Battery residual charging duration prediction method and device, medium, product and vehicle

Abstract

The invention provides a battery residual charging duration prediction method and device, a medium, a product and a vehicle. The method comprises the following steps: acquiring the maximum battery cell temperature, the minimum battery cell temperature, the initial charge state and the environment temperature of a battery in the current period; according to the maximum cell temperature, the minimum cell temperature and the initial charge state of the current period, predicting the initial charge state of the battery in the next period; predicting the maximum cell temperature and the minimum cell temperature of the battery in the next period according to the maximum cell temperature, the minimum cell temperature and the initial charge state of the current period and the environment temperature; iteratively calculating the state of charge of the battery until the state of charge exceeds a preset charging cut-off state of charge; and according to the number of iterations, obtaining the remaining charging duration of the battery. The method can improve the prediction accuracy of the residual charging duration of the battery.

Description

Technical Field

[0001] The present application relates to the field of battery charging technology, and in particular to a method, device, medium, product and vehicle for predicting remaining battery charging time. Background Art

[0002] As the core power source of electric vehicles, batteries need to be charged regularly to maintain their endurance. At present, when charging the batteries of electric vehicles, the remaining charging time of the batteries is usually predicted, and the estimated remaining charging time of the batteries is provided to users, so that users can reasonably arrange and plan their travel time. However, the current prediction method of the remaining charging time of the batteries is of low accuracy and cannot meet the needs of actual use.

[0003] Therefore, how to improve the accuracy of predicting the remaining charging time of the battery is an urgent problem to be solved. Summary of the invention

[0004] The present application provides a method, device, medium, product and vehicle for predicting the remaining charging time of a battery, so as to improve the prediction accuracy of the remaining charging time of the battery.

[0005] In a first aspect, the present application provides a method for predicting the remaining charging time of a battery, comprising:

[0006] Obtaining the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery in the current cycle;

[0007] Predicting the initial state of charge of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, and the initial state of charge of the current cycle;

[0008] Predicting the maximum cell temperature and the minimum cell temperature of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the current cycle;

[0009] Iteratively calculating the state of charge of the battery until it exceeds a preset charging cutoff state of charge;

[0010] The remaining charging time of the battery is obtained according to the number of iterations.

[0011] Optionally, predicting the maximum cell temperature and the minimum cell temperature of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the current cycle includes:

[0012] Predict the maximum cell temperature of the battery in the next cycle and the minimum cell temperature of the next cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, the ambient temperature, the duration of a single cycle, the self-heating temperature rise rate mapping relationship table, the thermal management temperature rise rate mapping relationship table, and the natural heat exchange temperature rise rate mapping relationship table of the current cycle;

[0013] Among them, the self-heating temperature rise rate mapping relationship table includes: the first mapping relationship between the battery cell temperature, the state of charge and the self-heating temperature rise rate; the thermal management temperature rise rate mapping relationship table includes: the second mapping relationship between the thermal management strategy and the thermal management temperature rise rate; the natural heat exchange temperature rise rate mapping relationship table includes: the third mapping relationship between the battery cell temperature, the ambient temperature and the natural heat exchange temperature rise rate.

[0014] Optionally, predicting the maximum cell temperature of the battery in the next cycle and the minimum cell temperature of the next cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, the ambient temperature, the self-heating temperature rise rate mapping relationship table, the thermal management temperature rise rate mapping relationship table, and the natural heat exchange temperature rise rate mapping relationship table of the current cycle includes:

[0015] Determine the self-heating temperature rise rate of the battery in the current cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the self-heating temperature rise rate mapping relationship table of the current cycle;

[0016] Determining a thermal management strategy of the battery in the current cycle according to the maximum battery cell temperature and the minimum battery cell temperature of the current cycle;

[0017] Determining the thermal management temperature rise rate of the battery in the current cycle according to the thermal management strategy of the battery in the current cycle and the thermal management temperature rise rate mapping relationship table;

[0018] Determine the natural heat exchange temperature rise rate of the battery in the current cycle according to the ambient temperature of the battery, the maximum battery cell temperature and the minimum battery cell temperature in the current cycle, and the natural heat exchange temperature rise rate mapping relationship table;

[0019] Determine the maximum cell temperature of the battery in the next cycle according to the maximum cell temperature of the current cycle, the duration of a single cycle, the self-heating temperature rise rate, the thermal management temperature rise rate, and the natural heat exchange temperature rise rate;

[0020] The minimum cell temperature of the battery in the next cycle is determined according to the minimum cell temperature of the current cycle, the duration of a single cycle, the self-heating temperature rise rate, the thermal management temperature rise rate and the natural heat exchange temperature rise rate.

[0021] Optionally, determining the self-heating temperature rise rate of the battery in the current cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the self-heating temperature rise rate mapping relationship table of the current cycle includes:

[0022] Determine a first initial temperature rise rate of the battery in the current cycle according to the maximum cell temperature of the current cycle, the initial state of charge, and the self-heating temperature rise rate mapping relationship table;

[0023] Determine a second initial temperature rise rate of the battery in the current cycle according to the minimum cell temperature of the current cycle, the initial state of charge, and the self-heating temperature rise rate mapping relationship table;

[0024] The self-heating temperature rise rate of the battery in the current cycle is determined according to the first initial temperature rise rate and the second initial temperature rise rate of the battery in the current cycle.

[0025] Optionally, determining the natural heat exchange temperature rise rate of the battery in the current cycle according to the ambient temperature of the battery, the maximum battery cell temperature and the minimum battery cell temperature in the current cycle, and the natural heat exchange temperature rise rate mapping relationship table includes:

[0026] Determining the temperature of the battery according to the maximum battery cell temperature and the minimum battery cell temperature of the current cycle;

[0027] The natural heat exchange temperature rise rate of the battery in the current cycle is determined according to the temperature of the battery, the ambient temperature of the battery, and the natural heat exchange temperature rise rate mapping relationship table.

[0028] Optionally, predicting the initial state of charge of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, and the initial state of charge of the current cycle includes:

[0029] According to the maximum battery cell temperature, the initial state of charge, and the charging current mapping relationship table, obtaining the first initial charging current of the battery in the current cycle; the charging current mapping relationship table includes: a fourth mapping relationship between the battery cell temperature, the state of charge and the charging current;

[0030] Acquire a second initial charging current of the battery in the current cycle according to the minimum battery cell temperature, the initial state of charge, and the charging current mapping relationship table;

[0031] Obtaining a charging current of the battery in the current cycle according to the first initial charging current and the second initial charging current;

[0032] The initial state of charge of the battery in the next cycle is predicted according to the charging current and the initial state of charge of the battery in the current cycle.

[0033] In a second aspect, the present application provides a charging processing method, comprising:

[0034] During the process of charging the battery, obtaining the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery;

[0035] Taking the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery as the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the first cycle, and using the method described in any one of the first aspects above to predict the remaining charging time of the battery;

[0036] Output the predicted remaining charging time of the battery.

[0037] In a third aspect, the present application provides a device for predicting the remaining charging time of a battery, the device comprising:

[0038] An acquisition module, used to acquire the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery in the current cycle;

[0039] A prediction module is used to predict the initial state of charge of the battery in the next cycle based on the maximum cell temperature, minimum cell temperature, and initial state of charge of the current cycle; predict the maximum cell temperature and minimum cell temperature of the battery in the next cycle based on the maximum cell temperature, minimum cell temperature, initial state of charge of the current cycle, and the ambient temperature; iteratively calculate the state of charge of the battery until it exceeds a preset charging cut-off state of charge; and obtain the remaining charging time of the battery based on the number of iterations.

[0040] In a fourth aspect, the present application provides a charging processing device, the device comprising:

[0041] An acquisition module, used for acquiring the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery during charging of the battery;

[0042] A prediction module, taking the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery as the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the first cycle, and using the method as described in any one of the first aspects to predict the remaining charging time of the battery;

[0043] The output module is used to output the predicted remaining charging time of the battery.

[0044] In a fifth aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0045] The memory stores computer-executable instructions;

[0046] The processor executes the computer-executable instructions stored in the memory to implement the method described in any one of the first aspect or the second aspect.

[0047] In a sixth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method described in any one of the first aspect or the second aspect.

[0048] In a seventh aspect, the present application provides a vehicle, comprising an electronic device and a battery; wherein the electronic device is used to execute the method described in any one of the first aspect or the second aspect above.

[0049] The battery remaining charging time prediction method, device, medium, product and vehicle provided in the embodiments of the present application can predict the battery's SOC in the current iteration cycle based on the battery's maximum cell temperature and minimum cell temperature in the process of predicting the battery's remaining charging time in an iterative manner, so as to obtain an SOC that is more in line with the actual charging situation. Since the SOC and ambient temperature will affect the change in battery temperature, based on these accurate data, the maximum cell temperature and minimum cell temperature of the battery in the next iteration cycle can be accurately predicted, thereby making the prediction of the battery's remaining charging time more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0051] Figure 1 A flowchart of a method for predicting the remaining charging time of a battery provided in an embodiment of the present application;

[0052] Figure 2 A schematic flow chart of a method for predicting the maximum cell temperature and the minimum cell temperature of a battery in the next cycle provided in an embodiment of the present application;

[0053] Figure 3 A schematic flow chart of a method for predicting the charging current of a battery in a current cycle provided in an embodiment of the present application;

[0054] Figure 4 A schematic diagram of a charging process provided in an embodiment of the present application;

[0055] Figure 5 A schematic diagram of another charging process method provided in an embodiment of the present application;

[0056] Figure 6 A schematic diagram of the structure of a device for predicting the remaining charging time of a battery provided in an embodiment of the present application;

[0057] Figure 7 A schematic diagram of the structure of a charging processing device provided in an embodiment of the present application;

[0058] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0059] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0060] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0061] As the core power source of electric vehicles, batteries need to be charged regularly to maintain their endurance. During the battery charging process, the prediction of the remaining charging time directly affects the user's daily travel planning and car experience. An accurate remaining charging time estimation method can help users know and reasonably arrange the charging time to avoid the inconvenience caused by long charging time.

[0062] The execution subject of the method provided in the embodiment of the present application can be any device on the vehicle for predicting the remaining charging time of the battery, for example, it can be a battery management system (BMS), or other devices on the vehicle that can implement the method, or a cloud server connected to the vehicle. The following embodiment is described using BMS as an example.

[0063] At present, the existing technology mainly uses the following methods to predict the remaining charging time:

[0064] The BMS pre-stores a static comparison table and a dynamic comparison table. The static comparison table is a two-dimensional array table consisting of temperature, voltage value and initial charging time value obtained based on a large amount of vehicle test data, and the dynamic comparison table is a two-dimensional array table consisting of temperature, voltage value and charging time. The charging time value in the dynamic comparison table is updated in real time according to the actual charging time of each charge.

[0065] Therefore, when charging the battery, the BMS can obtain a charging time based on the temperature and voltage of the battery according to the static comparison table and the dynamic comparison table. Then, the BMS determines the remaining predicted charging time from the two charging times. After charging is completed, the dynamic comparison table is updated according to the actual charging time.

[0066] There are many factors that affect the remaining charging time of the battery, such as self-heating of the battery during charging, influence of ambient temperature, etc. However, the above-mentioned prediction of the remaining charging time is only achieved through the temperature and voltage of the battery at a certain moment, and does not take into account the other factors mentioned above, resulting in poor accuracy of the remaining charging time predicted by the above method. In addition, the above-mentioned dynamic comparison table is only a mapping relationship between temperature, voltage value and charging time, and does not take into account all charging conditions, resulting in that when using the dynamic comparison table for prediction, it may be difficult to encounter a charging condition that meets the conditions / is exactly the same as the dynamic comparison table, further reducing the accuracy of the prediction.

[0067] In view of this, an embodiment of the present application provides a method for predicting the remaining charging time of a battery by integrating multiple factors to improve the accuracy of the prediction.

[0068] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0069] Figure 1 A flowchart of a method for predicting the remaining charging time of a battery provided in an embodiment of the present application. The method comprises:

[0070] S101. Obtain the maximum cell temperature, the minimum cell temperature, the initial SOC, and the ambient temperature of the battery in the current cycle.

[0071] Optionally, the remaining charging time of the battery is regarded as the superposition of multiple cycles, and the duration of each cycle is fixed. For example, the duration of each cycle can be defined as 0.1 seconds, and the remaining charging time of the battery can be the sum of multiple 0.1 seconds. The current cycle can be the cycle at the beginning of battery charging (for example, it can be 0-0.1 seconds since the battery was charged).

[0072] The battery is composed of a plurality of battery cells. The maximum battery cell temperature may be the maximum temperature of all battery cells of the battery, and the minimum battery cell temperature may be the minimum temperature of all battery cells of the battery.

[0073] The state of charge is the ratio of the remaining capacity of a battery after it has been used for a period of time or has been left unused for a long time to its capacity in a fully charged state, usually expressed as a percentage. Its value range is 0 to 1. When SOC = 0, it means that the battery is fully discharged, and when SOC = 1, it means that the battery is fully charged. The initial SOC mentioned here can be the SOC of the battery at the beginning of the cycle.

[0074] Optionally, the ambient temperature may be the temperature of a space where the battery is located. The ambient temperature may exchange heat with the battery, thereby affecting the remaining charging time of the battery.

[0075] Optionally, the maximum cell temperature, the minimum cell temperature and the SOC may be collected by the BMS during the battery charging process, and the ambient temperature may be obtained through a temperature sensor on the electric vehicle, or may be the ambient temperature of the location obtained from the cloud based on the location of the vehicle.

[0076] S102, predicting the initial SOC of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, and the initial SOC of the current cycle. The next cycle may be the cycle immediately following the current cycle, for example, the current cycle may be the cycle at the beginning of battery charging, for example, it may be 0-0.1 seconds from the time when the battery is charged, and the next cycle may be 0.1-0.2 seconds from the time when the battery is charged.

[0077] For example, the initial SOC of the next cycle is affected by the charging current and initial SOC of the current cycle. During the battery charging process, the charging current will change with the changes in the cell temperature and SOC. Therefore, the charging current of the battery in the current cycle can be predicted based on the maximum cell temperature, minimum cell temperature, and initial SOC of the current cycle. Then, based on the charging current and the initial SOC, the initial SOC of the battery in the next cycle is predicted.

[0078] For example, in the actual charging process, the minimum charging current that matches the battery cell temperature is usually selected based on the battery cell temperature to ensure the safety of the battery during the charging process. Therefore, when predicting the charging current of the battery, the charging current of the battery can be calculated based on the maximum battery cell temperature and the minimum battery cell temperature, so as to obtain the charging current that ensures the safety of battery charging, so as to make the prediction more consistent with the actual charging process, thereby improving the accuracy of the prediction.

[0079] Taking the current cycle as the nth cycle and the next cycle as n+1 as an example, the BMS can predict the initial SOC of the battery in the next cycle by the following formula (1):

[0080] SOC_n+1=SOC_n+(Ic_n*δt) / C (1)

[0081] Among them, SOC_n+1 is the predicted initial SOC of the battery in the n+1th cycle; SOC_n is the initial SOC of the nth cycle; Ic_n is the charging current of the nth cycle; δt is the duration of a single cycle; C is the available capacity of the battery. The minimum value of n is related to the setting of the initial value. Taking the initial value of 0 as an example, the value of n is greater than or equal to 0.

[0082] As mentioned above, the charging current can be predicted by the maximum cell temperature, the minimum cell temperature and the initial SOC of the current cycle. That is to say, there is a functional relationship between the charging current and the maximum cell temperature, the minimum cell temperature and the initial SOC of the current cycle. Therefore, it can also be used to substitute the calculation relationship containing the three variables of the maximum cell temperature, the minimum cell temperature and the initial SOC of the current cycle and capable of characterizing the charging current into the aforementioned formula (1) to replace the position of the variable in the formula, thereby achieving the purpose of predicting the initial SOC of the battery in the next cycle, saving the calculation link and improving the prediction efficiency.

[0083] It should be noted that in the subsequent embodiments, the processing using the charging current as a variable can be replaced and implemented in a similar manner, which will not be described in detail.

[0084] S103 , predicting the maximum cell temperature and the minimum cell temperature of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, the initial SOC, and the ambient temperature of the current cycle.

[0085] During the battery charging process, the cell temperature will change with the SOC and ambient temperature, as follows:

[0086] As the SOC increases, the rate of electrochemical reactions inside the battery will change. The higher the SOC, the more heat may be generated inside the battery, causing the battery cell temperature to rise. According to Joule's law, the greater the charging current, the more Joule heat is generated inside the battery, causing the battery cell temperature to rise. The ambient temperature directly affects the heat exchange efficiency between the battery and the external environment. In a high temperature environment, it becomes more difficult for the battery to dissipate heat to the external environment, resulting in a faster rate of temperature rise in the battery cell; in a low temperature environment, the battery needs to consume more energy to maintain its own temperature, resulting in a faster temperature drop in the battery cell.

[0087] Therefore, when predicting the battery cell temperature in the next cycle, the maximum and minimum cell temperatures of the battery in the next cycle can be predicted based on the maximum cell temperature, initial SOC, and ambient temperature of the current cycle, thereby ensuring the accuracy of predicting SOC using the maximum and minimum cell temperatures in the next cycle.

[0088] Exemplarily, the BMS can calculate the current self-heating temperature rise rate, thermal management temperature rise rate, and natural heat exchange temperature rise rate that affect the battery temperature rise rate based on the maximum cell temperature, minimum cell temperature, initial SOC, charging current, and ambient temperature of the current cycle, thereby accurately predicting the maximum cell temperature and minimum cell temperature of the battery in the next cycle based on the current self-heating temperature rise rate, thermal management temperature rise rate, and natural heat exchange temperature rise rate.

[0089] S104 , iteratively calculating the SOC of the battery until it exceeds a preset charging cutoff SOC.

[0090] Optionally, the charge cut-off SOC may also be a target SOC, which is used to define the battery charge cut-off condition, and may be preset in the BMS, preset at the factory, or user-defined, and there is no limitation on this. For example, the charge cut-off SOC value is 0.9.

[0091] It can be understood that the iterative calculation of the battery SOC refers to taking the next cycle indicated by S103 and S104 as the new current cycle to calculate the SOC, maximum cell temperature and minimum cell temperature of the next cycle of the new current cycle, and repeating this cycle until the cycle with the preset charging cut-off SOC appears, thereby terminating the iterative calculation process.

[0092] S105. Obtain the remaining charging time of the battery according to the number of iterations.

[0093] In the iterative calculation process, each iteration is a cycle, and the duration of each cycle is the same. Therefore, the remaining charging time T of the battery can be obtained according to the following formula (2):

[0094] T=M*δt (2)

[0095] Among them, if the iteration cycle is initially taken as 0, then M is the last cycle of the iterative calculation + 1; if the iteration cycle is initially taken as 1, then M is the last cycle of the iterative calculation, and also represents the final number of iterations of this iterative calculation, and δt is the duration of each cycle.

[0096] The method for predicting the remaining battery charging time provided in the embodiment of the present application can predict the SOC of the battery in the current iteration cycle based on the maximum cell temperature and the minimum cell temperature of the battery in the process of predicting the remaining battery charging time in an iterative manner, so as to obtain an SOC that is more in line with the actual charging situation. Since the SOC and the ambient temperature will affect the change of the battery temperature, based on these accurate data, the maximum cell temperature and the minimum cell temperature of the battery in the next iteration cycle can be accurately predicted, so that the prediction of the remaining battery charging time can be more accurate.

[0097] The following describes how to predict the maximum cell temperature and the minimum cell temperature of the battery in the next cycle based on the maximum cell temperature, the minimum cell temperature, the SOC, and the ambient temperature of the current cycle.

[0098] For example, the BMS can predict the maximum cell temperature of the battery in the next cycle and the minimum cell temperature of the next cycle based on the maximum cell temperature, minimum cell temperature, charge state, ambient temperature, duration of a single cycle, self-heating temperature rise rate mapping table, thermal management temperature rise rate mapping table, and natural heat exchange temperature rise rate mapping table of the current cycle.

[0099] First, the self-heating temperature rise rate mapping relationship table, the thermal management temperature rise rate mapping relationship table, and the natural heat exchange temperature rise rate mapping relationship table are introduced in detail.

[0100] (1) Self-heating temperature rise rate mapping table:

[0101] The self-heating temperature rise rate mapping relationship table may include, for example, a first mapping relationship between battery temperature, state of charge (SOC) and self-heating temperature rise rate. The table may be, for example, as shown in Table 1 below:

[0102] Table 1

[0103]

[0104] It should be understood that the horizontal axis in Table 1 is the SOC value, and the vertical axis is the battery temperature value. For example, assuming that the battery temperature is 30°C and the SOC is 20%, it can be seen from the table that the self-heating temperature rise rate of the battery is 0.00026°C / min / A 2 . Where A is the current unit ampere. The battery temperature is 30°C, the SOC is 20%, and the battery's self-heating temperature rise rate is 0.00026°C / min / A 2 These three constitute a first mapping relationship.

[0105] It should be noted that the above Table 1 is only an exemplary way of expressing each first mapping relationship in the self-heating temperature rise rate mapping relationship table. This embodiment does not limit the structure of the self-heating temperature rise rate mapping relationship table, and the division of battery temperature and SOC in the self-heating temperature rise rate mapping relationship table. For example, a finer granularity division can be used, or a coarser granularity division can be used.

[0106] (2) Thermal management temperature rise rate mapping table

[0107] The thermal management temperature rise rate mapping relationship table may include, for example: a second mapping relationship between thermal management strategies and thermal management temperature rise rates, which may be shown in the following Table 2:

[0108] Table 2

[0109] Thermal Management Strategies Temperature rise rate Heating level 1 0.5℃ / min Heating level 2 0.2℃ / min Cooling 1st gear -0.3℃ / min Cooling 2nd gear -0.6℃ / min

[0110] It should be understood that the first column of Table 2 is the thermal management strategy, and the second column is the temperature rise rate. For example, when the thermal management mode is heating level 1, the thermal management temperature rise rate is 0.5°C / min, and the two constitute a second mapping relationship.

[0111] It should be noted that the above Table 2 is only an exemplary representation of each second mapping relationship in the thermal management temperature rise rate mapping relationship table. This embodiment does not limit the structure of the thermal management temperature rise rate mapping relationship table, and the division of thermal management modes and gears in the thermal management temperature rise rate mapping relationship table. For example, the thermal management strategy may include heating gear 1, heating gear 2, heating gear n (n≥1), cooling gear 1, cooling gear 2, cooling gear n (n≥1).

[0112] (3) Natural heat transfer temperature rise rate mapping table

[0113] The natural heat exchange temperature rise rate mapping relationship table may include, for example, a third mapping relationship between battery temperature, ambient temperature and natural heat exchange temperature rise rate. The natural heat exchange temperature rise rate mapping relationship table may be, for example, as shown in the following Table 3:

[0114] Table 3

[0115]

[0116]

[0117] It should be understood that in the above Table 3, the horizontal axis is the battery temperature and the vertical axis is the ambient temperature. For example, when the ambient temperature is 30°C and the battery temperature is 15°C, the natural heat exchange temperature rise rate is 0.01°C / min, and the three form a mapping relationship. Among them, °C / min is the unit of the natural heat exchange temperature rise rate.

[0118] It should be noted that the above Table 3 only exemplifies a way of expressing each third mapping relationship in the natural heat exchange temperature rise rate mapping relationship table. This embodiment does not limit the structure of the natural heat exchange temperature rise rate mapping relationship table, nor does it limit the division of battery temperature and ambient temperature in the natural heat exchange temperature rise rate mapping relationship table.

[0119] The data in the above-mentioned self-heating temperature rise rate mapping relationship table, thermal management temperature rise rate mapping relationship table, and natural heat exchange temperature rise rate mapping relationship table are all constructed through offline measured data. Through these tables, the remaining charging time can be predicted more conveniently and quickly, omitting the calculation link. In addition, since the above tables are all measured values, the data in the above mapping relationship tables are more consistent with the actual situation of the battery, so the prediction of the remaining charging time based on these mapping relationship tables is more accurate.

[0120] Below, taking the self-heating temperature rise rate mapping relationship table, the thermal management temperature rise rate mapping relationship table, and the natural heat exchange temperature rise rate mapping relationship table as examples, we will further introduce how to combine the maximum cell temperature, minimum cell temperature, initial SOC, and ambient temperature of the current cycle to predict the maximum cell temperature of the battery in the next cycle and the minimum cell temperature of the next cycle.

[0121] Figure 2 A flow chart of a method for predicting the maximum cell temperature and the minimum cell temperature of a battery in the next cycle provided in an embodiment of the present application. The method comprises:

[0122] S201, determining the self-heating temperature rise rate of the battery in the current cycle according to the maximum battery cell temperature, the minimum battery cell temperature, the initial SOC, and the self-heating temperature rise rate mapping relationship table of the current cycle.

[0123] For example, the BMS can determine the first initial temperature rise rate of the battery in the current cycle based on the maximum cell temperature, initial SOC, and self-heating temperature rise rate mapping table of the current cycle; and determine the second initial temperature rise rate of the battery in the current cycle based on the minimum cell temperature, initial SOC, and self-heating temperature rise rate mapping table of the current cycle.

[0124] In this implementation, the BMS can determine the self-heating temperature rise rate of the battery in the current cycle according to the first initial temperature rise rate and the second initial temperature rise rate of the battery in the current cycle. For example, the smaller initial temperature rise rate of the first initial temperature rise rate and the second initial temperature rise rate can be selected as the self-heating temperature rise rate of the battery in the current cycle, or the average of the two initial self-heating temperature rise rates can be used as the self-heating temperature rise rate, or the maximum of the two initial self-heating temperature rise rates can be used as the self-heating temperature rise rate, etc.

[0125] For example, when the maximum cell temperature is -25°C, the minimum cell temperature is -30°C, and the initial SOC is 10%, by looking up the self-heating temperature rise rate mapping table shown in Table 1 above, it can be determined that the first initial temperature rise rate of the battery in the current cycle is 0.00042°C / min / A. 2 The second initial temperature rise rate is 0.00043℃ / min / A 2 In this implementation, for example, 0.00042°C / min / A 2 As the self-heating temperature rise rate of the battery in the current cycle.

[0126] S202: Determine a thermal management strategy of the battery in the current cycle according to the maximum cell temperature and the minimum cell temperature in the current cycle.

[0127] Optionally, the thermal management strategy may include heating level 1, heating level 2, heating level n (n≥1), cooling level 1, cooling level 2, cooling level n (n≥1).

[0128] Exemplarily, based on the principle of protecting the battery from being in a preset target operating temperature range during operation, a threshold value of the battery target operating temperature can be set in advance. When the maximum cell temperature or the minimum temperature exceeds the threshold, the BMS can select an appropriate strategy from the thermal management strategy to keep the battery in the target operating temperature range.

[0129] For example, if the maximum cell temperature of the current cycle is -25°C and the minimum cell temperature is -30°C, which are lower than the target operating temperature threshold, in order to keep the battery temperature within the target operating temperature range, the BMS determines that the thermal management strategy of the battery in the current cycle is heating level 1.

[0130] S203 . Determine the thermal management temperature rise rate of the battery in the current cycle according to the thermal management strategy of the battery in the current cycle and the thermal management temperature rise rate mapping relationship table.

[0131] Exemplarily, if the thermal management strategy of the current cycle is heating level 1, the thermal management temperature rise rate mapping relationship table shown in Table 2 is looked up to determine that the thermal management temperature rise rate of the battery in the current cycle is 0.5° C. / minute.

[0132] S204, determining the natural heat exchange temperature rise rate of the battery in the current cycle according to the ambient temperature of the battery, the maximum battery cell temperature and the minimum battery cell temperature in the current cycle, and the natural heat exchange temperature rise rate mapping relationship table.

[0133] In one implementation, the BMS can determine the battery temperature based on the maximum cell temperature and the minimum cell temperature of the current cycle. For example, the maximum cell temperature can be used as the battery temperature, the minimum cell temperature can be used as the battery temperature, or the average of the two can be used as the battery temperature. Assuming that the maximum cell temperature of the current cycle is 12°C and the minimum cell temperature is 10°C, in this example, for example, 10°C can be used as the battery temperature.

[0134] The BMS can determine the natural heat exchange temperature rise rate of the battery in the current cycle based on the battery temperature, the battery's ambient temperature, and the natural heat exchange temperature rise rate mapping relationship table.

[0135] For example, if the battery temperature is 10°C and the battery ambient temperature is 30°C, the natural heat exchange temperature rise rate mapping table shown in Table 3 above determines that the natural heat exchange temperature rise rate of the battery in the current cycle is 0.01°C / minute.

[0136] In another implementation, the BMS can determine the first natural heat exchange temperature rise rate of the battery in the current cycle based on the maximum cell temperature of the current cycle, the ambient temperature of the battery, and the natural heat exchange temperature rise rate mapping relationship table, and determine the second natural heat exchange temperature rise rate of the battery in the current cycle based on the minimum cell temperature of the current cycle, the ambient temperature of the battery, and the natural heat exchange temperature rise rate mapping relationship table. The BMS can select the larger natural heat exchange temperature rise rate between the first natural heat exchange temperature rise rate and the second natural heat exchange temperature rise rate as the natural heat exchange temperature rise rate of the current cycle, or select the smaller natural heat exchange temperature rise rate between the two as the natural heat exchange temperature rise rate of the current cycle, or take the average of the first natural heat exchange temperature rise rate and the second natural heat exchange temperature rise rate as the natural heat exchange temperature rise rate of the current cycle.

[0137] In the above steps, the self-heating temperature rise rate, the thermal management temperature rise rate and the natural heat exchange temperature rise rate are determined in no particular order and can be executed simultaneously or one of them can be executed first and then the other. There is no limitation on this.

[0138] S205, determining the maximum cell temperature of the battery in the next cycle according to the maximum cell temperature of the current cycle, the charging current, the duration of a single cycle, the self-heating temperature rise rate, the thermal management temperature rise rate and the natural heat exchange temperature rise rate.

[0139] Optionally, the maximum cell temperature of the battery in the next cycle is calculated as follows:

[0140] Tmax_n+1=Tmax_n+(K1_n*Ic_n 2 +K2_n+K3_n)*δt (3)

[0141] Among them, Tmax_n+1 is the maximum cell temperature of the battery in the n+1th cycle; Tmax_n is the maximum cell temperature of the nth cycle; Ic_n is the charging current of the nth cycle; δt is the duration of a single cycle; K1_n is the self-heating temperature rise rate of the nth cycle; K2_n is the thermal management temperature rise rate of the nth cycle; K3_n is the natural heat exchange temperature rise rate of the nth cycle.

[0142] S206, determining the minimum cell temperature of the battery in the next cycle according to the minimum cell temperature of the current cycle, the charging current, the duration of a single cycle, the self-heating temperature rise rate, the thermal management temperature rise rate and the natural heat exchange temperature rise rate.

[0143] Optionally, the maximum cell temperature of the battery in the next cycle is calculated as follows:

[0144] Tmin_n+1=Tmin_n+(K1_n*Ic_n 2 +K2_n+K3_n)*δt (4)

[0145] Among them, Tmin_n+1 is the minimum cell temperature of the battery in the n+1th cycle; Tmin_n is the minimum cell temperature of the nth cycle; Ic_n is the charging current of the nth cycle; δt is the duration of a single cycle; K1_n is the self-heating temperature rise rate of the nth cycle; K2_n is the thermal management temperature rise rate of the nth cycle; K3_n is the natural heat exchange temperature rise rate of the nth cycle.

[0146] There is no particular order in which step S205 and step S206 are executed. They can be executed simultaneously, or one can be executed first and then the other. There is no limitation on this.

[0147] As mentioned above, the charging current can be predicted by the maximum cell temperature, the minimum cell temperature, and the initial SOC of the current cycle. That is to say, there is a functional relationship between the charging current and the maximum cell temperature, the minimum cell temperature, and the initial SOC of the current cycle. Therefore, it can also be used to substitute the calculation relationship containing the three variables of the maximum cell temperature, the minimum cell temperature, and the initial SOC of the current cycle and capable of characterizing the charging current into the aforementioned formulas (3) and (4) to replace the position of the variable in the formula, thereby achieving the purpose of determining the maximum cell temperature and the minimum cell temperature of the battery in the next cycle, saving calculation steps and improving prediction efficiency.

[0148] During the battery charging process, the self-heating of the battery body, thermal management, and ambient temperature will all affect the temperature rise of the battery. Therefore, the self-heating temperature rise rate, thermal management temperature rise rate, and natural heat exchange temperature rise rate act together on the battery charging process, affecting the remaining charging time of the battery. Therefore, in the process of predicting the remaining charging time of the battery, the embodiment of the present application takes these factors into consideration and acts on the predicted battery temperature change, which can improve the accuracy of the predicted temperature, and then the battery charging process can be simulated based on the accurate temperature to obtain the accurate remaining charging time of the battery.

[0149] Consistent with the above, the embodiment of the present application can calculate the charging current of the battery based on the maximum cell temperature and the minimum cell temperature of the battery, thereby obtaining the charging current that ensures the safety of battery charging, so that the predicted state of charge is more consistent with the actual charging process, thereby improving the accuracy of the prediction. The following is an example of how to obtain the initial SOC of the battery in the next cycle:

[0150] Figure 3 A flow chart of a method for predicting the charging current of a battery in the current cycle provided in an embodiment of the present application. Figure 3 As shown, the above step S102 may include:

[0151] S301, obtaining a first initial charging current of the battery in a current cycle according to the maximum battery cell temperature, the initial SOC, and a charging current mapping relationship table; the charging current mapping relationship table includes: a fourth mapping relationship between the battery cell temperature, the SOC, and the charging current.

[0152] Table 4 is a charging current mapping relationship table provided in an embodiment of the present application. The table reflects the mapping relationship between the battery cell temperature, SOC and charging current.

[0153] Table 4

[0154]

[0155] It should be understood that the horizontal axis in the above Table 4 is the value of SOC, and the vertical axis is the value of battery cell temperature. For example, if the maximum battery cell temperature is 25°C and the SOC is 20%, it can be seen from the table that the first initial charging current of the current cycle is 100A.

[0156] It should be noted that the above Table 4 is only an exemplary way of expressing each fourth mapping relationship in the charging current mapping relationship table. This embodiment does not limit the structure of the charging current mapping relationship table, and the division of battery temperature and SOC in the charging current mapping relationship table. For example, a finer granularity division can be used, or a coarser granularity division can be used.

[0157] S302: Obtain a second initial charging current of the battery in the current cycle according to the minimum battery cell temperature, the initial SOC, and a charging current mapping relationship table.

[0158] Similar to step S301, if the minimum cell temperature is 20°C and the initial SOC is 20%, by looking up the charging current mapping table, it can be known that the second initial charging current of the current cycle is 90A.

[0159] S303: Obtain a charging current of the battery in a current cycle according to the first initial charging current and the second initial charging current.

[0160] Optionally, the smaller charging current between the first initial charging current and the second initial charging current is taken as the charging current of the battery in the current cycle. Using a smaller charging current can protect the battery. For example, when the first initial charging current is 100A and the second initial charging current is 90A, 90A is selected as the charging current of the battery in the current cycle. Alternatively, when the first initial charging current is 100A and the second initial charging current is 90A, the average of the first initial charging current 100A and the second initial charging current 90A, that is, 95A, is taken as the charging current of the battery in the current cycle. It should be noted that the above-mentioned determination of the self-heating temperature rise rate of the battery in the current cycle should be consistent with the method for determining the value of the charging current of the battery in the current cycle to ensure the rationality and accuracy of the prediction.

[0161] S304: predicting the initial SOC of the battery in the next cycle according to the charging current and the initial SOC of the battery in the current cycle.

[0162] Regarding how to calculate the SOC based on the charging current, please refer to the above description, which will not be repeated here.

[0163] The embodiments of the present application take into account that the charging current of the battery will be affected by the battery cell temperature and SOC during the charging process, and therefore provide a method for predicting the charging current of the battery in the current cycle based on the initial SOC and the battery cell temperature of the current cycle. Compared with predicting the remaining charging time of the battery using a constant charging current, this method can more accurately predict the remaining charging time of the battery.

[0164] The above prediction of the remaining charging time of the battery can be applied to the actual battery charging scenario of the vehicle, and can also be applied to the offline battery maintenance or experiment scenario. The following takes the charging scenario as an example to illustrate how to apply the above method. It should be noted that the method can be applied at any time of charging, or in other words, the following process can be executed in real time or periodically during the charging process.

[0165] Figure 4 A schematic diagram of a charging process provided in an embodiment of the present application. Figure 4 As shown, the method includes:

[0166] S401. During the process of charging the battery, obtain the maximum cell temperature, the minimum cell temperature, the SOC, and the ambient temperature of the battery.

[0167] S402, using the maximum cell temperature, minimum cell temperature, initial SOC, and ambient temperature of the battery as the maximum cell temperature, minimum cell temperature, initial SOC, and ambient temperature of the first cycle to predict the remaining charging time of the battery.

[0168] That is, the remaining battery charging time can be predicted by using the method for predicting the remaining battery charging time provided in the aforementioned method embodiment.

[0169] S403: Output the predicted remaining charging time of the battery.

[0170] The above prediction process is explained below through a specific example.

[0171] Figure 5 A schematic diagram of another charging process provided in an embodiment of the present application. Figure 5 As shown, the method may include, for example:

[0172] S501 , obtaining an initial maximum cell temperature Tmax_n, an initial minimum cell temperature Tmin_n, an initial state of charge SOC_n, and an ambient temperature Tc of a battery in an nth cycle.

[0173] It should be understood that when the iteration starts for the first time, the value of n is 0.

[0174] S502, determining the self-heating temperature rise rate K1_n of the battery in the nth cycle according to the maximum battery cell temperature Tmax_n, the minimum battery cell temperature Tmin_n, the SOC_n, and the self-heating temperature rise rate mapping relationship table of the nth cycle, and obtaining the charging current Ic_n of the battery in the nth cycle according to the maximum battery cell temperature Tmax_n, the minimum battery cell temperature Tmin_n, the state of charge, and the charging current mapping relationship table.

[0175] S503 , determining a thermal management strategy of the battery in the nth cycle according to the maximum battery cell temperature Tmax_n and the minimum battery cell temperature Tmin_n in the nth cycle.

[0176] S504: Determine the thermal management temperature rise rate K2_n of the battery in the nth cycle according to the thermal management strategy of the battery in the nth cycle and the thermal management temperature rise rate mapping relationship table.

[0177] S505. Determine the natural heat exchange temperature rise rate K3_n of the battery in the nth cycle according to the ambient temperature Tc of the battery, the maximum cell temperature Tmax_n and the minimum cell temperature Tmin_n of the nth cycle, and the natural heat exchange temperature rise rate mapping relationship table.

[0178] S506. Determine the maximum cell temperature Tmax_n+1 of the battery in the n+1th cycle according to the maximum cell temperature Tmax_n of the nth cycle, the charging current Ic_n, the duration of a single cycle, the self-heating temperature rise rate K1_n, the thermal management temperature rise rate K2_n and the natural heat exchange temperature rise rate K3_n.

[0179] S507. Determine the minimum cell temperature Tmin_n+1 of the battery in the (n+1)th cycle according to the minimum cell temperature Tmin_n of the nth cycle, the charging current Ic_n, the duration of a single cycle, the self-heating temperature rise rate K1_n, the thermal management temperature rise rate K2_n and the natural heat exchange temperature rise rate K3_n.

[0180] S508 , predicting the initial state of charge SOC_n+1 of the battery in the (n+1)th cycle according to the charging current Ic_n and the initial state of charge SOC_n of the battery in the nth cycle.

[0181] S509: Determine whether the initial state of charge SOC_n+1 of the battery in the (n+1)th cycle exceeds a preset charge cutoff SOC.

[0182] If yes, execute step S510.

[0183] If not, n+1 is used as the new n, and the process returns to step S502.

[0184] S510. Obtain the remaining charging time of the battery according to the iteration number n+1.

[0185] In the charging processing method provided in the embodiment of the present application, during the charging process of the battery, the self-heating of the battery body, the thermal management, and the ambient temperature will all affect the temperature rise of the battery. Therefore, the self-heating temperature rise rate, the thermal management temperature rise rate, and the natural heat exchange temperature rise rate act together on the battery charging process, affecting the remaining charging time of the battery. Therefore, in the process of predicting the remaining charging time of the battery, the embodiment of the present application takes these factors into consideration and acts on predicting the temperature change of the battery, which can improve the accuracy of the predicted temperature, and then the charging process of the battery can be simulated based on the accurate temperature to obtain the accurate remaining charging time of the battery.

[0186] Figure 6 A schematic diagram of a device for predicting the remaining charging time of a battery provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the device comprises:

[0187] An acquisition module 601 is used to acquire the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery in the current cycle;

[0188] The prediction module 602 is used to: predict the initial state of charge of the battery in the next cycle based on the maximum cell temperature, the minimum cell temperature, and the initial state of charge of the current cycle; predict the maximum cell temperature and the minimum cell temperature of the battery in the next cycle based on the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the current cycle; iteratively calculate the state of charge of the battery until it exceeds a preset charging cut-off state of charge; and obtain the remaining charging time of the battery based on the number of iterations.

[0189] Optionally, the prediction module 602 is specifically used to predict the maximum cell temperature of the battery in the next cycle and the minimum cell temperature of the next cycle based on the maximum cell temperature, minimum cell temperature, initial state of charge, ambient temperature, duration of a single cycle, self-heating temperature rise rate mapping relationship table, thermal management temperature rise rate mapping relationship table, and natural heat exchange temperature rise rate mapping relationship table of the current cycle.

[0190] Among them, the self-heating temperature rise rate mapping relationship table includes: the first mapping relationship between the battery cell temperature, the state of charge and the self-heating temperature rise rate; the thermal management temperature rise rate mapping relationship table includes: the second mapping relationship between the thermal management strategy and the thermal management temperature rise rate; the natural heat exchange temperature rise rate mapping relationship table includes: the third mapping relationship between the battery cell temperature, the ambient temperature and the natural heat exchange temperature rise rate.

[0191] For example, the prediction module 602 is specifically used to determine the self-heating temperature rise rate of the battery in the current cycle according to the maximum battery cell temperature, the minimum battery cell temperature, the initial state of charge, and the self-heating temperature rise rate mapping relationship table of the current cycle; determine the thermal management strategy of the battery in the current cycle according to the maximum battery cell temperature and the minimum battery cell temperature of the current cycle; determine the thermal management temperature rise rate of the battery in the current cycle according to the thermal management strategy of the battery in the current cycle, and the thermal management temperature rise rate mapping relationship table; determine the thermal management temperature rise rate of the battery in the current cycle according to the ambient temperature of the battery, The maximum cell temperature, the minimum cell temperature, and the natural heat exchange temperature rise rate mapping relationship table of the current cycle are used to determine the natural heat exchange temperature rise rate of the battery in the current cycle; the maximum cell temperature of the battery in the next cycle is determined according to the maximum cell temperature of the current cycle, the duration of a single cycle, the self-heating temperature rise rate, the thermal management temperature rise rate, and the natural heat exchange temperature rise rate; the minimum cell temperature of the battery in the next cycle is determined according to the minimum cell temperature of the current cycle, the duration of a single cycle, the self-heating temperature rise rate, the thermal management temperature rise rate, and the natural heat exchange temperature rise rate.

[0192] Exemplarily, the prediction module 602 is specifically used to determine the first initial temperature rise rate of the battery in the current cycle according to the maximum battery cell temperature, the initial state of charge, and the self-heating temperature rise rate mapping relationship table of the current cycle; determine the second initial temperature rise rate of the battery in the current cycle according to the minimum battery cell temperature, the initial state of charge, and the self-heating temperature rise rate mapping relationship table of the current cycle; determine the self-heating temperature rise rate of the battery in the current cycle according to the first initial temperature rise rate and the second initial temperature rise rate of the battery in the current cycle.

[0193] Exemplarily, the prediction module 602 is specifically used to determine the temperature of the battery according to the maximum cell temperature and the minimum cell temperature of the current cycle; and determine the natural heat exchange temperature rise rate of the battery in the current cycle according to the temperature of the battery, the ambient temperature of the battery, and the natural heat exchange temperature rise rate mapping relationship table.

[0194] Exemplarily, the prediction module 602 is specifically used to obtain the first initial charging current of the battery in the current cycle according to the maximum battery cell temperature, the initial state of charge, and the charging current mapping relationship table; the charging current mapping relationship table includes: a fourth mapping relationship between the battery cell temperature, the state of charge and the charging current; according to the minimum battery cell temperature, the initial state of charge, and the charging current mapping relationship table, obtain the second initial charging current of the battery in the current cycle; according to the first initial charging current and the second initial charging current, obtain the charging current of the battery in the current cycle; according to the charging current and the initial state of charge of the battery in the current cycle, predict the initial state of charge of the battery in the next cycle.

[0195] The specific implementation principles, processes and beneficial effects of the battery remaining charging time prediction device provided in the embodiment of the present application can be found in the aforementioned embodiments and will not be repeated here.

[0196] Figure 7 A schematic diagram of a charging processing device provided in an embodiment of the present application is shown in FIG. Figure 7 As shown, the device comprises:

[0197] The acquisition module 701 is used to acquire the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery during charging of the battery;

[0198] The prediction module 702 uses the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery as the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the first cycle, and adopts the method as described in any one of the first aspects to predict the remaining charging time of the battery;

[0199] The output module 703 is used to output the predicted remaining charging time of the battery.

[0200] The specific implementation principles, processes and beneficial effects of the charging processing device provided in the embodiments of the present application can be found in the aforementioned embodiments and will not be repeated here.

[0201] Figure 8 The electronic device 800 is a schematic diagram of a structure of an electronic device provided in an embodiment of the present application. The electronic device 800 may be, for example, any device for predicting the remaining charging time of a battery, such as the aforementioned BMS, a cloud server connected to a vehicle network, or the like.

[0202] like Figure 8As shown, the electronic device 800 may include: a memory 801, a processor 802, and a transceiver 803, wherein the memory 801 and the processor 802 communicate with each other; exemplarily, the memory 801, the processor 802, and the transceiver 803 may communicate with each other via a communication bus 804, the memory 801 is used to store a computer program, and the processor 802 executes the computer program to implement the method of the above embodiment.

[0203] Optionally, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The steps in the method embodiments disclosed in the present application may be directly implemented as being executed by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor.

[0204] An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, a method in any of the above method embodiments is implemented.

[0205] An embodiment of the present application also provides a computer program product, including a computer program, which implements the method in any of the above method embodiments when the computer program is executed by a processor.

[0206] An embodiment of the present application further provides a vehicle, comprising an electronic device and a battery; wherein the electronic device is used to execute a method in any of the above method embodiments, which will not be described in detail.

[0207] All or part of the steps of the above-mentioned method embodiments can be completed by hardware related to program instructions. The above-mentioned program can be stored in a readable memory. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the above-mentioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid state drive, magnetic tape, floppy disk, optical disc and any combination thereof.

[0208] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processing unit of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0209] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0210] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0211] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0212] In the present application, the term "include" and its variations may refer to non-restrictive inclusion; the term "or" and its variations may refer to "and / or". The terms "first", "second", etc. in the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. In the present application, "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the previously associated objects are in an "or" relationship.

Claims

1. A method for predicting the remaining charging time of a battery, characterized in that: The method comprises: Obtaining the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery in the current cycle; Predicting the initial state of charge of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, and the initial state of charge of the current cycle; Predicting the maximum cell temperature and the minimum cell temperature of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the current cycle; Iteratively calculating the state of charge of the battery until it exceeds a preset charging cutoff state of charge; The remaining charging time of the battery is obtained according to the number of iterations.

2. The method according to claim 1, characterized in that The method of predicting the maximum cell temperature and the minimum cell temperature of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the current cycle includes: Predict the maximum cell temperature of the battery in the next cycle and the minimum cell temperature of the next cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, the ambient temperature, the duration of a single cycle, the self-heating temperature rise rate mapping relationship table, the thermal management temperature rise rate mapping relationship table, and the natural heat exchange temperature rise rate mapping relationship table of the current cycle; Among them, the self-heating temperature rise rate mapping relationship table includes: the first mapping relationship between the battery cell temperature, the state of charge and the self-heating temperature rise rate; the thermal management temperature rise rate mapping relationship table includes: the second mapping relationship between the thermal management strategy and the thermal management temperature rise rate; the natural heat exchange temperature rise rate mapping relationship table includes: the third mapping relationship between the battery cell temperature, the ambient temperature and the natural heat exchange temperature rise rate.

3. The method according to claim 2, characterized in that The method predicts the maximum cell temperature and the minimum cell temperature of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, the ambient temperature, the self-heating temperature rise rate mapping relationship table, the thermal management temperature rise rate mapping relationship table, and the natural heat exchange temperature rise rate mapping relationship table of the current cycle, including: Determine the self-heating temperature rise rate of the battery in the current cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the self-heating temperature rise rate mapping relationship table of the current cycle; Determining a thermal management strategy of the battery in the current cycle according to the maximum battery cell temperature and the minimum battery cell temperature of the current cycle; Determining the thermal management temperature rise rate of the battery in the current cycle according to the thermal management strategy of the battery in the current cycle and the thermal management temperature rise rate mapping relationship table; Determine the natural heat exchange temperature rise rate of the battery in the current cycle according to the ambient temperature of the battery, the maximum battery cell temperature and the minimum battery cell temperature in the current cycle, and the natural heat exchange temperature rise rate mapping relationship table; Determine the maximum cell temperature of the battery in the next cycle according to the maximum cell temperature of the current cycle, the duration of a single cycle, the self-heating temperature rise rate, the thermal management temperature rise rate, and the natural heat exchange temperature rise rate; The minimum cell temperature of the battery in the next cycle is determined according to the minimum cell temperature of the current cycle, the duration of a single cycle, the self-heating temperature rise rate, the thermal management temperature rise rate and the natural heat exchange temperature rise rate.

4. The method according to claim 3, characterized in that The determining the self-heating temperature rise rate of the battery in the current cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the self-heating temperature rise rate mapping relationship table of the current cycle includes: Determine a first initial temperature rise rate of the battery in the current cycle according to the maximum cell temperature of the current cycle, the initial state of charge, and the self-heating temperature rise rate mapping relationship table; Determine a second initial temperature rise rate of the battery in the current cycle according to the minimum cell temperature of the current cycle, the initial state of charge, and the self-heating temperature rise rate mapping relationship table; The self-heating temperature rise rate of the battery in the current cycle is determined according to the first initial temperature rise rate and the second initial temperature rise rate of the battery in the current cycle.

5. The method according to claim 3, characterized in that: The determining the natural heat exchange temperature rise rate of the battery in the current cycle according to the ambient temperature of the battery, the maximum battery cell temperature and the minimum battery cell temperature of the current cycle, and the natural heat exchange temperature rise rate mapping relationship table includes: Determining the temperature of the battery according to the maximum battery cell temperature and the minimum battery cell temperature of the current cycle; The natural heat exchange temperature rise rate of the battery in the current cycle is determined according to the temperature of the battery, the ambient temperature of the battery, and the natural heat exchange temperature rise rate mapping relationship table.

6. The method according to claim 1, characterized in that The predicting the initial state of charge of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, and the initial state of charge of the current cycle includes: According to the maximum battery cell temperature, the initial state of charge, and the charging current mapping relationship table, obtaining the first initial charging current of the battery in the current cycle; the charging current mapping relationship table includes: a fourth mapping relationship between the battery cell temperature, the state of charge and the charging current; Acquire a second initial charging current of the battery in the current cycle according to the minimum battery cell temperature, the initial state of charge, and the charging current mapping relationship table; Obtaining a charging current of the battery in the current cycle according to the first initial charging current and the second initial charging current; The initial state of charge of the battery in the next cycle is predicted according to the charging current and the initial state of charge of the battery in the current cycle.

7. A charging processing method, characterized in that: The method comprises: During the process of charging the battery, obtaining the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery; Taking the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery as the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the first cycle, and using the method according to any one of claims 1 to 6 to predict the remaining charging time of the battery; Output the predicted remaining charging time of the battery.

8. A device for predicting remaining battery charging time, characterized in that: The device comprises: An acquisition module, used to acquire the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery in the current cycle; Prediction module for: Predicting the initial state of charge of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, and the initial state of charge of the current cycle; Predicting the maximum cell temperature and the minimum cell temperature of the battery in the next cycle according to the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the current cycle; Iteratively calculating the state of charge of the battery until it exceeds a preset charging cutoff state of charge; The remaining charging time of the battery is obtained according to the number of iterations.

9. A charging processing device, characterized in that: The device comprises: An acquisition module, used for acquiring the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery during charging of the battery; A prediction module, taking the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the battery as the maximum cell temperature, the minimum cell temperature, the initial state of charge, and the ambient temperature of the first cycle, and using the method according to any one of claims 1 to 6 to predict the remaining charging time of the battery; The output module is used to output the predicted remaining charging time of the battery.

10. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

12. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 7 when being executed by a processor.

13. A vehicle, characterized in that: The vehicle includes electronic equipment and a battery; Wherein, the electronic device is used to execute the method as described in any one of claims 1-7.

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