Method, device and vehicle for checking charging remaining time

Abstract

The application discloses a charging remaining time confirmation method, device and vehicle, relates to the battery charging technical field, and discloses a charging remaining time confirmation method, which comprises the following steps: obtaining current charging data of a battery pack, wherein the current charging data comprises a charging rate and an SOC value; obtaining a temperature rising charging time and a temperature rising charging end SOC value based on the current charging data and a first charging model; obtaining a constant temperature charging time based on the temperature rising charging end SOC value and a second charging model; and confirming a charging remaining time based on the temperature rising charging time and the constant temperature charging time.According to the basic characteristics of the temperature in the low-temperature fast charging process, the charging remaining time is calculated in sections, the fast and accurate calculation of the fast charging remaining time is realized in combination with a fitting method and given data, and the overcomplicated calculation and the model building and calculation of heat generation, heat conduction and heat dissipation are avoided.

Description

Technical Field

[0001] This application relates to the field of battery charging technology, and in particular to a method, apparatus, and vehicle for determining the remaining charging time. Background Technology

[0002] Low-temperature fast charging technology, as a key technology for improving the charging efficiency of electric vehicles and other devices using batteries as energy storage devices, often faces numerous challenges. One significant factor is the profound impact of the battery's state of charge (SOC) and the surrounding temperature environment on its charging behavior. Specifically, when a battery is at a low temperature, its internal chemical reactivity decreases, directly leading to the need for higher voltage or current to achieve the desired charging rate, resulting in a significant rate change. This rate change can not only exacerbate internal stress within the battery but also cause a sharp rise in battery temperature, further impacting battery life and safety performance.

[0003] Furthermore, low-temperature fast charging involves the complexity of battery pack heating and heat dissipation models. To ensure effective and safe charging of batteries in low-temperature environments, the battery pack typically needs to be preheated to raise the battery temperature to a suitable operating range. However, this heating process itself consumes additional energy and requires precise control to avoid overheating. Simultaneously, during charging, the internal chemical reactions of the battery generate heat, necessitating good heat dissipation capabilities from the battery pack to prevent safety issues caused by excessive temperature. Unfortunately, the diverse structures of battery packs, including the arrangement of individual cells and the selection of heat transfer media, make their heating and heat dissipation models quite complex and difficult to predict accurately.

[0004] Therefore, in the application of low-temperature fast charging technology, how to accurately predict the remaining charging time has become a key technical problem that urgently needs to be solved. Summary of the Invention

[0005] The main objective of this application is to provide a method, apparatus, and vehicle for determining the remaining charging time, aiming to solve the technical problem of difficulty in predicting the remaining charging time during low-temperature fast charging.

[0006] To achieve the above objectives, this application proposes a method for confirming the remaining charging time. The method includes: acquiring current charging data of the battery pack, the current charging data including charging rate and SOC value; acquiring a heating charging time and a heating charging end SOC value based on the current charging data and a first charging model; acquiring a constant temperature charging time based on the heating charging end SOC value and a second charging model; and confirming the remaining charging time based on the heating charging time and the constant temperature charging time.

[0007] In one embodiment, before the step of obtaining the heating charging time and the SOC value at the end of heating charging based on the current charging data and the first charging model, the method further includes: obtaining charging data of the battery pack at different temperatures; preprocessing and extracting features from the charging data to obtain a charging dataset to be fitted; fitting the charging rate and the SOC value based on the charging dataset to be fitted to obtain a relationship curve between the charging rate and the SOC value; and generating the first charging model based on the relationship curve between the charging rate and the SOC value.

[0008] In one embodiment, the steps of preprocessing and feature extraction of the charging data to obtain a charging dataset to be fitted include: removing abnormal data points from the charging data; performing interpolation at the removed locations to obtain preprocessed charging data; extracting features from the preprocessed charging data to obtain feature values; and organizing the preprocessed charging data based on the feature values ​​to generate a charging dataset to be fitted.

[0009] In one embodiment, after the step of generating the first charging model based on the relationship curve between the charging rate and the SOC value, the method further includes: obtaining the charging rate change rate within a preset temperature range; setting an adjustment coefficient based on the charging rate change rate; and correcting the first charging model based on the adjustment coefficient.

[0010] In one embodiment, the step of obtaining the constant temperature charging time based on the SOC value at the end of the temperature-raising charging and the second charging model includes: building a second charging model based on the rated relationship curve between the battery rate and the SOC value of the battery pack; obtaining a target SOC value; and obtaining the constant temperature charging time based on the SOC value at the end of the temperature-raising charging, the target SOC value, and the second charging model.

[0011] In one embodiment, after the step of confirming the remaining charging time based on the heating charging time and the constant temperature charging time, the method further includes: recording the actual charging time of the battery pack; comparing the actual charging time with the remaining charging time; and updating the first charging model and the second charging model when the deviation between the actual charging time and the remaining charging time is greater than a preset deviation range.

[0012] Furthermore, to achieve the above objectives, this application also proposes a device for confirming the remaining charging time. The device includes: a data acquisition module for acquiring current charging data of the battery pack, the current charging data including the charging rate and the State of Charge (SOC) value; a data calculation module for acquiring a heating-up charging time and a heating-up charging end SOC value based on the current charging data and a first charging model; for acquiring a constant-temperature charging time based on the heating-up charging end SOC value and a second charging model; and for confirming the remaining charging time based on the heating-up charging time and the constant-temperature charging time.

[0013] In addition, to achieve the above objectives, this application also proposes a vehicle, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the charging remaining time confirmation method as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the charging remaining time confirmation method as described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the method for confirming the remaining charging time as described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects:

[0017] Based on the basic temperature characteristics of the low-temperature charging process, the heating charging time is obtained through the current charging data of the battery pack and the first charging model. The constant temperature charging time is obtained through the SOC value at the end of the heating charging and the second charging model. The remaining charging time is calculated in segments. By combining the fitting method and the given data, the remaining fast charging time can be calculated quickly and accurately, avoiding overly complex calculations and the construction and calculation of models for heat generation, heat conduction, and heat dissipation. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating an embodiment of the method for confirming remaining charging time in this application;

[0021] Figure 2 A flowchart illustrating one implementation of the method for confirming the remaining charging time in this application;

[0022] Figure 3 A schematic diagram of a first charging model provided for an embodiment of the method for confirming the remaining charging time in this application;

[0023] Figure 4 A schematic diagram of a second charging model provided for an embodiment of the method for confirming the remaining charging time in this application;

[0024] Figure 5 A schematic diagram of the module structure of the charging remaining time confirmation device according to an embodiment of this application;

[0025] Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the method for confirming the remaining charging time in the embodiments of this application.

[0026] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0027] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0028] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0029] In the prior art, the estimation method is mainly based on the battery pack management system to estimate the remaining time based on the incoming current or the requested current.

[0030] The former scheme fails to consider the impact of initial temperature on the low incoming current, which may increase in the future. Furthermore, the incoming current cannot represent the future average level, leading to inaccurate calculation results. In the latter scheme, the actual incoming current does not follow the requested current, and the requested current is unstable, causing deviations in the estimation results. Additionally, the requested current theoretically requires temperature prediction information, introducing uncertainty.

[0031] In addition, calculations are also based on test data. Different SOC points, different battery pack temperature points, and ambient temperature points require a large amount of test data, resulting in a large amount of preliminary testing work.

[0032] Based on this, the main solution provided in this application embodiment is: to obtain the current charging data of the battery pack, the current charging data including the charging rate and the SOC value; to obtain the heating charging time and the SOC value at the end of heating charging based on the current charging data and a first charging model; to obtain the constant temperature charging time based on the SOC value at the end of heating charging and a second charging model; and to confirm the remaining charging time based on the heating charging time and the constant temperature charging time.

[0033] The solution provided in this application, based on the basic temperature characteristics of the low-temperature charging process, obtains the heating charging time through the current charging data of the battery pack and the first charging model, obtains the constant temperature charging time through the SOC value at the end of heating charging and the second charging model, calculates the remaining charging time in segments, and combines the fitting method and given data to quickly and accurately calculate the remaining fast charging time, avoiding overly complex calculations and the construction and calculation of models for heat generation, heat conduction, and heat dissipation.

[0034] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or vehicle capable of performing the above functions. The following description uses a vehicle as an example to illustrate this embodiment and the subsequent embodiments.

[0035] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the method for determining remaining charging time according to this application. In this embodiment, the method for determining remaining charging time includes steps S10 to S40:

[0036] Step S10: Obtain the current charging data of the battery pack, wherein the current charging data includes the charging rate and the SOC value.

[0037] It should be noted that the current charging data of the battery pack can be read from the battery management system (BMS) or other relevant sensors. In this solution, the charging rate and SOC value are the key factors to be monitored.

[0038] The charging rate (C-rate) represents the ratio of charging current to the battery's rated capacity. For example, 1C means the charging current equals the battery's rated capacity. The SOC (State of Charge) value represents the battery's current state of charge, usually expressed as a percentage (0%-100%).

[0039] Specifically, the current charging data of the battery pack is acquired by sending a data request to the BMS through programming or specific software tools. This typically involves sending specific commands or queries to request a specific dataset. The data returned by the BMS is usually sent in a specific format, such as binary. It is necessary to understand and parse these data formats to extract the required information. The charging rate and SOC value are then extracted from the returned data packets using vehicle algorithms.

[0040] Furthermore, data validation and error handling can be performed to verify the accuracy and reliability of the extracted data. Data accuracy can be ensured by comparing multiple data sources or performing consistency checks. If the data is incomplete, inaccurate, or unreadable, error handling mechanisms need to be implemented, such as logging errors, issuing alerts, or requesting data resending.

[0041] Furthermore, the extracted data is stored in appropriate storage media such as databases or files for subsequent analysis and use. The charging rate and SOC value are displayed through a user interface (such as a dashboard, display screen, or mobile application) so that the driver or technician can monitor the battery pack's charging status in real time.

[0042] Step S20: Based on the current charging data and the first charging model, obtain the heating charging time and the SOC value at the end of heating charging.

[0043] It should be noted that fast charging stations typically provide sufficient power to meet battery heating requirements. Generally, battery heating systems can reach 6kW, significantly exceeding the heat dissipation power due to low ambient temperatures. Therefore, fast charging of low-temperature batteries can be broadly divided into two phases based on battery temperature trends: a charging phase during the temperature rise process and a charging phase during the temperature stabilization process. This embodiment therefore provides the charging time steps for the charging phase during the temperature rise process.

[0044] As we can understand, the warm-up charging time refers to the period from the start of battery charging until the battery temperature reaches a certain stable level (or a specific temperature range). During this period, the battery gradually heats up due to internal chemical reactions and the heat generated by the current flowing through it. The length of the warm-up charging time is affected by a variety of factors.

[0045] Understandably, the first charging model characterizes the relationship between current charging data and the temperature-raising charging time. Specifically, the first charging model typically considers the following factors: battery internal resistance and heat generation, battery thermal capacity and thermal conductivity, battery chemical reactions, and environmental conditions. Based on these factors, the first charging model can usually establish a system of equations. By solving this system of equations, the temperature-raising charging time and the change in the battery's State of Charge (SOC) value during that time period can be obtained. This model can be a static model, used to predict the temperature-raising charging time under specific conditions; or it can be a dynamic model, capable of adjusting the prediction results in real time based on current charging data and environmental conditions.

[0046] This solution presents a method for constructing a first charging model. In a feasible implementation, it simplifies complex simultaneous equations and provides a simple and intuitive model for calculating the heating and charging time. Please refer to... Figure 2 , Figure 2 This is a flowchart illustrating one implementation method for confirming the remaining charging time in this application.

[0047] Step A10: Obtain charging data of the battery pack at different temperatures.

[0048] Understandably, the purpose of this step is to collect and analyze the charging behavior of the battery pack under different temperature conditions. Since the charging characteristics of the battery pack, such as the heating rate, SOC growth rate, and charging rate, are affected by temperature, the SOC gradually increases and the charging rate gradually increases as the battery heats up during charging. Therefore, obtaining charging data at different temperatures is crucial for understanding the charging behavior of the battery pack.

[0049] It should be noted that a series of representative temperature points are typically selected for data collection. These temperature points may include low temperatures, room temperatures, and high temperatures to cover the operating temperature range that the battery pack may encounter. Then, at each temperature point, conditions such as charging rate and charging voltage may need to be changed to obtain charging data under different charging strategies. Finally, a professional battery testing system or charging equipment is used to charge the battery pack and record various parameters during the charging process in real time, such as current, voltage, temperature, and SOC.

[0050] Step A20: Preprocess and extract features from the charging data to obtain the charging dataset to be fitted.

[0051] Understandably, the purpose of this step is to clean and organize the collected charging data in order to conduct subsequent data analysis and model fitting.

[0052] Specifically, preprocessing and feature extraction include: removing outlier data points from the charging data; performing interpolation at the removed locations to obtain preprocessed charging data; extracting features from the preprocessed charging data to obtain feature values; and organizing the preprocessed charging data based on the feature values ​​to generate a charging dataset to be fitted.

[0053] Understandably, due to potential errors or anomalies during data acquisition (such as sensor malfunctions or equipment instability), it is necessary to remove these outlier data points to ensure data quality. After removing outlier data points, interpolation can be performed at the removed locations to maintain data continuity. Interpolation methods include linear interpolation and polynomial interpolation; the specific method chosen depends on the characteristics of the data and the required processing.

[0054] Furthermore, feature values ​​that significantly influence charging behavior are extracted from the preprocessed charging data. These feature values ​​may include charging time, SOC change, temperature change, and charging rate. Based on the extracted feature values, the preprocessed charging data is organized to generate a structured dataset, which is the charging dataset to be fitted. This dataset will be used for subsequent model fitting and analysis.

[0055] Step A30: Fit the charging rate and the SOC value based on the charging dataset to be fitted, and obtain the relationship curve between the charging rate and the SOCC value.

[0056] It should be noted that the preprocessed charging dataset, i.e., the charging dataset to be fitted generated in step A20, is used to fit the relationship curve between the charging rate (C-rate) and the battery state of charge (SOC). This relationship curve can describe the change in SOCC value as the battery charging rate gradually increases with rising temperature. Charging data at a specific temperature are selected from the charging dataset to be fitted; these data should include SOCC values ​​and their corresponding charging rates.

[0057] It should be noted that mathematical methods such as polynomial fitting, exponential fitting, and machine learning algorithms are typically used to fit these data points and generate a curve showing the relationship between the charging rate and the SOCC value.

[0058] Step A40: Generate a first charging model based on the relationship curve between the charging rate and the SOCC value.

[0059] Specifically, during the battery temperature rise phase, the initial temperature is low, and the target heating temperature is high. Based on the battery pack's characteristic data, the difference between the charging rate at the target temperature and the charging rate at the current SOCC (State of Charge) is recorded as C. T-0 .

[0060] like Figure 3 As shown, Figure 3 This is a schematic diagram of the first charging model provided in the embodiment of the method for confirming the remaining charging time of this application. When the battery is charged and heated, the SOCC (Sodium Carbon Dioxide) gradually increases, and the battery charging rate also gradually increases. Approaching the target charging temperature, there is an upward curve relationship, characterized by rapid heat generation and a quick approach to the target temperature in the early stages due to full-power heating, followed by gradual approach to the target temperature due to heating adjustment in the later stages. Therefore, an arctangent function curve fitting is used to obtain the relationship between SOCC and charging rate during the heating process as the first charging model:

[0061] y = A*arctan(x) + C0.

[0062] Here, the variable x represents the increase in SOCC from the initial SOCC, with a maximum value of 100%. A is the scaling factor of the curve on the vertical axis, taking a value of C. T-0 / (π / 2), so that the maximum value of the function is C. T-0 C0 represents the initial temperature and the initial charge rate at the initial SOCC.

[0063] Step A50: Obtain the rate of change of charging rate within the preset temperature range.

[0064] It should be noted that the preset temperature range is usually a range of values ​​close to a constant temperature. At this time, the rate of change of the charging rate within the preset temperature range can well characterize the speed at which the charging rate approaches the charging rate at the target temperature.

[0065] Specifically, charging tests were conducted at each temperature point within a preset temperature range, and the changes in charging rate over time and SOCC were recorded.

[0066] Furthermore, based on the collected data, the rate of change of the charging rate at each temperature point is calculated. This can be achieved by differentiating the charging rate data or calculating the difference between adjacent data points.

[0067] Step A60: Set an adjustment coefficient based on the charging rate change rate.

[0068] It should be noted that in this step, we will set one or more adjustment coefficients based on the rate of change of the charging rate. These coefficients will be used for subsequent calibration of the first charging model. First, we analyze the relationship between the rate of change of the charging rate and temperature to determine which temperature ranges show significant changes in the charging rate. Based on the analysis results, we set different adjustment coefficients for different temperature ranges. These coefficients should reflect the trend and extent of the change in charging rate with temperature.

[0069] Step A70: Correct the first charging model based on the adjustment coefficient.

[0070] It should be noted that, based on the adjustment coefficient, relevant parameters in the first charging model are adjusted. This may involve modifying the mathematical formulas, algorithms, or logic within the model. New charging data or experimental data are used to verify the effectiveness of the corrected model. The differences between the model's predictions before and after correction and the actual data are compared to evaluate the effectiveness of the correction.

[0071] Understandably, if the calibration results are unsatisfactory, you can return to step A60 to readjust the adjustment coefficients and calibrate the model again. This process may require multiple iterations until the model meets the predetermined accuracy and reliability requirements.

[0072] Specifically, in the first charging model shown in this scheme, an adjustment coefficient B is introduced to obtain a new first charging model:

[0073] y = A*arctan(Bx) + C0.

[0074] The larger the adjustment coefficient B, the faster the charging rate approaches the target charging rate at that temperature; conversely, the smaller the B value, the slower the charging rate approaches the target charging rate. The adjustment coefficient B can be corrected using experimental data.

[0075] Specifically, given that the initial SOC is known, the charging rate at the initial temperature and the charging rate at the target temperature are both characteristic data of the battery, so the difference between the two can also be calculated, and the change process of the charging rate can be known.

[0076] At y = 0.9C T-0 At this point, the battery temperature rise process is approximated as ending, and the State of Charge (SOC) is obtained using an inverse function. Δ y represents the charging rate of the battery pack. Based on its meaning, within the battery pack's temperature rise range (SOC0, SOCΔ), the charging time is denoted as T1, in hours. The heating and charging time can then be calculated as follows:

[0077]

[0078] It is understandable that the SOC value at the end of the heating and charging phase is the SOC value at the end of the heating and charging phase, and the end of the heating and charging phase is the time point when the temperature rises to the preset target temperature.

[0079] In this embodiment, a first charging model for calculating the heating and charging time is presented in a simple and intuitive way. This model can provide users with useful information about the battery charging progress and help optimize charging strategies and improve charging efficiency.

[0080] Step S30: Based on the SOC value at the end of the heating and charging process and the second charging model, obtain the constant temperature charging time.

[0081] Understandably, the second charging model is a mathematical or algorithmic model specifically designed to describe the charging behavior of a battery under isothermal conditions. This model may consider various factors, including but not limited to the battery's chemical properties, thermal effects, and changes in voltage, current, and state of charge (SOC) during the charging process. Unlike the first charging model (which may focus on describing the battery's charging behavior during the heating phase), the second charging model focuses more on the isothermal phase, i.e., the charging process after the battery temperature has reached a preset value and remained stable.

[0082] It should be noted that constant-temperature charging time refers to the time required for the battery temperature to be maintained near a certain set value after reaching it during the charging process, by controlling the charging current until the battery is fully charged. This process aims to optimize charging efficiency, reduce thermal stress on the battery, and extend its lifespan.

[0083] Understandably, once the SOC value at the end of the warm-up charging process is obtained, it can be used as one of the input parameters in conjunction with the second charging model. This model will calculate the time required for isothermal charging based on the input SOC value and other possible parameters (such as battery temperature, target SOC, etc.).

[0084] In one feasible implementation, specific steps are provided: A second charging model is built based on the rated relationship curve between the battery pack's rate of return and State of Charge (SOC); a target SOC value is obtained; and the constant-temperature charging time is obtained based on the SOC value at the end of the temperature-raising charging process, the target SOC value, and the second charging model. It should be noted that the target SOC value is typically 100%, but can also be set to other values.

[0085] Specifically, by inputting the SOC-charging rate curve provided by the battery pack manufacturer, such as... Figure 4 As shown, Figure 4 A schematic diagram of a second charging model provided for an embodiment of the method for confirming the remaining charging time in this application.

[0086] Therefore, within the stable battery temperature range, T2 is the isothermal charging time, measured in hours. The isothermal charging time T2 can then be calculated as follows:

[0087]

[0088] Step S40: Based on the heating charging time and the constant temperature charging time, determine the remaining charging time.

[0089] It should be noted that the total charging time from the start of charging to full is obtained by adding the preheating charging time and the constant-temperature charging time. The remaining charging time is then obtained by subtracting the elapsed charging time (i.e., the current time minus the start time) from the total charging time. This calculated remaining time is then displayed in a user-friendly manner, such as on the charging device's screen or through voice prompts.

[0090] In this embodiment, based on the basic temperature characteristics of the low-temperature charging process, the heating charging time is obtained through the current charging data of the battery pack and the first charging model, and the constant temperature charging time is obtained through the SOC value at the end of the heating charging and the second charging model. The remaining charging time is calculated in segments, and the remaining fast charging time is calculated quickly and accurately by combining the fitting method and the given data, avoiding overly complex calculations and the construction and calculation of models such as heat generation, heat conduction, and heat dissipation.

[0091] In one feasible implementation, based on the above embodiments, the entire verification process can be further refined. The steps include: recording the actual charging time of the battery pack; comparing the actual charging time with the remaining charging time; and updating the first charging model and the second charging model when the deviation between the actual charging time and the remaining charging time is greater than a preset deviation range.

[0092] It should be noted that during the battery pack charging process, the total time from the start to the end of charging is recorded in real time. This includes the time for warm-up charging, the time for constant temperature charging, and any possible additional time (such as pause time, adjustment time, etc.).

[0093] It should be noted that, based on the heating and charging time and the isothermal charging time calculated in the previous steps (and possibly other known time factors), the remaining charging time is calculated. This calculated remaining time is then compared with the expected remaining time obtained through other methods (such as user input, system prediction, etc.).

[0094] It should be noted that this calculation involves the deviation between the actual charging time and the calculated remaining charging time. This deviation may be an absolute time difference or a relative percentage. This deviation is then compared to a preset deviation range. The preset deviation range is set based on the characteristics of the battery pack, charging conditions, and the user's desired level of accuracy.

[0095] It should be noted that if the deviation value is greater than the preset deviation range, it indicates that the currently used charging model (the first charging model and / or the second charging model) may not be accurate enough and needs to be updated. If the deviation value is within the preset deviation range, it indicates that the current model is accurate enough and can continue to be used.

[0096] It's important to note that when deciding to update the model, new charging data should be collected, including voltage, current, temperature, and SOC value. This data should reflect the actual behavior of the battery pack during charging. The new data should be used to train or calibrate the charging model to improve its accuracy and reliability. The updated model should be able to more accurately predict the warm-up charging time, the isothermal charging time, and the total charging time.

[0097] It should be noted that the updated model is applied to the actual charging process, and data continues to be collected to evaluate the model's performance. If biases or deficiencies are still found in the model, the above steps can be repeated for further optimization and iteration.

[0098] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the method for confirming the remaining charging time of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0099] This application also provides a device for confirming the remaining charging time, please refer to... Figure 5 The device for confirming the remaining charging time includes: a data acquisition module 10 for acquiring current charging data of the battery pack, the current charging data including charging rate and SOC value; a data calculation module 20 for acquiring heating charging time and heating charging end SOC value based on the current charging data and a first charging model; also for acquiring constant temperature charging time based on the heating charging end SOC value and a second charging model; and also for confirming the remaining charging time based on the heating charging time and the constant temperature charging time.

[0100] The charging remaining time confirmation device provided in this application, employing the charging remaining time confirmation method in the above embodiments, can solve the technical problem of difficulty in predicting the remaining charging time during low-temperature fast charging. Compared with the prior art, the beneficial effects of the charging remaining time confirmation device provided in this application are the same as those of the charging remaining time confirmation method provided in the above embodiments, and other technical features in the charging remaining time confirmation device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0101] This application provides a vehicle, the vehicle including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the charging remaining time confirmation method in Embodiment 1 above.

[0102] The following is for reference. Figure 6The diagram illustrates a structural schematic of a vehicle suitable for implementing embodiments of this application. The vehicle in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The vehicle shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0103] like Figure 6 As shown, the vehicle may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for vehicle operation. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the vehicle to communicate wirelessly or wiredly with other devices to exchange data. Although vehicles with various systems are shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0104] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0105] The vehicle provided in this application, employing the method for determining remaining charging time in the above embodiments, can solve the technical problem of difficulty in predicting remaining charging time during low-temperature fast charging. Compared with the prior art, the beneficial effects of the vehicle provided in this application are the same as those of the method for determining remaining charging time provided in the above embodiments, and other technical features of the vehicle are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0106] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0107] 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.

[0108] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the charging remaining time confirmation method in the above embodiments.

[0109] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0110] The aforementioned computer-readable storage medium may be included in the vehicle or may exist independently and not installed in the vehicle.

[0111] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a vehicle, cause the vehicle to: acquire current charging data of the battery pack, the current charging data including charging rate and SOC value; acquire a warm-up charging time and a warm-up charging end SOC value based on the current charging data and a first charging model; acquire a constant-temperature charging time based on the warm-up charging end SOC value and a second charging model; and determine the remaining charging time based on the warm-up charging time and the constant-temperature charging time.

[0112] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, 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 indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated 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 the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0114] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0115] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for determining the remaining charging time, thereby solving the technical problem of difficulty in predicting the remaining charging time during low-temperature fast charging. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the method for determining the remaining charging time provided in the above embodiments, and will not be repeated here.

[0116] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for confirming the remaining charging time.

[0117] The computer program product provided in this application can solve the technical problem of difficulty in predicting the remaining charging time during low-temperature fast charging. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the charging time confirmation method provided in the above embodiments, and will not be repeated here.

[0118] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for confirming remaining charging time, characterized in that, The method includes: Obtain the current charging data of the battery pack, wherein the current charging data includes the charging rate and the SOC value; Based on the current charging data and the first charging model, the heating charging time and the SOC value at the end of heating charging are obtained. Based on the SOC value at the end of the heating and charging process and the second charging model, the constant temperature charging time is obtained. Based on the heating-up charging time and the constant-temperature charging time, the remaining charging time is determined; Before the step of obtaining the heating-up charging time and the SOC value at the end of heating-up charging based on the current charging data and the first charging model, the method further includes: Obtain charging data for the battery pack at different temperatures; The charging data is preprocessed and features are extracted to obtain the charging dataset to be fitted; Based on the charging dataset to be fitted, the charging rate and the SOC value are fitted to obtain the relationship curve between the charging rate and the SOC value; Based on the relationship curve between the charging rate and the SOC value, a first charging model is generated; The steps of preprocessing and feature extraction of the charging data to obtain the charging dataset to be fitted include: Remove outlier data points from the charging data; Interpolation is performed at the rejection locations to obtain preprocessed charging data; Feature extraction is performed on the preprocessed charging data to obtain feature values; The preprocessed charging data is organized based on the aforementioned feature values ​​to generate a charging dataset to be fitted.

2. The method for confirming remaining charging time as described in claim 1, characterized in that, The step of generating the first charging model based on the relationship curve between the charging rate and the SOC value further includes: Obtain the rate of change of charging rate within a preset temperature range; An adjustment coefficient is set based on the charging rate change rate; The first charging model is corrected based on the adjustment coefficient.

3. The method for confirming remaining charging time as described in claim 2, characterized in that, The step of obtaining the isothermal charging time based on the SOC value at the end of the heating-up charging and the second charging model includes: A second charging model is built based on the rated relationship curve between the battery rate and the SOC value of the battery pack. Obtain the target SOC value; Based on the SOC value at the end of the heating and charging process, the target SOC value, and the second charging model, the constant temperature charging time is obtained.

4. The method for confirming remaining charging time as described in claim 1, characterized in that, The step of confirming the remaining charging time based on the heating charging time and the constant temperature charging time further includes: Record the actual charging time of the battery pack; Compare the actual charging time with the remaining charging time; When the deviation between the actual charging time and the remaining charging time is greater than a preset deviation range, the first charging model and the second charging model are updated.

5. A device for confirming remaining charging time, characterized in that, The device includes: The data acquisition module is used to acquire the current charging data of the battery pack, which includes the charging rate and the SOC value. The data calculation module is used to obtain the heating charging time and the SOC value at the end of heating charging based on the current charging data and the first charging model; it is also used to obtain the constant temperature charging time based on the SOC value at the end of heating charging and the second charging model; and it is also used to confirm the remaining charging time based on the heating charging time and the constant temperature charging time. The data acquisition module is further configured to acquire charging data of the battery pack at different temperatures; preprocess and extract features from the charging data to obtain a charging dataset to be fitted; fit the charging rate and the SOC value based on the charging dataset to be fitted to obtain a relationship curve between the charging rate and the SOC value; and generate a first charging model based on the relationship curve between the charging rate and the SOC value. The data acquisition module is also used to remove abnormal data points from the charging data; perform interpolation processing at the removal positions to obtain preprocessed charging data; extract features from the preprocessed charging data to obtain feature values; and organize the preprocessed charging data based on the feature values ​​to generate a charging dataset to be fitted.

6. A vehicle, characterized in that, The vehicle includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for confirming the remaining charging time as claimed in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the method for confirming the remaining charging time as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the method for confirming the remaining charging time as described in any one of claims 1 to 4.

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