Battery charging time prediction method, and vehicle, program product and storage medium

Abstract

A battery charging time prediction method, and a vehicle, a program product and a storage medium. The battery charging time prediction method comprises: on the basis of the current SOC and end-of-charge SOC of a target battery, determining from a plurality of preset SOC intervals all target SOC intervals sequentially passed during charging (110); executing prediction processing for each target SOC interval (120), wherein the prediction processing comprises: on the basis of a predicted temperature change speed, predicting a target temperature interval of the target battery when being charged within the target SOC interval (121), on the basis of the target SOC interval and the target temperature interval, determining a corresponding first calibrated charging parameter (122), and on the basis of the first calibrated charging parameter, predicting charging time within the target SOC interval (123); and on the basis of the charging time within all the target SOC intervals, determining the total length of charging time (130). By means of establishing the correspondences among SOC intervals, temperature intervals and calibrated charging parameters, charging time prediction including a temperature prediction function is realized, thereby improving the precision of charging time prediction in an extreme temperature environment.

Description

A battery charging time prediction method, vehicle, software product, and storage medium

[0001] Cross-reference of related applications

[0002] This application claims priority to Chinese patent application CN202410673981.X, filed on May 28, 2024, entitled “A method for predicting battery charging time, a vehicle, a program product and a storage medium”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of battery technology, and more specifically, to a battery charging time prediction method, a vehicle, a program product, and a storage medium. Background Technology

[0004] With the continuous upgrading of new energy vehicle charging technology, charging speeds have significantly improved, leading to a marked increase in users' reliance on information regarding the remaining charging time. Various battery states, such as State of Charge (SOC) and State of Health (SOH), are affected by environmental factors during charging, resulting in inaccurate predictions of charging time in related technologies and significantly impacting user experience.

[0005] Summary of the Invention

[0006] The purpose of this application is to provide a battery charging time prediction method, vehicle, program product, and storage medium to achieve the technical effect of improving the accuracy of charging time prediction.

[0007] A first aspect of this application provides a battery charging time prediction method, the method comprising:

[0008] Based on the current SOC and the charging cutoff SOC of the target battery, determine all target SOC intervals that will be passed through sequentially during charging from multiple preset SOC intervals.

[0009] For each of the target SOC intervals, a prediction process is performed, the prediction process including:

[0010] Based on the predicted rate of temperature change, the target temperature range that the target battery will pass through during charging within the target SOC range is predicted; based on the target SOC range and the target temperature range, the corresponding first calibration charging parameters are determined; and based on the first calibration charging parameters, the charging time within the target SOC range is predicted.

[0011] The total charging time is determined based on the charging time for all target SOC ranges.

[0012] In the above implementation process, by establishing the correspondence between the SOC range, temperature range and calibrated charging parameters, and combining temperature prediction and charging time prediction, the influence of temperature changes on charging parameters is also taken into account in the prediction of charging time, thus realizing charging time prediction with temperature prediction function and improving the accuracy of charging time prediction under extreme temperature conditions.

[0013] Furthermore, the method also includes:

[0014] Obtain the state characteristics of the target battery;

[0015] Predict the rate of temperature change of the target battery during the charging process based on the state characteristics.

[0016] In the above implementation process, the temperature change rate is predicted using the state characteristics of the target battery, thereby revealing the temperature change trend of the target battery during charging. Furthermore, based on the temperature change rate, temperature prediction can be combined with charging time prediction, thus improving the accuracy of charging time prediction.

[0017] Further, predicting the rate of temperature change of the target battery during charging based on the state characteristics includes:

[0018] The state features are input into the trained temperature prediction model to obtain the output temperature change rate.

[0019] In the above implementation process, the temperature prediction model is used to predict the rate of temperature change, and the temperature prediction model is combined with the charging time calculation to improve the accuracy of time prediction.

[0020] Further, predicting the target temperature range that the target battery will pass through while charging within the target SOC range based on the predicted rate of temperature change includes:

[0021] Based on the predicted rate of temperature change, determine whether a temperature range crossing occurs during charging within the target SOC range;

[0022] The target temperature range corresponding to the target SOC range is determined based on the judgment result.

[0023] In the above implementation process, the predicted temperature change rate is used to predict whether the target battery has crossed the temperature range during charging in each target SOC range, and then the corresponding target temperature range and the corresponding first calibration charging parameters are determined to complete the charging time prediction process.

[0024] Further, determining whether a temperature range crossing occurs during charging within the target SOC range based on the predicted temperature change rate includes:

[0025] Determine the first temperature range corresponding to the target SOC range, and determine the corresponding second calibration charging parameters based on the target SOC range and the first temperature range;

[0026] Determine the starting SOC of the target SOC range within the first temperature range, and the starting temperature of the first temperature range;

[0027] Based on the rate of temperature change, a first time is determined from the initial temperature to the boundary value of the first temperature range, and a second time is determined from the initial SOC to the target SOC range based on the second calibrated charging parameters.

[0028] At least based on the first time and the second time, it is determined whether a temperature range crossing occurs during charging within the target SOC range.

[0029] In the above implementation process, for each target SOC range, the first time from the starting temperature to the boundary value of the corresponding first temperature range is calculated, and the second time from the starting SOC to the boundary value of the range is calculated within the target SOC range. Based on the first time and the second time, it is determined whether the target SOC range has crossed the temperature range, thereby determining the corresponding target temperature range and the corresponding first calibration charging parameters, and completing the charging time prediction process.

[0030] Furthermore, for the first target SOC range, the first temperature range corresponding to the first target SOC range is the temperature range in which the current temperature of the target battery is located; the starting SOC of the first target SOC range is the current SOC of the target battery; and the starting temperature of the first temperature range is the current temperature of the target battery.

[0031] For non-first target SOC intervals, the starting SOC of the target SOC interval is the lower limit of the target SOC interval.

[0032] Furthermore, the method also includes:

[0033] If it is determined that the current target SOC interval has not crossed temperature intervals, the first temperature interval corresponding to the current target SOC interval is determined to be the first temperature interval corresponding to the next target SOC interval.

[0034] If it is determined that the current target SOC interval has crossed a temperature interval, the second temperature interval after the crossing is determined as the first temperature interval corresponding to the next target SOC interval.

[0035] Further, the step of determining whether a temperature range crossing occurs during charging within the target SOC range, based at least on the first time and the second time, includes:

[0036] If the first time is less than the second time, it is determined that a temperature range crossing occurred during charging within the target SOC range.

[0037] Further, the step of determining whether a temperature range crossing occurs during charging within the target SOC range, based at least on the first time and the second time, includes:

[0038] If the first time is greater than the second time, determine each reference SOC interval that is before the target SOC interval and has the same first temperature interval as the target SOC interval;

[0039] Obtain the third time when the target battery is charged in each of the reference SOC intervals;

[0040] If the first time is greater than the sum of the second time and the third time, it is determined that the target SOC range has not experienced a temperature range crossing.

[0041] If the first time is less than the sum of the second time and the third time, it is determined that the target SOC range has experienced a temperature range crossing.

[0042] Further, determining the target temperature range corresponding to the target SOC range based on the judgment result includes:

[0043] If the judgment result indicates that a temperature range crossing has occurred, the target temperature range corresponding to the target SOC range is determined to include the first temperature range before the crossing and the second temperature range after the crossing.

[0044] If the judgment result indicates that no temperature range crossing has occurred, the target temperature range corresponding to the target SOC range is determined to be the first temperature range.

[0045] Further, the step of predicting the charging time within the target SOC range based on the first calibrated charging parameters includes:

[0046] For a target SOC range where no temperature range crossing occurs, a corresponding first calibration charging parameter is determined based on the target SOC range and the first temperature range, and the charging time within the target SOC range is predicted based on the first calibration charging parameter.

[0047] For a target SOC range where a temperature range crossing occurs, a first calibration charging parameter is determined based on the SOC range and the first temperature range. The charging time within the first SOC sub-range before the crossing and the target SOC at the time of the crossing are predicted based on the first calibration charging parameter. A second calibration charging parameter is determined based on the SOC range and the second temperature range. The charging time within the second SOC sub-range after the crossing is predicted based on the second calibration charging parameter and the target SOC. The charging time of the target SOC range is determined based on the charging time within the first SOC sub-range and the charging time within the second SOC sub-range.

[0048] In the above implementation process, a temperature prediction model is trained using cloud databases and cloud computing power and then deployed to the vehicle, enabling temperature change prediction during the charging process across the entire temperature range. Simultaneously, by combining the temperature prediction model with a charging time algorithm, the vehicle can achieve high-precision charging time estimation across the entire temperature range.

[0049] A second aspect of this application provides a vehicle, the vehicle comprising:

[0050] A battery, and a battery management system connected to the battery;

[0051] The battery management system includes a processor and a memory for storing processor-executable instructions;

[0052] Wherein, when the processor invokes the executable instructions, it implements the operation of any of the methods described in the first aspect.

[0053] A third aspect of this application provides a computer program product, the computer program product including a computer program, which, when executed by a processor, implements any of the methods described in the first aspect.

[0054] A fourth aspect of this application provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of any of the methods described in the first aspect. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 is a flowchart illustrating a battery charging time prediction method provided in an embodiment of this application;

[0057] Figure 2-6 is a flowchart illustrating another battery charging time prediction method provided in an embodiment of this application;

[0058] Figure 7 is a schematic diagram of the charging path in the battery charging map provided in the embodiments of this application;

[0059] Figure 8 is a hardware structure diagram of a BMS in a vehicle provided in an embodiment of this application. Detailed Implementation

[0060] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0061] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0062] Battery states such as SOC and SOH are affected by environmental factors such as temperature during charging. For example, the accuracy of calculating remaining charging time is significantly lower in low-temperature environments than in normal-temperature environments, thus greatly impacting the user's driving experience.

[0063] To improve the accuracy of battery charging time prediction, this application provides a battery charging time prediction method. Exemplarily, the method can be used in various electrical devices with charging time prediction requirements, such as vehicles and aircraft. As shown in Figures 1-2, the prediction method includes steps 110-130.

[0064] Step 110: Based on the current SOC and the charging cutoff SOC of the target battery, determine all target SOC intervals that will be passed through sequentially during charging from multiple preset SOC intervals.

[0065] Step 120: Perform prediction processing for each of the target SOC intervals.

[0066] The prediction process includes steps 121-123 as shown in Figure 2.

[0067] Step 121: Based on the predicted rate of temperature change, predict the target temperature range that the target battery will pass through when charging within the target SOC range;

[0068] Step 122: Determine the corresponding first calibration charging parameters based on the target SOC range and the target temperature range;

[0069] Step 123: Predict the charging time within the target SOC range based on the first calibrated charging parameters.

[0070] After completing step 120, proceed to step 130.

[0071] Step 130: Determine the total charging time based on the charging time of all target SOC ranges.

[0072] For example, the prediction method can be triggered after the power supply device, such as a charging station, is connected to the battery. It can also be executed periodically during the target battery process.

[0073] In this embodiment, the battery SOC is pre-divided into multiple SOC intervals. For example, the SOC intervals can be divided in 5% increments, resulting in [0, 5%], [5%, 10%], ..., [95%, 100%]. The SOC range corresponding to each interval can be flexibly set according to requirements, and is not limited to 5%. Furthermore, the SOC ranges corresponding to each SOC interval can be the same (i.e., equally spaced) or different (i.e., non-equally spaced), and this embodiment does not impose any restrictions on this.

[0074] Similarly, the temperatures the battery may experience are pre-divided into multiple temperature ranges, for example, in 10°C increments, resulting in [-10°C, 0°C], [0°C, 10°C], [10°C, 20°C]...[40°C, 50°C]. The temperature range for each range can be flexibly set according to requirements, and is not limited to 10°C. Furthermore, the temperature ranges for each range can be the same or different; this embodiment does not impose any restrictions.

[0075] Based on this, a correspondence between the SOC range, temperature range, and calibration charging parameters can be established in advance. This correspondence can be represented, for example, but not limited to, in the form of tables, functions, or neural network models. Once a certain SOC range and a certain temperature range are known, the corresponding calibration charging parameters can be determined through this correspondence.

[0076] Taking the correspondence between SOC range, temperature range, and calibrated charging parameters as an example, this table is also called a battery charging map. Table 1 shows a battery charging map as one example.

[0077] Table 1

[0078] As can be seen, after knowing a certain SOC range and a certain temperature range, the corresponding calibration charging parameters can be found in Table 1 above. The calibration charging parameters are used to determine the charging time required to complete the charging of the SOC range within the specified temperature range.

[0079] For example, calibrating charging parameters may include calibrating charging current and / or calibrating charging time, etc. The calibration process for the calibrated charging current can be found in relevant technologies and will not be elaborated here. After determining the calibrated charging current, the calibrated charging time can be determined based on the SOC range corresponding to the SOC interval, the battery capacity, and the calibrated charging current. Specifically, it satisfies the following formula (1):

[0080] Among them, t ij I represents the calibration charging time corresponding to the i-th SOC range and the j-th temperature range. ij Let ΔSOC be the calibration charging current corresponding to the i-th SOC interval and the j-th temperature interval, where 1 ≤ i ≤ n; 1 ≤ j ≤ m. i Let be the SOC range corresponding to the i-th SOC interval. Cap is the battery capacity of the target battery.

[0081] Based on this, when executing the prediction method provided in this embodiment, the current SOC and current temperature of the target battery to be charged are first obtained. The current SOC refers to the SOC of the target battery when the prediction method is executed, and the current temperature refers to the battery temperature when the prediction method is executed. Furthermore, the charging cutoff SOC can be obtained. The charging cutoff SOC can be preset by the Battery Management System (BMS) or set by the user as needed, such as 100%, 90%, etc.

[0082] Based on the current SOC and the charging cutoff SOC, multiple target SOC intervals can be determined sequentially during charging from the aforementioned SOC intervals. For example, if the target battery starts charging at an SOC of 13% and the charging cutoff SOC is 98%, then the target SOC intervals sequentially passed during charging include: [10%, 15%], [15%, 20%]...[95%, 100%], a total of 20 target SOC intervals.

[0083] During charging, the battery may generate heat, or the battery's thermal management system may adjust the battery temperature, including heating it when it's too cold and cooling it when it's too hot. Therefore, the battery temperature will change during charging. This change in battery temperature will in turn affect the predicted charging time. Thus, for each target SOC range, the prediction process shown in Figure 2 can be executed sequentially.

[0084] The predictive processing specifically includes: first, predicting the target temperature range traversed during charging within the target SOC range based on the predicted rate of temperature change. The target temperature range includes at least one. If a temperature range is crossed during charging within the target SOC range, then the target SOC range corresponds to multiple target temperature ranges.

[0085] Subsequently, based on the target SOC range and its corresponding target temperature range, the first calibration charging parameters can be determined. The first calibration charging parameters include a first calibration charging current and / or a first calibration charging time. Thus, the charging time within the target SOC range can be predicted based on the first calibration charging parameters. The charging time refers to the time taken for the target battery to charge within the target SOC range.

[0086] After performing the above prediction process sequentially for all target SOC intervals, the charging time for each target SOC interval can be obtained. Based on the charging times of all target SOC intervals, the total charging time required for the target battery to charge from the current SOC to the charging cutoff SOC can be determined. For example, the charging times corresponding to all target SOC intervals can be added together to obtain the total charging time.

[0087] As can be seen, the battery charging time prediction method provided in this application establishes a correspondence between the SOC range, temperature range and calibrated charging parameters, combines temperature prediction and charging time prediction, and takes into account the influence of temperature changes on charging parameters in the prediction of charging time, thereby realizing charging time prediction with temperature prediction function and improving the accuracy of charging time prediction under extreme temperature conditions.

[0088] The following provides a detailed description of steps 110-130.

[0089] In some embodiments, the process of predicting the rate of temperature change may include steps 310-320 as shown in Figure 3.

[0090] Step 310: Obtain the state characteristics of the target battery.

[0091] For example, the target battery comprises multiple battery cells, and the temperature of each battery cell may be different at the same time. Based on this, the state characteristics of the target battery may include, but are not limited to: battery cell voltage, the highest battery temperature at the sampling time, the lowest battery temperature at the sampling time, SOC, SOH, charging current, and charger information group.

[0092] A charger is a power supply device that provides electrical energy to electrical equipment, such as a charging station. The charger information group may include, but is not limited to, the charger's maximum output power, maximum output current, maximum output voltage, etc.

[0093] Step 320: Predict the rate of temperature change of the target battery during the charging process based on the state characteristics.

[0094] For example, a mapping relationship between state characteristics and temperature change rate can be established in advance. This mapping relationship can be a functional expression or a neural network model, etc. Based on the state characteristics of the target battery, the temperature change rate of the target battery during charging can be predicted. The temperature change rate is a vector, including the direction and rate of temperature change. Temperature change methods include heating and cooling directions.

[0095] As can be seen, this embodiment utilizes the state characteristics of the target battery to predict the rate of temperature change, thereby revealing the temperature change trend of the target battery during charging. Furthermore, based on the rate of temperature change, temperature prediction can be combined with charging time prediction, thus improving the accuracy of charging time prediction.

[0096] Regarding the prediction of the temperature change rate based on state characteristics in step 320, in some embodiments, the prediction can be based on machine learning techniques. For example, a temperature prediction model can be pre-trained based on machine learning techniques. Thus, performing step 320 can include:

[0097] The state features are input into the trained temperature prediction model to obtain the output temperature change rate.

[0098] The training process of the temperature prediction model is described below:

[0099] 1) Building the database

[0100] The system collects historical charging data from electrical devices, such as vehicles, or all charging data from the research and development phase. This historical charging data can include charging station information, battery information, and charging process data. A database is then built in the cloud based on this historical charging data. Specifically, in the vehicle's historical charging data, thermal management can be enabled to adjust the battery temperature during charging. That is, the battery temperature data in the historical charging data can include battery temperature data adjusted based on thermal management, or battery temperature data without thermal management adjustment.

[0101] For example, the historical charging data may include: ① Battery type, such as ternary lithium batteries, lithium iron phosphate batteries, etc. Different types of batteries exhibit different temperature change characteristics during charging. ② Charging current and charging voltage, which are key factors affecting battery temperature changes. ③ Ambient temperature, which has a significant impact on the battery temperature during charging. ④ Initial battery temperature, which affects its temperature change during charging. ⑤ Battery state information, including SOC and SOH, which directly affect the magnitude of the charging current. ⑦ Charging pile data, including the charging pile's identification number, maximum power transmitted by the charging pile, maximum output current, maximum charging voltage, etc. Charging pile data can be obtained through the charging pile's log system or monitoring equipment.

[0102] Furthermore, to enhance the generalization ability of the temperature prediction model, data can be collected under various charging conditions, such as different charging station brands, different charging station power, different charging periods, different ambient temperatures, and different battery packs. This helps the model learn the relationships between more variables, thereby more accurately predicting battery temperature changes.

[0103] 2) Preprocess historical charging data

[0104] The preprocessing includes data cleaning and data standardization.

[0105] Due to the continuity of vehicle data, the proportion of outliers in the collected data will be very low when the vehicle is functioning normally. Therefore, the isolated forest method, which is the most efficient way to deal with low outliers, can be used for data cleaning. Moreover, the isolated forest method can ensure that multiple isolated trees are built at the same time, and data with different characteristics can be cleaned simultaneously.

[0106] Of course, data cleaning methods are not limited to the isolated forest method. Those skilled in the art can choose other data cleaning methods based on the proportion of outliers in historical charging data.

[0107] Furthermore, since the data scale varies across dimensions at different temperatures, it's impossible to train the model uniformly using the same method. Therefore, a data standardization method is used to adjust the data scale uniformly. By normalizing the input features, it's possible to prevent certain features from having an excessive impact on the model's training results, thereby improving the model's performance and stability. Optionally, Z-Score Normalization can be used to make the data conform to a normal distribution. The specific standardization process can be found in relevant technical documents and will not be elaborated here. Besides Z-standardization, those skilled in the art can choose other standardization methods according to their actual needs.

[0108] 3) Build a temperature prediction model

[0109] For example, an Extreme Learning Machine (ELM) model can be chosen as the temperature prediction model. ELM is characterized by its fast learning speed, strong generalization ability, simple algorithm structure, and high flexibility. It has a unique advantage in feature extraction where battery temperature is strongly coupled with multiple parameters.

[0110] Specifically, the temperature prediction model constructed in this embodiment includes an input layer, a hidden layer, and an output layer. The input layer comprises seven neurons, used to input state features including battery cell voltage, maximum battery temperature, minimum battery temperature, SOC, SOH, charging current, and charger information arrays. The activation function for the input layer can be a ReLU (Rectified Linear Unit) function.

[0111] The hidden layer consists of seven neurons, used to construct connections between the seven input parameters. The activation function for the input layer can be the sigmoid function.

[0112] The output layer consists of two neurons, which output the predicted rate of temperature change and the highest temperature. The activation function for the input layer can be the Softmax function (normalized exponential function). The highest temperature includes the highest temperature in the highest temperature curve during charging and the highest temperature in the lowest temperature curve. Since the battery comprises multiple cells, the highest and lowest temperatures of the battery can be sampled simultaneously. The highest temperature curve can be obtained using the highest temperatures from all sampling times, and the lowest temperature curve can be obtained using the lowest temperatures from all sampling times.

[0113] Of course, in addition to ELM, those skilled in the art can choose other neural network models as temperature prediction models according to actual needs.

[0114] 4) Model Training

[0115] Preprocessed historical charging data can be used as training samples for supervised training of the temperature prediction model. For example, the output weights can be obtained by calculating the linear relationship between the hidden layer output and the target output through regularization.

[0116] Optionally, Ridge Regression (also known as L2 regularization) can be used as the model's regularization term. By using the sum of squared weights of the model's parameters as a penalty, the model's parameters are appropriately reduced. Furthermore, early stopping can be added simultaneously to more effectively prevent excessively large parameter values ​​and avoid the model becoming overly sensitive, building upon the ridge regression. Introducing these regularization terms into the loss function minimizes the original loss function during training while keeping the model's complexity (i.e., parameter size) relatively low, making it suitable for embedded deployment in vehicles and increasing its practical applications.

[0117] After the model training is completed, a trained temperature prediction model can be obtained, which can be used to predict the rate of temperature change of the target battery during the charging process, as well as the highest temperature, based on the state characteristics of the target battery.

[0118] Thus, through the embodiments described above, the rate of temperature change of the target battery during the charging process can be predicted. Then, step 121 is executed to predict the target temperature range that the target battery will traverse during charging at each target SOC interval, based on the predicted rate of temperature change.

[0119] Based on any of the above embodiments, the prediction process of the target temperature range in step 121 may include steps 410-420 as shown in Figure 4.

[0120] Step 410: Based on the predicted rate of temperature change, determine whether a temperature range crossing occurs during charging within the target SOC range;

[0121] Step 420: Determine the target temperature range corresponding to the target SOC range based on the judgment result.

[0122] As illustrated in the examples above, for the same SOC range, different temperature ranges will result in different calibrated charging parameters. These calibrated charging parameters affect the calculated charging time within the target SOC range. Therefore, it is necessary to determine whether a temperature range transition has occurred during charging within each target SOC range based on the rate of temperature change. If a temperature range transition has occurred, it indicates that the target battery is being charged using different calibrated charging parameters within that target SOC range.

[0123] Subsequently, the target temperature range corresponding to each target SOC range can be determined based on the judgment result. For example, if the judgment result indicates no crossing occurred, the corresponding target temperature range includes one. If the judgment result indicates a crossing occurred, the corresponding target temperature range includes at least two, and the number of target temperature ranges matches the number of crossings. For example, the number of target temperature ranges equals the number of crossings plus one.

[0124] As can be seen, this embodiment predicts whether the target battery has crossed the temperature range during charging in each target SOC range by predicting the rate of temperature change, and then determines the corresponding target temperature range and the corresponding first calibration charging parameters to complete the charging time prediction process.

[0125] Regarding the determination process of temperature range crossing in step 410, in some embodiments, it may include steps 411-414 as shown in Figure 5.

[0126] Step 411: Determine the first temperature range corresponding to the target SOC range, and determine the corresponding second calibration charging parameters based on the target SOC range and the first temperature range.

[0127] For example, for the target SOC range that the target battery first enters during charging, that is, the first target SOC range, the corresponding first temperature range is the temperature range where the current temperature of the target battery is located.

[0128] It can be seen that the current SOC range of the target battery is the first target SOC range. Furthermore, based on the current temperature of the target battery, the first temperature range corresponding to the first target SOC range can be determined.

[0129] For the first target SOC range, if it is determined that no temperature range crossing occurs during charging within the first target SOC range, then the first temperature range corresponding to the first target SOC range is determined as the first temperature range corresponding to the next target SOC range.

[0130] For example, if [SOC2, SOC3] is the first target SOC range, and based on the current temperature of the target battery, the first temperature range corresponding to [SOC2, SOC3] is [Temp1, Temp2]. If it is determined that no temperature range crossing occurs during charging within [SOC2, SOC3], then [Temp1, Temp2] is determined to be the next target SOC range, i.e., the first temperature range corresponding to [SOC3, SOC4].

[0131] Conversely, if it is determined that a temperature range crossing occurred during charging within the range [SOC2, SOC3], and the new temperature range is [Temp2, Temp3], then the new [Temp2, Temp3] range is determined as the next target SOC range, i.e., the first temperature range corresponding to [SOC3, SOC4]. Type-specific processing is then performed for each subsequent target SOC range.

[0132] Based on this example, we can conclude the following: If it is determined that the current target SOC interval has not experienced a temperature range crossing, the first temperature interval corresponding to the current target SOC interval is determined to be the first temperature interval corresponding to the next target SOC interval; if it is determined that the current target SOC interval has experienced a temperature range crossing, the second temperature interval after the crossing is determined to be the first temperature interval corresponding to the next target SOC interval.

[0133] This completes the determination of the first temperature range corresponding to the target SOC range. Subsequently, based on the target SOC range and the first temperature range, the corresponding second calibration charging parameters can be determined. Similarly, the second calibration charging parameters include the second calibration charging current and / or the second calibration charging time.

[0134] It's important to distinguish here that the first calibration charging parameters are determined based on the target SOC range and the target temperature range. The target temperature range encompasses all temperature ranges the target battery traverses while charging within the target SOC range. The second calibration charging parameters are determined based on the target SOC range and the first temperature range. When the target battery is charging within the target SOC range, if no temperature range is crossed, it only traverses the first temperature range; if a temperature range is crossed, it traverses more than just the first temperature range.

[0135] Step 412: Determine the starting SOC of the target SOC range within the first temperature range, and the starting temperature of the first temperature range.

[0136] The initial SOC refers to the SOC at which the target battery begins charging within the target SOC range. Therefore, for the first target SOC range, since the corresponding first temperature range is the temperature range where the current temperature is located, the initial SOC of the first target SOC range within the first temperature range is the current SOC. The starting temperature of the first temperature range is the current temperature of the target battery.

[0137] For example, if the target battery starts charging at a SOC of 13% and the current temperature is 15°C, then [10%, 15%] is the first target SOC range, and [10°C, 20°C] is the first temperature range corresponding to the first target SOC range. Simultaneously, 13% is the starting SOC of the first target SOC range, and 15°C is the starting temperature of the first temperature range.

[0138] For target SOC intervals that are not the first target SOC interval, that is, target SOC intervals after the first target SOC interval, the starting SOC is the lower limit of the target SOC interval.

[0139] It is understandable that each target SOC range has two boundary values, including an upper limit and a lower limit. For non-first target SOC ranges, the target battery will enter the target SOC range for charging from its lower limit, so the initial SOC is the lower limit of that range.

[0140] Continuing with the example above, [15%, 20%] is the second target SOC interval, which is not the first target SOC interval. Its starting SOC is the lower limit of this interval, which is 15%. If it is determined that [10%, 15%] has not crossed a temperature interval, then the first temperature interval corresponding to [15%, 20%] is still [10℃, 20℃], and the starting temperature is 13%. If it is determined that [10%, 15%] has crossed a temperature interval in the direction of increasing temperature, then the first temperature interval corresponding to [15%, 20%] is the crossed [20℃, 30℃], and the starting temperature is 20%.

[0141] This completes the acquisition of the initial SOC and initial temperature.

[0142] Step 413: Determine the first time from the starting temperature to the boundary value of the first temperature range based on the temperature change rate, and determine the second time from the starting SOC to the target SOC range based on the second calibration charging parameters.

[0143] For example, based on the direction of change in the rate of temperature change, it can be determined which boundary value the initial temperature changes towards within the first temperature range. If the direction of change is heating, the initial temperature changes towards the upper limit; if the direction of change is cooling, the initial temperature changes towards the lower limit. Based on the initial temperature, the boundary value of the first temperature range, and the rate of temperature change, the first time can be calculated. The specific calculation formula is shown in formula (2):

[0144] Among them, t j Let ΔTemp be the first time interval of the j-th temperature range, where 1 ≤ j ≤ m. j HeatRate is the difference between the boundary value and the starting temperature of the j-th temperature range. j Let be the predicted rate of temperature change in the j-th temperature range.

[0145] Taking the above example, for the first temperature range [10℃, 20℃], the initial temperature is 13℃. If the rate of temperature change indicates an upward trend, then the boundary of the first temperature range is the upper limit of 20℃. Based on the rate of temperature change, the first time required for the temperature to rise from the initial temperature of 13℃ to 20℃ during charging can be calculated.

[0146] Furthermore, for example, during the charging process, the target battery will be charged from the initial SOC to the boundary value of the target SOC range, i.e., the upper limit. If the second calibration charging parameter includes the second calibration charging current, the second time can be calculated based on the initial SOC, the upper limit of the target SOC range, the second calibration charging current, and the battery capacity. For the specific calculation formula, please refer to formula (1) above.

[0147] Taking the above example, for the target SOC range [10%, 15%], its initial SOC is 13%, and the boundary value of this range is 15%. Thus, based on the second calibration charging parameters and the battery capacity, the second time required for the target battery to charge from the initial SOC of 13% to 15% can be calculated.

[0148] Furthermore, the second calibration charging parameter can be the second calibration charging time. Taking the i-th SOC interval as the target SOC interval as an example, if its corresponding first temperature interval is the j-th temperature interval, the second time can be calculated based on the initial SOC, the upper limit of the target SOC interval, and the second calibration charging time. The specific calculation formula is shown in formula (3).

[0149] Among them, t i The second time interval for the i-th target SOC interval; t ij The calibration charging time corresponding to the i-th SOC interval and the j-th temperature interval, i.e., the second calibration charging time; SOC i+1 Let SOC be the upper limit of the i-th target SOC interval; strat,i Let ΔSOC be the starting SOC of the i-th SOC interval; i Let be the SOC range corresponding to the i-th SOC interval.

[0150] It can be seen that if SOC strat If the lower limit of the i-th target SOC interval is given, then the second time is equal to the second calibrated charging time.

[0151] Taking the above example, for the target SOC range [10%, 15%], its initial SOC is 13%, and the boundary value of this range is 15%. If the second calibration charging time is t... 10-15 The second time t required for the target battery to charge from an initial SOC of 13% to 15% is then calculated. 13-15 =t 10-15 (15%-13%) / (5%).

[0152] Step 414: Determine whether a temperature range crossing occurs during charging within the target SOC range, based at least on the first time and the second time.

[0153] For example, by comparing the magnitudes of the first time and the second time, it can be determined whether a temperature range crossing occurs during charging within the target SOC range.

[0154] As can be seen, in this embodiment, for each target SOC range, the first time from the starting temperature to the boundary value of the corresponding first temperature range is calculated, and the second time from the starting SOC to the boundary value of the range is calculated within the target SOC range. Based on the first time and the second time, it is determined whether the target SOC range has experienced a temperature range crossing, thereby determining the corresponding target temperature range and the corresponding first calibration charging parameters, and completing the charging time prediction process.

[0155] In some embodiments, if it is determined that a temperature range crossing has occurred within the target SOC range, the target SOC range can be divided into a first SOC sub-range before the crossing and a second SOC sub-range after the crossing, based on the target SOC at the time of the temperature crossing. Then, the second SOC sub-range is determined as the next target SOC range, the second temperature range after the crossing is determined as the first temperature range corresponding to the next target SOC range, the target SOC is determined as the starting SOC of the next target SOC range, and the process returns to the step of determining whether a temperature range crossing has occurred during charging within the target SOC range. This allows for the determination of situations where a target SOC range experiences multiple consecutive temperature range crossings. A specific example can be seen in Figure 7 showing the SOC range [60%, 65%].

[0156] Regarding step 414, which determines whether a temperature range crossing has occurred within the target SOC range based on the first and second times, some embodiments may include:

[0157] If the first time is less than the second time, it is determined that a temperature range crossing occurred during charging within the target SOC range.

[0158] Understandably, if the first time interval is shorter than the second time interval, it means that the battery temperature has already reached the upper limit of the first temperature range before charging to the upper limit of the target SOC range. Therefore, a temperature range crossing will occur when charging within the target SOC range.

[0159] Furthermore, in some embodiments, if the first time is greater than the second time, it is further determined whether a temperature range crossing has occurred through steps 4141-4144 as shown in FIG6.

[0160] Step 4141: Determine each reference SOC interval that is prior to the target SOC interval and has the same first temperature interval as the target SOC interval.

[0161] Step 4142: Obtain the third time when the target battery is charged in each of the reference SOC intervals.

[0162] Step 4143: If the first time is greater than the sum of the second time and the third time, it is determined that the target SOC interval has not experienced a temperature range crossing.

[0163] Step 4144: If the first time is less than the sum of the second time and the third time, it is determined that the target SOC interval has crossed a temperature range.

[0164] The following will use specific examples to describe how to determine whether a temperature range crossing has occurred, based at least on the first and second time points.

[0165] Figure 7 illustrates the temperature and SOC range changes of a target battery during charging, i.e., the charging path in the battery charging map. Assume the target battery's current SOC before charging is 56%, the current temperature is 12℃, and the charging cutoff SOC is 79%. It can be seen that during the charging process from 56% to 79%, the target SOC ranges include [55%, 60%], [60%, 65%], [65%, 70%], [70%, 75%], and [75%, 80%]. Furthermore, during charging within the [55%, 60%] range, one temperature range transition occurs, from [10℃, 15℃] to [15℃, 20℃]. During charging within the [60%, 65%] range, two temperature range transitions occur, from [15℃, 20℃] to [20℃, 25℃], and then to [25℃, 30℃]. During charging within the [65%, 70%] range, the battery temperature remained within [25℃, 30℃]. During charging within the [70%, 75%] range, a temperature range shift occurred, moving from [25℃, 30℃] to [20℃, 25℃]. During charging within the [75%, 80%] range, the battery temperature remained within [20℃, 25℃], and the charging process was completed at 79%.

[0166] The following describes how to determine whether a temperature range crossing has occurred for each target SOC interval, based at least on the first and second time points.

[0167] For the first target SOC range [55%, 60%], the initial SOC of this range is the current SOC of the target battery before charging begins, which is 56%. Based on the current temperature of the target battery, the first temperature range corresponding to the first target SOC range [55%, 60%] can be determined as [10℃, 15℃], with an initial temperature of 12℃. Thus, based on the predicted temperature change rate, the direction of battery temperature change is upward, and the first time required for the target battery to heat up from 12℃ to 15℃ can be calculated. The second calibration charging parameters corresponding to the target SOC range [55%, 60%] and the first temperature range [10℃, 15℃] can also be determined. Based on the second calibration charging parameters, the second time required for the target battery to charge from 56% to 60% can be calculated.

[0168] By comparing the magnitudes of the first and second time intervals, it can be seen that the first time interval is shorter than the second time interval, indicating that the battery temperature had already reached the upper limit of the first temperature range of 15°C before the target battery was charged from 56% to 60%. Therefore, it can be determined that a temperature range crossing occurred within the range of [55%, 60%], and the second temperature range after the crossing is [15°C, 20°C].

[0169] Since it was determined that a temperature range crossing occurred within the [55%, 60%] range, in order to further determine whether multiple temperature range crossings occurred within the [55%, 60%] range, the target SOC corresponding to the time when the temperature range crossing occurred can be calculated based on the first time and the second calibrated charging parameters.

[0170] For example, if the second calibration charging parameter is the second calibration charging current, the target SOC can be calculated based on the first time, the second calibration charging current, the battery capacity, and the initial SOC. The specific calculation formula is shown in formula (4).

[0171] Among them, SOC end,i Let t be the target SOC of the i-th SOC interval. j For the first time in the j-th temperature interval (the first temperature interval), I ij This is the second calibration charging current corresponding to the i-th SOC range and the j-th temperature range.

[0172] For example, if the second calibration charging parameter is the second calibration charging time, the target SOC can be calculated based on the first time, the second calibration charging time, and the initial SOC. The specific calculation formula is shown in formula (5).

[0173] Among them, t ij The second calibrated charging time corresponding to the i-th SOC interval and the j-th temperature interval, ΔSOC iLet be the SOC range corresponding to the i-th SOC interval.

[0174] Calculations using formula (4) or formula (5) show that the target SOC in the [55%, 60%] range is 58%, meaning the target battery experienced a temperature range transition when charged from 56% to 58%. At this point, [55%, 60%] can be divided into a first SOC sub-range [55%, 58%] and a second SOC sub-range [58%, 60%]. The second SOC sub-range [58%, 60%] is determined as the next target SOC range, and the second temperature range [15℃, 20℃] after the transition is determined as the first temperature range of the next target SOC range [58%, 60%]. The target SOC of 58% is the starting SOC of the next target SOC range [58%, 60%]. Further analysis is then conducted to determine whether a temperature range transition has occurred within [58%, 60%].

[0175] For the target SOC range [58%, 60%], based on the predicted temperature change rate, the direction of battery temperature change is known to be upward, allowing calculation of the first time required for the target battery to heat up from 15°C to 20°C. Furthermore, the second calibration charging parameters corresponding to the target SOC range [58%, 60%] and the first temperature range [15°C, 20°C] can be determined. Based on these second calibration charging parameters, the second time required for the target battery to charge from 58% to 60% can be calculated.

[0176] By comparing the magnitudes of the first and second times, it can be seen that the first time is greater than the second time. Based on the embodiment shown in Figure 6, reference SOC intervals can be identified that are prior to the target SOC interval [58%, 60%] and have the same first temperature interval [15℃, 20℃] as the target SOC interval [58%, 60%]. However, according to historical calculation results, the reference SOC interval does not exist in the charging path. Therefore, the third time for the target battery to charge within the reference SOC interval is 0. At this point, it can be determined that the first time is greater than the sum of the second and third times, and it is judged that the target SOC interval [58%, 60%] has not experienced a temperature interval crossing. That is, when the target battery is charged to 60%, the battery temperature has not yet reached 20%.

[0177] Since no temperature range crossing occurred within the [58%, 60%] interval, the first temperature interval [15℃, 20℃] corresponding to [58%, 60%] is determined to be the first temperature interval corresponding to the next target SOC interval [60%, 65%]. Furthermore, since [60%, 65%] is not the first target SOC interval, its starting SOC is the lower limit of the interval, 60%.

[0178] For the target SOC range [60%, 65%], similar to the process described above, calculate the first time required for the target battery to heat up from 15°C to 20°C, and calculate the second time required for the target battery to charge from 60% to 65%.

[0179] By comparing the magnitudes of the first and second times, it can be seen that the first time is greater than the second time. Based on the embodiment shown in Figure 6, according to historical calculation results, the reference SOC range [58%, 60%), which is the same as the target SOC range [60%, 65%] and has the same first temperature range [15℃, 20℃], is determined to be before the target SOC range [60%, 65%]. The third time when the target battery is charged within the reference SOC range [58%, 60%] is obtained. By comparing the magnitudes of the first time and the sum of the second and third times, it can be seen that the first time is less than the sum of the second and third times. It is determined that the target SOC range [60%, 65%] has undergone a temperature range transition, and the second temperature range after the transition is [20℃, 25℃].

[0180] Similar to the above process, after determining that a temperature range crossing has occurred within the [60%, 65%] interval, the [60%, 65%] interval can be further divided into a first SOC sub-interval and a second SOC sub-interval. Then, it can be determined whether multiple temperature range crossings have occurred within the [60%, 65%] interval. This process is repeated for each target SOC interval, until the last target SOC interval [75%-80%].

[0181] This completes the determination of temperature range crossings. Next, step 420 above can be executed: based on the determination results, determine the target temperature range corresponding to each target SOC range.

[0182] For example, if the judgment result indicates that a temperature range crossing has occurred, the target temperature range corresponding to the target SOC range can be determined to include the first temperature range before the crossing and the second temperature range after the crossing.

[0183] Continuing with the charging path shown in Figure 7, as mentioned above, [55%, 60%], [60%, 65%], and [70%, 75%] involve temperature range crossings. Therefore, the target temperature range corresponding to [55%, 60%] includes the first temperature range [10℃, 15℃] before the crossing and the second temperature range [15℃, 20℃] after the crossing. Similarly, the target temperature range corresponding to [60%, 65%] includes [15℃, 20℃], [20℃, 25℃], and [25℃, 30℃]. Furthermore, the target temperature range corresponding to [70%, 75%] includes the first temperature range [25℃, 30℃] before the crossing and the second temperature range [20℃, 25℃] after the crossing.

[0184] For example, if the judgment result indicates that no temperature range crossing has occurred, the target temperature range corresponding to the target SOC range can be determined as the first temperature range.

[0185] Please refer to Figure 7 for the charging path. As mentioned above, there is no temperature range crossing between [65%, 70%] and [75%, 80%]. Therefore, the target temperature range corresponding to [65%, 70%] can be determined as the first temperature range [25℃, 30℃]; and the target temperature range corresponding to [75%, 80%] can be determined as the first temperature range [20℃, 25℃].

[0186] This completes the process of determining the target temperature range. Subsequently, for each target SOC range and its corresponding target temperature range, the corresponding first calibration charging parameters can be determined. Based on these first calibration charging parameters, the charging time within each target SOC range can be predicted.

[0187] For example, for a target SOC range where no temperature range crossing occurs, a corresponding first calibration charging parameter is determined based on the target SOC range and the first temperature range, and the charging time within the target SOC range is predicted based on the first calibration charging parameter.

[0188] Referring again to the charging path shown in Figure 7, for the target SOC range [65%, 70%] and its corresponding first temperature range [25℃, 30℃] where no temperature range crossing occurs, the corresponding first calibration charging parameters are determined. Based on these first calibration charging parameters, the charging time t within the target SOC range [65%, 70%] is predicted. 65-70 .

[0189] Similarly, for the target SOC range [75%, 80%] and its corresponding first temperature range [20℃, 25℃] where no temperature range crossing occurs, the corresponding first calibration charging parameters are determined. Based on these first calibration charging parameters, the charging time t for charging from 75% to 78% within the target SOC range [75%, 80%] is predicted. 75-79 .

[0190] For example, for a target SOC range where a temperature range crossing occurs, a first first calibration charging parameter is determined based on the SOC range and the first temperature range, and the charging time in the first SOC sub-range before the crossing and the target SOC at the time of the crossing are predicted based on the first first calibration charging parameter; a second first calibration charging parameter is determined based on the SOC range and the second temperature range, and the charging time in the second SOC sub-range after the crossing is predicted based on the second first calibration charging parameter and the target SOC; the charging time of the target SOC range is determined based on the charging time in the first SOC sub-range and the charging time in the second SOC sub-range.

[0191] Referring again to the charging path shown in Figure 7, for the target SOC range [55%, 60%] where the temperature range crossing occurs and its corresponding first temperature range [10℃, 15℃], the first calibration charging parameters are determined. Based on the first calibration charging parameters, the target SOC of 58% at the time of the crossing is determined, and the charging time t within the first SOC sub-range [55%, 58%] is predicted. 55-58 Then, based on the target SOC range [55%, 60%] and its corresponding second temperature range [15℃, 20℃], the corresponding second first calibration charging parameters are determined. And based on the second first calibration charging parameters, the charging time t within the second SOC sub-range [58%, 60%] is predicted. 58-60 And determine the charging time t for the target SOC range [55%, 60%]. 55-60 =t 55-58 +t 58-60 And so on.

[0192] Finally, by summing up the charging times for each target SOC range, the total charging time for the target battery can be obtained.

[0193] As can be seen, the battery charging time prediction method provided in this application combines cloud databases and cloud computing power to train a temperature prediction model, which is then deployed to the vehicle, thereby enabling temperature change prediction during the charging process across the entire temperature range. Furthermore, by combining the temperature prediction model with a charging time algorithm, the vehicle can achieve high-precision charging time estimation across the entire temperature range.

[0194] Based on the methods described in any of the above embodiments, this application also provides a vehicle. At the hardware level, the vehicle includes a battery and a battery management system (BMS) connected to the battery. It may also include other hardware required for other services. As shown in Figure 8, the BMS includes a processor, an internal bus, and a memory, and may also include other hardware required for other services. The memory stores executable instructions. When the processor calls the executable instructions, it implements a battery charging time prediction method as described in any of the above embodiments.

[0195] Based on the methods described in any of the above embodiments, this application also provides a computer program product, which includes one or more computer programs or instructions. The computer program or instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. When executed by a processor, the computer program implements a battery charging time prediction method as described in any of the above embodiments.

[0196] This application also provides a computer storage medium storing a computer program, which, when executed by a processor, can be used to perform a battery charging time prediction method as described in any of the above embodiments.

[0197] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0198] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0199] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0200] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0201] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0202] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0203] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for predicting battery charging time, characterized in that, The method includes: Based on the current SOC and the charging cutoff SOC of the target battery, determine all target SOC intervals that will be passed through sequentially during charging from multiple preset SOC intervals. For each of the target SOC intervals, a prediction process is performed, the prediction process including: Based on the predicted rate of temperature change, the target temperature range that the target battery will pass through during charging within the target SOC range is predicted; based on the target SOC range and the target temperature range, the corresponding first calibration charging parameters are determined; and based on the first calibration charging parameters, the charging time within the target SOC range is predicted. The total charging time is determined based on the charging time for all target SOC ranges.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the state characteristics of the target battery; Predict the rate of temperature change of the target battery during the charging process based on the state characteristics.

3. The method according to claim 2, characterized in that, The step of predicting the rate of temperature change of the target battery during charging based on the state characteristics includes: The state features are input into the trained temperature prediction model to obtain the output temperature change rate.

4. The method according to any one of claims 1-3, characterized in that, The step of predicting the target temperature range that the target battery will pass through during charging within the target SOC range based on the predicted rate of temperature change includes: Based on the predicted rate of temperature change, determine whether a temperature range crossing occurs during charging within the target SOC range; The target temperature range corresponding to the target SOC range is determined based on the judgment result.

5. The method according to claim 4, characterized in that, The step of determining whether a temperature range crossing occurs during charging within the target SOC range based on the predicted rate of temperature change includes: Determine the first temperature range corresponding to the target SOC range, and determine the corresponding second calibration charging parameters based on the target SOC range and the first temperature range; Determine the starting SOC of the target SOC range within the first temperature range, and the starting temperature of the first temperature range; Based on the rate of temperature change, a first time is determined from the initial temperature to the boundary value of the first temperature range, and a second time is determined from the initial SOC to the target SOC range based on the second calibrated charging parameters. At least based on the first time and the second time, it is determined whether a temperature range crossing occurs during charging within the target SOC range.

6. The method according to claim 5, characterized in that, For the first target SOC range, the first temperature range corresponding to the first target SOC range is the temperature range in which the current temperature of the target battery is located; the starting SOC of the first target SOC range is the current SOC of the target battery; and the starting temperature of the first temperature range is the current temperature of the target battery. For non-first target SOC intervals, the starting SOC of the target SOC interval is the lower limit of the target SOC interval.

7. The method according to claim 5, characterized in that, The method further includes: If it is determined that the current target SOC interval has not crossed temperature intervals, the first temperature interval corresponding to the current target SOC interval is determined to be the first temperature interval corresponding to the next target SOC interval. If it is determined that the current target SOC interval has crossed a temperature interval, the second temperature interval after the crossing is determined as the first temperature interval corresponding to the next target SOC interval.

8. The method according to claim 5, characterized in that, The determination of whether a temperature range crossing occurs during charging within the target SOC range, based at least on the first time and the second time, includes: If the first time is less than the second time, it is determined that a temperature range crossing occurred during charging within the target SOC range.

9. The method according to claim 5, characterized in that, The determination of whether a temperature range crossing occurs during charging within the target SOC range, based at least on the first time and the second time, includes: If the first time is greater than the second time, determine each reference SOC interval that is before the target SOC interval and has the same first temperature interval as the target SOC interval; Obtain the third time when the target battery is charged in each of the reference SOC intervals; If the first time is greater than the sum of the second time and the third time, it is determined that the target SOC range has not experienced a temperature range crossing. If the first time is less than the sum of the second time and the third time, it is determined that the target SOC range has experienced a temperature range crossing.

10. The method according to claim 4, characterized in that, The step of determining the target temperature range corresponding to the target SOC range based on the judgment result includes: If the judgment result indicates that a temperature range crossing has occurred, the target temperature range corresponding to the target SOC range is determined to include the first temperature range before the crossing and the second temperature range after the crossing. If the judgment result indicates that no temperature range crossing has occurred, the target temperature range corresponding to the target SOC range is determined to be the first temperature range.

11. The method according to claim 10, characterized in that, The prediction of charging time within the target SOC range based on the first calibrated charging parameters includes: For a target SOC range where no temperature range crossing occurs, a corresponding first calibration charging parameter is determined based on the target SOC range and the first temperature range, and the charging time within the target SOC range is predicted based on the first calibration charging parameter. For a target SOC range where a temperature range crossing occurs, a first calibration charging parameter is determined based on the SOC range and the first temperature range. The charging time within the first SOC sub-range before the crossing and the target SOC at the time of the crossing are predicted based on the first calibration charging parameter. A second calibration charging parameter is determined based on the SOC range and the second temperature range. The charging time within the second SOC sub-range after the crossing is predicted based on the second calibration charging parameter and the target SOC. The charging time of the target SOC range is determined based on the charging time within the first SOC sub-range and the charging time within the second SOC sub-range.

12. A vehicle, characterized in that, The vehicles include: A battery, and a battery management system connected to the battery; The battery management system includes a processor and a memory for storing processor-executable instructions; Wherein, when the processor invokes the executable instructions, it implements the operation of any one of the methods described in claims 1-11.

13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-11.

14. A computer-readable storage medium, characterized in that, It stores computer instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1-11.

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