A battery thermal management method, apparatus, storage medium, and vehicle for a vehicle.

Abstract

This invention discloses a battery thermal management method, apparatus, storage medium, and vehicle for a vehicle. The method includes: acquiring historical driving data of the vehicle, wherein the historical driving data includes at least one of the following: the historical remaining battery charge and the vehicle driving time corresponding to the historical remaining charge; determining predicted driving data of the vehicle based on the historical driving data, wherein the predicted driving data represents the vehicle's operating state over a future period; determining a predicted temperature and a predicted temperature rise rate of the battery over the future period based on the predicted driving data and the battery's temperature characteristics, wherein the temperature characteristics include at least one of the following: the battery's initial temperature, the ambient temperature of the battery, and the battery's coolant temperature; and performing thermal management of the battery over the future period based on the predicted temperature and the predicted temperature rise rate. This invention solves the technical problem of difficulty in ensuring the reliability of the battery and the entire vehicle.

Description

Technical Field

[0001] This invention relates to the field of batteries, and more specifically, to a battery thermal management method, apparatus, storage medium, and vehicle for a vehicle. Background Technology

[0002] Currently, battery thermal management strategies typically rely on the battery's current temperature for activation and control, without considering future operating conditions. Furthermore, to avoid impacting passenger cabin comfort, thermal management is usually only activated after the battery temperature exceeds the optimal range. However, relying solely on the current temperature for thermal management cannot effectively reduce the battery temperature in a timely manner. This makes it difficult to ensure the battery operates within a suitable temperature range and increases the energy consumption of activating thermal management, ultimately leading to technical issues that compromise the reliability of the battery and the entire vehicle.

[0003] There is currently no effective solution to the aforementioned technical problems that make it difficult to guarantee the reliability of batteries and vehicles. Summary of the Invention

[0004] This invention provides a battery thermal management method, apparatus, storage medium, and vehicle for vehicles, to at least solve the technical problem of difficulty in ensuring the reliability of batteries and the entire vehicle.

[0005] According to one aspect of the present invention, a battery thermal management method for a vehicle is provided. The method may include: acquiring historical driving data of the vehicle, wherein the historical driving data includes at least one of the following: historical remaining battery charge and vehicle driving time corresponding to the historical remaining charge; determining predicted driving data of the vehicle based on the historical driving data, wherein the predicted driving data represents the vehicle's operating state over a future period; determining a predicted temperature and a predicted temperature rise rate of the battery over the future period based on the predicted driving data and the battery's temperature characteristics, wherein the temperature characteristics include at least one of the following: the battery's initial temperature, the ambient temperature of the battery, and the battery's coolant temperature; and performing thermal management of the battery over the future period based on the predicted temperature and the predicted temperature rise rate.

[0006] Optionally, based on the predicted driving data and the battery's temperature characteristics, the predicted temperature and predicted temperature rise rate of the battery over a future period are determined, including: inputting the predicted driving data and temperature characteristics into a target model for prediction to obtain the predicted temperature and predicted temperature rise rate, wherein the target model is obtained by iteratively training on experimental data from at least one vehicle.

[0007] Optionally, the battery thermal management method of the vehicle may further include: acquiring the average temperature of the battery; comparing the average temperature with a temperature threshold of the battery to obtain a comparison result; and determining the future duration based on the comparison result.

[0008] Optionally, determining the future duration based on the comparison result includes: in response to the comparison result that the average temperature exceeds a temperature threshold, determining the future duration as a first future duration, wherein the first future duration is used to predict the predicted temperature and the predicted temperature rise rate; and in response to the comparison result that the average temperature is less than a temperature threshold, determining the future duration as a second future duration, wherein the second future duration is used to predict the predicted temperature and the predicted temperature rise rate.

[0009] Optionally, thermal management of the battery over a future period is performed based on the predicted temperature and the predicted temperature rise rate, including: cooling the battery in response to a first predicted temperature exceeding a cooling temperature threshold; cooling the battery based on the relationship between a first cell temperature at the start time and the cooling temperature threshold in response to a first predicted temperature being less than the cooling temperature threshold and the predicted temperature rise rate exceeding a first instantaneous temperature rise rate; and cooling the battery in response to a first predicted temperature being less than the cooling temperature threshold and the predicted temperature rise rate being less than a first instantaneous temperature rise rate.

[0010] Optionally, thermal management of the battery over a future period based on the predicted temperature and the predicted temperature rise rate further includes: heating the battery in response to a second predicted temperature of the battery not exceeding a heating temperature threshold of the battery, wherein the second predicted temperature is much lower than the first predicted temperature; heating the battery based on the relationship between the second cell temperature of the battery at the initial moment and the heating temperature threshold in response to a second predicted temperature exceeding the heating temperature threshold and the predicted temperature rise rate not exceeding a second instantaneous temperature rise rate of the battery, wherein the second instantaneous temperature rise rate is much lower than the first instantaneous temperature rise rate and the second cell temperature is much lower than the first cell temperature; and heating the battery in response to a second predicted temperature exceeding the heating temperature threshold and the predicted temperature rise rate exceeding the second instantaneous temperature rise rate.

[0011] According to one aspect of the present invention, a battery thermal management device for a vehicle is provided. The device may include: an acquisition unit for acquiring historical driving data of the vehicle, wherein the historical driving data includes at least one of the following: historical remaining battery charge and vehicle driving time corresponding to the historical remaining charge; a first determination unit for determining predicted driving data of the vehicle based on the historical driving data, wherein the predicted driving data represents the vehicle's operating state over a future period; a second determination unit for determining a predicted temperature and a predicted temperature rise rate of the battery over a future period based on the predicted driving data and the battery's temperature characteristics, wherein the temperature characteristics include at least one of the following: the battery's initial temperature, the ambient temperature of the battery, and the battery's coolant temperature; and a thermal management unit for performing thermal management of the battery over a future period based on the predicted temperature and the predicted temperature rise rate.

[0012] According to another aspect of the present invention, a computer-readable storage medium is also provided. The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the battery thermal management method for a vehicle according to the present invention.

[0013] According to another aspect of the present invention, a processor is also provided. The processor is used to run a program, wherein the program, when run by the processor, executes the battery thermal management method for a vehicle according to the embodiments of the present invention.

[0014] According to another aspect of the present invention, a vehicle is also provided, which is used to perform the battery thermal management method of the vehicle according to the present invention.

[0015] In this embodiment of the invention, historical driving data of the vehicle is acquired, and based on the acquired historical driving data, the predicted driving data of the vehicle is determined, that is, the operating state of the vehicle in the future is determined. Based on the predicted driving data and the temperature characteristics of the battery, the predicted temperature and predicted temperature rise rate of the battery in the future are determined. Based on the determined predicted temperature and predicted temperature rise rate, thermal management of the battery is performed in the future, thereby achieving the purpose of timely reducing the temperature of the battery. This solves the technical problem of difficulty in ensuring the reliability of the battery and the whole vehicle, and achieves the technical effect of ensuring the reliability of the battery and the whole vehicle. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart of a battery thermal management method for a vehicle according to an embodiment of the present invention;

[0018] Figure 2 This is a flowchart of a data-driven power battery thermal management strategy optimization method according to an embodiment of the present invention;

[0019] Figure 3 This is a flowchart of a method for predicting the performance parameters of a power battery according to an embodiment of the present invention;

[0020] Figure 4 This is a flowchart of an optimization method for battery thermal management activation conditions according to an embodiment of the present invention;

[0021] Figure 5(a) is a flowchart of an optimization method for thermal management activation conditions in different thermal management demand scenarios according to an embodiment of the present invention;

[0022] Figure 5(b) is a flowchart of an optimization method for thermal management activation conditions in different thermal management demand scenarios according to an embodiment of the present invention;

[0023] Figure 6 This is a schematic diagram of a data-driven power battery thermal management strategy optimization device according to an embodiment of the present invention;

[0024] Figure 7 This is a schematic diagram of a thermal management device for a vehicle battery according to an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Example 1

[0028] According to an embodiment of the present invention, a battery thermal management method for a vehicle is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] Figure 1 This is a flowchart of a battery thermal management method for a vehicle according to an embodiment of the present invention. The method may include the following steps:

[0030] Step S101: Obtain the vehicle's historical driving data.

[0031] In the technical solution provided by step S101 of the present invention, the historical driving data can be historical data obtained from the cloud. The historical driving data can include at least one of the following: historical vehicle speed, historical battery voltage, historical battery current, historical battery temperature, historical average heating power of the battery, historical average cooling power of the battery, historical remaining battery charge, and vehicle driving time corresponding to the historical remaining charge, etc. This is only an example and is not specifically limited.

[0032] Optionally, historical driving data of the vehicle can be downloaded from the cloud, and redundant data in the historical driving data can be deleted while retaining the valid data in the historical driving data.

[0033] Step S102: Based on historical driving data, determine the predicted driving data of the vehicle.

[0034] In the technical solution provided by step S102 of the present invention, the predicted driving data can be used to represent the operating status of the vehicle in the future. The predicted driving data may include, but is not limited to: the vehicle's driving speed in the future, the vehicle's battery power in the future, and the battery usage time corresponding to different vehicle speeds and power in the future.

[0035] Optionally, after acquiring the vehicle's historical driving data, the predicted driving data of the vehicle can be determined based on the historical driving data. For example, based on various types of historical driving data such as the vehicle's historical speed, battery's historical voltage, battery's historical current, battery's historical temperature, battery's historical average heating power, battery's historical average cooling power, battery's historical remaining charge, and the vehicle's driving time corresponding to the historical remaining charge, the weights corresponding to each data point can be determined. After the weights are determined and adjusted, the predicted driving data of the vehicle can be determined based on the relationship between each data point and the weights corresponding to that data point, thereby obtaining the prediction result of the vehicle's future driving conditions.

[0036] Step S103: Based on the predicted driving data and the battery temperature characteristics, determine the predicted temperature and predicted temperature rise rate of the battery over the future period.

[0037] In the technical solution provided by step S103 of the present invention, the temperature characteristics of the battery include at least one of the following: the initial temperature of the battery, the ambient temperature of the battery, and the coolant temperature of the battery. The predicted temperature can be represented by T. 预测 The predicted temperature rise rate can be represented by dT. 预测 / dt is used to represent it.

[0038] Optionally, after determining the vehicle's predicted driving data based on historical driving data, the predicted battery temperature and predicted temperature rise rate over a future period are determined based on the predicted driving data and the battery's temperature characteristics. For example, based on the predicted driving data, battery temperature characteristics, and remaining battery capacity, the predicted battery temperature T over a future period can be determined. 预测 and the predicted rate of temperature rise dT 预测 / dt.

[0039] Step S104: Based on the predicted temperature and the predicted rate of temperature rise, perform thermal management on the battery over the future.

[0040] In the technical solution provided by step S104 of the present invention, the above-mentioned thermal management can be to cool the battery or to heat the battery.

[0041] Optionally, after determining the predicted temperature and predicted temperature rise rate of the battery over a future period based on predicted driving data and battery temperature characteristics, thermal management of the battery is performed over the future period based on the predicted temperature and predicted temperature rise rate. For example, the battery can be cooled based on the relationship between the predicted temperature and a cooling temperature threshold, and the relationship between the predicted temperature rise rate and the instantaneous temperature rise rate; or the battery can be heated based on the relationship between the predicted temperature and a heating temperature threshold, and the relationship between the predicted temperature rise rate and the instantaneous temperature rise rate. The cooling temperature threshold can be a temperature limit T at which the battery needs to immediately initiate cooling. cool-limit The heating temperature threshold can be the temperature limit T at which the battery needs to immediately start heating. heat-limit The instantaneous temperature rise rate can include: the highest instantaneous temperature rise rate v under cooling conditions. ΔTmax And the lowest instantaneous rate of temperature rise v under heating conditions. ΔTmin .

[0042] In steps S101 to S104 of this application, historical driving data of the vehicle is obtained, and based on the obtained historical driving data, the predicted driving data of the vehicle is determined, that is, the operating status of the vehicle in the future is determined. Based on the predicted driving data and the temperature characteristics of the battery, the predicted temperature and predicted temperature rise rate of the battery in the future are determined. Based on the determined predicted temperature and predicted temperature rise rate, thermal management of the battery is performed in the future, thereby achieving the purpose of timely reducing the temperature of the battery. This solves the technical problem of difficulty in ensuring the reliability of the battery and the whole vehicle, and achieves the technical effect of ensuring the reliability of the battery and the whole vehicle.

[0043] The method described in this embodiment will be further described below.

[0044] As an optional embodiment, step S103, based on the predicted driving data and the battery temperature characteristics, determines the predicted temperature and predicted temperature rise rate of the battery over a future period, including: inputting the predicted driving data and temperature characteristics into the target model for prediction, and obtaining the predicted temperature and predicted temperature rise rate.

[0045] In this embodiment, the target model can be a power battery performance prediction model obtained by iteratively training experimental data from at least one vehicle. The experimental data from at least one vehicle can include parameters such as battery voltage, battery current, maximum and minimum temperatures of individual battery cells, remaining battery capacity, coolant temperature, flow rate, and ambient temperature. This experimental data can cover various usage scenarios such as different temperatures, different remaining battery capacity, and heating or cooling the battery. This is only an example and is not specifically limited.

[0046] Optionally, after determining the vehicle's predicted driving data based on historical driving data, the obtained predicted driving data and temperature characteristics are input into the power battery performance prediction model for prediction, which yields the predicted temperature T. 预测 and the predicted rate of temperature rise dT 预测 / dt.

[0047] Optionally, parameters such as the battery's initial temperature, remaining battery capacity, coolant temperature, flow rate, and ambient temperature are used as inputs, and the battery voltage, battery current, battery temperature, and coolant temperature are used as outputs to establish a neural network-based power battery performance prediction model. Experimental data from the vehicle are input into the established power battery performance prediction model for iterative training, resulting in a converged power battery performance prediction model. This converged model can include the functional relationships between the battery voltage, battery current, battery temperature, and coolant temperature and the various input parameters, as shown in the following equation:

[0048] T t =f(t,T0,SOC,T) inlet Q flow ,T amb (1)

[0049] Where t can be used to represent time, T0 can be the initial temperature of the battery, and T t T can be the temperature of the battery at time t. inlet This can be the temperature at which the battery coolant enters the water, Q. flow This can be the battery coolant flow rate, T amb It can be the ambient temperature.

[0050] As an optional embodiment, the battery thermal management method for the vehicle may further include: acquiring the average temperature of the battery; comparing the average temperature with a temperature threshold of the battery to obtain a comparison result; and determining the future duration based on the comparison result.

[0051] In this embodiment, the aforementioned average temperature can be represented by T. avg Let t represent the temperature threshold, which can be 15℃, and the future duration, which can be the temperature prediction time. The temperature prediction time can be represented by t. 预测 This is just an example and is not intended to be specific.

[0052] Optionally, the average temperature of the battery is obtained, and compared with the battery's temperature threshold to obtain the comparison result, that is, the relationship between the average temperature and the temperature threshold can be obtained. Based on the relationship between the average temperature and the temperature threshold, the future duration can be determined, that is, the temperature prediction time t can be determined. 预测 .

[0053] As an optional embodiment, determining the future duration based on the comparison result includes: determining the future duration as a first future duration in response to the comparison result that the average temperature exceeds a temperature threshold; and determining the future duration as a second future duration in response to the comparison result that the average temperature is less than a temperature threshold.

[0054] In this embodiment, the first future duration can be used to predict the predicted temperature and the predicted temperature rise rate. The first future duration can be 5 minutes. The second future duration can be used to predict the predicted temperature and the predicted temperature rise rate. The second future duration can be 10 minutes. This is only an example and is not specifically limited.

[0055] Optionally, after comparing the average temperature with the battery's temperature threshold and obtaining the comparison result, the future duration can be determined based on the relationship between the average temperature and the temperature threshold. For example, if the comparison result is that the average temperature exceeds the temperature threshold, the temperature prediction time is determined to be 5 minutes; if the comparison result is that the average temperature is less than the temperature threshold, the temperature prediction time is determined to be 10 minutes.

[0056] As an optional embodiment, step S104, based on the predicted temperature and the predicted temperature rise rate, performs thermal management on the battery over a future period, including: cooling the battery in response to a first predicted temperature exceeding a cooling temperature threshold; cooling the battery based on the relationship between a first cell temperature at the start time and the cooling temperature threshold in response to a first predicted temperature being less than the cooling temperature threshold and the predicted temperature rise rate exceeding a first instantaneous temperature rise rate; and cooling the battery in response to a first predicted temperature being less than the cooling temperature threshold and the predicted temperature rise rate being less than a first instantaneous temperature rise rate.

[0057] In this embodiment, the first predicted temperature can be the predicted highest monomer temperature T. 预测-max The aforementioned cooling temperature threshold can be the temperature limit T at which the battery needs to be cooled immediately. cool-limit The aforementioned first instantaneous temperature rise rate can be the highest instantaneous temperature rise rate v under cooling conditions. ΔTmax The aforementioned first cell temperature can be the highest cell temperature T at the initial moment of the battery. 0max .

[0058] Optionally, after determining the predicted temperature and predicted temperature rise rate of the battery over a future period based on predicted driving data and battery temperature characteristics, it is determined whether the first predicted temperature of the battery exceeds the battery's cooling temperature threshold. If the first predicted temperature of the battery exceeds the battery's cooling temperature threshold, the battery is directly cooled. If the first predicted temperature of the battery is less than the battery's cooling temperature threshold, it is determined whether the predicted temperature rise rate exceeds the battery's first instantaneous temperature rise rate. If the predicted temperature rise rate exceeds the battery's first instantaneous temperature rise rate, the battery is cooled according to the relationship between the battery's first single-cell temperature at the beginning and the cooling temperature threshold. If the predicted temperature rise rate is less than the battery's first instantaneous temperature rise rate, cooling needs to be activated in advance, and the conventional cooling strategy can be used.

[0059] Optionally, in response to a predicted temperature being lower than a cooling temperature threshold and a predicted temperature rise rate exceeding the battery's first instantaneous temperature rise rate, cooling the battery based on the relationship between the battery's first cell temperature at the initial moment and the cooling temperature threshold can be achieved through the following steps: For T... 0max Is it greater than or equal to (T) cool-limit -5) Make a judgment if T 0max ≥(T cool-limit -5), then immediately turn on the cooling; if T 0max <(T cool-limit -5), then set the target cooling temperature to T. cool-target =T cool-limit -10, in t 预测 From T time 预测-maxCool down to T cool-target As a standard, calculate the temperature T at which cooling needs to be turned on in advance. cool-on .

[0060] As an optional embodiment, step S104, which involves thermal management of the battery over a future period based on the predicted temperature and the predicted temperature rise rate, further includes: heating the battery in response to the second predicted temperature of the battery not exceeding a heating temperature threshold of the battery; heating the battery based on the relationship between the second single-cell temperature of the battery at the initial moment and the heating temperature threshold in response to the second predicted temperature being greater than the heating temperature threshold and the predicted temperature rise rate not exceeding the second instantaneous temperature rise rate of the battery; and heating the battery in response to the second predicted temperature being greater than the heating temperature threshold and the predicted temperature rise rate being greater than the second instantaneous temperature rise rate.

[0061] In this embodiment, the second predicted temperature can be much lower than the first predicted temperature, and the second predicted temperature can be the predicted lowest monomer temperature T. 预测-min The aforementioned heating temperature threshold can be the temperature limit T at which the battery needs to immediately activate heating. heat-limit The second instantaneous temperature rise rate can be much smaller than the first instantaneous temperature rise rate, and the second instantaneous temperature rise rate can be the lowest instantaneous temperature rise rate v under heating conditions. ΔTmin The second cell temperature can be much lower than the first cell temperature, and the second cell temperature can be the lowest cell temperature T at the start of the battery. 0min .

[0062] Optionally, after determining the predicted temperature and predicted temperature rise rate of the battery over a future period based on predicted driving data and battery temperature characteristics, it is determined whether the second predicted temperature of the battery exceeds the battery's heating temperature threshold. If the second predicted temperature of the battery does not exceed the battery's heating temperature threshold, the battery is directly heated. If the second predicted temperature of the battery is greater than the battery's heating temperature threshold, it is determined whether the predicted temperature rise rate exceeds the battery's second instantaneous temperature rise rate. If the predicted temperature rise rate does not exceed the battery's second instantaneous temperature rise rate, the battery is heated according to the relationship between the battery's second single-cell temperature at the initial moment and the heating temperature threshold. If the predicted temperature rise rate is greater than the battery's second instantaneous temperature rise rate, heating needs to be started in advance, and it can be started according to the conventional heating strategy.

[0063] Optionally, in response to the second predicted temperature being greater than the heating temperature threshold and the predicted temperature rise rate not exceeding the battery's second instantaneous temperature rise rate, heating the battery based on the relationship between the battery's second single-cell temperature at the initial moment and the heating temperature threshold can be achieved through the following steps: For T... 0min Is it less than or equal to (T) heat-limit -5) Make a judgment if T0min ≤(T heat If -limit--5), then heating will start immediately; if T 0min >(T heat-limit -5), then set the target cooling temperature to T. heat-target =T heat-limit +10, to be in t 预测 From T time 预测-min Heat up to T heat-target As a standard, calculate the temperature T that needs to be turned on in advance. heatl-on .

[0064] This embodiment acquires historical driving data of the vehicle, determines the vehicle's predicted driving data based on the acquired historical driving data, that is, determines the vehicle's operating status in the future. Based on the predicted driving data and the battery's temperature characteristics, it determines the predicted temperature and predicted temperature rise rate of the battery in the future. Based on the determined predicted temperature and predicted temperature rise rate, it performs thermal management on the battery in the future, thereby solving the technical problem of difficulty in ensuring the reliability of the battery and the whole vehicle, and achieving the technical effect of ensuring the reliability of the battery and the whole vehicle.

[0065] Example 2

[0066] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.

[0067] Current battery thermal management strategies typically rely on the battery's current temperature for activation and control, neglecting future operating conditions. Furthermore, to avoid impacting passenger comfort, thermal management is usually only activated after the battery temperature exceeds its optimal range. However, relying solely on current temperature for thermal management fails to promptly reduce battery temperature, leading to technical challenges in ensuring the reliability of both the battery and the vehicle. Therefore, a new battery thermal management method is needed to guarantee the reliability of both the battery and the vehicle as a whole.

[0068] In one related technology, a temperature control method for a power battery system is disclosed. This method includes: acquiring temperature and current parameters of the battery pack in the power battery system; and employing fuzzy control and predictive control strategies to control the coolant outlet temperature of the temperature control module based on the temperature and current parameters, thereby controlling the battery pack temperature within a set temperature range. However, this method only uses fuzzy control and predictive control strategies to control the coolant outlet temperature of the temperature control module based on the temperature and current parameters. It cannot distinguish between different thermal management requirement scenarios to optimize the activation conditions of battery thermal management, thus making it difficult to guarantee the reliability of the battery and the entire vehicle.

[0069] However, this invention proposes a data-driven optimization method for power battery thermal management strategy. By predicting future driving conditions based on historical data in the cloud and establishing a battery performance prediction model to predict battery parameters, the method optimizes the activation conditions of battery thermal management by setting different battery temperature prediction times, setting thermal management activation temperature limits based on the battery's optimal operating temperature limits, and differentiating different thermal management demand scenarios based on the predicted temperature and average temperature rise rate range. This achieves the goal of timely reducing battery temperature, solves the technical problem of difficulty in ensuring the reliability of battery and vehicle use, and realizes the technical effect of ensuring the reliability of battery and vehicle use.

[0070] Figure 2 This is a flowchart of a data-driven power battery thermal management strategy optimization method according to an embodiment of the present invention, such as... Figure 2 As shown, the method may include the following steps:

[0071] Step S201: Predict future driving conditions based on historical data in the cloud. Weights are assigned and adjusted for various types of historical data according to different vehicle speeds, SOCs and corresponding driving times, and the weighted results are used to obtain the prediction results for future driving conditions.

[0072] After obtaining the weighted prediction results for future driving conditions, the process proceeds to step S202, where a power battery performance prediction model is established. This model includes modules for electrical, thermal, flow field, and usage strategies. By inputting the predicted future driving conditions obtained in step one, along with basic parameters such as ambient temperature, initial battery temperature, remaining battery charge (State of Charge, SOC), coolant temperature, and flow rate, the model can predict the battery temperature T at a specified future time. 预测 and the average temperature rise rate dT 预测 / dt.

[0073] Predicting the battery temperature T at a specified future time. 预测 and the average temperature rise rate dT 预测 After / dt, proceed to step S203 to determine the predicted temperature T. 预测 and the average temperature rise rate dT 预测 The conditions for enabling battery thermal management are optimized within the range of / dt.

[0074] Figure 3 This is a flowchart of a method for predicting the performance parameters of a power battery according to an embodiment of the present invention, such as... Figure 3 As shown, the method may include the following steps:

[0075] Step S301: Using the battery initial temperature, SOC, coolant temperature, flow rate, and ambient temperature as inputs, and the battery voltage, current, temperature, and coolant temperature as outputs, establish a power battery performance prediction model based on a neural network model.

[0076] After establishing a power battery performance prediction model based on a neural network model, proceed to step S302, where N sets of real test data are taken, where N is a positive integer. The test data needs to include parameters such as battery voltage, current, maximum and minimum temperature of individual cells, SOC, coolant temperature, flow rate, and ambient temperature, and cover various usage scenarios such as different temperatures, SOC, heating, and cooling.

[0077] Next, in step S303, the N sets of real experimental data from step S302 are input into the model for iterative training. The converged model includes the functional relationships between battery voltage, current, temperature, and coolant temperature and each input parameter. The functional relationship corresponding to battery temperature is shown in the following formula, which can be used to calculate battery temperature parameters:

[0078] T t =f(t,T0,SOC,T) inlet Q flow ,T amb (1)

[0079] Where t can be time, T0 can be the initial temperature of the battery, and T t T can be the temperature of the battery at time t. inlet This can be the temperature at which the battery coolant enters the water, Q. flow This can be the battery coolant flow rate, T amb It can be the ambient temperature.

[0080] After obtaining the converged model, proceed to step S304. Based on the converged model from step S303, and by inputting the future predicted operating conditions and basic parameters such as ambient temperature, initial battery temperature, SOC, coolant temperature, and flow rate, set the operating condition running time t. 预测 t can be calculated using the following formula. 预测 Battery temperature T at time 预测 and the average temperature rise rate dT over a specified period of time 预测 / dt:

[0081] T 预测 =f(t) 预测 ,T0,SOC,T inlet Q flow ,T amb (2)

[0082]

[0083] Where Δt can be the evaluation time for the average temperature rise rate, T 预测-Δt It can be (t) 预测-Δt The battery temperature at time Δt is usually defined as 3 min.

[0084] Optionally, the operating time t 预测 Based on the average battery temperature T avg The interval in which T is located is set, if T avg If the temperature is ≥15℃, then set t. 预测 =5min, if T avg If the temperature is <15℃, then set t 预测 =10min.

[0085] Figure 4 This is a flowchart of an optimization method for battery thermal management activation conditions according to an embodiment of the present invention, such as... Figure 4 As shown, the method may include the following steps:

[0086] Step S401, using the upper and lower limits of the battery's optimal operating temperature T opt-uplimit and T opt-downlimit Based on this, the design margin ΔT is considered separately. uplimit and ΔT downlimit The temperature limit T for batteries that require immediate cooling / heating cool-limit and T heat-limit .

[0087] Alternatively, the temperature limit T at which the battery needs to immediately activate cooling / heating can be obtained using the following formula. cool-limit and T heat-limit :

[0088] T cool-limit =T opt-uplimit +ΔT uplimit (4)

[0089] T heat-limit =T opt-downlimit -ΔT downlimit (5)

[0090] The temperature limit T at which the battery requires immediate cooling / heating is obtained. cool-limit and T heat-limit Next, proceed to step S402, based on the highest instantaneous temperature rise rate v under cooling conditions during bench or assembly testing. ΔTmax And the lowest instantaneous rate of temperature rise v under heating conditions. ΔTmin The temperature rise rate limits were determined for cooling and heating conditions, respectively.

[0091] After determining the temperature rise rate limits under cooling and heating conditions, proceed to step S403, T预测 Including the predicted results T for the highest and lowest monomer temperatures 预测-max and T 预测-min Using this as a judgment variable, the conditions for activating thermal management are optimized according to different thermal management needs.

[0092] Figure 5(a) is a flowchart of an optimization method for thermal management activation conditions under different thermal management demand scenarios according to an embodiment of the present invention. As shown in Figure 5(a), the method may include the following steps:

[0093] Step S501: Determine that the battery has a cooling requirement.

[0094] After determining that the battery has a cooling requirement, proceed to step S502 to process T. 预测-max Is it greater than or equal to T? cool-limit Make a judgment.

[0095] If T 预测-max ≥T cool-limit If so, proceed to step S503 and immediately turn on the cooling.

[0096] If T 预测-max <T cool-limit Then proceed to step S504, and process dT 预测 Is / dt greater than or equal to v? ΔTmax Make a judgment.

[0097] If dT 预测 / dt≥v ΔTmax Then proceed to step S505, for T 0max Is it greater than or equal to (T) cool-limit -5) Make a judgment if T 0max ≥(T cool-limit -5), then proceed to step S506, immediately activate cooling, if T 0max <(T cool-limit -5), then proceed to step S507, and set the target cooling temperature to T. cool-target =T cool-limit -10, in t 预测 From T time 预测-max Cool down to T cool-target As a standard, calculate the temperature T at which cooling needs to be turned on in advance. cool-on .

[0098] If dT 预测 / dt <v ΔTmax If the condition is met, proceed to step S508. There is no need to turn on the cooling in advance; simply turn it on using the standard cooling strategy.

[0099] Alternatively, the temperature T that needs to be turned on in advance can be calculated using the following formula. cool-on:

[0100] T cool-on =T 预测 -(P cool *t 预测 ) / cm (6)

[0101] Among them, P cool Δt represents the average cooling power over a time period, c represents the specific heat capacity of a single battery cell, and m represents the weight of all battery cells.

[0102] Figure 5(b) is a flowchart of another method for optimizing thermal management activation conditions for different thermal management demand scenarios according to an embodiment of the present invention. As shown in Figure 5(b), the method may include the following steps:

[0103] Step S511: Determine that the battery has a heating requirement.

[0104] After determining that the battery has a heating requirement, proceed to step S512 to process T. 预测-min Is it less than or equal to T? heat-limit Make a judgment.

[0105] If T 预测-min ≤T heat-limit If so, proceed to step S513 and immediately start heating.

[0106] If T 预测-min >T heat-limit Then proceed to step S514, and process dT 预测 Is / dt less than or equal to v? ΔTmin Make a judgment.

[0107] If dT 预测 / dt≤v ΔTmin Then proceed to step S515, for T 0min Is it less than or equal to (T) heat-limit -5) Make a judgment if T 0min ≤(T heat If -limit--5), then proceed to step S516, immediately start heating, if T 0min >(T heat-limit -5), then proceed to step S517, and set the target cooling temperature to T. heat-target =T heat-limit +10, to be in t 预测 From T time 预测-min Heat up to T heat-target As a standard, calculate the temperature T that needs to be turned on in advance. heatl-on .

[0108] If dT 预测 / dt>vΔTmin If the condition is met, proceed to step S518. There is no need to start heating in advance; simply start heating according to the conventional heating strategy.

[0109] Alternatively, the temperature T that needs to be turned on in advance can be calculated using the following formula. heat-on :

[0110] T heat-on =T 预测 +(P heat *t 预测 ) / cm (7)

[0111] Among them, P heat It can be the average heating power over a time period of Δt.

[0112] Alternatively, the average cooling power and average heating power over a time period Δt can be determined using the following formula:

[0113] P cool =f(T) amb ) = c 液 *ρ 液 *Q flow-cool *Δt*(T inlet-cool -T outlet-cool (8)

[0114] P heat =f'(T amb ) = c 液 *ρ 液 *Q flow-heat *Δt*(T outlet-heat -T inlet-heat (9)

[0115] Among them, c 液 It can be the specific heat capacity of the coolant, ρ 液 Q can be the density of the coolant. flow-cool and Q flow-heat The coolant flow rate can be divided into coolant flow rate under cooling conditions and coolant flow rate under heating conditions, T inlet-cool and T inlet-heat The inlet temperature of the coolant can be divided into the coolant inlet temperature under cooling conditions and the coolant inlet temperature under heating conditions, T. outlet-cool and T outlet-heat These can be the coolant outlet temperature under cooling conditions and the coolant outlet temperature under heating conditions, respectively.

[0116] Optionally, following the above method, the calculated average cooling power and average heating power under different ambient temperatures are used to form P. cool and P heatThe table ensures that when calculating the temperature at which cooling and heating are started in advance, the corresponding average cooling power and average heating power can be obtained by looking up the table using the ambient temperature.

[0117] Figure 6 This is a schematic diagram of a data-driven power battery thermal management strategy optimization device according to an embodiment of the present invention, as shown below. Figure 6 As shown, the device may include: a condition prediction module 601, a battery temperature prediction module 602, a prediction time judgment module 603, a thermal management demand judgment module 604, a thermal management early activation judgment module 605, an ambient temperature acquisition module 606, and a thermal management demand power judgment module 607. The condition prediction module 601 can be used to acquire future condition inputs; the battery temperature prediction module 602 can be used to predict the battery temperature and temperature rise rate at a specific moment based on the future condition inputs; the prediction time judgment module 603 can be used to determine the moment when the temperature needs to be predicted based on the current battery temperature; the thermal management demand judgment module 604 can be used to determine cooling or heating needs based on the predicted battery temperature; the thermal management early activation judgment module 605 can be used to determine whether thermal management needs to be activated in advance and the activation temperature conditions based on the predicted battery temperature and temperature rise rate, combined with the target temperature and the cooling or heating power obtained from a lookup table; the ambient temperature acquisition module 606 can be used to acquire the current ambient temperature; and the thermal management demand power judgment module 607 can be used to obtain the cooling or heating power requirements based on the ambient temperature and a lookup table.

[0118] Optionally, the operating condition prediction module can input the predicted future operating conditions into the battery temperature prediction module, and the prediction time judgment module can input the determined prediction time into the battery temperature prediction module. The battery temperature prediction module can predict the battery temperature and temperature rise rate at a specified time according to the input prediction time. Specifically, the predicted battery temperature is input into the thermal management demand judgment module and the thermal management early activation judgment module, the temperature rise rate is input into the thermal management early activation judgment module, and the ambient temperature acquisition module can input the acquired ambient temperature into the thermal management demand power judgment module. The thermal management demand judgment module can determine the cooling or heating demand based on the predicted battery temperature and input the judgment result into the thermal management demand power judgment module and the thermal management early activation judgment module. The thermal management demand power judgment module can input the cooling or heating demand and the corresponding power demand into the thermal management early activation judgment module.

[0119] Optionally, the operating condition prediction module is invoked to predict future driving conditions based on historical data in the cloud. After weighting, the predicted future driving conditions are obtained and input into the battery temperature prediction module. The prediction time judgment module is then applied, based on the average battery temperature T. avgWithin a given timeframe, the temperature prediction time is set differently. Input is then sent to the battery temperature prediction module. Using the inputs from the first two steps, the battery temperature prediction module is invoked. This module applies a power battery performance prediction model based on a neural network model. Based on future predicted operating conditions, ambient temperature, initial battery temperature, SOC, coolant temperature, flow rate, and other basic parameters, the battery temperature T at a specified future time is predicted. 预测 and the average temperature rise rate dT 预测 / dt, predicts the battery temperature and inputs it into the thermal management demand judgment module and the thermal management early activation judgment module. The temperature rise rate is input into the thermal management early activation judgment module. The thermal management demand judgment module is called. Based on the predicted battery temperature, the current thermal management demand scenario is determined, such as cooling demand or heating demand. The thermal management demand power judgment and thermal management early activation judgment modules are input. The ambient temperature acquisition module is called to obtain the current ambient temperature. The thermal management demand power judgment module is input. The thermal management early activation judgment module is called. Combining the predicted battery temperature, temperature rise rate and target temperature, it is determined whether thermal management needs to be activated in advance. If the result of the previous step is that thermal management needs to be activated in advance, but does not need to be activated immediately, the thermal management power demand judgment module is called. Based on the ambient temperature, the cooling or heating power demand is obtained by looking up a table. The thermal management early activation judgment module is input to calculate the temperature conditions for early activation.

[0120] For example, if the power battery thermal management strategy optimization device is installed in a pure electric vehicle, and assuming that the operating condition prediction module predicts that the future driving condition is a long-term continuous high-speed driving condition, then the operating logic of the strategy optimization device in this vehicle is as follows:

[0121] (1) The Battery Management System (BMS) reports the current average battery temperature T. avg The temperature is 33℃. The prediction time determination module is called to determine the prediction time t. 预测 Input the battery temperature prediction module for 5 minutes;

[0122] (2) Call the battery temperature prediction module, input the prediction conditions and prediction time, and obtain t. 预测 The predicted maximum single-cell temperature is 40℃, with an average temperature rise rate of 5℃ / min. Since the model presets the upper and lower limits of the optimal battery operating temperature to be 35℃ and 15℃ respectively, a design margin ΔT is reserved. uplimit and ΔT downlimit The temperatures T are 15℃ and 25℃ respectively, which are the temperature limits T at which cooling / heating needs to be activated immediately. cool-limit and T heat-limit The temperatures are 50℃ and -10℃ respectively; and the highest instantaneous temperature rise rate v under cooling conditions was measured on the bench / in-vehicle. ΔTmaxThe rate is 3℃ / min. The thermal management requirement judgment and thermal management early activation judgment modules are invoked. Based on the current scenario, it is determined that cooling needs to be activated. The activation temperature condition needs to be based on the highest unit temperature T at the start of the operating condition. 0max Further assessment;

[0123] (3) The highest unit temperature T reported by BMS at the initial moment of operation. 0max It is 35℃, lower than (T cool-limit -5) = 40℃, set the target cooling temperature as T cool-target The ambient temperature is 35℃. The ambient temperature acquisition module is called to collect the current ambient temperature, which is 25℃. The thermal management power demand judgment module is called to find the corresponding cooling power of 3kW. The thermal management early start judgment module is input and the temperature condition for early start of cooling is calculated to be 38℃. That is, when the highest single cell temperature of the battery reaches 38℃, cooling needs to be turned on.

[0124] In this embodiment, future driving conditions are predicted based on historical data from the cloud. The predicted future driving conditions, along with basic parameters such as ambient temperature, initial battery temperature, state of charge (SOC), coolant temperature, and flow rate, are input into the power battery performance prediction model for prediction, thereby obtaining the battery temperature T at a specified future time. 预测 and the average temperature rise rate dT 预测 / dt, by determining the predicted temperature T 预测 and the average temperature rise rate dT 预测 The / dt range optimizes the battery thermal management activation conditions, thereby solving the technical problem of difficulty in ensuring the reliability of battery and vehicle use, and achieving the technical effect of ensuring the reliability of battery and vehicle use.

[0125] Example 3

[0126] According to embodiments of the present invention, a battery thermal management device for a vehicle is also provided. It should be noted that this battery thermal management device can be used to execute a battery thermal management method for a vehicle as described in Embodiment 1.

[0127] Figure 7 This is a schematic diagram of a vehicle battery thermal management device according to an embodiment of the present invention. Figure 7 As shown, the battery thermal management device 700 of the vehicle may include: an acquisition unit 701, a first determination unit 702, a second determination unit 703, and a thermal management unit 704.

[0128] The acquisition unit 701 is used to acquire the vehicle's historical driving data, wherein the historical driving data includes at least one of the following: the battery's historical remaining charge and the vehicle's driving time corresponding to the historical remaining charge.

[0129] The first determining unit 702 is used to determine the predicted driving data of the vehicle based on historical driving data, wherein the predicted driving data is used to represent the operating status of the vehicle in the future time period.

[0130] The second determining unit 703 is used to determine the predicted temperature and predicted temperature rise rate of the battery over a future period based on the predicted driving data and the temperature characteristics of the battery, wherein the temperature characteristics include at least one of the following: the initial temperature of the battery, the ambient temperature of the battery, and the coolant temperature of the battery.

[0131] Thermal management unit 704 is used to perform thermal management of the battery over a future period of time based on predicted temperature and predicted rate of temperature rise.

[0132] Optionally, the second determining unit 703 may include: a prediction module, used to input the predicted driving data and temperature features into the target model for prediction, to obtain the predicted temperature and the predicted temperature rise rate, wherein the target model is obtained by iterative training on experimental data of at least one vehicle.

[0133] Optionally, the first determining unit 702 may include: an acquisition module for acquiring the average temperature of the battery; a comparison module for comparing the average temperature with a temperature threshold of the battery to obtain a comparison result; and a determining module for determining the future duration based on the comparison result.

[0134] Optionally, the determining module may include: a first response submodule, configured to determine a future duration as a first future duration in response to a comparison result that the average temperature exceeds a temperature threshold, wherein the first future duration is used to predict the predicted temperature and the predicted temperature rise rate; and a second response submodule, configured to determine a future duration as a second future duration in response to a comparison result that the average temperature is less than a temperature threshold, wherein the second future duration is used to predict the predicted temperature and the predicted temperature rise rate.

[0135] Optionally, the thermal management unit 704 may include: a first response module, configured to cool the battery in response to a first predicted temperature of the battery exceeding a cooling temperature threshold of the battery; a second response module, configured to cool the battery based on the relationship between a first single cell temperature of the battery at the initial moment and a cooling temperature threshold in response to a first predicted temperature being less than a cooling temperature threshold and a predicted temperature rise rate exceeding a first instantaneous temperature rise rate of the battery; and a third response module, configured to cool the battery in response to a first predicted temperature being less than a cooling temperature threshold and a predicted temperature rise rate being less than a first instantaneous temperature rise rate.

[0136] Optionally, the thermal management unit 704 may further include: a fourth response module, configured to heat the battery in response to a second predicted temperature of the battery not exceeding a heating temperature threshold of the battery, wherein the second predicted temperature is much lower than the first predicted temperature; a fifth response module, configured to heat the battery in response to a second predicted temperature exceeding a heating temperature threshold and a predicted temperature rise rate not exceeding a second instantaneous temperature rise rate of the battery, based on the relationship between the second single-cell temperature of the battery at the initial moment and the heating temperature threshold, wherein the second instantaneous temperature rise rate is much lower than the first instantaneous temperature rise rate, and the second single-cell temperature is much lower than the first single-cell temperature; and a sixth response module, configured to heat the battery in response to a second predicted temperature exceeding a heating temperature threshold and a predicted temperature rise rate exceeding a second instantaneous temperature rise rate.

[0137] In this embodiment, an acquisition unit is used to acquire historical driving data of the vehicle, wherein the historical driving data includes at least one of the following: the historical remaining battery charge and the vehicle driving time corresponding to the historical remaining battery charge; a first determination unit is used to determine the predicted driving data of the vehicle based on the historical driving data, wherein the predicted driving data is used to represent the operating state of the vehicle in the future; a second determination unit is used to determine the predicted temperature and predicted temperature rise rate of the battery in the future based on the predicted driving data and the temperature characteristics of the battery, wherein the temperature characteristics include at least one of the following: the initial temperature of the battery, the ambient temperature of the battery, and the coolant temperature of the battery; a thermal management unit is used to perform thermal management of the battery in the future based on the predicted temperature and predicted temperature rise rate, thereby solving the technical problem of difficulty in ensuring the reliability of the battery and the whole vehicle, and achieving the technical effect of ensuring the reliability of the battery and the whole vehicle.

[0138] Example 4

[0139] According to an embodiment of the present invention, a computer-readable storage medium is also provided, the storage medium including a stored program, wherein the program executes the battery thermal management method for a vehicle in Embodiment 1.

[0140] Example 5

[0141] According to an embodiment of the present invention, a processor is also provided for running a program, wherein the program is executed by the processor to perform the battery thermal management method of the vehicle in Embodiment 1.

[0142] Example 6

[0143] According to an embodiment of the present invention, a vehicle is also provided for performing the battery thermal management method of any of the vehicles in Embodiment 1.

[0144] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0145] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0147] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0149] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0150] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A battery thermal management method for a vehicle, characterized in that, include: Obtain historical driving data of the vehicle, wherein the historical driving data includes at least one of the following: the historical remaining charge of the battery and the vehicle driving time corresponding to the historical remaining charge; Based on the historical driving data and the weights corresponding to the data types of the historical driving data, the predicted driving data of the vehicle is determined, wherein the predicted driving data is used to represent the operating status of the vehicle in the future. Based on the predicted driving data and the temperature characteristics of the battery, the predicted temperature and predicted temperature rise rate of the battery in the future time period are determined, wherein the temperature characteristics include at least one of the following: the initial temperature of the battery, the ambient temperature of the battery, and the coolant temperature of the battery. Based on the predicted temperature and the predicted rate of temperature rise, thermal management of the battery is performed over the predicted future duration, including: In response to the battery's first predicted temperature exceeding a cooling temperature threshold, the battery is directly cooled, wherein the first predicted temperature is the predicted highest single-cell temperature T. 预测-max The cooling temperature threshold is the temperature limit T at which the battery needs to be cooled immediately. cool-limit ; In response to the first predicted temperature being less than the cooling temperature threshold and the predicted temperature rise rate exceeding the first instantaneous temperature rise rate of the battery, it is further determined that if the highest single-cell temperature T at the initial moment... 0MAX ≥ (T) cool-limit If the temperature is -5℃, immediately turn on the cooling; if T 0MAX < (T) cool-limit If the temperature is -5℃, then the cooling target is set to T. cool-target =T cool-limit -10℃, to predict the temperature at time t 预测 From T 预测-max Cool down to T cool-target As a standard, calculate the temperature T at which cooling needs to be turned on in advance. cool-on Wherein, the first instantaneous temperature rise rate is the highest instantaneous temperature rise rate under cooling conditions; In response to the first predicted temperature being less than the cooling temperature threshold and the predicted temperature rise rate being less than the first instantaneous temperature rise rate, there is no need to activate the cooling in advance; it can be activated according to the conventional cooling strategy.

2. The method according to claim 1, characterized in that, Based on the predicted driving data and the temperature characteristics of the battery, the predicted temperature and predicted temperature rise rate of the battery within the future time period are determined, including: The predicted driving data and the temperature features are input into the target model for prediction to obtain the predicted temperature and the predicted temperature rise rate, wherein the target model is obtained by iterative training on experimental data from at least one vehicle.

3. The method according to claim 1, further comprising: Obtain the average temperature of the battery; The average temperature is compared with the temperature threshold of the battery to obtain the comparison result; Based on the comparison results, the future duration is determined.

4. The method according to claim 3, characterized in that, Based on the comparison results, determining the future duration includes: In response to the comparison result that the average temperature exceeds the temperature threshold, the future duration is determined to be a first future duration, wherein the first future duration is used to predict the predicted temperature and the predicted temperature rise rate; In response to the comparison result that the average temperature is less than the temperature threshold, the future duration is determined to be a second future duration, wherein the second future duration is used to predict the predicted temperature and the predicted temperature rise rate.

5. The method according to claim 1, characterized in that, The method further includes: The temperature T that needs to be turned on in advance is calculated based on the following formula. cool-on ; T cool-on =T 预测-max -(P cool t 预测 ) / c m; Among them, P cool The average cooling power is used to represent the average temperature rise rate over the evaluation period Δt, c represents the specific heat capacity of a single battery cell, and m represents the weight of all battery cells.

6. The method according to claim 1, characterized in that, Based on the predicted temperature and the predicted temperature rise rate, thermal management of the battery over the future duration further includes: In response to the fact that the second predicted temperature of the battery does not exceed the heating temperature threshold of the battery, the battery is directly heated, wherein the second predicted temperature is much lower than the first predicted temperature; In response to the second predicted temperature being greater than the heating temperature threshold and the predicted temperature rise rate not exceeding the second instantaneous temperature rise rate of the battery, it is further determined that if the lowest single-cell temperature T at the start time... 0min ≤(T heat-limit If the temperature is -5℃, then immediately turn on the heating. 0min > (T) heat-limit If the temperature is -5℃, then the target heating temperature is set to T. heat-target =T heat-limit +10℃, to predict the temperature at time t 预测 From T 预测-min Heat up to T heat-target As a standard, calculate the temperature T that needs to be turned on in advance. heat-on Wherein, the second instantaneous temperature rise rate is much smaller than the first instantaneous temperature rise rate, T 0min Much smaller than T 0MAX ; In response to the second predicted temperature being greater than the heating temperature threshold and the predicted temperature rise rate being greater than the second instantaneous temperature rise rate, heating does not need to be started in advance; it can be started according to the conventional heating strategy.

7. A battery thermal management device for a vehicle, characterized in that, include: An acquisition unit is used to acquire historical driving data of a vehicle, wherein the historical driving data includes at least one of the following: the historical remaining charge of the battery and the vehicle driving time corresponding to the historical remaining charge; The first determining unit is used to determine the predicted driving data of the vehicle based on the historical driving data and the weights corresponding to the data types of the historical driving data, wherein the predicted driving data is used to represent the operating status of the vehicle in the future time period. The second determining unit is configured to determine the predicted temperature and predicted temperature rise rate of the battery in the future time period based on the predicted driving data and the temperature characteristics of the battery, wherein the temperature characteristics include at least one of the following: the initial temperature of the battery, the ambient temperature of the battery, and the coolant temperature of the battery. A thermal management unit is configured to perform thermal management on the battery over the future duration based on the predicted temperature and the predicted temperature rise rate. The thermal management unit is further configured to directly cool the battery in response to a first predicted temperature exceeding a cooling temperature threshold, wherein the first predicted temperature is the predicted highest single-cell temperature T. 预测-max The cooling temperature threshold is the temperature limit T at which the battery needs to be cooled immediately. cool-limit In response to the first predicted temperature being less than the cooling temperature threshold and the predicted temperature rise rate exceeding the first instantaneous temperature rise rate of the battery, it is further determined that if the highest single-cell temperature T at the initial moment... 0MAX ≥ (T) cool-limit If the temperature is -5℃, immediately turn on the cooling; if T 0MAX < (T) cool-limit If the temperature is -5℃, then the cooling target is set to T. cool-target =T cool-limit -10℃, to predict the temperature at time t 预测 From T 预测-max Cool down to T cool-target As a standard, calculate the temperature T at which cooling needs to be turned on in advance. cool-on Wherein, the first instantaneous temperature rise rate is the highest instantaneous temperature rise rate under cooling conditions; in response to the first predicted temperature being less than the cooling temperature threshold and the predicted temperature rise rate being less than the first instantaneous temperature rise rate, there is no need to start cooling in advance, and it can be started according to the conventional cooling strategy.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the battery thermal management method of the vehicle according to any one of claims 1 to 6.

9. A processor, characterized in that, The processor is used to run a program, wherein the program, when run by the processor, executes the battery thermal management method of the vehicle according to any one of claims 1 to 6.

10. A vehicle, characterized in that, The vehicle is used to perform the battery thermal management method of the vehicle according to any one of claims 1 to 6.

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