CN115224751A - Method, device, electronic device and medium for obtaining the remaining time of battery charging - Google Patents

Abstract

The present application discloses a method, device, electronic device and medium for obtaining the remaining time of battery charging. The method is to input the current charging environment information, current power and current charging power into a preset charging duration prediction model after obtaining the current charging environment information, current electric quantity and current charging power corresponding to the rechargeable battery in the charging state, and predict the charging duration through the charging duration prediction model. The model analyzes the current charging environment information, current power and current charging power, and outputs the charging time required by the rechargeable battery from the current power to the total power of the rechargeable battery; and determines the output charging time as the remaining charging time of the rechargeable battery. Compared with the remaining time actively reported by the BMS in the prior art and the remaining time obtained through the charging power and the required charging power, the remaining charging time obtained by this method has a higher accuracy and improves the user experience.

Description

Method, device, electronic device and medium for obtaining the remaining time of battery charging

technical field

The present application relates to the technical field of battery charging, and in particular, to a method, device, electronic device and medium for obtaining the remaining time of battery charging.

Background technique

With the development of society, people's awareness of environmental protection has gradually increased, and more and more people are using new energy vehicles. At present, most of the new energy vehicles in our country are electric vehicles. Among them, electric vehicles are mainly powered by the electric energy stored in the power battery to drive the vehicle, which has the advantages of zero pollution and zero emission. However, the power battery capacity of an electric vehicle is limited, and the vehicle needs to be charged when the electric vehicle is used. Normally, it takes a period of time for a battery to be fully charged, which is called the remaining charging time, that is, the remaining charging time of the battery is the time it takes to fully charge the battery from the current power level when the battery is in a charging state.

At present, you can obtain the solution of the remaining battery charging time in the following ways:

Scheme 1: In the case that the battery management system (BMS) actively reports the remaining full time, the reported remaining full time is determined as the remaining battery charging time. The remaining time reported by the BMS is based on the current full battery. It is determined by the ratio of the required charging power W to the product of the battery charging voltage and charging current.

Scheme 2: When the battery management system (BMS) does not actively report the remaining full time, calculate the state of charge (SOC) of the battery according to the current power of the battery and the total battery capacity indicated by the BMS. ) to 100 still needed charging power, then according to the current charging power of the vehicle and the required charging power, calculate the remaining battery charging time, that is, the remaining battery charging time t = required charging power W/current power P. The SOC is used to represent the ratio of the remaining power of the battery to the total power of the battery under the same conditions. When SOC=0, it means that the battery is fully discharged, and when SOC=1, it means that the battery is fully charged.

However, since the magnitude of the charging current changes with the temperature during the charging process, there is a problem with the accuracy of the remaining battery charging time obtained by the above solution.

As shown in Figure 1, a schematic diagram of the comparison between the remaining charging time reported by the BMS and the actual remaining charging time of an electric vehicle during one charging process.

In this coordinate system, the horizontal axis is the actual time, the vertical axis is the remaining charging time (minutes), curve A represents the curve distribution of the actual remaining charging time at each actual time, and curve B represents the remaining charging time curve reported by the BMS at each actual time Distribution, if the charging start SOC=46%, end SOC=100%. It can be seen that the remaining duration of each actual time reported by the BMS is a broken line, and the error around 19:03 is large.

SUMMARY OF THE INVENTION

The embodiments of the present application provide a method, device, electronic device and medium for obtaining the remaining charging time of a battery, which solve the above problems in the prior art, and can obtain the remaining charging time with high accuracy _.

In a first aspect, a method for obtaining the remaining time of battery charging is provided, and the method may include:

Obtain the current charging environment information, current power and current charging power corresponding to the rechargeable battery in the charging state;

Input the current charging environment information, the current power and the current charging power into a preset charging duration prediction model, and the current charging environment information, the current power and the current charging duration are determined by the charging duration prediction model. The charging power is analyzed, and the charging time required by the rechargeable battery from the current power to the total power of the rechargeable battery is output; wherein, the charging time prediction model is based on the charging environment information in the historical charging data, historical The current power and each charging power are obtained by iterative training of the neural network;

The output charging duration is determined as the remaining charging duration of the rechargeable battery.

In a possible implementation manner, the training process of the charging duration prediction model includes:

Obtain the historical charging data of each rechargeable battery, the historical charging data includes the charging environment information of the rechargeable battery in the charging state, the historical current power, charging power and the actual remaining charging time of the corresponding rechargeable battery; the actual remaining charging time is The real time required to charge from the historical current power to the total power of the rechargeable battery;

The charging environment information, the historical current power and the charging power of the rechargeable batteries in the charging state are used as training samples, and the actual remaining charging time corresponding to each training sample is used as a sample of the training sample label, perform iterative training on the neural network, and determine the current neural network that meets the preset iterative conditions as the charging duration prediction model.

In a possible implementation manner, the historical charging data further includes historical charging end power;

Get historical charging data for each rechargeable battery, including:

Obtain the candidate historical charging data of each rechargeable battery;

Screening the candidate historical charging data to obtain historical charging data that meets preset screening conditions; wherein the preset screening conditions include that the historical end-of-charge power is the total power of the corresponding rechargeable battery, and the actual The remaining charging time is longer than the preset time.

In a possible implementation manner, the charging environment information includes a charging location and a corresponding ambient temperature;

The charging power includes required charging power and actual charging power corresponding to the rechargeable battery.

In a possible implementation manner, the process of determining the current power level includes:

obtaining the state of charge SOC of the rechargeable battery; the state of charge SOC represents the ratio of the remaining power of the rechargeable battery to the total power of the rechargeable battery under the same conditions;

If the rechargeable battery is located in an electric vehicle, searching for a preset mapping relationship between the vehicle type and the total power of the rechargeable battery according to the vehicle type of the electric vehicle, and determining the total power of the rechargeable battery corresponding to the vehicle type;

The product of the determined total power of the rechargeable battery and the state of charge SOC of the rechargeable battery is determined as the current power of the rechargeable battery.

In a possible implementation manner, after determining the output charging duration as the remaining charging duration of the rechargeable battery, the method further includes:

The remaining charging duration is sent to the terminal that establishes the communication connection, so that the terminal displays the remaining charging duration.

In a possible implementation manner, after determining the output charging duration as the remaining charging duration of the rechargeable battery, the method further includes:

A duration prediction log of the rechargeable battery is generated, and the duration prediction log includes the remaining charging duration of the current charging of the rechargeable battery.

In a second aspect, a device for obtaining the remaining time of battery charging is provided, and the device may include:

an acquisition unit, used for acquiring the current charging environment information, current power and current charging power corresponding to the rechargeable battery in the charging state;

The input unit is used to input the current charging environment information, the current power and the current charging power into a preset charging duration prediction model, and the current charging environment information, the current charging duration prediction model The power and the current charging power are analyzed, and the charging time required by the rechargeable battery from the current power to the total power of the rechargeable battery is output; wherein, the charging duration prediction model is based on historical charging data. Environmental information, historical current power and charging power are obtained by iterative training of the neural network;

A determining unit, configured to determine the output charging duration as the remaining charging duration of the rechargeable battery.

In a possible implementation, the apparatus further includes a training unit;

The training unit is used to perform the following steps:

Obtain the historical charging data of each rechargeable battery, the historical charging data includes the charging environment information of the rechargeable battery in the charging state, the historical current power, charging power and the actual remaining charging time of the corresponding rechargeable battery; the actual remaining charging time is The real time required to charge from the historical current power to the total power of the rechargeable battery;

The charging environment information, the historical current power and the charging power of the rechargeable batteries in the charging state are used as training samples, and the actual remaining charging time corresponding to each training sample is used as a sample of the training sample label, perform iterative training on the neural network, and determine the current neural network that meets the preset iterative conditions as the charging duration prediction model.

In a possible implementation manner, the historical charging data further includes historical charging end power; the training unit is further configured to:

Obtain the candidate historical charging data of each rechargeable battery;

Screening the candidate historical charging data to obtain historical charging data that meets preset screening conditions; wherein the preset screening conditions include that the historical end-of-charge power is the total power of the corresponding rechargeable battery, and the actual The remaining charging time is longer than the preset time.

In a possible implementation manner, the charging environment information includes a charging location and a corresponding ambient temperature;

The charging power includes required charging power and actual charging power corresponding to the rechargeable battery.

In a possible implementation manner, the obtaining unit is further configured to obtain the state of charge SOC of the rechargeable battery; the state of charge SOC indicates that the remaining power of the rechargeable battery is the same as that of the rechargeable battery under the same conditions. The ratio of total electricity;

The determining unit is further configured to, if the rechargeable battery is located in the electric vehicle, search for a preset mapping relationship between the vehicle type and the total power of the rechargeable battery according to the vehicle type of the electric vehicle, and determine the corresponding vehicle type. The total charge of the rechargeable battery;

And, the product of the determined total power of the rechargeable battery and the state of charge SOC of the rechargeable battery is determined as the current power of the rechargeable battery.

In a possible implementation manner, the apparatus further includes a sending unit;

The sending unit is configured to send the remaining charging duration to a terminal that establishes a communication connection, so that the terminal displays the remaining charging duration.

In a possible implementation manner, the apparatus further includes a log generating unit;

The log generating unit is configured to generate a duration prediction log of the rechargeable battery, where the duration prediction log includes the remaining charging duration of the current charging of the rechargeable battery.

In a third aspect, an electronic device is provided, the electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

memory for storing computer programs;

The processor is configured to implement any one of the method steps described in the first aspect above when executing the program stored in the memory.

In a fourth aspect, a computer-readable storage medium is provided, and a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method steps of any one of the above-mentioned first aspect are implemented.

The method for obtaining the remaining battery charging time provided by the embodiment of the present application is: after obtaining the current charging environment information, current power and current charging power corresponding to the rechargeable battery in the charging state, input the current charging environment information, current power and current charging power into the preset The set charging duration prediction model analyzes the current charging environment information, current power and current charging power through the charging duration prediction model, and outputs the charging duration required by the rechargeable battery from the current power to the total power of the rechargeable battery; among them, the charging duration prediction The model is obtained by iterative training of the neural network according to the charging environment information in the historical charging data, the historical current power and the charging power; and the output charging time is determined as the remaining charging time of the rechargeable battery. The method uses machine learning technology to analyze the multi-dimensional feature information such as charging environment information, current power and charging power in the charging big data to obtain the remaining charging time of the rechargeable battery, which is the same as the remaining time actively reported by the BMS in the prior art. , and compared with the remaining time obtained through the charging power and the required charging power, it has a higher accuracy rate and improves the user experience.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present application more clearly, the following briefly introduces the accompanying drawings that need to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, so It should not be regarded as a limitation of the scope. For those of ordinary skill in the art, other related drawings can also be obtained from these drawings without any creative effort.

1 is a schematic diagram of a comparison between the remaining charging duration reported by a BMS and the actual remaining charging duration provided by an embodiment of the present application;

FIG. 2 is a schematic flowchart of a method for obtaining a remaining battery charging time according to an embodiment of the present application;

3 is a schematic diagram of a training process of a charging duration prediction model provided by an embodiment of the present application;

FIG. 4 is a schematic diagram of the comparison of the remaining charging duration obtained by the embodiment of the present application, the remaining charging duration reported by the BMS, and the actual remaining charging duration, respectively, according to an embodiment of the present application;

FIG. 5 is a schematic structural diagram of a device for acquiring remaining battery charging time provided by an embodiment of the present application;

FIG. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the protection of the present application.

When charging a rechargeable battery, the user usually wants to see the remaining charging time of the battery. For example, a user of an electric vehicle, after knowing the remaining charging time of the rechargeable battery of the vehicle, can do some other tasks within that time. and ensure that you arrive at the charging location in time to pick up the car when the rechargeable battery is fully charged. Therefore, the embodiment of the present application acquires the remaining charging time of the rechargeable battery when the rechargeable battery starts to be charged, and informs the user of the remaining charging time, which not only provides better convenience for the user, but also greatly improves the user experience. In addition, damage to the rechargeable battery due to excessive charging time can be avoided.

The method for obtaining the remaining charging time of the battery provided by the embodiment of the present application can be applied to a charging device for charging the battery.

The charging device can be communicatively connected with a user's terminal; wherein, the terminal can be a user equipment (UserEquipment, UE) such as a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a personal digital assistant (PDA), a tablet computer (PAD), etc. ), handheld devices, in-vehicle devices, wearable devices, computing devices or other processing devices connected to wireless modems, Mobile Stations (MS), Mobile Terminals, and the like.

Considering the same rechargeable battery, under the conditions of the same ambient temperature, the same city or the same charging station, it has a consistent charging law, that is, under the above characteristic conditions, the rechargeable battery has the same remaining time for two charges at the target SOC. sexual characteristics. The embodiment of the present application obtains the remaining charging time under multi-dimensional conditions by making full use of the charging big data of historical statistics and based on the machine learning technology.

The preferred embodiments of the present application will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not intended to limit the present application. The embodiments in and features in the embodiments can be combined with each other.

FIG. 2 is a schematic flowchart of a method for obtaining a remaining battery charging time according to an embodiment of the present application. As shown in Figure 2, the method may include:

Step S210: Acquire current charging environment information, current electric quantity and current charging power corresponding to the rechargeable battery in the charging state.

For the acquisition of current charging environment information:

Since the allowable charging current for the rechargeable battery is different at different ambient temperatures or at different charging locations, such as charging stations in different locations, the charging time is changed, so the current charging environment information can include the charging location, such as the location of the charging station The ambient temperature corresponding to the charging location;

Moreover, different charging currents and/or different charging voltages will affect the charging time of the rechargeable battery, so it is necessary to obtain the current charging power.

For the acquisition of the current charging power:

The current charging power may include required charging power and actual charging power corresponding to the rechargeable battery.

Among them, the required charging power refers to the charging power requested by the rechargeable battery from the charging device, which includes the requested required charging voltage and the required charging current; the actual charging power refers to the charging power fed back by the charging device to the rechargeable battery, which includes the actual charging voltage and actual charging current. The required charging power is the same or similar to the actual charging power.

In the specific implementation, for the current power, the same rechargeable battery can request the same charging power from different charging devices, that is, the required power is the same, but due to the different models or manufacturers of different charging devices, the charging power fed back by different charging devices to the rechargeable battery may be different. , that is, the actual power may be different, but it satisfies the condition that the required charging power is the same as or similar to the actual charging power. Therefore, the current charging power can include the combined power of the required charging power and the actual charging power corresponding to the rechargeable battery; or, the current charging power can be Either the actual charging power or the actual charging power.

It should be noted that, in order to obtain a more accurate remaining charging time, the current charging power includes the combined power of the required charging power corresponding to the rechargeable battery and the actual charging power.

For the acquisition of current power:

In the specific implementation, the state of charge SOC of the rechargeable battery is obtained; (discharge experiment method, open circuit voltage method, neural network method, etc.); the state of charge SOC represents the ratio of the remaining power of the rechargeable battery to the total power of the rechargeable battery under the same conditions ;

It should be noted that the state of charge SOC of the rechargeable battery can be obtained by methods such as the open circuit voltage method and the neural network method. Since the open circuit voltage method and the neural network method are methods for detecting SOC in the prior art, the embodiments of the present application are described here. I won't go into details.

After that, the product of the total power of the rechargeable battery and the state of charge SOC of the rechargeable battery is determined as the current power of the rechargeable battery. The total power of the rechargeable battery is the rated power or the maximum discharge capacity of the rechargeable battery.

In one example, if the rechargeable battery is located in an electric vehicle, that is, the total power of the rechargeable battery cannot be known, and the mapping relationship between the preset vehicle type and the total power of the rechargeable battery can be searched according to the vehicle type of the electric vehicle to determine the vehicle type. The total power of the corresponding rechargeable battery;

Among them, the mapping relationship between the preset vehicle type and the total power of the rechargeable battery is calculated based on the existing vehicle purchase order, and the vehicle purchase order mainly selects the order after 2019.

For example, the mapping relationship between the preset vehicle type and the total power of the rechargeable battery can be shown in Table 1:

Table 1

From Table 1, when the vehicle type is BYD E200 Pro, the total power of the corresponding rechargeable battery is 43 kWh; when the vehicle type is BYD BYD e6, the total power of the corresponding rechargeable battery is 82 kWh; the vehicle type is Tesla Model When S, the total power of the corresponding rechargeable battery is 90 kWh; when the vehicle type is Great Wall C30EV, the total power of the corresponding rechargeable battery is 26.57 kWh.

In summary, the charging city corresponding to the rechargeable battery in the charging state, the location of the charging station, the ambient temperature, the vehicle type to which the rechargeable battery belongs, the total power of the rechargeable battery corresponding to the vehicle type, the state of charge SOC, the charging demand voltage, and the charging voltage can be obtained. Demand current, actual charging voltage, and charging actual current have a total of 10 characteristic information.

Step S220: Input the current charging environment information, current power and current charging power into a preset charging duration prediction model to obtain the charging duration required by the rechargeable battery from the current power to the total power of the rechargeable battery.

The charging duration prediction model is obtained by iterative training of the neural network according to the charging environment information in the historical charging data, the historical current power and the charging power. Among them, the charging duration prediction model can include three network layers: input layer, hidden layer and output layer.

In an example, the input layer may include 10 neurons (that is, corresponding to 10 feature information), the hidden layer includes 64 neurons, and a Rectified Linear Unit (ReLU) is used as the activation function and a mini-batch The Mini-batch gradient descent (MBGD) function is used as the gradient descent optimizer, the output layer includes one neuron, and the Mean Square Error (MSE) algorithm can be used as the loss function of the model.

After that, the current charging environment information, the current power and the current charging power are analyzed through the charging duration prediction model, and the charging duration required by the rechargeable battery from the current power to the total power of the rechargeable battery is output.

In one embodiment, as shown in Figure 3, the training process of the charging duration prediction model includes:

Step S31 , acquiring historical charging data of each rechargeable battery.

In the specific implementation, the candidate historical charging data of each rechargeable battery is obtained; the candidate historical charging data may include the charging environment information of the rechargeable battery in the charging state, the historical current power, the charging power, the historical charging end power and the corresponding charging The actual remaining time to charge the battery. Wherein, the actual remaining charging time is the real time required to charge from the historical current power to the total power of the rechargeable battery.

According to the preset screening conditions, the candidate historical charging data is screened to obtain the historical charging data that satisfies the preset screening conditions; the preset screening conditions may include that the historical charging end power is the total power of the corresponding rechargeable battery, and the actual remaining charging time longer than the preset duration.

Specifically, from the candidate historical charging data, select the historical charging data in which the end-of-charge power of the rechargeable battery is the total power of the rechargeable battery, that is, the charging from the historical current power to full, that is, the first charge of the total power of the rechargeable battery is obtained. historical charging data to ensure the accuracy of the trained model; then, select the historical charging data whose remaining charging time is greater than the preset time from the first historical charging data, that is, delete the short remaining charging time from the first historical charging data The duration, such as 10 minutes of historical charging data, further ensures the accuracy of the trained model.

It should be noted that, the historical charging data whose remaining charging time is longer than the preset duration can also be selected from the candidate historical charging data, and then the historical charging data whose charging end power is the total power of the rechargeable battery can be selected. This is an embodiment of the present application. The selection sequence of the historical charging data is not limited here.

Step S32: Use the charging environment information, historical current power and charging power of each rechargeable battery in the charging state as a training sample, use the actual remaining charging time corresponding to each training sample as a sample label of the training sample, and iterate the neural network. train.

In the specific implementation, the charging environment information, historical current power and charging power of each rechargeable battery in the charging state are used as training samples, and the actual remaining charging time corresponding to each training sample is used as the sample label of the training sample. Iterative training, output the charging time corresponding to each training sample. The neural network includes preset model parameters.

In the iterative training process, a preset loss function, such as the MSE algorithm, is used to calculate the loss of the charging duration corresponding to any training sample and the actual remaining charging duration of the corresponding training sample to obtain the loss value of the training sample, and based on the loss value Update the model parameters of the trained current neural network.

Step S33 , determining the current neural network trained and satisfying the preset iteration condition as the charging duration prediction model.

If the calculated loss value is not greater than the preset loss threshold, or the number of iterations reaches the preset number threshold, the loss value or the current neural network corresponding to the iteration number is determined as the charging duration prediction model.

The preset iteration condition may also be other iteration end condition, which is not limited in this embodiment of the present application.

Step S230: Determine the charging duration output by the charging duration prediction model as the remaining charging duration of the rechargeable battery.

In a possible implementation, after obtaining the remaining charging time, the obtained remaining charging time may be sent to the terminal that establishes the communication connection, so that the terminal can display the remaining charging time for the user to watch.

In another possible implementation, after obtaining the remaining charging duration, a duration prediction log of the rechargeable battery may be generated for online effect evaluation, wherein the duration prediction log may include the current charging time of the rechargeable battery. Remaining time for charging.

It can be understood that the duration prediction log may also include the charging city involved in the charging process of the rechargeable battery, the location of the charging station, the ambient temperature, the vehicle type to which the rechargeable battery belongs, the total power of the rechargeable battery corresponding to the vehicle type, and the state of charge SOC. , charging demand voltage, charging demand current, charging actual voltage, charging actual current and other information.

In an example, as shown in FIG. 4 , when an electric vehicle is charged at a power station, a schematic diagram of the comparison of the remaining charging time obtained by the embodiment of the present application, the remaining charging time reported by the BMS, and the actual remaining charging time, respectively.

In the coordinate system shown in FIG. 4 , the horizontal axis represents the actual time, and the vertical axis represents the remaining charging time at different actual times, wherein curve 1 is the curve corresponding to the remaining charging time obtained in the embodiment of the present application; curve 2 is the BMS report The curve corresponding to the remaining charging time of , and curve 3 is the curve corresponding to the actual remaining charging time.

It can be seen from this that curve 1 is relatively smooth; curve 2 is not smooth, with many breakpoints, that is, the total jump in the remaining charging time; curve 1 is closer to curve 3, that is, the remaining charging time obtained in the embodiment of the present application is closer to the actual Remaining time for charging.

The method for obtaining the remaining battery charging time provided by the embodiment of the present application is: after obtaining the current charging environment information, current power and current charging power corresponding to the rechargeable battery in the charging state, input the current charging environment information, current power and current charging power into the preset The set charging duration prediction model analyzes the current charging environment information, current power and current charging power through the charging duration prediction model, and outputs the charging duration required by the rechargeable battery from the current power to the total power of the rechargeable battery; among them, the charging duration prediction The model is obtained by iterative training of the neural network according to the charging environment information, historical current power and charging power in the historical charging data; and the output charging time is determined as the remaining charging time of the rechargeable battery.

The method uses machine learning technology to analyze the multi-dimensional feature information such as charging environment information, current power and charging power in the charging big data to obtain the remaining charging time of the rechargeable battery, which is the same as the remaining time actively reported by the BMS in the prior art. , and compared with the remaining time obtained through the charging power and the required charging power, it has a higher accuracy rate and improves the user experience.

Corresponding to the above method, an embodiment of the present application further provides a device for obtaining the remaining time of battery charging. As shown in FIG. 5 , the device for obtaining the remaining time for battery charging may include: an obtaining unit 510 , an input unit 520 , and a determining unit 530 ;

The obtaining unit 510 is configured to obtain the current charging environment information, current electric quantity and current charging power corresponding to the rechargeable battery in the charging state;

The input unit 520 is configured to input the current charging environment information, the current power and the current charging power into a preset charging duration prediction model, and the current charging environment information, the current charging duration prediction model is The current power and the current charging power are analyzed, and the charging duration required by the rechargeable battery from the current power to the total power of the rechargeable battery is output; wherein, the charging duration prediction model is based on the charging duration in the historical charging data. Environmental information, historical current power and charging power are obtained by iterative training of the neural network;

The determining unit 530 is configured to determine the output charging duration as the remaining charging duration of the rechargeable battery.

In a possible implementation manner, the apparatus further includes a training unit 540;

The training unit 540 is configured to perform the following steps:

Obtain the historical charging data of each rechargeable battery, the historical charging data includes the charging environment information of the rechargeable battery in the charging state, the historical current power, charging power and the actual remaining charging time of the corresponding rechargeable battery; the actual remaining charging time is The real time required to charge from the historical current power to the total power of the rechargeable battery;

The charging environment information, the historical current power and the charging power of the rechargeable batteries in the charging state are used as training samples, and the actual remaining charging time corresponding to each training sample is used as a sample of the training sample label, perform iterative training on the neural network, and determine the current neural network that meets the preset iterative conditions as the charging duration prediction model.

In a possible implementation manner, the historical charging data further includes historical charging end power; the training unit 540 is further configured to:

Obtain the candidate historical charging data of each rechargeable battery;

Screening the candidate historical charging data to obtain historical charging data that meets preset screening conditions; wherein the preset screening conditions include that the historical end-of-charge power is the total power of the corresponding rechargeable battery, and the actual The remaining charging time is longer than the preset time.

In a possible implementation manner, the charging environment information includes a charging location and a corresponding ambient temperature;

The charging power includes required charging power and actual charging power corresponding to the rechargeable battery.

In a possible implementation manner, the obtaining unit 510 is further configured to obtain the state of charge SOC of the rechargeable battery; the state of charge SOC represents the remaining power of the rechargeable battery and the total power of the rechargeable battery under the same conditions The ratio of electricity;

The determining unit 530 is further configured to, if the rechargeable battery is located in an electric vehicle, search for a preset mapping relationship between the vehicle type and the total power of the rechargeable battery according to the vehicle type of the electric vehicle, and determine the charging corresponding to the vehicle type the total charge of the battery;

And, the product of the determined total power of the rechargeable battery and the state of charge SOC of the rechargeable battery is determined as the current power of the rechargeable battery.

In a possible implementation manner, the apparatus further includes a sending unit 550;

The sending unit 550 is configured to send the remaining charging duration to a terminal that establishes a communication connection, so that the terminal displays the remaining charging duration.

In a possible implementation manner, the apparatus further includes a log generating unit 560;

The log generating unit 560 is configured to generate a duration prediction log of the rechargeable battery, where the duration prediction log includes the remaining charging duration of the current charging of the rechargeable battery.

The functions of each functional unit of the apparatus for obtaining the remaining battery charging time provided by the above embodiments of the present application can be implemented through the above method steps. Therefore, the functions of each unit in the apparatus for obtaining the remaining battery charging time provided by the embodiments of the present application The specific working process and beneficial effects will not be repeated here.

An embodiment of the present application also provides an electronic device, as shown in FIG. 6 , including a processor 610 , a communication interface 620 , a memory 630 and a communication bus 640 , wherein the processor 610 , the communication interface 620 , and the memory 630 pass through the communication bus 640 complete communication with each other.

a memory 630 for storing computer programs;

When the processor 610 is used to execute the program stored in the memory 630, the following steps are implemented:

Obtain the current charging environment information, current power and current charging power corresponding to the rechargeable battery in the charging state;

Input the current charging environment information, the current power and the current charging power into a preset charging duration prediction model, and the current charging environment information, the current power and the current charging duration are determined by the charging duration prediction model. The charging power is analyzed, and the charging time required by the rechargeable battery from the current power to the total power of the rechargeable battery is output; wherein, the charging time prediction model is based on the charging environment information in the historical charging data and the historical current The electricity and each charging power are obtained by iterative training of the neural network;

The output charging duration is determined as the remaining charging duration of the rechargeable battery.

In a possible implementation manner, the training process of the charging duration prediction model includes:

Obtain the historical charging data of each rechargeable battery, the historical charging data includes the charging environment information of the rechargeable battery in the charging state, the historical current power, charging power and the actual remaining charging time of the corresponding rechargeable battery; the actual remaining charging time is The real time required to charge from the historical current power to the total power of the rechargeable battery;

The charging environment information, the historical current power and the charging power of the rechargeable batteries in the charging state are used as training samples, and the actual remaining charging time corresponding to each training sample is used as a sample of the training sample label, perform iterative training on the neural network, and determine the current neural network that meets the preset iterative conditions as the charging duration prediction model.

In a possible implementation manner, the historical charging data further includes historical charging end power;

Get historical charging data for each rechargeable battery, including:

Obtain the candidate historical charging data of each rechargeable battery;

Screening the candidate historical charging data to obtain historical charging data that meets preset screening conditions; wherein the preset screening conditions include that the historical end-of-charge power is the total power of the corresponding rechargeable battery, and the actual The remaining charging time is longer than the preset time.

In a possible implementation manner, the charging environment information includes a charging location and a corresponding ambient temperature;

The charging power includes required charging power and actual charging power corresponding to the rechargeable battery.

In a possible implementation manner, the process of determining the current power level includes:

obtaining the state of charge SOC of the rechargeable battery; the state of charge SOC represents the ratio of the remaining power of the rechargeable battery to the total power of the rechargeable battery under the same conditions;

If the rechargeable battery is located in an electric vehicle, searching for a preset mapping relationship between the vehicle type and the total power of the rechargeable battery according to the vehicle type of the electric vehicle, and determining the total power of the rechargeable battery corresponding to the vehicle type;

The product of the determined total power of the rechargeable battery and the state of charge SOC of the rechargeable battery is determined as the current power of the rechargeable battery.

In a possible implementation manner, after determining the output charging duration as the remaining charging duration of the rechargeable battery, the method further includes:

The remaining charging duration is sent to the terminal that establishes the communication connection, so that the terminal displays the remaining charging duration.

In a possible implementation manner, after determining the output charging duration as the remaining charging duration of the rechargeable battery, the method further includes:

A duration prediction log of the rechargeable battery is generated, and the duration prediction log includes the remaining charging duration of the current charging of the rechargeable battery.

The above-mentioned communication bus may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an Extended Industry Standard Architecture (Extended Industry Standard Architecture, EISA) bus or the like. The communication bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of presentation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

The communication interface is used for communication between the above electronic device and other devices.

The memory may include random access memory (Random Access Memory, RAM), and may also include non-volatile memory (Non-Volatile Memory, NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

The above-mentioned processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; may also be a digital signal processor (Digital Signal Processing, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

Since the implementation manners and beneficial effects of each component of the electronic device in the above-mentioned embodiment to solve the problem can be achieved by referring to the steps in the embodiment shown in FIG. 2 , the specific working process and beneficial effects of the electronic device provided by the embodiment of the present application The effect will not be repeated here.

In yet another embodiment provided by the present application, a computer-readable storage medium is also provided, where instructions are stored in the computer-readable storage medium, and when the computer-readable storage medium is run on a computer, the computer is made to execute any one of the foregoing embodiments. The method for obtaining the remaining battery charging time.

In yet another embodiment provided by the present application, a computer program product including instructions is also provided, which, when running on a computer, enables the computer to execute the method for obtaining the remaining battery charging time in any of the foregoing embodiments .

Those skilled in the art should understand that the embodiments in the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein .

The embodiments of the present application are described with reference to the flowcharts and/or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

Although the preferred embodiments of the embodiments of the present application have been described, additional changes and modifications to these embodiments may be made by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

Obviously, those skilled in the art can make various changes and modifications to the embodiments in the embodiments of the present application without departing from the spirit and scope of the embodiments in the embodiments of the present application. In this way, if these modifications and variations of the embodiments in the embodiments of the present application fall within the scope of the claims in the embodiments of the present application and their equivalents, the embodiments of the present application are also intended to include these modifications and variations.

Claims (10)

1. A method for obtaining the remaining duration of battery charging, wherein the method comprises: Obtain the current charging environment information, current power and current charging power corresponding to the rechargeable battery in the charging state; Input the current charging environment information, the current power and the current charging power into a preset charging duration prediction model, and the current charging environment information, the current power and the current charging duration are determined by the charging duration prediction model. The charging power is analyzed, and the charging time required by the rechargeable battery from the current power to the total power of the rechargeable battery is output; wherein, the charging time prediction model is based on the charging environment information in the historical charging data, historical The current power and each charging power are obtained by iterative training of the neural network; The output charging duration is determined as the remaining charging duration of the rechargeable battery. 2. The method of claim 1, wherein the training process of the charging duration prediction model comprises: Obtain the historical charging data of each rechargeable battery, the historical charging data includes the charging environment information of the rechargeable battery in the charging state, the historical current power, charging power and the actual remaining charging time of the corresponding rechargeable battery; the actual remaining charging time is The real time required to charge from the historical current power to the total power of the rechargeable battery; The charging environment information, the historical current power and the charging power of the rechargeable batteries in the charging state are used as training samples, and the actual remaining charging time corresponding to each training sample is used as a sample of the training sample label, perform iterative training on the neural network, and determine the current neural network that meets the preset iterative conditions as the charging duration prediction model. 3. The method according to claim 2, wherein the historical charging data further comprises historical charging end power; Get historical charging data for each rechargeable battery, including: Obtain the candidate historical charging data of each rechargeable battery; Screening the candidate historical charging data to obtain historical charging data that meets preset screening conditions; wherein the preset screening conditions include that the historical end-of-charge power is the total power of the corresponding rechargeable battery, and the actual The remaining charging time is longer than the preset time. 4. The method according to claim 1 or 2, wherein the charging environment information comprises a charging position and a corresponding ambient temperature; The charging power includes required charging power and actual charging power corresponding to the rechargeable battery. 5. The method according to claim 1 or 2, wherein the determination process of the current electric quantity comprises: obtaining the state of charge SOC of the rechargeable battery; the state of charge SOC represents the ratio of the remaining power of the rechargeable battery to the total power of the rechargeable battery under the same conditions; If the rechargeable battery is located in an electric vehicle, searching for a preset mapping relationship between the vehicle type and the total power of the rechargeable battery according to the vehicle type of the electric vehicle, and determining the total power of the rechargeable battery corresponding to the vehicle type; The product of the determined total power of the rechargeable battery and the state of charge SOC of the rechargeable battery is determined as the current power of the rechargeable battery. 6. The method of claim 1, wherein after determining the output charging duration as the remaining charging duration of the rechargeable battery, the method further comprises: The remaining charging duration is sent to the terminal that establishes the communication connection, so that the terminal displays the remaining charging duration. 7. The method of claim 1, wherein after determining the output charging duration as the remaining charging duration of the rechargeable battery, the method further comprises: A duration prediction log of the rechargeable battery is generated, and the duration prediction log includes the remaining charging duration of the current charging of the rechargeable battery. 8. A device for obtaining the remaining charging time of a battery, wherein the device comprises: an acquisition unit, used for acquiring the current charging environment information, current power and current charging power corresponding to the rechargeable battery in the charging state; The input unit is used to input the current charging environment information, the current power and the current charging power into a preset charging duration prediction model, and the current charging environment information, the current charging duration prediction model The power and the current charging power are analyzed, and the charging time required by the rechargeable battery from the current power to the total power of the rechargeable battery is output; wherein, the charging duration prediction model is based on historical charging data. Environmental information, historical current power and charging power are obtained by iterative training of the neural network; A determining unit, configured to determine the output charging duration as the remaining charging duration of the rechargeable battery. 9. An electronic device, characterized in that the electronic device comprises a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; memory for storing computer programs; The processor is configured to implement the method steps of any one of claims 1-7 when executing the program stored in the memory. 10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method steps of any one of claims 1-7 are implemented.

Images