Battery temperature prediction method, device, vehicle and storage medium

Abstract

The embodiments of the present application provide a battery temperature prediction method, device, vehicle, and storage medium. These methods can, after determining a driving route, predict battery temperature changes based on real-time battery power changes along the navigation route, thereby obtaining a relatively accurate battery temperature prediction value. The battery temperature prediction method comprises the following steps: obtaining a real-time power change curve for the navigation route taken to the destination; obtaining first real-time battery status information at time T1; obtaining a first real-time current; determining a first temperature change of the battery from time T1 to time T2 based on the first real-time current; obtaining a temperature prediction value at time T2; determining a second real-time battery voltage at time T2 based on the first real-time current; obtaining a second real-time current; determining a second temperature change of the battery from time T2 to time T3 based on the second real-time current; obtaining a temperature prediction value at time T3, and so on, to obtain a temperature prediction value at time Tn.

Description

Technical field

[0001] Embodiments of the present application relate to the field of power battery technology, and in particular to a method, device, vehicle, and storage medium for predicting battery temperature. [Background Technology]

[0002] Electric vehicles are becoming increasingly popular, with battery technology serving as the core of electric vehicles. Battery temperature significantly impacts performance, lifespan, and safety. For example, battery performance degrades significantly at low temperatures, while lifespan degrades rapidly at high temperatures. Overheating can also easily lead to thermal runaway. Therefore, accurately estimating battery temperature fluctuations can make battery use safer and more efficient.

[0003] In the existing technology, vehicle battery temperature prediction is usually based on a table corresponding to the battery's average power value and temperature value to predict the battery temperature change during the current trip. However, the vehicle battery temperature changes in real time during actual driving. Predicting the battery temperature based solely on the average driving speed and average power will result in a large difference between the final predicted temperature and the actual temperature, resulting in low battery temperature prediction accuracy. [Summary of the invention]

[0004] The embodiments of the present application provide a battery temperature prediction method, device, vehicle, and storage medium. After determining a driving route, the battery temperature change can be predicted based on the real-time changes in battery power along the navigation route, thereby obtaining a more accurate battery temperature prediction value.

[0005] In a first aspect, an embodiment of the present application provides a method for predicting battery temperature, the method comprising:

[0006] Obtaining a real-time power change curve of a navigation route taken to a destination, wherein the real-time power change curve is used to indicate a power change trend over time when the vehicle travels along the navigation route;

[0007] Acquire first real-time status information of the battery at time T1, where the first real-time status information includes a first real-time voltage and a first real-time temperature;

[0008] Obtaining a first real-time current based on a ratio of a first real-time power at time T1 to the first real-time voltage in the real-time power change curve;

[0009] determining a first temperature change of the battery from time T1 to time T2 based on the first real-time current;

[0010] Summing the first real-time temperature and the first temperature change to obtain a temperature prediction value at time T2;

[0011] determining a second real-time voltage of the battery at time T2 based on the first real-time current;

[0012] Obtaining a second real-time current based on a ratio of the second real-time power at time T2 in the real-time power change curve to the second real-time voltage;

[0013] determining a second temperature change of the battery from time T2 to time T3 based on the second real-time current;

[0014] The temperature prediction value at time T2 and the second temperature change are summed to obtain the temperature prediction value at time T3, and so on to obtain the temperature prediction value at time Tn, where time Tn is the time when the vehicle reaches the destination.

[0015] In an embodiment of the present application, before driving, the vehicle will plan a route through the navigation system and obtain a power change curve of the vehicle battery corresponding to the navigation route. The current value of the battery at time T1 can be obtained by the ratio of the power value at time T1 to the voltage value at time T1. Then, based on the current value at time T1, the temperature change from time T1 to time T2 is obtained, thereby obtaining a temperature prediction value at time T2. The current of the battery at time T2 is the ratio of the power at time T2 determined by the power curve to the voltage at time T2 determined by the current at time T1. The change from time T2 to time T3 is obtained based on the current at time T2, thereby obtaining a temperature prediction value at time T3. And so on, thereby obtaining a temperature prediction value at time Tn based on the navigation route. By obtaining the real-time temperature change value at the corresponding time based on the real-time current value at each time, and continuously calculating the temperature prediction value at the final time, the obtained temperature prediction result is more accurate than the temperature prediction value obtained by using the average power to correspond.

[0016] Optionally, the calculation formula for the first temperature change is as follows:

[0017]

[0018] Where n is a positive integer not less than 1. When n=1, ΔT n is the first temperature change of the battery from the time T1 to the time T2; Tn is the first real-time current of the battery at time T1; ΔR n-1 is the internal resistance of the battery at the time T1; Δt is the sampling interval; q n-1 is the heat exchange value between the battery and the external environment from time T1 to time T2; C is the specific heat capacity of the battery; and m is the weight of the battery.

[0019] In the embodiment of the present application, the temperature change from T1 to T2 is related to the battery power at T1. The electric power at T1 can be determined by obtaining the battery current at T1 and the corresponding internal resistance of the battery cell, subtracting the heat exchanged with the external environment at this time, and finally dividing by a fixed heat capacity value to calculate the temperature change from T1 to T2.

[0020] Optionally, the calculation formula of the second real-time voltage is as follows:

[0021] U Tn =U Tn-1 -I Tn-1 *R Tn-1

[0022] Where n is a positive integer not less than 1. When n=2, U Tn is the second real-time voltage of the battery at time T2; U Tn-1 is the first real-time voltage of the battery at the time T1; I Tn-1 is the first real-time current of the battery at the time T1; R Tn-1 is the system internal resistance of the battery at the time T1.

[0023] In the embodiment of the present application, since the discharge process of the battery is a chemical reaction, the electrolyte concentration inside the battery continues to decrease during the discharge process, so the battery voltage will continue to decrease during the discharge process. The battery voltage at the next moment can be obtained by subtracting the voltage drop from the battery voltage at the previous moment, thereby obtaining a battery voltage that conforms to the actual discharge process of the battery.

[0024] Optionally, the first real-time temperature is a first minimum real-time temperature, and summing the first real-time temperature and the first temperature change to obtain a temperature prediction value at time T2 includes:

[0025] Summing the first lowest real-time temperature and the first temperature change to obtain a predicted lowest temperature value at time T2;

[0026] Summing the temperature prediction value at time T2 and the second temperature change to obtain the temperature prediction value at time T3 includes:

[0027] The lowest temperature prediction value at time T2 and the second temperature change are summed to obtain the lowest temperature prediction value at time T3, and so on to obtain the lowest temperature prediction value at time Tn.

[0028] In the embodiment of the present application, multiple temperature sensors are installed on the vehicle battery. Different sensor positions may result in different measured temperatures. When the temperature collected at the initial moment is the lowest temperature, the temperature prediction value obtained by repeated calculation is the lowest temperature prediction value, that is, the possible lowest temperature of the battery. When it is determined that the battery needs to be heated up, the lowest temperature prediction value needs to be used as a benchmark. It is believed that when the lowest temperature prediction values ​​of the battery meet the required temperature, the battery temperature must be within the required temperature range, which facilitates subsequent operations.

[0029] Optionally, the first real-time temperature is a first maximum real-time temperature, and summing the first real-time temperature and the first temperature change to obtain a temperature prediction value at time T2 includes:

[0030] Summing the first maximum real-time temperature and the first temperature change to obtain a maximum temperature prediction value at time T2;

[0031] Summing the temperature prediction value at time T2 and the second temperature change to obtain the temperature prediction value at time T3 includes:

[0032] The maximum temperature prediction value at time T2 and the second temperature change are summed to obtain the maximum temperature prediction value at time T3, and so on to obtain the maximum temperature prediction value at time Tn.

[0033] In the embodiment of the present application, multiple temperature sensors are installed on the vehicle battery. Different sensor positions may result in different measured temperatures. When the temperature collected at the initial moment is the highest temperature, the temperature prediction value obtained by repeated calculation is the highest temperature prediction value, that is, the possible highest temperature of the battery. When it is determined that the battery needs to be cooled, the highest temperature prediction value needs to be used as a benchmark. It is believed that when the highest temperature prediction values ​​of the battery meet the required temperature, the battery temperature must be within the required temperature range, which facilitates subsequent operations.

[0034] Optionally, the internal resistance of the battery cell at the time T1 is related to the battery temperature at the time T1 and the first state of charge at the time T1. The calculation formula of the first state of charge is as follows:

[0035]

[0036] Where n is a positive integer not less than 1. When n=1, SOC Tn is the first state of charge of the battery at the time T1; SOC Tn-1 is the state of charge of the battery at the last moment T1; I Tn is the first real-time current of the vehicle at the time T1; I0 is the rated current of the battery.

[0037] In the embodiment of the present application, the size of the internal resistance of the battery cell is directly related to the state of charge of the battery. Therefore, it is necessary to continuously calculate the battery state of charge at each moment to obtain the corresponding battery cell internal resistance value at each moment. Since the battery state of charge will change with the discharge current of the battery, the current size at each moment can be substituted into the formula for continuous calculation, so as to obtain the battery state of charge that conforms to the actual discharge process of the battery.

[0038] Optionally, a real-time power change curve of the navigation route taken to the destination is obtained, including:

[0039] Obtaining a real-time speed change curve of a navigation route taken to a destination, wherein the real-time speed change curve is used to indicate a speed change trend of the vehicle over time when the vehicle travels along the navigation route;

[0040] The real-time power change curve is determined based on the real-time speed change curve.

[0041] In an embodiment of the present application, the vehicle navigation system determines a navigation route based on the destination and obtains a real-time speed change curve of the vehicle on the guide route, that is, the curve is used to indicate the change trend of the vehicle's driving speed on the navigation route over time, so that the real-time power change curve can be obtained based on the real-time speed change curve.

[0042] In a second aspect, an embodiment of the present application provides a device for predicting battery temperature, the device comprising:

[0043] a first acquiring unit, configured to acquire a real-time power variation curve of a navigation route taken by the vehicle to the destination, wherein the real-time power variation curve is used to indicate a power variation trend over time of the vehicle when the vehicle travels along the navigation route;

[0044] a second acquiring unit, configured to acquire first real-time status information of the battery at time T1, wherein the first real-time status information includes a first real-time voltage and a first real-time temperature;

[0045] a first processing unit, configured to obtain a first real-time current based on a ratio of a first real-time power to the first real-time voltage at time T1 in the real-time power change curve;

[0046] a first determining unit, configured to determine a first temperature change of the battery from time T1 to time T2 based on the first real-time current;

[0047] a second processing unit, configured to sum the first real-time temperature and the first temperature change to obtain a temperature prediction value at time T2;

[0048] a second determining unit, configured to determine a second real-time voltage of the battery at time T2 based on the first real-time current;

[0049] a third processing unit, configured to obtain a second real-time current based on a ratio of the second real-time power to the second real-time voltage at time T2 in the real-time power change curve;

[0050] a third determining unit, configured to determine a second temperature change of the battery from time T2 to time T3 based on the second real-time current;

[0051] The fourth processing unit is used to sum the temperature prediction value at time T2 and the second temperature change to obtain the temperature prediction value at time T3, and so on to obtain the temperature prediction value at time Tn, where time Tn is the time when the vehicle travels to the destination.

[0052] Optionally, the calculation formula for the first temperature change is as follows:

[0053]

[0054] Where n is a positive integer not less than 1. When n=1, ΔT n is the first temperature change of the battery from the time T1 to the time T2; Tn is the first real-time current of the battery at time T1; ΔR n-1 is the internal resistance of the battery at the time T1; Δt is the sampling interval; q n-1 is the heat exchange value between the battery and the external environment from time T1 to time T2; C is the specific heat capacity of the battery; and m is the weight of the battery.

[0055] Optionally, the calculation formula of the second real-time voltage is as follows:

[0056] U Tn =U Tn-1 -I Tn-1 *R Tn-1

[0057] Where n is a positive integer not less than 1. When n=2, U Tn is the second real-time voltage of the battery at time T2; U Tn-1 is the first real-time voltage of the battery at the time T1; I Tn-1 is the first real-time current of the battery at the time T1; R Tn-1 is the system internal resistance of the battery at the time T1.

[0058] Optionally, the first real-time temperature is a first minimum real-time temperature, and the second processing unit is specifically configured to:

[0059] Summing the first lowest real-time temperature and the first temperature change to obtain a predicted lowest temperature value at time T2;

[0060] The fourth processing unit is specifically configured to:

[0061] The lowest temperature prediction value at time T2 and the second temperature change are summed to obtain the lowest temperature prediction value at time T3, and so on to obtain the lowest temperature prediction value at time Tn.

[0062] Optionally, the first real-time temperature is a first maximum real-time temperature, and the second processing unit is specifically configured to:

[0063] Summing the first maximum real-time temperature and the first temperature change to obtain a maximum temperature prediction value at time T2;

[0064] The fourth processing unit is specifically configured to:

[0065] The maximum temperature prediction value at time T2 and the second temperature change are summed to obtain the maximum temperature prediction value at time T3, and so on to obtain the maximum temperature prediction value at time Tn.

[0066] Optionally, the internal resistance of the battery cell at the time T1 is related to the battery temperature at the time T1 and the first state of charge at the time T1. The calculation formula of the first state of charge is as follows:

[0067]

[0068] Where n is a positive integer not less than 1. When n=1, SOC Tn is the first state of charge of the battery at the time T1; SOC Tn-1 is the state of charge of the battery at the last moment T1; I Tn is the first real-time current of the vehicle at the time T1; I0 is the rated current of the battery.

[0069] Optionally, the first acquiring unit is specifically configured to:

[0070] Obtaining a real-time speed change curve of a navigation route taken to a destination, wherein the real-time speed change curve is used to indicate a speed change trend of the vehicle over time when the vehicle travels along the navigation route;

[0071] The real-time power change curve is determined based on the real-time speed change curve.

[0072] In a third aspect, an embodiment of the present application provides a vehicle, comprising a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement steps of the method described in the embodiment of the first aspect.

[0073] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the embodiment of the first aspect.

[0074] It should be understood that the second to fourth aspects of the embodiments of the present application are consistent with the technical solutions of the first aspect of the embodiments of the present application, and the beneficial effects achieved by each aspect and the corresponding feasible implementation methods are similar and will not be repeated.

Brief Description of the Drawings

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0076] Figure 1 A schematic flow chart of a method for predicting battery temperature provided in an embodiment of the present application;

[0077] Figure 2 A schematic diagram of the structure of a battery temperature prediction device provided in an embodiment of the present application;

[0078] Figure 3 A schematic structural diagram of a vehicle provided in an embodiment of the present application. [Specific implementation method]

[0079] In order to better understand the technical solutions of this specification, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0080] It should be clear that the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this specification.

[0081] The terms used in the examples of this application are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a," "an," "the," and "the" used in the examples of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0082] At present, electric vehicles are being used more and more widely, and vehicle batteries are the core of electric vehicles. The temperature of the battery has an important impact on its own performance, life and safety.

[0083] The applicant has discovered through research that in related technologies, the prediction of vehicle battery temperature is usually based on a table of the battery's average power value and temperature value to predict the temperature change of the battery during the current trip. However, the vehicle's battery temperature changes in real time during actual driving. Predicting the battery temperature based solely on the average driving speed and average power will result in a large difference between the final predicted temperature and the actual temperature, resulting in low accuracy in battery temperature prediction.

[0084] In view of this, an embodiment of the present application provides a battery temperature prediction method. In this method, before driving, a vehicle will plan a route through a navigation system and obtain a power change curve of the vehicle battery corresponding to the navigation route. The current value of the battery at time T1 can be obtained by the ratio of the power value at time T1 to the voltage value at time T1. Then, based on the current value at time T1, the temperature change from time T1 to time T2 is obtained, thereby obtaining a temperature prediction value at time T2. The current of the battery at time T2 is the ratio of the power at time T2 determined by the power curve to the voltage at time T2 determined by the current at time T1. The change from time T2 to time T3 is obtained based on the current at time T2, thereby obtaining a temperature prediction value at time T3. And so on, thereby obtaining a temperature prediction value at time Tn based on the navigation route. By obtaining the real-time temperature change value at the corresponding time based on the real-time current value at each time, and continuously calculating the temperature prediction value at the final time, the obtained temperature prediction result is more accurate than the temperature prediction value obtained by using average power to correspond.

[0085] The following describes the technical solutions provided by the embodiments of the present invention in conjunction with the accompanying drawings. Figure 1 , an embodiment of the present invention provides a method for predicting battery temperature, the process of which is described as follows:

[0086] Step 101: Acquire a real-time power variation curve of a navigation route taken to a destination. The real-time power variation curve is used to indicate a power variation trend over time when the vehicle is traveling along the navigation route.

[0087] In an embodiment of the present application, after the driver inputs the destination into the navigation system, the vehicle's navigation system will determine a specific navigation route based on the destination and obtain a real-time power change curve of the vehicle battery on the corresponding navigation route. The real-time power change curve can be used to predict the battery temperature when the vehicle reaches the destination.

[0088] Considering that after the vehicle's navigation system determines a specific navigation route, it can obtain a speed change trend curve of the vehicle while driving based on the navigation route, but there is no direct correspondence between the vehicle's speed change and the vehicle battery temperature change.

[0089] Therefore, in the embodiment of the present application, a battery power change trend curve related to battery temperature change is obtained based on the vehicle speed change trend curve, thereby facilitating the prediction of battery temperature using the power change trend curve.

[0090] As a possible implementation method, a real-time speed change curve of the navigation route taken to the destination is obtained, and the real-time speed change curve is used to indicate the speed change trend of the vehicle over time when traveling along the navigation route; and a real-time power change curve is determined based on the real-time speed change curve.

[0091] Step 102: Acquire first real-time status information of the battery at time T1, where the first real-time status information includes a first real-time voltage and a first real-time temperature.

[0092] Step 103: Obtain a first real-time current based on a ratio of the first real-time power to the first real-time voltage at time T1 in the real-time power variation curve.

[0093] Step 104 : Determine a first temperature change of the battery from time T1 to time T2 based on the first real-time current.

[0094] Step 105: summing the first real-time temperature and the first temperature variation to obtain a temperature prediction value at time T2.

[0095] In an embodiment of the present application, time T1 is set as the initial time, and the first real-time power corresponding to time T1 can be obtained based on the real-time power curve, as well as the first real-time voltage of the battery at time T1, and the first real-time current at time T1 can be obtained based on the ratio of the first real-time power to the first real-time voltage. The first temperature variable from time T1 to time T2 can be obtained through the first real-time current, and the first real-time temperature of the battery at time T1 and the first temperature change from time T1 to time T2 are summed and calculated to determine the temperature prediction value at time T2.

[0096] Considering that the discharge current of the battery will cause heat to be generated, which will cause the temperature of the battery to change, and the heat generated by different currents is also different.

[0097] Therefore, in the embodiment of the present application, the corresponding temperature change is calculated based on the current of the battery at each moment and the corresponding internal resistance of the battery cell. The calculated temperature change can more accurately reflect the battery temperature change between two adjacent moments.

[0098] As a possible implementation, the calculation formula for the first temperature change is as follows:

[0099]

[0100] Where n is a positive integer not less than 1. When n=1, ΔT n is the first temperature change of the battery from T1 to T2; I Tn is the first real-time current of the battery at time T1; ΔR n-1 is the internal resistance of the battery at time T1; Δt is the sampling interval; q n-1 is the heat exchange value between the battery and the external environment from T1 to T2; C is the specific heat capacity of the battery; m is the weight of the battery.

[0101] Considering that the internal resistance of the battery cell is related to the battery temperature and the battery state of charge, if the internal resistance of the battery cell is obtained based solely on the initial battery state of charge, the error in calculating the subsequent temperature change will be large.

[0102] Therefore, in an embodiment of the present application, the battery state of charge corresponding to each moment is calculated, and the internal core resistance of the battery is obtained based on the state of charge at each moment and the battery temperature at each moment, so that the calculated internal core resistance of the battery is more consistent with the change of the internal core resistance during the actual use of the battery.

[0103] As a possible implementation, the internal resistance of the battery cell at time T1 is related to the battery temperature at time T1 and the first state of charge at time T1. The calculation formula of the first state of charge is as follows:

[0104]

[0105] Where n is a positive integer not less than 1. When n=1, SOC Tn The first state of charge of the battery at time T1; SOC Tn-1 is the state of charge of the battery at the moment before T1; I Tn is the first real-time current of the vehicle at time T1; I0 is the rated current of the battery.

[0106] Step 106: Determine a second real-time voltage of the battery at time T2 based on the first real-time current.

[0107] Considering that the battery discharge process is a chemical reaction, the electrolyte concentration inside the battery continuously decreases during the discharge process, so the battery voltage will continuously decrease during the discharge process. Therefore, in the embodiment of the present application, the battery voltage at the next moment can be obtained by subtracting the voltage drop from the battery voltage at the previous moment, thereby obtaining a battery voltage that conforms to the actual battery discharge process.

[0108] As a possible implementation, the calculation formula of the second real-time voltage is as follows:

[0109] U Tn =U Tn-1 -I Tn-1 *R Tn-1

[0110] Where n is a positive integer not less than 1. When n=2, U Tn is the second real-time voltage of the battery at time T2; U Tn-1 is the first real-time voltage of the battery at time T1; I Tn-1 is the first real-time current of the battery at time T1; R Tn-1 is the system internal resistance of the battery at time T1.

[0111] Step 107: Obtain the second real-time current based on the ratio of the second real-time power to the second real-time voltage at time T2 in the real-time power variation curve.

[0112] Step 108: Determine a second temperature change of the battery from time T2 to time T3 based on the second real-time current.

[0113] Step 109: summing the temperature prediction value at time T2 and the second temperature variation to obtain the temperature prediction value at time T3, and so on to obtain the temperature prediction value at time Tn, where time Tn is the time when the vehicle reaches the destination.

[0114] In an embodiment of the present application, for the temperature prediction value at time T3, the second real-time power corresponding to time T2 can be first obtained based on the real-time power curve, and the second real-time voltage at time T2 can be obtained based on the first real-time current at time T1, and the second real-time current at time T2 can be obtained based on the ratio of the second real-time power to the second real-time voltage. The second temperature variable from time T2 to time T3 can be obtained through the second real-time current. The second real-time temperature of the battery at time T2 and the second temperature change from time T2 to time T3 are summed and calculated to determine the temperature prediction value at time T3.

[0115] Considering that time T1 is set as the initial time, the battery temperature corresponding to this time is directly obtained by the temperature sensor. Due to the differences in the installation positions of the temperature sensors, the initial temperature values ​​obtained are different, and there are also multiple results for the predicted temperature values ​​calculated based on the initial temperature values.

[0116] Therefore, in an embodiment of the present application, the highest real-time temperature and the lowest real-time temperature are determined from the temperatures obtained by the temperature sensor, and temperature prediction is performed based on the highest real-time temperature and the lowest real-time temperature. The prediction result obtained is a predicted temperature range from the lowest temperature prediction value to the highest temperature prediction value, thereby ensuring that the temperature of any part of the battery is within the predicted temperature range.

[0117] As a possible implementation method, when the first real-time temperature is the first minimum real-time temperature, the first minimum real-time temperature is summed with the first temperature change to obtain the minimum temperature prediction value at time T2; the minimum temperature prediction value at time T2 is summed with the second temperature change to obtain the minimum temperature prediction value at time T3, and so on, to obtain the minimum temperature prediction value at time Tn.

[0118] As another possible implementation, when the first real-time temperature is the first maximum real-time temperature, the first maximum real-time temperature is summed with the first temperature change to obtain the maximum temperature prediction value at time T2; the maximum temperature prediction value at time T2 is summed with the second temperature change to obtain the maximum temperature prediction value at time T3, and so on, to obtain the maximum temperature prediction value at time Tn.

[0119] See Figure 2 Based on the same inventive concept, an embodiment of the present application provides a battery temperature prediction device, which includes: a first acquisition unit 201, a second acquisition unit 202, a first processing unit 203, a first determination unit 204, a second processing unit 205, a second determination unit 206, a third processing unit 207, a third determination unit 208 and a fourth processing unit 209.

[0120] The first acquisition unit 201 is used to acquire a real-time power change curve of the navigation route taken by the vehicle to the destination, where the real-time power change curve is used to indicate a power change trend over time when the vehicle is traveling along the navigation route;

[0121] The second acquiring unit 202 is configured to acquire first real-time status information of the battery at time T1, where the first real-time status information includes a first real-time voltage and a first real-time temperature;

[0122] The first processing unit 203 is configured to obtain a first real-time current based on a ratio of the first real-time power to the first real-time voltage at time T1 in the real-time power change curve;

[0123] A first determining unit 204 is configured to determine a first temperature change of the battery from time T1 to time T2 based on the first real-time current;

[0124] The second processing unit 205 is configured to sum the first real-time temperature and the first temperature variation to obtain a temperature prediction value at time T2;

[0125] A second determining unit 206 is configured to determine a second real-time voltage of the battery at time T2 based on the first real-time current;

[0126] The third processing unit 207 is configured to obtain a second real-time current based on a ratio of the second real-time power to the second real-time voltage at time T2 in the real-time power change curve;

[0127] a third determining unit 208, configured to determine a second temperature change of the battery from time T2 to time T3 based on the second real-time current;

[0128] The fourth processing unit 209 is configured to sum the temperature prediction value at time T2 and the second temperature variation to obtain the temperature prediction value at time T3, and so on to obtain the temperature prediction value at time Tn, where time Tn is the time when the vehicle reaches the destination.

[0129] Optionally, the calculation formula for the first temperature change is as follows:

[0130]

[0131] Where n is a positive integer not less than 1. When n=1, ΔT n is the first temperature change of the battery from T1 to T2; I Tn is the first real-time current of the battery at time T1; ΔR n-1 is the internal resistance of the battery at time T1; Δt is the sampling interval; q n-1 is the heat exchange value between the battery and the external environment from T1 to T2; C is the specific heat capacity of the battery; m is the weight of the battery.

[0132] Optionally, the second real-time voltage is calculated as follows:

[0133] U Tn =U Tn-1 -I Tn-1 *R Tn-1

[0134] Where n is a positive integer not less than 1. When n=2, U Tn is the second real-time voltage of the battery at time T2; U Tn-1 is the first real-time voltage of the battery at time T1; I Tn-1 is the first real-time current of the battery at time T1; R Tn-1 is the system internal resistance of the battery at time T1.

[0135] Optionally, the first real-time temperature is a first minimum real-time temperature, and the second processing unit 205 is specifically configured to:

[0136] Summing the first lowest real-time temperature and the first temperature change to obtain a predicted lowest temperature value at time T2;

[0137] The fourth processing unit 209 is specifically configured to:

[0138] The lowest temperature prediction value at time T2 and the second temperature change are summed to obtain the lowest temperature prediction value at time T3, and so on to obtain the lowest temperature prediction value at time Tn.

[0139] Optionally, the first real-time temperature is a first maximum real-time temperature, and the second processing unit 205 is specifically configured to:

[0140] Summing the first maximum real-time temperature and the first temperature change to obtain a maximum temperature prediction value at time T2;

[0141] The fourth processing unit 209 is specifically configured to:

[0142] The maximum temperature prediction value at time T2 and the second temperature change are summed to obtain the maximum temperature prediction value at time T3, and so on to obtain the maximum temperature prediction value at time Tn.

[0143] Optionally, the internal resistance of the battery cell at time T1 is related to the battery temperature at time T1 and the first state of charge at time T1. The calculation formula of the first state of charge is as follows:

[0144]

[0145] Where n is a positive integer not less than 1. When n=1, SOC Tn The first state of charge of the battery at time T1; SOC Tn-1 is the state of charge of the battery at the moment before T1; I Tn is the first real-time current of the vehicle at time T1; I0 is the rated current of the battery.

[0146] Optionally, the first acquiring unit 201 is specifically configured to:

[0147] Obtain a real-time speed change curve of the navigation route taken to the destination. The real-time speed change curve is used to indicate the speed change trend of the vehicle over time when traveling along the navigation route;

[0148] A real-time power variation curve is determined based on the real-time speed variation curve.

[0149] See Figure 3 Based on the same inventive concept, an embodiment of the present application provides a vehicle, which includes at least one processor 301, and the processor 301 is used to execute a computer program stored in a memory to implement the embodiment of the present application. Figure 1The steps of the battery temperature prediction method are shown.

[0150] Optionally, the processor 301 may specifically be a central processing unit, a specific ASIC, or one or more integrated circuits for controlling program execution.

[0151] Optionally, the vehicle may further include a memory 302 connected to the at least one processor 301. The memory 302 may include ROM, RAM, and disk storage. The memory 302 is used to store data required by the processor 301 when it is running, that is, it stores instructions that can be executed by the at least one processor 301. The at least one processor 301 executes the instructions stored in the memory 302 to execute the following operations: Figure 1 The method shown. The number of the memory 302 is one or more. The memory 302 is Figure 3 It is shown together with the figure, but it should be noted that the memory 302 is not a required functional module, so Figure 3 Shown in dashed lines.

[0152] The physical devices corresponding to the first acquisition unit 201, the second acquisition unit 202, the first processing unit 203, the first determination unit 204, the second processing unit 205, the second determination unit 206, the third processing unit 207, the third determination unit 208 and the fourth processing unit 209 can all be the aforementioned processor 301. Figure 1 Therefore, for the functions that can be achieved by each functional module in the device, please refer to Figure 1 The corresponding description in the illustrated embodiment will not be repeated here.

[0153] The embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores computer instructions, which, when executed on a computer, cause the computer to execute the following Figure 1 method.

[0154] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for predicting battery temperature, characterized in that: The method comprises: Obtaining a real-time power change curve of a navigation route taken to a destination, wherein the real-time power change curve is used to indicate a power change trend over time when the vehicle travels along the navigation route; Acquire first real-time status information of the battery at time T1, where the first real-time status information includes a first real-time voltage and a first real-time temperature; Obtaining a first real-time current based on a ratio of the first real-time power at time T1 to the first real-time voltage in the real-time power change curve; determining a first temperature change of the battery from time T1 to time T2 based on the first real-time current; Summing the first real-time temperature and the first temperature change to obtain a temperature prediction value at time T2; determining a second real-time voltage of the battery at time T2 based on the first real-time current; obtaining a second real-time current based on a ratio of the second real-time power at time T2 in the real-time power change curve to the second real-time voltage; determining a second temperature change of the battery from time T2 to time T3 based on the second real-time current; Summing the temperature prediction value at time T2 and the second temperature change to obtain the temperature prediction value at time T3, and so on to obtain the temperature prediction value at time Tn, where time Tn is the time when the vehicle reaches the destination; The calculation formulas for the first temperature change and the second temperature change are as follows: ； Where n is a positive integer not less than 1. hour, is the first temperature change of the battery from the time T1 to the time T2; is the first real-time current of the battery at the time T1; is the internal resistance of the battery at the time T1, is the heat exchange value between the battery and the external environment from time T1 to time T2; when n=2, is the second temperature change of the battery from the time T2 to the time T3; is the second real-time current of the battery at the time T2; is the internal resistance of the battery cell at the time T2, is the heat exchange value between the battery and the external environment from time T2 to time T3; is the sampling interval; is the specific heat capacity of the battery; is the weight of the battery.

2. The method according to claim 1, characterized in that The calculation formula of the second real-time voltage is as follows: ； Where n is a positive integer not less than 1. hour, is the second real-time voltage of the battery at the time T2; is the first real-time voltage of the battery at the time T1; is the first real-time current of the battery at the time T1; is the system internal resistance of the battery at the time T1.

3. The method according to claim 1, characterized in that The first real-time temperature is a first minimum real-time temperature, and the first real-time temperature and the first temperature change are summed to obtain the temperature prediction value at time T2, including: Summing the first lowest real-time temperature and the first temperature change to obtain a predicted lowest temperature value at time T2; Summing the temperature prediction value at time T2 and the second temperature change to obtain the temperature prediction value at time T3 includes: The lowest temperature prediction value at time T2 and the second temperature change are summed to obtain the lowest temperature prediction value at time T3, and so on to obtain the lowest temperature prediction value at time Tn.

4. The method according to claim 1, wherein The first real-time temperature is a first maximum real-time temperature, and the first real-time temperature and the first temperature change are summed to obtain the temperature prediction value at time T2, including: Summing the first maximum real-time temperature and the first temperature change to obtain a maximum temperature prediction value at time T2; Summing the temperature prediction value at time T2 and the second temperature change to obtain the temperature prediction value at time T3 includes: The maximum temperature prediction value at time T2 and the second temperature change are summed to obtain the maximum temperature prediction value at time T3, and so on to obtain the maximum temperature prediction value at time Tn.

5. The method according to claim 2, characterized in that The internal resistance of the battery cell at time T1 is related to the battery temperature at time T1 and the first state of charge at time T1. The calculation formula of the first state of charge is as follows: ； Where n is a positive integer not less than 1. hour, is the first state of charge of the battery at the time T1; is the state of charge of the battery at the previous moment T1; is the first real-time current of the vehicle at the time T1; is the rated current of the battery.

6. The method according to claim 1, characterized in that Obtain real-time power change curves along the navigation route to the destination, including: Obtaining a real-time speed change curve of a navigation route taken to a destination, wherein the real-time speed change curve is used to indicate a speed change trend of the vehicle over time when the vehicle travels along the navigation route; The real-time power change curve is determined based on the real-time speed change curve.

7. A battery temperature prediction device, characterized in that: The device comprises: a first acquiring unit, configured to acquire a real-time power variation curve of a navigation route taken by the vehicle to the destination, wherein the real-time power variation curve is used to indicate a power variation trend over time of the vehicle when the vehicle travels along the navigation route; a second acquiring unit, configured to acquire first real-time status information of the battery at time T1, wherein the first real-time status information includes a first real-time voltage and a first real-time temperature; a first processing unit, configured to obtain a first real-time current based on a ratio of the first real-time power at time T1 to the first real-time voltage in the real-time power change curve; a first determining unit, configured to determine a first temperature change of the battery from time T1 to time T2 based on the first real-time current; a second processing unit, configured to sum the first real-time temperature and the first temperature change to obtain a temperature prediction value at time T2; a second determining unit, configured to determine a second real-time voltage of the battery at time T2 based on the first real-time current; a third processing unit, configured to obtain a second real-time current based on a ratio of the second real-time power at time T2 to the second real-time voltage in the real-time power change curve; a third determining unit, configured to determine a second temperature change of the battery from time T2 to time T3 based on the second real-time current; a fourth processing unit, configured to sum the temperature prediction value at time T2 and the second temperature change to obtain a temperature prediction value at time T3, and so on to obtain a temperature prediction value at time Tn, where time Tn is the time when the vehicle reaches the destination; The calculation formulas for the first temperature change and the second temperature change are as follows: ； Where n is a positive integer not less than 1. hour, is the first temperature change of the battery from the time T1 to the time T2; is the first real-time current of the battery at the time T1; is the internal resistance of the battery at the time T1, is the heat exchange value between the battery and the external environment from time T1 to time T2; when n=2, is the second temperature change of the battery from the time T2 to the time T3; is the second real-time current of the battery at the time T2; is the internal resistance of the battery cell at the time T2, is the heat exchange value between the battery and the external environment from time T2 to time T3; is the sampling interval; is the specific heat capacity of the battery; is the weight of the battery.

8. A vehicle, characterized in that: The vehicle includes at least one processor and a memory connected to the at least one processor, and the at least one processor is configured to implement the steps of the method according to any one of claims 1 to 6 when executing a computer program stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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