A battery charging remaining duration prediction method and device and storage medium

Abstract

Embodiments of the present application provide a method, device and storage medium for measuring the remaining charging duration of a battery, the method comprising: obtaining real-time charging data of a first battery built in a first vehicle, the real-time charging data comprising chip data, battery state data, charging voltage and charging current at a charging start time; inputting the real-time charging data into a preset prediction model to obtain an average charging current of the first battery during the charging process; determining a remaining charging duration of the first battery according to a target charging amount and the average charging current, the target charging amount being a historical electric quantity of the first battery when fully charged or an identified electric quantity; and outputting and displaying the remaining charging duration. The present scheme can improve the prediction accuracy of the remaining charging duration.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of batteries, in particular to a battery charging remaining time prediction method, device and storage medium. BACKGROUND

[0002] The charging remaining time is usually predicted according to the charging current table provided by the battery manufacturer. However, in actual charging, the battery temperature changes, the charging pile output current fluctuates, and the vehicle electrical appliances consume electricity, etc., resulting in a large error in the table lookup prediction of the charging remaining time.

[0003] Therefore, a black box model is usually built using historical charging data, such as building a machine learning and deep learning training model, and inputting real vehicle data online to the trained model to predict the charging remaining time. Although the error is reduced, the prediction accuracy is generally low. SUMMARY

[0004] Embodiments of the present application provide a battery charging remaining time prediction method, device and storage medium, which can improve the prediction accuracy of the charging remaining time.

[0005] In a first aspect, embodiments of the present application provide a battery charging remaining time prediction method, which comprises:

[0006] obtaining real-time charging data of a first battery built-in a first vehicle, the real-time charging data comprising chip data, battery state data, charging voltage and charging current at a charging start time;

[0007] inputting the real-time charging data into a preset prediction model to obtain an average charging current of the first battery in a charging process;

[0008] determining a charging remaining time of the first battery according to a target charging amount and the average charging current, the target charging amount being a historical electric quantity of the first battery when fully charged;

[0009] outputting and displaying the charging remaining time.

[0010] In a second aspect, embodiments of the present application further provide a device for predicting a battery charging remaining time, which comprises:

[0011] an input and output module configured to obtain real-time charging data of a first battery built-in a first vehicle, the real-time charging data comprising chip data, battery state data, charging voltage and charging current at a charging start time;

[0012] a processing module configured to input the real-time charging data into a preset prediction model to obtain an average charging current of the first battery in a charging process;

[0013] The processing module is further configured to determine a remaining charging duration of the first battery according to a target charging amount and the average charging current, the target charging amount being a historical charging amount of the first battery when fully charged;

[0014] The display module is configured to output and display the remaining charging duration through the input / output module.

[0015] In some embodiments, before the input / output module inputs the real-time charging data into the preset prediction model, the processing module is further configured to:

[0016] obtain historical charging data of the first battery, the historical charging data including charging data of at least one time of charging the first battery;

[0017] train the preset prediction model with the historical charging data, so that the preset prediction model has the characteristic of predicting the average current in the battery charging process.

[0018] In some embodiments, the historical charging amount satisfies at least one of the following:

[0019] an average value of at least one historical full charging amount;

[0020] an actual charging amount at the last time of full charging;

[0021] an actual charging amount at any time of full charging;

[0022] a historical charging amount at full charging;

[0023] a historical charging amount at the last time of full charging;

[0024] an average historical charging amount of at least two times of full charging;

[0025] The identification amount satisfies at least one of the following:

[0026] a rated charging amount of the first battery;

[0027] an actual charging capacity of the first battery;

[0028] a preset charging amount, the preset charging amount including a preset proportion of the rated charging amount or a preset proportion of the actual charging capacity.

[0029] In some embodiments, before the processing module determines the remaining charging duration of the first battery according to the target charging amount and the average charging current, the processing module is further configured to:

[0030] obtain a real full charging amount of the first battery according to a capacity fade coefficient, a rated charging amount and a state of health of the first battery;

[0031] calculate a remaining battery state of charge of the first battery according to a current starting battery state of charge and a current ending battery state of charge of the first battery;

[0032] obtain the target charging amount according to the real full charge amount and the remaining battery state of charge.

[0033] In some embodiments, the processing module is further configured to:

[0034] determine a first vehicle type and a first battery type of the first vehicle;

[0035] determine the average charging current according to the first vehicle type, the first battery type, and a preset correspondence relationship, the preset correspondence relationship being a correspondence relationship between the first vehicle type, the first battery type, and the average charging current;

[0036] determine a remaining charging duration of the battery according to the target charging amount and the average charging current, the target charging amount being a historical charging amount of the first battery when fully charged.

[0037] In some embodiments, the processing module is further configured to:

[0038] determine a first vehicle type and a first battery type of the first vehicle;

[0039] if the first vehicle and a second vehicle predicted by the preset prediction model satisfy a preset matching condition, determine that an average charging current corresponding to the second vehicle is the average charging current of the first vehicle, wherein the preset matching condition comprises at least one of the following: the first vehicle type matches a second vehicle type of the second vehicle, and the first battery type matches the second vehicle type of the second vehicle.

[0040] In some embodiments, the remaining charging duration of the battery is determined according to the target charging amount and the average charging current, and the target charging amount at least satisfies one of the following:

[0041] the target charging amount is a historical charging amount of the first battery when fully charged;

[0042] the target charging amount is a rated charging amount of the first battery;

[0043] the target charging amount is a historical charging amount of the first battery when fully charged last time;

[0044] the target charging amount is an average historical charging amount of the first battery when fully charged at least twice;

[0045] the target charging amount is a historical full charging amount of a second battery of the second vehicle.

[0046] In some embodiments, the processing module is further configured to:

[0047] determining a decay coefficient of the first battery and battery usage data, the battery usage data comprising a total battery usage time length, a battery charging frequency, and a battery charging time length at a historical charging time;

[0048] determining matching data of the first battery and the second battery according to the battery usage data;

[0049] determining the target charging amount according to the matching data and the decay coefficient.

[0050] In a third aspect, an embodiment of the present application further provides a processing device, comprising a processor and a memory, the memory storing a computer program, and the processor calling the computer program in the memory to execute steps in any battery charging remaining time prediction method provided by an embodiment of the present application.

[0051] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, the computer readable storage medium storing a plurality of instructions, and the instructions being adapted to be loaded by a processor to execute steps in any battery charging remaining time prediction method provided by an embodiment of the present application.

[0052] From the above, in an embodiment of the present application, since the real-time charging data is input into the preset prediction model, the prediction result output based on the real-time charging data can accurately represent the average charging current of the first battery in the charging process, and thus, when the charging remaining time of the first battery is determined according to the target charging amount and the average charging current, the charging remaining time can be determined more objectively and accurately. Compared with the existing battery mechanism model, the preset prediction model of the present application fully considers the multi-dimensional data of the battery, and thus can output a more accurate charging remaining time. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0054] Figure 1 is a schematic diagram of an architecture of the preset prediction model in the present application;

[0055] Figure 2 is a schematic diagram of a flow of the method for measuring the battery charging remaining time in the present application;

[0056] Figure 3A structural diagram of a device for measuring the remaining charging time of a battery in the present application;

[0057] Figure 4 A structural diagram of a processing device in the present application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative work fall within the scope of protection of the present application.

[0059] In the following description, specific embodiments of the present application will be described with reference to steps and symbolic representations of operations by one or more computers. Unless otherwise indicated, these descriptions and representations are the means used by the computers to manipulate data in a structure-based form. Computer 1000 executes a part of instructions 1002 maybe specially adapted to perform certain operations but software could be written to adapt the processor to perform those operations in real-time. The description and representation are the means used by the computers to manipulate data in a structure-based form.

[0060] The principles of the present application are operable with numerous other general purpose or special purpose computing, communications, environments or configurations. Examples of well-known computing systems, environments, and configurations that can be suitable for use with the present application include, but are not limited to, hand-held or laptop devices, personal computers, servers, multiprocessor systems, microcomputer-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments that include any of the above systems or devices.

[0061] The terms "first", "second", and "third" and the like in the present application are used to distinguish different objects, rather than to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion.

[0062] First, before introducing the embodiments of the present application, the related content about the application background of the present application is introduced.

[0063] The execution subject of the risk detection method provided in the application can be a device provided in the application, or a server device, a physical host, a vehicle-mounted terminal, or a user equipment (UE) processing device integrated with the device. The device can be implemented in hardware or software. The UE can be a terminal device such as a smartphone, a tablet computer, a notebook computer, a palm computer, a desktop computer, or a personal digital assistant (PDA).

[0064] Next, a battery charging remaining time prediction method provided in the application is introduced.

[0065] The method for measuring the battery charging remaining time provided in the application can be applied to all battery charging scenarios. In the embodiments of the application, the battery charging scenario of a vehicle is taken as an example. When necessary, the vehicle can be replaced by any type of terminal device, which is not limited in the application.

[0066] First, the process of feature learning of the vehicle and accurate prediction of the battery charging remaining time, that is, the model training stage, is introduced, so as to effectively predict the battery charging remaining time based on the trained model. The embodiments of the application include the following steps.

[0067] Obtaining historical charging data of the first battery, the historical charging data including charging data of at least one time of charging the first battery;

[0068] Training the historical charging data on the preset prediction model, so that the preset prediction model has the characteristics of predicting the average current in the battery charging process.

[0069] For example, considering that the actual charging current in the actual charging process does not completely match the charging current table provided by the battery cell manufacturer (due to reasons such as change of battery temperature in actual charging, fluctuation of charging pile output current, and power consumption of vehicle-mounted electrical appliances), the following takes offline learning as an example. As shown in FIG. 2, the historical charging data is learned based on a machine learning model. The model input features are the SOC, charging current, charging voltage, battery temperature, and battery SOH at the charging start time. The model output label is the average current in the actual charging process. The model is a machine learning model such as Xgboost, LSTM, and one-dimensional convolution. Figure 1

[0070] In the vehicle scenario, the preset prediction model can be arranged in the cloud / vehicle end. The charging behavior of each vehicle can be learned and the model can be trained. The vehicle portrait can be performed to identify the charging behavior characteristics of each vehicle and improve the prediction accuracy of the charging remaining time. In other scenarios, the preset prediction model can be arranged in the cloud / terminal device, which is not limited in the application.

[0071] ​After the training of the model is completed in the above embodiment, the trained model can be used to effectively predict the remaining charging duration of the battery. Specifically, as shown in Figure 2 The embodiment of the present application can include the following steps:

[0072] 101. Real-time charging data of a first battery built in a first vehicle is acquired.

[0073] The real-time charging data includes chip data at the start of charging, battery state data, charging voltage, and charging current. Since the first battery includes a battery capacity monitoring chip and a battery management protection chip, the chip data includes chip data of the battery capacity monitoring chip and chip data of the battery management protection chip.

[0074] 102. The real-time charging data is input into a preset prediction model to obtain an average charging current of the first battery during the charging process.

[0075] In some embodiments, the preset prediction model is obtained by training a model using historical charging data of the first battery, and the historical charging data includes charging data of at least one charging of the first battery.

[0076] 103. The remaining charging duration of the first battery is determined according to a target charging capacity and the average charging current.

[0077] In the embodiment of the present application, the remaining charging duration can also be output and displayed for the user to intuitively view.

[0078] In some embodiments, the target charging capacity is an identified capacity of the first battery, or a historical capacity of the first battery when fully charged. Since the first battery will experience at least one fully charged state, the target charging capacity in the embodiment of the present application is a set of capacities. The set of capacities includes at least one historical capacity of the first battery when fully charged. In some embodiments, the historical capacity satisfies at least one of the following:

[0079] an average value of at least one historical full charging capacity;

[0080] an actual capacity at the last full charging;

[0081] an actual capacity at any full charging;

[0082] a historical capacity at full charging;

[0083] a historical capacity at the last full charging;

[0084] an average historical capacity of at least two full chargings;

[0085] The identified electric quantity satisfies at least one of the following:

[0086] The rated electric quantity of the first battery;

[0087] The actual charging capacity of the first battery;

[0088] The preset charging quantity includes a preset proportion of the rated electric quantity or a preset proportion of the actual charging capacity.

[0089] In some other embodiments, the target charging quantity can be an identified electric quantity of the first battery.

[0090] In some embodiments of the present application, before determining the remaining charging duration of the first battery according to the target charging quantity and the average charging current, the method further comprises:

[0091] According to the electric quantity decay coefficient of the first battery, the rated electric quantity and the state of health (SOH) of the power battery, the real full charging quantity of the first battery is obtained;

[0092] According to the current start of charge (State of Charge, SOC) and the cut-off SOC of the first battery, the remaining SOC of the first battery is calculated;

[0093] According to the real full charging quantity and the remaining SOC, the target charging quantity is obtained.

[0094] In some embodiments, the battery charging remaining time depends on the full charging quantity of the battery and the current during the charging process, so if the charging quantity and the current of the battery can be accurately calculated, the battery charging remaining time can be accurately calculated. Based on the machine learning / deep learning model, the average charging current can be obtained. Optionally, a calculation formula of the charging quantity is as follows:

[0095] Charging quantity = (114 * SOH / 100-2) * ((96.3-Start_SOC) / 100)

[0096] The above calculation formula takes the rated electric quantity of the battery as 114 Ah and the full charging SOC as 96.3 as an example. The charging quantity needs to consider the electric quantity decay caused by the battery life decay. The rated electric quantity multiplied by the SOH is the real full charging quantity of the battery. Then, the remaining SOC is calculated according to the current SOC of the battery (Start_SOC) and the cut-off SOC (96.3 as an example), and then the charging quantity is obtained. Then, the remaining charging time is calculated according to the charging quantity and the average current during the charging process.

[0097] Since there are many vehicles of the same model on the market, some vehicles are used first, so there are many historical charging data which are more objective, and the same type of vehicle or the same type of battery usually has similar attenuation coefficients, therefore, the historical charging data also has certain reference value. In order to quickly output the remaining charging time, the remaining charging time of the same vehicle or the same type of battery can be reused, which mainly includes the following two aspects:

[0098] Aspect 1: determining the remaining charging time according to a preset correspondence

[0099] In some embodiments, the method further comprises:

[0100] determining a first vehicle model and a first battery type of the first vehicle;

[0101] determining the average charging current according to the first vehicle model, the first battery type and a preset correspondence, the preset correspondence being a correspondence between the first vehicle model, the first battery type and the average charging current;

[0102] determining the remaining charging time of the battery according to a target charging amount and the average charging current, the target charging amount being a historical electric quantity of the first battery when fully charged.

[0103] Aspect 2: determining the remaining charging time according to a collaborative filtering algorithm

[0104] In some embodiments, the method further comprises:

[0105] determining a first vehicle model and a first battery type of the first vehicle;

[0106] if the first vehicle and a second vehicle meet a preset matching condition, determining that the average charging current corresponding to the second vehicle is the average charging current of the first vehicle, wherein the second vehicle includes a vehicle historically predicted by the preset prediction model.

[0107] The preset matching condition includes at least one of the following: the first vehicle model matches a second vehicle model of the second vehicle, and the first battery type matches the second vehicle model of the second vehicle.

[0108] determining the remaining charging time of the battery according to a target charging amount and the average charging current, the target charging amount meeting at least one of the following:

[0109] the target charging amount is a historical electric quantity of the first battery when fully charged;

[0110] the target charging amount is a rated electric quantity of the first battery;

[0111] The target charging amount is a historical charging amount of the second battery of the second vehicle.

[0112] The target charging amount is an average historical charging amount of the first battery for at least two times of full charging.

[0113] The target charging amount is a historical full charging amount of the second battery of the second vehicle.

[0114] In some embodiments, the method further comprises:

[0115] determining a decay coefficient of the first battery and battery usage data of the first battery, the battery usage data comprising a total battery usage time, a battery charging frequency, and a battery charging time during historical charging;

[0116] determining matching data of the first battery and the second battery according to the battery usage data;

[0117] determining the target charging amount according to the matching data and the decay coefficient.

[0118] The matching data can comprise:

[0119] a ratio of the total battery usage time of the first battery to a historical usage time of the second battery;

[0120] a ratio of the battery charging frequency of the first battery to a historical charging frequency of the second battery;

[0121] a ratio of the battery charging time of the first battery during historical charging to a battery charging time of the second battery during historical charging; and

[0122] a target historical stage of the decay of the first battery to the decay of the second battery.

[0123] Specifically, since the battery usage data comprises the total battery usage time, the battery charging frequency, and the battery charging time during historical charging of the first battery, and the second battery is the battery of the second vehicle predicted by the preset prediction model, the second battery of the second vehicle has a certain reference value for determining the target charging amount of the first battery of the first vehicle. The target charging amount a of the second battery can be determined directly according to the above matching data, and then the target charging amount a of the second battery is determined as the target charging amount b of the first battery. Alternatively, considering that the first battery decays, a decay coefficient of the first battery is introduced, and a value calculated according to the decay coefficient and the target charging amount a is the target charging amount b of the first battery.

[0124] It can be seen that, compared with the prior art, in the embodiment of the application, since the real-time charging data is input into the preset prediction model, the prediction result output based on the real-time charging data can accurately represent the average charging current of the first battery in the charging process, and therefore, when the charging remaining duration of the first battery is determined according to the target charging amount and the average charging current, the charging remaining duration can be more objectively and accurately determined. Compared with the existing battery mechanism model, the preset prediction model of the application fully considers the dimensional data of the battery, and therefore can output more accurate charging remaining duration. Specifically, the charging remaining time calculation formula is disassembled and solved by combining the machine learning model and the battery mechanism model, that is, the charging behavior of each vehicle is learned and the model is trained, and the vehicle portrait is performed to identify the charging behavior characteristics of each vehicle, and the charging remaining time prediction accuracy is improved, so that the prediction accuracy of the preset prediction model is improved, and the prediction accuracy of the preset prediction model of the application is obviously higher than that of the battery mechanism model and the machine learning model.

[0125] In order to better implement the method of the application, the embodiment of the application further provides a device 20 for predicting the charging remaining duration of a battery.

[0126] Please refer to Figure 3 , Figure 3 A structural schematic diagram of the device 20 for predicting the charging remaining duration of a battery is provided, wherein the device 20 for measuring the charging remaining duration of a battery can specifically include the following structure:

[0127] An input and output module 201 is configured to acquire real-time charging data of a first battery built in a first vehicle, wherein the real-time charging data includes chip data, battery state data, charging voltage and charging current at a charging starting time;

[0128] A processing module 202 is configured to input the real-time charging data into a preset prediction model and output a prediction result, wherein the prediction result is an average charging current of the first battery in a charging process;

[0129] The processing module 202 is further configured to determine a charging remaining duration of the first battery according to a target charging amount and the average charging current, wherein the target charging amount is a historical electric quantity of the first battery when it is fully charged;

[0130] A display module 203 is configured to output and display the charging remaining duration through the input and output module 201.

[0131] In some embodiments, before the input and output module 201 inputs the real-time charging data into the preset prediction model, the processing module is further configured to:

[0132] obtaining historical charging data of the first battery, the historical charging data comprising charging data of at least one time of charging the first battery;

[0133] training the preset prediction model according to the historical charging data, so that the preset prediction model has a characteristic of predicting an average current in a battery charging process.

[0134] In some embodiments, the historical capacity satisfies at least one of the following:

[0135] an average value of historical capacities of at least two times of full charging;

[0136] an actual capacity at a last time of full charging;

[0137] an actual capacity at any time of full charging;

[0138] a historical capacity at full charging;

[0139] a historical capacity at a last time of full charging;

[0140] an average historical capacity of at least two times of full charging;

[0141] the identification capacity satisfies at least one of the following:

[0142] a rated capacity of the first battery;

[0143] an actual charging capacity of the first battery;

[0144] a preset charging capacity, the preset charging capacity comprising a preset proportion of the rated capacity or a preset proportion of the actual charging capacity.

[0145] In some embodiments, before determining the remaining duration of charging the first battery according to the target charging capacity and the average charging current, the processing module 202 is further configured to:

[0146] obtaining a real full charging capacity of the first battery according to a capacity attenuation coefficient, a rated capacity and a state of health of the first battery;

[0147] calculating a remaining state of charge of the first battery according to a current start state of charge and a current end state of charge of the first battery;

[0148] obtaining the target charging capacity according to the real full charging capacity and the remaining state of charge of the first battery.

[0149] In some embodiments, the processing module is further configured to:

[0150] determining a first vehicle type and a first battery type of the first vehicle;

[0151] determine the average charging current according to the first vehicle model, the first battery type and a preset correspondence relationship, the preset correspondence relationship being a correspondence relationship between the first vehicle model, the first battery type and the average charging current;

[0152] determine a remaining charging duration of the battery according to a target charging amount and the average charging current, the target charging amount being a historical charging amount of the first battery when fully charged.

[0153] In some embodiments, the processing module 202 is further configured to:

[0154] determine a first vehicle model and a first battery type of the first vehicle;

[0155] if the first vehicle and a second vehicle predicted by the preset prediction model satisfy a preset matching condition, determine that an average charging current corresponding to the second vehicle is the average charging current of the first vehicle, wherein the preset matching condition comprises at least one of the following: the first vehicle model matches a second vehicle model of the second vehicle, and the first battery type matches the second vehicle model of the second vehicle;

[0156] determine a remaining charging duration of the battery according to a target charging amount and the average charging current, the target charging amount satisfying at least one of the following:

[0157] the target charging amount is a historical charging amount of the first battery when fully charged;

[0158] the target charging amount is a rated charging amount of the first battery;

[0159] the target charging amount is a historical charging amount of the first battery when fully charged last time;

[0160] the target charging amount is an average historical charging amount of the first battery when fully charged at least twice;

[0161] the target charging amount is a historical full charging amount of a second battery of the second vehicle.

[0162] In some embodiments, the processing module 202 is further configured to:

[0163] determine a decay coefficient of the first battery and battery usage data, the battery usage data comprising a total battery usage duration, a battery charging frequency and a historical battery charging duration;

[0164] determine matching data of the first battery and the second battery according to the battery usage data;

[0165] determine the target charging amount according to the matching data and the decay coefficient.

[0166] In the embodiments of the present application, since the real-time charging data is input into the preset prediction model, the prediction result output based on the real-time charging data can accurately represent the average charging current of the first battery during the charging process, and therefore, when the remaining charging duration of the first battery is determined according to the target charging amount and the average charging current, the remaining charging duration can be more objectively and accurately determined. Compared with the existing battery mechanism model, the preset prediction model of the present application fully considers the dimensional data of the battery, and therefore can output more accurate remaining charging duration.

[0167] The present application also provides a processing device, which is described in detail with reference to Figure 4 , Figure 4 A structural schematic diagram of the processing device of the present application is shown, and specifically, the processing device provided by the present application includes a processor, which is used to implement the steps in the corresponding embodiments when executing the computer program stored in the memory, or is used to implement the functions of the modules in the corresponding embodiments when executing the computer program stored in the memory. Figure 2 Figure 3

[0168] For example, the computer program can be divided into one or more modules / units, one or more modules / units are stored in the memory and executed by the processor to complete the present application. One or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which is used to describe the execution process of the computer program in the computer device.

[0169] The processing device can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the schematic diagram is only an example of the processing device and does not constitute a limitation on the processing device, and can include more or fewer components than the schematic diagram, or combine certain components, or different components, for example, the processing device can also include an input / output device, a network access device, a bus, etc., and the processor, the memory, the input / output device and the network access device are connected through the bus.

[0170] ​​The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the processing device, and connects various parts of the processing device through various interfaces and lines.

[0171] The memory can be used to store computer programs and / or modules, and the processor realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data (such as audio data, video data, etc.) created according to the use of the processing device, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0172] The display screen is used to display characters of at least one character type output by the input and output unit.

[0173] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device, processing device and corresponding modules thereof can be referred to as Figure 2 the description in the corresponding embodiments, and will not be described here in detail.

[0174] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by instructions controlling relevant hardware. The instructions can be stored in a computer readable storage medium and loaded and executed by the processor.

[0175] To this end, an embodiment of the present application provides a computer readable storage medium, wherein a plurality of instructions are stored, the instructions can be loaded by a processor to execute the steps of the present application as Figure 1 The steps of the present application in the corresponding embodiments can refer to the specific operations of the steps of the present application in the corresponding embodiments Figure 2 The descriptions of the present application in the corresponding embodiments are not repeated here.

[0176] The computer readable storage medium can include a read only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0177] The instructions stored in the computer readable storage medium can execute the steps of the present application as Figure 2 Therefore, the beneficial effects of the present application as Figure 2 The beneficial effects of the present application as

[0178] The above describes in detail the battery charging duration prediction method, device and storage medium provided by the present application. The principle and implementation manner of the present application are described by applying specific examples in the embodiments of the present application. The above embodiment descriptions are only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation manner and application range can be changed according to the idea of the present application. In conclusion, the content of the present description should not be understood as a limitation of the present application.

Claims

1. A method for predicting battery charging time, characterized in that, The method includes: The real-time charging data of the first battery built into the first vehicle is obtained. The real-time charging data includes chip data at the start of charging, battery status data, charging voltage and charging current. The chip data includes chip data of the battery power monitoring chip and chip data of the battery management and protection chip. The real-time charging data is input into a preset prediction model to obtain the average charging current of the first battery during the charging process; The actual fully charged capacity of the first battery is obtained based on the battery's charge decay coefficient, rated capacity, and battery health status. The remaining state of charge of the first battery is calculated based on the current state of charge of the first battery during startup and the state of charge of the first battery during shutdown. The target charge amount is obtained based on the actual fully charged capacity and the remaining battery state of charge. The charging time of the first battery is determined based on the target charging amount and the average charging current.

2. The method according to claim 1, characterized in that, The preset prediction model is obtained by training the model using the historical charging data of the first battery, which includes charging data from at least one charging of the first battery.

3. The method according to claim 1, characterized in that, The method further includes: Determine the first vehicle model and the first battery type; The average charging current is determined based on the first vehicle model, the first battery type, and a preset correspondence, wherein the preset correspondence is the correspondence between the first vehicle model, the first battery type, and the average charging current.

4. The method according to claim 3, characterized in that, If the first vehicle and the second vehicle meet the preset matching conditions, then the average charging current corresponding to the second vehicle is determined to be the average charging current, and the second vehicle is a vehicle including the historical predictions of the preset prediction model. The preset matching conditions include at least one of the following: the first vehicle model matches the second vehicle model of the second vehicle, and the first battery type matches the second battery type of the second vehicle.

5. The method according to claim 4, characterized in that, The method further includes: Determine the degradation coefficient and battery usage data of the first battery, including the total battery usage time, the number of battery charging cycles, and the battery charging time during historical charging. The matching data between the first battery and the second battery is determined based on the battery usage data; The target charging amount is determined based on the matching data and the attenuation coefficient.

6. A device for predicting the remaining charging time of a battery, characterized in that, The device for predicting the remaining battery charging time includes: The input / output module is used to acquire real-time charging data of the first battery built into the first vehicle. The real-time charging data includes chip data at the start of charging, battery status data, charging voltage and charging current. The chip data includes chip data of the battery power monitoring chip and chip data of the battery management and protection chip. The processing module is used to input the real-time charging data into a preset prediction model to obtain the average charging current of the first battery during the charging process. The processing module is further configured to obtain the actual fully charged capacity of the first battery based on the battery capacity decay coefficient, rated capacity, and battery health status; calculate the remaining battery state of charge of the first battery based on the current starting battery state of charge and the ending battery state of charge; and obtain the target charging amount based on the actual fully charged capacity and the remaining battery state of charge. The processing module is also used to determine the remaining charging time of the first battery based on the target charging amount and the average charging current. The display module is used to output and display the remaining charging time through the input / output module.

7. A processing device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the method as described in any one of claims 1 to 5 when it invokes the computer program in the memory.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted for loading by a processor to perform the method of any one of claims 1 to 5.

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