, the at least one temperature zone being divided according to the difference in heat exchange effect between the battery and the external environment; a predicting module configured to use the electrothermal coupling model, based on the parameters of the electrothermal control path, to predict the battery state change path under the electrothermal control path, wherein the battery state change path includes the change path of the at least one temperature parameter; and a control module configured to perform electrothermal control on the battery based on the battery state change path under the electrothermal control path.

[0016] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute, when executing the computer program, a battery electrothermal control method of the first aspect and any embodiment thereof described above.

[0017] Fourthly, this application provides a computer-readable storage medium storing program code for computer execution, the program code including a battery electrothermal control method for performing the first aspect and any embodiment of the first aspect.

[0018] Fifthly, embodiments of this application provide a computer program including instructions for performing the electrothermal control method of the battery in the first aspect and any embodiment of the first aspect.

[0019] In this application, an electrothermal coupling model is defined, which includes a thermal model for predicting at least one temperature zone of the battery and at least one temperature parameter that corresponds one-to-one. This allows for temperature parameter calculations to be performed based on the cells in the corresponding temperature zone, eliminating the need to use all cells in the battery for each temperature parameter, thus significantly reducing the computational load. Furthermore, the at least one temperature zone is determined based on the difference in heat exchange effects between the battery and the external environment, ensuring the accuracy of temperature calculations at key locations. This enables the electrothermal coupling model to be implemented for prediction in a real vehicle. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of a battery electrothermal control method provided in an embodiment of this application.

[0021] Figure 2 This is a schematic diagram showing the temperature zones in a battery according to an embodiment of the present application's method for controlling the electric heating of a battery.

[0022] Figure 3 This is a schematic diagram of the temperature display of a single-sided cold plate cell in a battery electrothermal control method provided in an embodiment of this application.

[0023] Figure 4 This is a schematic diagram of battery charging segment data for a battery electrothermal control method provided in an embodiment of this application.

[0024] Figure 5 This is a schematic diagram of the electrothermal coupling model in a battery electrothermal control method provided in this application embodiment.

[0025] Figure 6 This is a flowchart illustrating the process of a battery electrothermal control method provided in an embodiment of this application.

[0026] Figure 7 This is a schematic diagram of the structure of a battery heating control device provided in an embodiment of this application.

[0027] Figure 8 This is a schematic structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0030] The terms "first" and "second," etc., used in the specification and claims of this application are used to distinguish different objects, not to describe a specific order of objects. For example, "first target object" and "second target object," etc., are used to distinguish different target objects, not to describe a specific order of target objects.

[0031] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0032] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0033] The capacity and safety of power batteries are significantly affected by temperature during charging and discharging. During charging, at low temperatures, the battery impedance increases due to factors such as decreased electrolyte ionic conductivity, resulting in a substantial reduction in the charging current the battery can handle. At high temperatures, battery side reactions accelerate, potentially leading to safety issues such as thermal runaway. Similarly, during discharging, the increased impedance causes a significant decrease in the battery's discharge power. Furthermore, in the field of new energy vehicles, kinetic energy recovery is an important method for reducing energy consumption. During vehicle operation, battery discharging and charging (recharging caused by kinetic energy recovery) typically occur simultaneously.

[0034] The temperature sensitivity of power batteries and the lag in the actual thermal management effect make it very important to predict the battery state during charging and discharging.

[0035] Most related technical solutions still employ calibration methods. This involves using a matrix boundary table provided by the battery supplier, showing the charging current and discharging power at different states of charge (SOC) and temperatures, to calibrate suitable charging and discharging currents and battery thermal management strategies for each SOC and temperature. This approach struggles to cover all scenarios and typically only addresses a limited number of acceptance conditions.

[0036] With the advent of the artificial intelligence (AI) wave, many engineers hope to use AI algorithms online to predict the state change path of batteries under electrothermal control paths (battery charging and discharging current and thermal management methods), so as to control the battery using the predicted state change path. Therefore, a model capable of predicting battery state online is the core of the entire algorithm framework. This model needs to have efficient computing power while ensuring prediction accuracy to meet the real-time requirements of automotive applications, and support the prediction of future states.

[0037] In related technologies, there are many modeling methods. For example, for cell-level modeling, Comsol's multiphysics simulation often uses a 1D+3D modeling approach, combining a one-dimensional electrochemical model with a three-dimensional single-cell model. This method requires inputting a large number of electrochemical parameters (such as membrane porosity, ion diffusion coefficient, conductivity, etc.), and the calculation accuracy is highly dependent on the accuracy of these parameters. Due to the difficulty in obtaining parameters and the enormous computational load, this type of model is mainly used for cell mechanism research and is difficult to extend to the entire battery pack level. Another example is battery-level modeling, which mainly uses finite element thermal simulation to establish a complete geometric model including components such as the lower tray, upper cover, cooling plate, and thermally conductive adhesive, and then performs mesh generation and physics field solving. The accuracy of this method is closely related to the model parameters, the number and quality of the mesh, and is mainly used for structural optimization and mechanism verification in the laboratory stage, making it difficult to meet the real-time control requirements of actual vehicles. Both of these modeling methods generally suffer from the following problems: First, the parameter requirements are large and difficult to obtain; second, the computational load is enormous, far exceeding the computing power of automotive hardware; third, the resulting model structure is complex and difficult to convert into embedded code for automotive deployment. Furthermore, the models obtained from the above modeling directly output the temperature of all cells in the battery, and then perform unified post-processing to obtain the required temperature parameters (e.g., the highest battery temperature, the lowest battery temperature, and the average battery temperature), which further increases the computational load for predicting temperature parameters.

[0038] To address the aforementioned technical issues, this application defines an electrothermal coupling model. This model includes a thermal model used to predict at least one temperature zone of the battery and at least one temperature parameter, ensuring that temperature parameter calculations can be performed based on the cells within the corresponding temperature zone, eliminating the need to calculate using all cells in the battery for each temperature parameter. This significantly reduces the computational load. Furthermore, the at least one temperature zone is defined based on the difference in heat exchange between the battery and the external environment, guaranteeing the accuracy of temperature calculations at key locations. This allows the electrothermal coupling model to be implemented in a real vehicle for prediction.

[0039] The following combination Figure 1 This application will be described in detail.

[0040] Figure 1 This is a schematic flowchart of a battery electrothermal control method provided in an embodiment of this application, in order to solve the above-mentioned technical problems. Figure 1 The schematic flowchart of the battery electrothermal control method shown includes steps S110 to S130.

[0041] Step S110: Determine the electrothermal coupling model, wherein the electrothermal coupling model includes an electrothermal model and a thermal model that are coupled to each other. The thermal model is used to predict at least one temperature parameter. The at least one temperature parameter corresponds one-to-one with at least one temperature zone of the battery. The thermal model predicts the temperature parameter corresponding to the target temperature zone based on the cells in the target temperature zone. The target temperature zone is any one of the at least one temperature zone. The at least one temperature zone is divided according to the difference in heat exchange effect between the battery and the external environment.

[0042] An electrothermal coupling model consists of mutually coupled electrical and thermal models. Mutual coupling can be understood as the output of model A being the input of model B, and vice versa. In other words, in an electrothermal coupling model, the output of the electrical model is the input of the thermal model, and vice versa.

[0043] At least one temperature parameter is a parameter index characterizing the battery temperature. For example, the temperature parameter may include one or more of the following: maximum battery temperature (Tmax), average battery temperature (Tavg), and minimum battery temperature (Tmin).

[0044] A thermal model is used to predict at least one temperature parameter. This can be understood as follows: by inputting the corresponding input parameters into the thermal model, at least one temperature parameter is output by the thermal model. Here, "prediction" means that the thermal model outputs at least one temperature parameter for a future time period. For example, the thermal model outputs at least one temperature parameter for the next time period or at least one temperature parameter for the next time cycle.

[0045] At least one temperature parameter corresponds one-to-one with at least one temperature zone of the battery. In other words, the calculation of the first temperature parameter is performed using the cells within the corresponding temperature zone. This avoids the need for prediction calculations based on all cells in the battery when calculating each temperature parameter, thus reducing the computational load.

[0046] In some embodiments, the thermal model predicts the temperature parameters corresponding to the target temperature zone based on the cells within the target temperature zone. This can be understood as the thermal model treating multiple cells within the target temperature zone as uniform cells and predicting the temperature parameters corresponding to the target temperature zone.

[0047] At least one temperature zone is defined based on the difference in heat exchange efficiency between the battery and the external environment. For example, temperature zone 1 has a poorer heat exchange efficiency with the external environment than other temperature zones. Temperature zone 1 could be the core layer of the battery. Alternatively, temperature zone 2 has a better heat exchange efficiency with the external environment than other temperature zones. Temperature zone 2 could be the outer layer of the battery. And finally, temperature zone 3 has a heat exchange efficiency that falls somewhere in between the temperature zones. Temperature zone 3 could be the middle layer of the battery.

[0048] For example, see Figure 2 , Figure 2 This is a schematic diagram showing the temperature zones in a battery according to an embodiment of the present application's method for controlling the electric heating of a battery. Figure 2 The battery is divided into an outer layer, a middle layer, and a core layer. The outer layer is temperature zone 2, the middle layer is temperature zone 3, and the core layer is temperature zone 1.

[0049] The correspondence between temperature zones and temperature parameters is based on certain criteria. If the temperature in temperature zone 1 is significantly higher than other temperature zones, then the temperature parameter corresponding to temperature zone 1 can be the battery's highest temperature. If the temperature in temperature zone 2 is significantly lower than other temperature zones, then the temperature parameter corresponding to temperature zone 2 can be the battery's lowest temperature. If the temperature in temperature zone 3 is located at the midpoint between at least one temperature zone, then the temperature parameter corresponding to temperature zone 3 can be the battery's average temperature. This ensures the accuracy of temperature calculations at critical locations. In other words, calculating each temperature parameter using a thermal model guarantees high accuracy / meets accuracy requirements for battery temperature calculations.

[0050] Batteries generate heat during charging and discharging, but external air can cool them through natural circulation or via a cooling system / heat sink. Therefore, it can be determined that a battery contains temperature zones with inconsistent temperatures, caused by differences in heat exchange between the battery and the external environment.

[0051] The electrothermal coupling model is used to predict the battery state change path. It can be understood as predicting the parameters representing the battery state change path. Alternatively, it can be understood as predicting the battery state change path under an electrothermal control path based on the parameters of that path. Based on its working principle, the electrothermal coupling model predicts a battery state change path that includes at least the battery state in the target time domain.

[0052] Step S120: Using the electrothermal coupling model, based on the parameters of the electrothermal control path, predict the battery state change path under the electrothermal control path, wherein the battery state change path includes the change path of at least one temperature parameter.

[0053] In some embodiments, the parameters of an electrothermal control path may include one or more of requested current, thermal management power, and state of charge. The requested current, thermal management power, and state of charge may differ between different electrothermal control paths. The parameters of an electrothermal control path can uniquely identify it.

[0054] Using an electrothermal coupling model, the state-of-the-art (SOA) path of a battery under electrothermal control can be predicted. For example, the SOA path may include one or more of the predicted current, predicted temperature, and predicted state of charge (SOC) within the target time domain under the electrothermal control path. The predicted temperature may include one or more of the battery's average temperature, minimum temperature, and maximum temperature.

[0055] In some embodiments, to build an electrothermal coupling model, it is necessary to build a thermal model and an electrical model separately, and then couple the built thermal model and electrical model together to generate the electrothermal coupling model.

[0056] The thermal model is built based on the battery's temperature. It determines at least one temperature parameter corresponding to at least one temperature zone of the battery at the current moment. Using this at least one temperature parameter, a target model is constructed to predict at least one temperature parameter corresponding to at least one temperature zone of the battery at the next moment. In other words, the target model is used to predict at least one temperature parameter at the next moment based on at least one temperature parameter at the current moment, where at least one temperature parameter corresponds one-to-one with at least one temperature zone.

[0057] In some embodiments, the time interval between the current moment and the next moment can be represented by a sub-period, an iteration period, a time period, or a sampling point. For example, the moment before the iteration is called the current moment, and the moment after one iteration is called the next moment. Another example is that the moment before the sub-period is the current moment, and the moment after a single sub-period is the next moment. Yet another example is that the current moment is the t-th sampling point, and the next moment is the (t+1)-th sampling point.

[0058] In some embodiments, a thermal model is determined based on a target model. This can be understood as using the target model as the thermal model.

[0059] In some embodiments, the step of building the target model may include building target sub-models for each temperature zone, integrating the target sub-models to obtain the target model. That is, it eliminates the need for 3D modeling of the entire battery, significantly reducing the computational load of prediction using the electrothermal coupling model, thus enabling predictive control in a real vehicle using the electrothermal coupling model.

[0060] For example, the battery includes at least one of the following: a first temperature zone, a second temperature zone, and a third temperature zone. The heat exchange effect between the first temperature zone and the external environment is better than the heat exchange effect between the second and third temperature zones and the external environment. The heat exchange effect between the second temperature zone and the external environment is better than the heat exchange effect between the third temperature zone and the external environment. For example, see... Figure 2 The first temperature zone is temperature zone 2, the second temperature zone is temperature zone 3, and the third temperature zone is temperature zone 1.

[0061] Accordingly, a target model is constructed based on at least one temperature parameter corresponding to at least one temperature zone of the battery at the current moment, to predict at least one temperature parameter corresponding to at least one temperature zone of the battery at the next moment, including: Construct a first objective sub-model based on the temperature parameters corresponding to the first temperature zone of the battery at the current moment, and calculate the temperature parameters corresponding to the first temperature zone of the battery at the next moment. A second objective sub-model is constructed based on the temperature parameters corresponding to the second temperature zone of the battery at the current moment, and the temperature parameters corresponding to the second temperature zone of the battery at the next moment are calculated. Construct a third objective sub-model based on the temperature parameters corresponding to the third temperature zone of the battery at the current moment, and calculate the temperature parameters corresponding to the third temperature zone of the battery at the next moment. Determine the target model based on at least one of the first target sub-model, the second target sub-model, and the third target sub-model.

[0062] In some embodiments, at least one temperature zone corresponds one-to-one with a temperature parameter. For example, the first temperature zone corresponds to the battery's highest temperature, the second temperature zone corresponds to the battery's average temperature, and the third temperature zone corresponds to the battery's lowest temperature. That is, building the first target sub-model can be a process of building a first target sub-model based on the battery's highest temperature corresponding to the first temperature zone at the current moment, and calculating the battery's highest temperature corresponding to the first temperature zone at the next moment. Building the second target sub-model can be a process of building a second target sub-model based on the battery's average temperature corresponding to the first temperature zone at the current moment, and calculating the battery's average temperature corresponding to the first temperature zone at the next moment. Building the third target sub-model can be a process of building a third target sub-model based on the battery's lowest temperature corresponding to the first temperature zone at the current moment, and calculating the battery's lowest temperature corresponding to the first temperature zone at the next moment.

[0063] In related technologies, heat generation is often estimated using current and internal resistance, and the heat dissipation capacity of the water-cooling system is combined to roughly predict the battery temperature change trend. However, this method can only calculate the average temperature and is difficult to reflect the temperature distribution within the battery pack. In reality, the heat generation of the battery pack comes not only from the battery cells themselves, but also from components such as connecting plates; and the heat dissipation process, in addition to the water-cooling plate, is also affected by environmental conditions, especially convective heat transfer related to vehicle speed during driving. Furthermore, the heat transfer conditions of cells in different locations vary, and the heat transfer of the connecting plates also causes temperature differences at different points in a single cell.

[0064] In some embodiments, the battery may include multiple cells, each cell including at least one region with inconsistent temperature. For example, a cell may include one or more of the following: a region near the heat exchange plate, a strong cooling plate heat exchange region near the cold plate, a core region, a bottom tray-affected region, etc. That is, a single cell includes at least one region, and there is an inconsistency in temperature between the regions. The temperature inconsistency between the regions is affected by the inconsistent heat exchange effect of the side beam bottom plate liquid cooling plate and the heat generated by the top heat exchange plate.

[0065] Following the steps described above for determining at least one temperature parameter corresponding to at least one temperature zone of the battery at the current moment, this may include determining a first cell located in at least one temperature zone of the battery, and determining at least one temperature parameter corresponding to at least one region of the first cell at the current moment. Constructing a target model based on the at least one temperature parameter corresponding to the at least one temperature zone of the battery at the current moment, and predicting at least one temperature parameter corresponding to the at least one temperature zone of the battery at the next moment, may include constructing a target model based on the at least one temperature parameter corresponding to the at least one region of the first cell at the current moment, and calculating the at least one temperature parameter corresponding to the at least one region of the first cell at the next moment.

[0066] In some embodiments, depending on the cell structure, the minimum battery temperature typically needs to be determined based on a first minimum battery temperature (Tmin1) and a second minimum battery temperature (Tmin2). That is, for each temperature zone corresponding to the minimum battery temperature, sub-models are built to calculate the first minimum battery temperature and the second minimum battery temperature at the next moment based on the current first minimum battery temperature and the second minimum battery temperature. Based on these two sub-models, a target sub-model for calculating the minimum battery temperature is built.

[0067] The following describes the process of building the target model, using a battery cell structure with a single-sided cold plate and cold plate cooling as an example. (See also...) Figure 3 , Figure 3 This is a schematic diagram of the temperature display of a single-sided cold plate cell in a battery electrothermal control method provided in an embodiment of this application. Figure 3 The cell temperature display area includes the middle area for calculating Tavg, the top area for calculating Tmax, the bottom area for calculating Tmin1, and the side area for calculating Tmin2.

[0068] For building the second objective sub-model, the middle region of the mid-temperature zoned cell can be selected as a uniform cell and used as the source for calculating the battery's average temperature. The formula for calculating the battery's average temperature at the next moment is shown in the following formula (1): (1) In formula (1), , , , These represent the average battery temperature, highest battery temperature, lowest battery temperature, and ambient temperature at the t-th sampling point, respectively, all in °C. , , , Δt represents the fitting coefficients for the corresponding heat sources. Mcell is the sampling interval in seconds. Ccell is the cell mass in kg. Ccell is the cell specific heat capacity in J / (kg·K). P_cell is the heat generation power of the cell body in W. Pcoolt is the heat exchange power between the cell and the external liquid cooling system in W. Formula (1) is the sub-model built to calculate the average battery temperature at the next moment.

[0069] P_cell is calculated using the following formula (2): (2) In formula (2), f(I,SOC,T) is a lookup function. The heating power is obtained by looking up the table based on the current I, the average state of charge (SOC) of the battery, and the cell temperature. If there is no heat meter, the voltage and OCV can be calculated based on the equivalent circuit model and obtained using the Bernadi formula.

[0070] Pcoolt is calculated using the following formula (3): (3) In formula (3), CooltT_in is the battery inlet water temperature (in °C). CooltT_out is the battery outlet water temperature (in °C). ρcoolt is the coolant density (in kg / m3). Ccoolt is the coolant specific heat capacity (in J / (kg·K)). CooltFlow is the coolant flow rate (in L / min). num is the number of cells included in the battery.

[0071] For the construction of the first target sub-model, the top region of the high-temperature zone cell is selected as the uniform cell and used as the source for calculating the battery's highest temperature. The formula for calculating the battery's highest temperature at the next moment is shown in the following formula (4): (4) In formula (4), , , , These represent the fitting coefficients for the corresponding heat sources. Formula (4) is the sub-model built to calculate the battery's highest temperature at the next moment.

[0072] P_busbar is calculated using the following formula (5): (5) In formula (5), The resistance of a single cell is measured in Ω.

[0073] To build the third objective sub-model, it is necessary to build separate sub-models to calculate the minimum temperature of the first and second batteries at the next time step.

[0074] For constructing the sub-model of the lowest temperature of the first battery, the bottom region of the low-temperature zone cell is selected as the uniform cell and used as the calculation source for the lowest temperature of the first battery. The calculation formula for the lowest temperature of the first battery at the next moment is shown in the following formula (6): (6) In formula (6), , , The coefficient of fit for the corresponding heat source is not represented by 'not'.

[0075] For building a sub-model of the lowest temperature of the second battery, the side region of the low-temperature zone cell is selected as a uniform cell and used as the calculation source for the lowest temperature of the second battery. The calculation formula for the lowest temperature of the second battery differs when thermal management control is enabled and disabled.

[0076] When thermal management control is activated, the formula for calculating the minimum temperature of the second battery at the next moment is shown in the following formula (7): (7) In formula (7), This represents the fitting coefficient for the corresponding heat source.

[0077] When thermal management control is not enabled, the formula for calculating the minimum temperature of the second battery at the next moment is shown in the following formula (8): (8) Based on the sub-models of the first and second battery minimum temperatures built respectively, a third target sub-model is built. The building formula is shown in formula (9) below: (9) Formulas (6)-(9) are the sub-models built to calculate the minimum battery temperature at the next moment.

[0078] In some embodiments, , and The heat transfer coefficient of the battery pack to the environment should be used as the basis. This coefficient should be derived from the battery's insulation data under different wind speeds during hub testing, or from the battery's external heat transfer effect under different wind speeds obtained during the design phase based on finite element simulation. , and The calculated / simulated values ​​of the battery's external heat transfer coefficient under different wind speeds are usually not significantly different.

[0079] The solution proposed in this application overcomes the limitation of computational power in related technologies, meeting the real-time requirements of real vehicles. The best existing finite element thermal simulation models require hundreds of thousands or even millions of meshes coupled with multiphysics, with single calculations taking hours and relying solely on high-performance laboratory clusters. While cell-level P2D models offer high accuracy, they require loading dozens of difficult-to-obtain electrochemical parameters, and the computational load varies with the number of cells, making them unsuitable for automotive computing power. This application's solution, through "battery pack temperature partitioning + cell structure partitioning + non-critical path simplification," significantly reduces computational load while maintaining thermal model accuracy. It can be transcoded and embedded into the vehicle controller, solving the core pain point of related technologies being "only usable offline and unsuitable for vehicle use." This achieves both computational efficiency and vehicle-grade compatibility.

[0080] In some embodiments, the steps for building a thermal model are described above. The thermal model built above includes multiple coefficients. For example, to This coefficient requires coefficient fitting. Coefficient fitting needs to be based on fitting data adapted to various operating conditions. For example, various operating conditions may include one or more of the following: low temperature, normal temperature, high temperature with / without thermal management charging conditions; low temperature, normal temperature, high temperature with / without discharging conditions. For example, in actual coefficient fitting, the coefficient fitting operation can be performed based on at least 12 sets of fitting data (test data).

[0081] In some embodiments, different temperature parameters correspond to different coefficients. When fitting the coefficients of different temperature parameters, certain requirements can be imposed on the fitting coefficients. For example, when fitting the coefficient of Tmax, experimental values ​​can be used for Tmin and Tavg, and the fitness calculation only calculates Tmax and the experimental Tmax, which results in a better fitting effect. The same applies when fitting the coefficients of Tmin and Tavg.

[0082] In some embodiments, coefficient fitting can be performed using a fitting algorithm. For example, a particle swarm optimization (PSO) algorithm can be used, which iteratively selects the population with the lowest fitness (fit coefficients).

[0083] Following the above description of the electrical model construction, in some embodiments, the electrical model is related to the battery's charge and discharge data at multiple temperatures and multiple states of charge. The electrical model can be constructed based on this data. The electrical model can also be called an equivalent circuit model. For example, the electrical model can be a first-order RC equivalent circuit model.

[0084] A generative electrical model is constructed by fitting parameters from multiple states of charge and multiple temperatures. These parameters can be either HPPC parameters or DCR parameters.

[0085] For example, the parameters are HPPC parameters. HPPC charge / discharge data for each temperature and state of charge are selected, and the charging and discharging data are extracted separately using simulation tools (e.g., Matlab or Python). See [link to documentation]. Figure 4 , Figure 4 This is a schematic diagram of battery charging segment data for a battery electrothermal control method provided in an embodiment of this application. Figure 4 The charging segment data includes five points: a, b, c, d, and e. R0 is obtained by dividing the current by the voltage difference between segments ab and dc, while R1 and C1 can be calculated from segments bc and de.

[0086] In some embodiments, the electrothermal coupling model is used to predict the battery state change path of a battery, and it predicts the battery state change path under the electrothermal control path based on the parameters of the electrothermal control path. In practical applications, the parameters included in the battery state change path to be predicted can be set, and the model output of the electrothermal coupling model can be determined based on the parameter settings. Based on the model output, as well as the signal transmission process and model interaction process between the electrical model and the thermal model, the thermal model and the electrical model are combined to generate the electrothermal coupling model.

[0087] In some embodiments, the output of the thermal model includes the input of the electrical model.

[0088] In some embodiments, the electrothermal coupling model can also output the boundary current value of the battery. The boundary current value can include the safe charging current and the safe discharging current. The safe charging current can be expressed as... The unit is ampere-ampere (A), and the safe discharge current can be expressed as: The unit is ampere-ampere (A). The boundary current value of the battery output by the electrothermal coupling model can be obtained through a lookup table module or a current boundary calculation module.

[0089] For example, consider the current boundary calculation module. The current boundary calculation module can be constructed by separately building sub-modules for calculating the safe charging current and the safe discharging current. The formula for calculating the safe charging current is shown in formula (10) below, and the formula for calculating the safe discharging current is shown in formula (11) below: (10) (11) This represents the upper limit of the battery's safe voltage. This represents the lower boundary of the battery's safe voltage, expressed in V, max(OCV( )) and min(OCV( )) respectively represent and The maximum and minimum values ​​of OCV are calculated by combining them with Tmax, Tmin, and Tavg, respectively, and the unit is V. , , and These represent the charging internal resistance (ohms), charging polarization internal resistance (ohms), discharging internal resistance (ohms), and polarization internal resistance (ohms), respectively, with units of Ω.

[0090] In some embodiments, the current boundary calculation module requires the battery's state of charge (SOC) at the next moment when in use. Therefore, the electrothermal coupling model may also include an ampere-hour integration module. The ampere-hour integration module is used to calculate the SOC at the next moment based on the battery's SOC at the current moment. The calculation formula is shown in formula (12) below: (12) In formula (12), Cap_cell represents the battery capacity in Ah, and SOC is in percentage form.

[0091] In some embodiments, an electrothermal coupling model can be constructed based on a thermal model, an electrical model, a current boundary calculation module, and an ampere-hour integration module. In this case, the signal inputs to the electrothermal coupling model are Tmax(t), Tmin(t), Tavg(t), Ireq, SOCmax(t), SOCmin(t), and Pcoolt(t), where Ireq represents the model input request current. Based on the input signal at time t, the thermal model module calculates the battery temperature at time t+1, and the ampere-hour integration module calculates the SOC at time t+1. The battery temperature and SOC at time t+1 are then input into the equivalent circuit module and the current boundary calculation module to obtain the battery's highest voltage, lowest voltage, maximum charging current boundary, and maximum discharging current boundary at time t+1. Exemplarily, the constructed electrothermal coupling model can be displayed using simulation software, such as Simulink.

[0092] In some embodiments, the electrothermal coupling model is a convective heat transfer coefficient model related to vehicle speed, established to address the strong correlation between environmental heat transfer and vehicle speed during driving. In other words, the electrothermal coupling model is built through partitioned modeling and path simplification (retaining heat transfer paths that significantly affect temperature distribution (such as water-cooled plates, heat exchangers, and environmental heat transfer), while simplifying paths with less impact, thus reducing computational dimensions). This significantly reduces model complexity and computational load while ensuring the accuracy of temperature calculations at key locations, enabling the model to run in real-time on the vehicle controller. Compared to traditional finite element thermal simulation, the method provided in this application significantly reduces computational load and avoids dependence on a large number of difficult-to-obtain material parameters.

[0093] In related technologies, vehicle controllers typically employ a single-call, single-step calculation model, resulting in onboard models that can only output instantaneous state estimates based on currently collected data, failing to achieve true prediction functionality. This is because prediction requires input signals from future moments, which cannot be obtained in advance during real-time calculations. To address this technical problem, in some embodiments, the thermal model and the electrical model are coupled together to generate an electrothermal coupled model. This can include: coupling the thermal model and the electrical model together to generate an initial electrothermal coupled model; and building an electrothermal coupled model based on the initial electrothermal coupled model using an inner loop mode. The initial electrothermal coupled model is invoked to achieve prediction of a single sub-time domain within the target time domain. The electrothermal coupled model is then invoked to achieve prediction of the target time domain.

[0094] Based on the above process of building an electrothermal coupling model using each module, it can be understood that this process can also include rebuilding the electrothermal coupling model built from each module through an internal loop mode to generate a new electrothermal coupling model. In other words, the model built from each module can be called the initial electrothermal coupling model.

[0095] In some embodiments, the inner loop mode can also be called the inner iteration mode.

[0096] In related technologies, long-term time-domain simulation calculations are often implemented in computer workstation simulation environments and are commonly used in the product design stage. However, in actual vehicle controllers, simulation models cannot perform real-time long-term time-domain prediction calculations in sync with the vehicle due to computational complexity and the inability to iterate. Based on the solution of this application embodiment, an electrothermal coupling model is built using an inner loop mode, enabling single-step long-term time-domain computation. This is significant for the practical use of the model in the controller, making real-time predictive control by AI algorithms possible. Specifically, in some embodiments, building an electrothermal coupling model using an inner loop mode involves modifying the loop-related parameters in the initial electrothermal coupling model to generate the electrothermal coupling model. Loop-related parameters may include time-domain variables and input parameters.

[0097] Identify the time-domain dependent variables and input parameters in the initial electrothermal coupling model; based on the inner loop mode, modify the time-domain dependent variables to generate a reference electrothermal coupling model; change the input parameters in the reference electrothermal coupling model to the target derived parameters to generate an electrothermal coupling model.

[0098] For example, changes to the input parameters can be made by the target derived parameters being dynamically derived parameters, as long-term calculations require dynamic input in the long time domain, and the model input request current and the cell-to-external liquid cooling heat exchange power can be input in matrix form. Alternatively, the target derived parameters can be fixed-value derived parameters, which can be set according to fixed values ​​if they remain unchanged in the long time domain, and the corresponding model input request current and cell-to-external liquid cooling heat exchange power can be selected based on the number of inner loop iterations.

[0099] In some embodiments, time-domain dependent variables include clock parameters and an integration module. Based on the inner loop mode, changing the time-domain dependent variables can be done by replacing the clock parameters based on the product of the number of inner loop iterations and the duration of a single iteration; and by replacing the integration module based on the increment calculated per iteration, thus generating a reference electrothermal coupling model.

[0100] For example, modify the time-domain dependent variables in the model. If time-domain dependent variables exist in the model, they should be deleted and replaced. For example, time-domain dependent variables may include the clock and the integration module. The clock can be replaced with the number of inner loop iterations × the duration represented by a single iteration. In the heat accumulation and ampere-hour integration modules, there may be integration modules. The integration module can only reflect a single step of time and cannot be used. The integral quantity can be changed to the increment calculated per iteration.

[0101] See Figure 5 , Figure 5 This is a schematic diagram of the electrothermal coupling model in a battery electrothermal control method provided in this application embodiment. That is, each time the electrothermal coupling model is called for calculation, the battery temperature rise and accumulated state of charge obtained from previous iterations are added to the initial temperature and initial state of charge to obtain the starting temperature and initial state of charge for the current iteration. Since the model input request current may exceed the battery charge / discharge current boundary as the state of charge and temperature change during the iterative calculation process, a comparison between the model input request current and the current boundary is required to obtain a suitable calculation current, Ismu.

[0102] The system operates through an internal iterative system. When the number of iterations is less than the set number, a Triger event is triggered, invoking the electrothermal coupling model submodule. `Time_cal_nstep` represents the computation length represented by the current iteration count. This internal iterative system can also be called a stateflow or an internal loop system.

[0103] In some embodiments, the electrothermal coupling model calculates the battery temperature rise and battery state of charge increment for each iteration, iteratively accumulates these values ​​within an inner iterative system, and outputs them to the electrothermal coupling model, taking effect when the electrothermal coupling model is invoked in the next iteration. In other words, through an inner loop mode, an inner loop system is constructed within the initial electrothermal coupling model to generate the electrothermal coupling model. Accordingly, using the electrothermal coupling model, based on the parameters of the electrothermal control path, predicting the battery state change path under the electrothermal control path can include: The parameters of the electrothermal control path are used as the input of the current iteration of the electrothermal coupling model. The first battery state of the battery under the electrothermal control path is generated using the electrothermal coupling model. Using the internal loop system, the input and output of the electrothermal coupling model in the current iteration are accumulated to generate the accumulated battery state of the battery under the electrothermal control path. The accumulated battery state is used as the input for the next iteration. Using the electrothermal coupling model, the second battery state under the electrothermal control path is generated. Using an internal loop system, the battery state and the second battery state are accumulated to generate a reference battery state. The input of the current iteration of the electrothermal coupling model is updated using the reference battery state, and the process returns to execute the step of generating the first battery state under the electrothermal control path using the electrothermal coupling model, until the iteration stops. Based on the accumulated battery states generated in each iteration, the battery state change path under the electrothermal control path is determined. Parameters contained in the reference battery state can be represented by delta. For example, the highest battery temperature in the reference battery state can be represented as delt_Tmax_step, and the highest battery temperature obtained after n iterations can be represented as delt_Tmax_nstep.

[0104] In some embodiments, the step of accumulating the battery state and the second battery state using the internal circulation system to generate the reference battery state can be implemented through the accumulation module in the electrothermal coupling model. The accumulation module calculates the temperature (Tmax(t+nstep), Tmin(t+nstep), Tavg(t+nstep)) and SOC (SOCmax(t+nstep), SOCmin(t+nstep)) of the current predicted inner iteration step based on the accumulated temperature and SOC changes during the iteration process. In other words, the electrothermal coupling model can be constructed by combining the thermal model, electrical model, current boundary calculation module, ampere-hour integration module, and accumulation module.

[0105] The solution applied in this application overcomes the limitation of "single-step estimation" in related technologies, achieving forward-looking decision support. The best-in-class simplified vehicle models can only perform single-step state estimation based on currently collected data (such as real-time current and temperature), and cannot output future states. This invention, through an internal iteration mechanism, can complete the rolling deduction of "current state → future multi-step states" in a single model call: without waiting for future real input signals, it can directly combine the preset electrothermal paths (charge and discharge current curves, thermal management strategies) of the AI ​​algorithm for internal loop calculation, outputting key parameters such as the highest / lowest cell temperature and SOC for future multi-step states. In other words, when the vehicle controller calls the model once, it performs multi-step state deduction within the model through internal iteration based on the electrothermal path input by the AI ​​algorithm. During the deduction process, the internal iteration system stores and updates the future battery state, completing the prediction of the future battery state without the input of actual collected future signal values. That is to say, multi-step prediction can be achieved without relying on future input signals, providing a basis for the AI ​​algorithm to evaluate different electrothermal control paths.

[0106] Step S130: Perform electrothermal control on the battery based on the battery state change path under the electrothermal control path.

[0107] Given the battery state change path under the output electrothermal control path of the electrothermal coupling model, the safe charging current boundary and safe discharging power boundary of the battery are calculated based on the battery state change path, and the battery is electrothermally controlled using the safe charging current boundary and safe discharging power boundary.

[0108] In some embodiments, the electrothermal control path may include multiple paths. For any given electrothermal control path, the control path parameters for determining the electrothermal control path in step S110 and step S120 above are executed. For each battery state change path under the electrothermal control path, dimensional scores are performed on the battery temperature and / or battery state of charge dimensions. Based on the dimensional scores corresponding to each electrothermal control path, a target electrothermal control path is determined from the multiple electrothermal control paths, and the battery is electrothermally controlled according to the target electrothermal control path.

[0109] In some embodiments, the electrothermal coupling model may only perform the step of predicting the battery state change path under each electrothermal control path. The remaining steps are performed by the AI ​​algorithm. In other words, the AI ​​algorithm can invoke the electrothermal coupling model through the controller, and the result of each invocation can reflect the predicted battery state change path in the time domain under one electrothermal control path.

[0110] In some embodiments, the target electrothermal control path can be the optimal electrothermal control path.

[0111] The following explains the specific implementation of combining AI algorithms with electrothermal coupling models to determine the selection of the target electrothermal control path as the control variable.

[0112] The AI ​​algorithm is invoked on the controller once per first duration. Each invocation requires the AI ​​algorithm to predict the battery's operating state under different electrothermal control paths for a second duration, in order to select the appropriate electrothermal control path as the control variable. The first duration is much shorter than the second duration. For example, the first duration is 10 seconds, and the second duration is 300 seconds. Exemplarily, the AI ​​algorithm samples at sampling point t. Assuming the vehicle is charging while driving, the AI ​​algorithm has three selectable electrothermal control paths: (Ireq=300A, CooltT_in=25℃, CooltFlow=20L / min), (Ireq=300A, CooltT_in=35℃, CooltFlow=15L / min), and (Ireq=300A, CooltT_in=25℃, CooltFlow=20L / min). The coolant flow rate (CooltFlow) and coolant temperature (CooltT_in) can be used to represent the thermal management power. The AI ​​algorithm calls three electrothermal coupling models (single-step time domain) to perform single-step calculations and obtain the battery state under three electrothermal control paths. Based on the predicted value after the second time period, the AI ​​algorithm evaluates the battery from dimensions such as whether the battery temperature has risen to the target value, whether the change in battery SOC is equal to the model input requested current, and whether the state of charge should be consumed in the second time period of discharge (if not, it means that the battery cannot meet the model input requested current during discharge). The AI ​​algorithm then selects the target electrothermal control path as the control variable.

[0113] In this embodiment, an electrothermal coupling model suitable for AI calculations within a real vehicle controller is established. This model can predict the battery's state change path under different electrothermal control paths within the target time domain through a single-step call. In other words, it provides forward-looking data support for the AI ​​algorithm to "pre-select the optimal electrothermal control path," a core function that related technologies cannot achieve. This solves the problems of the lack of high-precision models available for AI calculations, the difficulty of adapting conventional computer workstation simulation models to real vehicle controllers, and the limitation of related models to only real-time state estimation, thus helping the AI ​​algorithm predict and control the charging and discharging process of the power battery.

[0114] For ease of understanding, the following provides an embodiment of a battery electrothermal control method according to the present application. See also Figure 6 , Figure 6 This is a flowchart illustrating the process of a battery electrothermal control method provided in an embodiment of this application. Figure 6 The flowchart shown includes steps S610 to S650.

[0115] Step S610: Establish the equivalent circuit model.

[0116] To facilitate large-scale data processing, we selected a first-order RC equivalent circuit model to fit the equivalent electrothermal coupling model parameters at multiple different SOCs and temperatures. Therefore, we selected HPPC charge-discharge data at various temperatures and SOCs, and used tools such as Matlab or Python to extract the charging and discharging data separately. For the discharging segment, see [link to discharging data]. Figure 4 R0 is obtained by dividing the current by the voltage difference between segments ab and dc, while R1 and C1 can be calculated from segments bc and de.

[0117] If the battery's DCR parameters are available, an equivalent circuit model can be built using these parameters.

[0118] Step S620: Establish a thermal model.

[0119] The battery temperature can be roughly divided into three zones: outer layer, middle layer, and core layer. Different temperature zones experience varying degrees of heat exchange from the external environment. Figure 2 As shown, the high temperature zone, low temperature zone, and medium temperature zone are used as examples for explanation.

[0120] The battery cell is divided into an area near the heat exchange plate, a strong cooling plate heat exchange zone near the cold plate, a core area, and a bottom tray influence zone. Taking a single-sided cold plate and cold plate cooling as an example, the temperature distribution of a single battery cell is as follows: Figure 3 The following example will be used for illustration.

[0121] The temperature is inconsistent between the cells inside the battery, and there are also temperature inconsistencies between different areas within a single cell. The temperature inconsistencies within the cells are mainly due to the inconsistent heat exchange between each cell and the external battery environment and the liquid cooling system. The temperature inconsistencies between different areas within a single cell are due not only to the inconsistent heat exchange effects of the side beams, bottom plate, and liquid cooling plates, but also to the influence of heat generated by the top heat exchanger.

[0122] Based on the above analysis, it is first necessary to establish formulas to calculate the heat generation P_cell of the main body of the battery cell, the heat generation P_busbar of the battery cell bar, and the heat exchange between the battery cell and the external liquid cooling Pcoolt, all in W. The calculation formulas are shown in formulas (2), (3), and (5).

[0123] The middle region of the medium-temperature zone cell is selected as the uniform cell and used as the calculation source for Tavg. The calculation formula is shown in formula (1). The top region of the high-temperature zone cell is selected as the uniform cell and used as the calculation source for Tmax. The calculation formula is shown in formula (4). The bottom region of the low-temperature zone cell is selected as the uniform cell and used as the calculation source for Tmin1. The calculation formula is shown in formula (6). The side of the low-temperature zone cell is selected as the calculation source for Tmin2. Tmin2 may be the low temperature during high-temperature cooling. The calculation formulas are shown in formulas (7) and (8). The calculation formula for Tmin(t) is shown in formula (9).

[0124] The coefficient fitting data should include at least 12 sets of test data for low temperature, normal temperature, high temperature charging conditions with / without thermal management enabled, and low temperature, normal temperature, high temperature discharging conditions with / without thermal management enabled.

[0125] For the 12 sets of test data, the particle swarm optimization (PSO) algorithm can be used for parameter fitting. The PSO algorithm selects the population with the lowest fitness (fit coefficient) in the iteration. To achieve better parameter fitting, when fitting the Tmax coefficient, the experimental values ​​can be used for Tmin and Tavg, and the fitness calculation only needs to calculate Tmax and the experimental Tmax. This results in a better fit, and the same applies to the fit coefficients of Tmin and Tavg.

[0126] Step S630: Establish the ampere-hour integration module, the current boundary calculation module, and the accumulation module.

[0127] The ampere-hour integration module, the calculation formula for ampere-hour integration is shown in formula (12). The current boundary calculation module, if the supplier provides the corresponding safe value at SOC and temperature, can be obtained through the lookup table module, otherwise it can be obtained through voltage calculation, the calculation formula is shown in formula (10) and formula (11).

[0128] Accumulation module: Calculates the temperature (Tmax(t+nstep), Tmin(t+nstep), Tavg(t+nstep)) and SOC (SOCmax(t+nstep), SOCmin(t+nstep)) of the current prediction iteration step based on the accumulated temperature and SOC changes during the iteration process.

[0129] Step S640: Build an electrothermal coupling model for single-step time-domain calculation using an inner loop mode.

[0130] Taking Simulink as an example. Modify time-domain related variables in the model. If time-domain related variables exist in the model, they need to be deleted and replaced, such as the clock and the integral module. The clock can be replaced with the number of inner loop iterations multiplied by the duration of a single iteration. In the heat accumulation and ampere-hour integration modules, there may be integral modules. The integral module can only reflect the time of a single step and cannot be used. The integral quantity can be changed to the increment calculated per iteration. Establish dynamic derived parameters as input parameters. Long-time domain calculations require long-time domain dynamic input. Ireq and Pcoolt (CooltT_in, CooltFlow) can be input in matrix form. If they are fixed in the long-time domain, they can also be set according to fixed values. Select the corresponding Ireq and Pcoolt according to the number of inner loop iterations. Establish the inner loop iteration system, such as... Figure 5 As shown. Figure 5 Within the process, the stateflow loop iterates. When the number of iterations is less than the set number, a Triger event is triggered, invoking the electrothermal coupling model submodule. Time_cal_nstep represents the computation length represented by the current iteration number. The electrothermal coupling model calculates the temperature rise and SOC increase for each iteration, continuously accumulating these values ​​within the internal iterative system's stateflow and outputting them to the electrothermal coupling model, which takes effect in the next iteration when the electrothermal coupling model is called. Each time the electrothermal coupling model is called for calculation, the accumulated battery temperature rise and accumulated battery SOC obtained from previous iterations are added to the initial temperature and SOC to obtain the starting temperature and SOC for the current iteration. Since Ireq may exceed the battery charge / discharge current boundary as SOC and temperature change during iterative calculations, a comparison between Ireq and the current boundary is necessary to obtain a suitable computational current Ismu.

[0131] Step S650, combined with AI explanation of the use of single-step long-time domain calculation electrothermal coupling model.

[0132] The AI ​​algorithm is called on the controller every 10 seconds. Each time the AI ​​algorithm is called, it needs to predict the battery's operating state over 300 seconds under different electrothermal control paths in order to select the appropriate electrothermal control path as the control variable.

[0133] Specifically, the AI ​​algorithm samples at sampling point t. Assuming the battery is in the process of discharging while driving, the AI ​​algorithm has three selectable electrothermal control paths: (Ireq=300A, CooltT_in=25℃, CooltFlow=20L / min), (Ireq=300A, CooltT_in=35℃, CooltFlow=15L / min), and (Ireq=300A, CooltT_in=25℃, CooltFlow=20L / min). The AI ​​algorithm calls three single-step long-time domain electrothermal coupling models, performs single-step calculations, and obtains the battery state change paths under the three electrothermal control paths. Based on the predicted value after 300 seconds, the AI ​​algorithm evaluates the battery from dimensions such as whether the battery temperature has risen to the target value, whether the change in battery SOC is equal to the SOC that should be consumed after 300 seconds of discharge (if not, it means that the battery cannot meet Ireq during the discharge process), and selects the optimal electrothermal control path as the control variable.

[0134] In this embodiment, the battery is divided into temperature zones and cell structure zones, retaining critical heat transfer paths while simplifying non-critical paths. The maximum and minimum temperatures within the battery are accurately calculated by comprehensively considering the influence of the environment, water cooling system, and heat exchanger on the temperature at critical locations. The equivalent circuit model (ECM) is coupled with the aforementioned thermal model to construct an integrated electrothermal system. An innovative internal iteration mechanism is introduced to predict future states in a single-step calculation. The electrothermal coupling model provided in this embodiment can run efficiently on automotive hardware, supporting AI algorithms to predict battery state changes under different electrothermal control paths (charge / discharge current and thermal management strategies), thereby optimizing current control and thermal management strategies during the charging and discharging process.

[0135] With the above Figure 1 Corresponding to the embodiment of the battery's electrothermal control method shown, this specification also provides embodiments of the battery's electrothermal control device. Figure 7 This is a schematic diagram of the structure of a battery heating control device provided in an embodiment of this application. Figure 7 As shown, the battery's heating control device includes: The determination module 710 is configured to determine an electrothermal coupling model, wherein the electrothermal coupling model includes an electrically coupled model and a thermal model, the thermal model is used to predict at least one temperature parameter, the at least one temperature parameter corresponds one-to-one with at least one temperature zone of the battery, the thermal model predicts the temperature parameter corresponding to the target temperature zone based on the cells in the target temperature zone, the target temperature zone is any one of the at least one temperature zone, the at least one temperature zone is divided according to the difference in heat exchange effect between the battery and the external environment; The prediction module 720 is configured to use the electrothermal coupling model to predict the battery state change path of the battery under the electrothermal control path based on the parameters of the electrothermal control path, wherein the battery state change path includes the change path of the at least one temperature parameter. The control module 730 is configured to perform electrothermal control on the battery based on the battery state change path of the battery under the electrothermal control path.

[0136] In some embodiments, the battery's electrothermal control device further includes a construction module configured to: determine at least one temperature parameter corresponding to at least one temperature zone of the battery at the current moment; construct a target model based on the at least one temperature parameter corresponding to the at least one temperature zone of the battery at the current moment, predict the at least one temperature parameter corresponding to the at least one temperature zone of the battery at the next moment, and determine a thermal model based on the target model; construct an electrical model based on battery charge and discharge data at multiple temperatures and multiple states of charge; and couple the thermal model and the electrical model to generate an electrothermal coupling model.

[0137] In some embodiments, the at least one temperature zone includes at least one of the following: a first temperature zone, a second temperature zone, and a third temperature zone, wherein the heat exchange effect between the first temperature zone and the external environment is better than the heat exchange effect between the second temperature zone and the third temperature zone and the external environment, and the heat exchange effect between the second temperature zone and the external environment is better than the heat exchange effect between the third temperature zone and the external environment; the construction module is further configured to construct a first target sub-model based on the temperature parameters corresponding to the first temperature zone of the battery at the current moment, and calculate the temperature parameters corresponding to the first temperature zone of the battery at the next moment; construct a second target sub-model based on the temperature parameters corresponding to the second temperature zone of the battery at the current moment, and calculate the temperature parameters corresponding to the second temperature zone of the battery at the next moment; construct a third target sub-model based on the temperature parameters corresponding to the third temperature zone of the battery at the current moment, and calculate the temperature parameters corresponding to the third temperature zone of the battery at the next moment; and determine a target model based on at least one of the first target sub-model, the second target sub-model, and the third target sub-model.

[0138] In some embodiments, the battery includes a plurality of cells, each cell including at least one region with inconsistent temperatures; the construction module is further configured to determine a first cell located in at least one temperature zone of the battery, and to determine at least one temperature parameter corresponding to the at least one region of the first cell at the current time; the construction of a target model based on the at least one temperature parameter corresponding to the at least one temperature zone of the battery at the current time, and predicting at least one temperature parameter corresponding to the at least one temperature zone of the battery at the next time, includes: constructing a target model based on the at least one temperature parameter corresponding to the at least one region of the first cell at the current time, and calculating the at least one temperature parameter corresponding to the at least one region of the first cell at the next time.

[0139] In some embodiments, the building module is further configured to determine the model output of the electrothermal coupling model; based on the model output, the signal transmission process and model interaction process between the thermal model and the electrical model, combine the thermal model and the electrical model to generate the electrothermal coupling model, wherein the output of the thermal model includes the input of the electrical model, and the output of the electrical model includes the input of the thermal model.

[0140] In some embodiments, the building module is further configured to couple the thermal model and the electrical model to generate an initial electrothermal coupling model, wherein the initial electrothermal coupling model is invoked to make predictions for a single sub-time domain in the target time domain; and an electrothermal coupling model is built based on the initial electrothermal coupling model through an inner loop mode, wherein the electrothermal coupling model is invoked to make predictions for the target time domain.

[0141] In some embodiments, the building module is further configured to: identify time-domain dependent variables in the initial electrothermal coupling model; modify the time-domain dependent variables based on the inner loop mode to generate a reference electrothermal coupling model; identify input parameters in the reference electrothermal coupling model; and modify the input parameters to target derived parameters based on the inner loop mode to generate an electrothermal coupling model.

[0142] In some embodiments, the time-domain related variables include clock parameters and an integration module; the construction module is further configured to determine the number of inner loop iterations and the duration of a single iteration; replace the clock parameters based on the product of the number of inner loop iterations and the duration of a single iteration; determine the single iteration calculation increment; and replace the integration module based on the single iteration calculation increment to generate a reference electrothermal coupling model.

[0143] In some embodiments, the building module is further configured to construct an inner loop system in the initial electrothermal coupling model through an inner loop mode to generate an electrothermal coupling model; the prediction module 720 is further configured to use the parameters of the electrothermal control path as the input of the current iteration of the electrothermal coupling model, and use the electrothermal coupling model to generate a first battery state of the battery under the electrothermal control path; use the inner loop system to accumulate the input and output of the electrothermal coupling model in the current iteration to generate an accumulated battery state of the battery under the electrothermal control path; use the accumulated battery state as the input of the next iteration, and use the electrothermal coupling model to generate a second battery state of the battery under the electrothermal control path; use the inner loop system to accumulate the accumulated battery state and the second battery state to generate a reference battery state, use the reference battery state to update the input of the current iteration of the electrothermal coupling model, and return to execute the step of using the electrothermal coupling model to generate the first battery state of the battery under the electrothermal control path, until the iteration stop condition is reached, and determine the battery state change path of the battery under the electrothermal control path based on the accumulated battery states generated in each iteration.

[0144] In some embodiments, the electrothermal control path includes multiple paths; the control module 730 is further configured to determine a dimensional score of each battery state change path in the dimensions of battery temperature and / or battery state of charge based on the battery state change path of each of the electrothermal control paths; determine a target electrothermal control path from the multiple electrothermal control paths based on the dimensional score of each battery state change path; and perform electrothermal control on the battery based on the target electrothermal control path.

[0145] In this application, an electrothermal coupling model is defined, which includes a thermal model for predicting at least one temperature zone of the battery and at least one temperature parameter that corresponds one-to-one. This allows for temperature parameter calculations to be performed based on the cells in the corresponding temperature zone, eliminating the need to use all cells in the battery for each temperature parameter, thus significantly reducing the computational load. Furthermore, the at least one temperature zone is determined based on the difference in heat exchange effects between the battery and the external environment, ensuring the accuracy of temperature calculations at key locations. This enables the electrothermal coupling model to be implemented for prediction in a real vehicle.

[0146] The above is a schematic scheme of a battery heating control device according to this embodiment. It should be noted that the technical solution of this battery heating control device is similar to that described above. Figure 1 The technical solutions for the battery's electrothermal control method shown belong to the same concept. Details not described in detail in the technical solution for the battery's electrothermal control device can be found above. Figure 1 The technical solution of the battery's electrothermal control method is described.

[0147] Figure 8 This is a schematic structural diagram of a computer device provided in an embodiment of this application. Figure 8 The dashed lines in the diagram indicate that the unit or module is optional. The computer device 800 can be used to implement the methods described in the above method embodiments.

[0148] Computer device 800 may include one or more processors 810. The processor 810 can support the computer device 800 in implementing the methods described in the preceding method embodiments. The processor 810 may be a general-purpose processor or a special-purpose processor. For example, the processor may be a central processing unit (CPU). Alternatively, the processor may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0149] The computer device 800 may also include one or more memories 820. The memories 820 store a computer program that can be executed by the processor 810, causing the processor 810 to perform the methods described in the preceding method embodiments. The memories 820 may be independent of the processor 810 or integrated within the processor 810.

[0150] The computer device 800 may also include a transceiver 830, through which the processor 810 can communicate with other devices. For example, the processor 810 can send and receive data with other devices through the transceiver 830.

[0151] In one embodiment of this application, the aforementioned components of the computer device 800 and Figure 8 Other components not shown can also be connected to each other. It should be understood that... Figure 8 The computer device structural block diagram shown is for illustrative purposes only and is not intended to limit the scope of this application. Those skilled in the art can add or replace other components as needed.

[0152] The above is an illustrative scheme of a computer device according to this embodiment. It should be noted that the technical solution of this computer device and the technical solution of the battery heating control method described above belong to the same concept. For details not described in detail in the technical solution of the computer device, please refer to the description of the technical solution of the battery heating control method described above.

[0153] In addition, this application also proposes a computer-readable storage medium storing a computer program. When the computer program is executed by a computer, it implements the operation in the battery electrothermal control method provided in the above embodiments. The specific steps will not be described in detail here.

[0154] This application also provides a computer program product. The computer program product includes a program / instructions. When executed by a processor, the computer program / instructions implement the steps of the above-described battery electrothermal control method.

[0155] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity / operation / object from another, and do not necessarily require or imply any such actual relationship or order between these entities / operations / objects; the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system 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 system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0156] For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and relevant details can be found in the description of the method embodiments. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate. Some or all of the modules can be selected according to actual needs to achieve the purpose of this application. Those skilled in the art can understand and implement this without creative effort.

[0157] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0158] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods in the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, television, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0159] The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method of electro-thermal control of a battery, characterized by, The method comprises: determining an electro-thermal coupling model, wherein the electro-thermal coupling model comprises an electric model and a thermal model coupled with each other, the thermal model is used to predict at least one temperature parameter corresponding to at least one temperature zone of the battery, the thermal model predicts the temperature parameter corresponding to a target temperature zone based on a battery cell in the target temperature zone, the target temperature zone is any one of the at least one temperature zone, and the at least one temperature zone is obtained by dividing according to differences in heat exchange effects between the battery and an external environment; using the electro-thermal coupling model, predicting a battery state change path of the battery under an electro-thermal control path based on parameters of the electro-thermal control path, wherein the battery state change path comprises a change path of the at least one temperature parameter; based on the battery state change path of the battery under the electro-thermal control path, electro-thermal controlling the battery.

2. The method of claim 1, wherein, The electro-thermal coupling model is built by: determining at least one temperature parameter corresponding to at least one temperature zone of the battery at a current time; building a target model for predicting at least one temperature parameter corresponding to at least one temperature zone of the battery at a next time based on at least one temperature parameter corresponding to at least one temperature zone of the battery at the current time, and determining a thermal model based on the target model; building the electric model based on charging and discharging data of the battery under multiple temperatures and multiple states of charge; coupling the thermal model and the electric model with each other to generate an electro-thermal coupling model.

3. The method of claim 2, wherein, The at least one temperature zone comprises at least one of the following: a first temperature zone, a second temperature zone, and a third temperature zone, the heat exchange effect between the first temperature zone and an external environment is better than that between the second temperature zone and the external environment and that between the third temperature zone and the external environment, and the heat exchange effect between the second temperature zone and the external environment is better than that between the third temperature zone and the external environment; The building of the target model for predicting at least one temperature parameter corresponding to at least one temperature zone of the battery at a next time based on at least one temperature parameter corresponding to at least one temperature zone of the battery at a current time comprises: building a first target sub-model for calculating a temperature parameter corresponding to the first temperature zone of the battery at a next time based on a temperature parameter corresponding to the first temperature zone of the battery at a current time; building a second target sub-model for calculating a temperature parameter corresponding to the second temperature zone of the battery at a next time based on a temperature parameter corresponding to the second temperature zone of the battery at a current time; building a third target sub-model for calculating a temperature parameter corresponding to the third temperature zone of the battery at a next time based on a temperature parameter corresponding to the third temperature zone of the battery at a current time; determining a target model according to at least one of the first target sub-model, the second target sub-model, and the third target sub-model.

4. The method according to claim 2 or 3, characterized in that, The battery comprises a plurality of battery cells, and the battery cell comprises at least one region with inconsistent temperatures; The determination of at least one temperature parameter corresponding to at least one temperature zone of the battery at a current time comprises: determining a first battery cell located in at least one temperature partition of the battery, and determining at least one temperature parameter corresponding to the at least one region of the first battery cell at a current time point; the building is based on at least one temperature parameter corresponding to at least one temperature partition of the battery at the current time point, to predict the target model of at least one temperature parameter corresponding to at least one temperature partition of the battery at the next time point, comprising: building is based on at least one temperature parameter corresponding to the at least one region of the first battery cell at the current time point, to calculate the target model of at least one temperature parameter corresponding to the at least one region of the first battery cell at the next time point.

5. The method of claim 2, wherein, the mutual coupling of the thermal model and the electrical model generates an electro-thermal coupling model, comprising: determining the model output of the electro-thermal coupling model; based on the signal transmission process and the model interaction process between the model output, the thermal model and the electrical model, the thermal model and the electrical model are combined to generate the electro-thermal coupling model, wherein the output of the thermal model includes the input of the electrical model, and the output of the electrical model includes the input of the thermal model.

6. The method of claim 2, wherein, the mutual coupling of the thermal model and the electrical model generates an electro-thermal coupling model, comprising: intercoupling the thermal model and the electrical model to generate an initial electro-thermal coupling model, wherein the initial electro-thermal coupling model is called to realize the prediction of a single sub-time domain in a target time domain; by an inner loop mode, based on the initial electro-thermal coupling model, an electro-thermal coupling model is built, wherein the electro-thermal coupling model is called to realize the prediction of the target time domain.

7. The method of claim 6, wherein, the electro-thermal coupling model is built by the inner loop mode based on the initial electro-thermal coupling model, comprising: identifying time domain related variables in the initial electro-thermal coupling model; based on the inner loop mode, the time domain related variables are changed to generate a reference electro-thermal coupling model; identifying input parameters in the reference electro-thermal coupling model; based on the inner loop mode, the input parameters are changed to target derived parameters to generate an electro-thermal coupling model.

8. The method of claim 7, wherein, the time domain related variables include clock parameters and integral modules; based on the inner loop mode, the time domain related variables are changed to generate a reference electro-thermal coupling model, comprising: determining the number of inner loop iterations and the length of single iteration; based on the product of the number of inner loop iterations and the length of single iteration, the clock parameters are replaced; determining the single iteration calculation increment; based on the single iteration calculation increment, the integral module is replaced to generate a reference electro-thermal coupling model.

9. The method according to any one of claims 6-8, characterized in that, the electro-thermal coupling model is built by the inner loop mode based on the initial electro-thermal coupling model, comprising: by an inner loop mode, an inner loop system is constructed in the initial electro-thermal coupling model to generate an electro-thermal coupling model; using the electro-thermal coupling model, based on the parameters of the electro-thermal control path, to predict the battery state change path of the battery under the electro-thermal control path, comprising: taking the parameters of the electro-thermal control path as the input of the current iteration of the electro-thermal coupling model, using the electro-thermal coupling model to generate the first battery state of the battery under the electro-thermal control path; The internal circulation system is used to accumulate the input of the electro-thermal coupling model in the current iteration, the output of the electro-thermal coupling model in the current iteration, and generate an accumulated battery state of the battery under the electro-thermal control path; The accumulated battery state is used as the input of the next iteration, and the electro-thermal coupling model is used to generate a second battery state of the battery under the electro-thermal control path; The internal circulation system is used to accumulate the accumulated battery state and the second battery state, generate a reference battery state, update the input of the electro-thermal coupling model in the current iteration with the reference battery state, and return to execute the step of using the electro-thermal coupling model to generate the first battery state of the battery under the electro-thermal control path until a stop iteration condition is reached, and the battery state change path of the battery under the electro-thermal control path is determined based on the accumulated battery state generated in each iteration.

10. The method of claim 1, wherein, The electro-thermal control path includes multiple; The electro-thermal control of the battery based on the battery state change path of the battery under the electro-thermal control path includes: Based on the battery state change path of the battery under each electro-thermal control path, determine the dimension score of each battery state change path in the dimension of battery temperature and / or battery state of charge; Based on the dimension score of each battery state change path, determine a target electro-thermal control path from multiple electro-thermal control paths, and perform electro-thermal control on the battery based on the target electro-thermal control path.

11. An electric heating control device for a battery, characterized by The device includes: A determination module configured to determine an electro-thermal coupling model, wherein the electro-thermal coupling model includes an electric model and a thermal model coupled with each other, the thermal model is used to predict at least one temperature parameter corresponding to at least one temperature partition of a battery, the thermal model predicts the temperature parameter corresponding to a target temperature partition based on a cell in the target temperature partition, the target temperature partition is any one of the at least one temperature partition, and the at least one temperature partition is obtained by partitioning according to the difference in heat exchange effect between the battery and the external environment; A prediction module configured to use the electro-thermal coupling model to predict a battery state change path of the battery under an electro-thermal control path based on parameters of the electro-thermal control path, wherein the battery state change path includes a change path of the at least one temperature parameter; A control module configured to perform electro-thermal control on the battery based on the battery state change path of the battery under the electro-thermal control path.

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