CN115224751A - Method and device for acquiring remaining battery charging time, electronic equipment and medium - Google Patents
Method and device for acquiring remaining battery charging time, electronic equipment and medium Download PDFInfo
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- H—ELECTRICITY
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- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
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Abstract
The application discloses a method and a device for acquiring remaining battery charging time, electronic equipment and a medium. After current charging environment information, current electric quantity and current charging power corresponding to a rechargeable battery in a charging state are obtained, inputting the current charging environment information, the current electric quantity and the current charging power into a preset charging duration prediction model, analyzing the current charging environment information, the current electric quantity and the current charging power through the charging duration prediction model, and outputting the charging duration required by the rechargeable battery from the current electric quantity to the total electric quantity of the rechargeable battery; and determines the output charging period as the charging remaining period of the rechargeable battery. Compared with the residual time actively reported by the BMS in the prior art and the residual time obtained through the charging power and the required charging electric quantity, the charging residual time obtained by the method has higher accuracy rate and improves the user experience.
Description
Technical Field
The present disclosure relates to the field of battery charging technologies, and in particular, to a method and an apparatus for obtaining a remaining battery charging time, an electronic device, and a medium.
Background
With the development of society, people's environmental protection consciousness is gradually strengthened, and more people use new energy vehicle. At present, most new energy vehicles in China are electric vehicles, wherein the electric vehicles mainly provide power for the running of the vehicles by electric energy stored in power batteries, and have the advantages of zero pollution and zero emission. However, the power battery capacity of the electric vehicle is limited, and the electric vehicle needs to be charged when the vehicle is used. Generally, a battery needs a period of time to be fully charged, which is called a charge remaining time period, that is, the charge remaining time period is a time period required for the battery to be charged from a current charge amount to be fully charged when the battery is in a charged state.
The scheme for acquiring the remaining time of battery charging can be obtained by the following method:
in the scheme 1, under the condition that a Battery Management System (BMS) actively reports a full charge remaining time, the reported full charge remaining time is determined as a Battery charge remaining time, wherein the remaining time actively reported by the BMS is determined according to a ratio of a charging electric quantity W required by the current full charge to a product of a Battery charge voltage and a charge current.
In the scheme 2, under the condition that a Battery Management System (BMS) does not actively report the remaining full Charge time, according to the current electric quantity Of the Battery and the total Battery capacity indicated by the BMS, the remaining charging electric quantity required from the State Of Charge (SOC) to 100 Of the Battery is calculated, and then according to the current charging power and the required charging electric quantity Of the vehicle, the remaining Battery Charge time, that is, the remaining Battery Charge time t = the required charging electric quantity W/the current power P, is calculated. Where SOC is used to represent a ratio of a remaining capacity of the battery to a total capacity of the battery under the same condition, indicating that the battery is completely discharged when SOC =0, and indicating that the battery is completely charged when SOC = 1.
However, the accuracy of the remaining charging time of the battery obtained by the above scheme is problematic because the magnitude of the charging current varies with the temperature during the charging process.
As shown in fig. 1, a schematic diagram of comparing the remaining charging time reported by the BMS of an electric vehicle during a charging process with the actual remaining charging time.
In the coordinate system, the horizontal axis is actual time, the vertical axis is charging remaining time (minutes), the curve a represents the curve distribution of the actual charging remaining time at each actual time, the curve B represents the curve distribution of the charging remaining time reported by the BMS at each actual time, and if the initial SOC =46% of the charging, the end SOC =100%. It can be seen that the remaining duration of each real time reported by the BMS is a broken line, and the error around 19.
Disclosure of Invention
The embodiment of the application provides a method, a device, an electronic device and a medium for acquiring the remaining time of battery charging, solves the problems in the prior art, and can acquire the remaining time of battery charging with high accuracy 。
In a first aspect, a method for acquiring a remaining battery charging time is provided, where the method may include:
acquiring current charging environment information, current electric quantity and current charging power corresponding to a rechargeable battery in a charging state;
inputting the current charging environment information, the current electric quantity and the current charging power into a preset charging duration prediction model, analyzing the current charging environment information, the current electric quantity and the current charging power through the charging duration prediction model, and outputting the charging duration required by the rechargeable battery from the current electric quantity to the total electric quantity of the rechargeable battery; the charging duration prediction model is obtained by performing iterative training on a neural network according to each charging environment information, historical current electric quantity and each charging power in historical charging data;
and determining the output charging time period as the charging remaining time period of the rechargeable battery.
In one possible embodiment, the training process of the charging duration prediction model includes:
acquiring historical charging data of each rechargeable battery, wherein the historical charging data comprises charging environment information of the rechargeable battery in a charging state, historical current electric quantity, charging power and actual charging remaining time of the corresponding rechargeable battery; the actual charging remaining time length is the real time length required for charging from the historical current electric quantity to the total electric quantity of the rechargeable battery;
and taking the charging environment information, the historical current electric quantity and the charging power of each rechargeable battery in a charging state as training samples, taking the actual charging remaining time corresponding to each training sample as a sample label of the training sample, performing iterative training on the neural network, and determining the trained current neural network meeting preset iterative conditions as a charging time prediction model.
In one possible embodiment, the historical charging data further includes a historical end-of-charge;
acquiring historical charging data of each rechargeable battery, wherein the historical charging data comprises the following steps:
acquiring candidate historical charging data of each rechargeable battery;
screening the candidate historical charging data to obtain historical charging data meeting preset screening conditions; the preset screening condition comprises that the historical charging ending electric quantity is the total electric quantity of the corresponding rechargeable battery, and the actual charging remaining time is longer than the preset time.
In one possible embodiment, the charging environment information includes a charging location and a corresponding ambient temperature;
the charging power comprises required charging power and actual charging power corresponding to the rechargeable battery.
In one possible embodiment, the determining of the current amount of power includes:
acquiring the state of charge (SOC) of the rechargeable battery; the state of charge (SOC) represents the ratio of the residual capacity of the rechargeable battery to the total capacity of the rechargeable battery under the same condition;
if the rechargeable battery is located in the electric vehicle, searching a preset mapping relation between the vehicle type and the total electric quantity of the rechargeable battery according to the vehicle type of the electric vehicle, and determining the total electric quantity of the rechargeable battery corresponding to the vehicle type;
and determining the product of the determined total electric quantity of the rechargeable battery and the SOC of the rechargeable battery as the current electric quantity of the rechargeable battery.
In one possible embodiment, after determining the output charging period as the remaining charging period of the rechargeable battery, the method further includes:
and sending the residual charging time length to a terminal establishing communication connection so that the terminal displays the residual charging time length.
In one possible embodiment, after determining the output charging period as the remaining charging period of the rechargeable battery, the method further includes:
and generating a duration prediction log of the rechargeable battery, wherein the duration prediction log comprises the charging remaining duration of the current charging of the rechargeable battery.
In a second aspect, an apparatus for obtaining a remaining battery charging time is provided, and the apparatus may include:
the device comprises an acquisition unit, a charging unit and a control unit, wherein the acquisition unit is used for acquiring current charging environment information, current electric quantity and current charging power corresponding to a rechargeable battery in a charging state;
the input unit is used for inputting the current charging environment information, the current electric quantity and the current charging power into a preset charging duration prediction model, analyzing the current charging environment information, the current electric quantity and the current charging power through the charging duration prediction model, and outputting the charging duration required by the rechargeable battery from the current electric quantity to the total electric quantity of the rechargeable battery; the charging duration prediction model is obtained by performing iterative training on a neural network according to each charging environment information, historical current electric quantity and each charging power in historical charging data;
a determination unit configured to determine the output charging time period as a charging remaining time period of the rechargeable battery.
In one possible embodiment, the apparatus further comprises a training unit;
the training unit is used for executing the following steps:
acquiring historical charging data of each rechargeable battery, wherein the historical charging data comprises charging environment information of the rechargeable battery in a charging state, historical current electric quantity, charging power and actual charging remaining time of the corresponding rechargeable battery; the actual charging remaining time length is the actual time length required for charging from the historical current electric quantity to the total electric quantity of the rechargeable battery;
and taking the charging environment information, the historical current electric quantity and the charging power of each rechargeable battery in a charging state as training samples, taking the actual charging remaining time corresponding to each training sample as a sample label of the training sample, performing iterative training on the neural network, and determining the trained current neural network meeting preset iterative conditions as a charging time prediction model.
In one possible embodiment, the historical charging data further includes a historical end-of-charge; the training unit is further configured to:
acquiring candidate historical charging data of each rechargeable battery;
screening the candidate historical charging data to obtain historical charging data meeting preset screening conditions; the preset screening condition comprises that the historical charging ending electric quantity is the total electric quantity of the corresponding rechargeable battery, and the actual charging remaining time is longer than the preset time.
In one possible embodiment, the charging environment information includes a charging location and a corresponding ambient temperature;
the charging power comprises required charging power and actual charging power corresponding to the rechargeable battery.
In a possible embodiment, the obtaining unit is further configured to obtain a state of charge SOC of the rechargeable battery; the state of charge (SOC) represents the ratio of the residual capacity of the rechargeable battery to the total capacity of the rechargeable battery under the same condition;
the determining unit is further configured to, if the rechargeable battery is located in an electric vehicle, find a mapping relationship between a preset vehicle type and a total electric quantity of the rechargeable battery according to the vehicle type of the electric vehicle, and determine the total electric quantity of the rechargeable battery corresponding to the vehicle type;
and determining the product of the determined total electric quantity of the rechargeable battery and the SOC of the rechargeable battery as the current electric quantity of the rechargeable battery.
In one possible embodiment, the apparatus further comprises a transmitting 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 one possible embodiment, the apparatus further comprises a log generation unit;
the log generating unit is configured to generate a duration prediction log of the rechargeable battery, where the duration prediction log includes a remaining charging duration of the rechargeable battery in the current charging.
In a third aspect, an electronic device is provided, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus;
a memory for storing a computer program;
a processor adapted to perform the method steps of any of the above first aspects when executing a program stored in the memory.
In a fourth aspect, a computer-readable storage medium is provided, having stored therein a computer program which, when executed by a processor, performs the method steps of any of the above first aspects.
The method for acquiring the remaining time of battery charging provided by the embodiment of the application comprises the steps of inputting current charging environment information, current electric quantity and current charging power into a preset charging time prediction model after acquiring the current charging environment information, the current electric quantity and the current charging power corresponding to a rechargeable battery in a charging state, analyzing the current charging environment information, the current electric quantity and the current charging power through the charging time prediction model, and outputting the charging time required by the rechargeable battery from the current electric quantity to the total electric quantity of the rechargeable battery; the charging duration prediction model is obtained by performing iterative training on a neural network according to each charging environment information, historical current electric quantity and each charging power in historical charging data; and determines the output charging period as the charging remaining period of the rechargeable battery. The method analyzes the multi-dimensional characteristic information such as charging environment information, current electric quantity and charging power in the charging big data through the machine learning technology to obtain the charging remaining time of the rechargeable battery, and 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 electric quantity, the method has higher accuracy and improves user experience.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic diagram illustrating a comparison between a remaining charging time reported by a BMS and an actual remaining charging time according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a method for acquiring a remaining battery charging time according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of a training process of a charging duration prediction model according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram illustrating comparison between the remaining charging time period obtained in the embodiment of the present application and the remaining charging time period reported by the BMS, and an actual remaining charging time period, respectively;
fig. 5 is a schematic structural diagram of an apparatus for acquiring remaining battery charging time according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
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, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without any creative effort belong to the protection scope of the present application.
When charging a rechargeable battery, a user usually wants to see the remaining charging time of the battery, for example, a user using an electric vehicle can do some other things in the time after knowing the remaining charging time of the rechargeable battery of the vehicle, and can ensure that the user can arrive at a charging place to lift a vehicle in time when the rechargeable battery is full. Therefore, when the rechargeable battery starts to be charged, the charging remaining time of the rechargeable battery is acquired, and the charging remaining time is informed to the user, so that better convenience can be provided for the user, and the user experience can be greatly improved. In addition, the damage to the rechargeable battery due to overlong charging time can be avoided.
The method for acquiring the remaining time of battery charging provided by the embodiment of the application can be applied to charging equipment for charging the battery.
The charging device can be in communication connection with a terminal of a user; the Terminal may be a User Equipment (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), a handheld device, a vehicle-mounted device, a wearable device, a computing device or other processing device connected to a wireless modem, a Mobile Station (MS), a Mobile Terminal (Mobile Terminal), or the like.
Under the condition of the same ambient temperature, the same city or the same charging station, the charging system has the consistent charging rule by considering the same rechargeable battery, namely the rechargeable battery has the characteristic of consistency in the time length of the two-time charging remaining at the target SOC under the characteristic condition. According to the embodiment of the application, the charging remaining duration under the multi-dimensional condition is obtained by fully utilizing the historical charging big data and based on the machine learning technology.
The preferred embodiments of the present application will be described below with reference to the accompanying drawings of the specification, it should be understood that the preferred embodiments described herein are merely for illustrating and explaining the present application, and are not intended to limit the present application, and that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Fig. 2 is a schematic flowchart of a method for acquiring a remaining battery charging time according to an embodiment of the present disclosure. As shown in fig. 2, the method may include:
step S210, current charging environment information, current electric quantity, and current charging power corresponding to the rechargeable battery in the charging state are obtained.
Acquiring current charging environment information:
since the charging time duration changes due to different allowed charging currents for the rechargeable battery at different ambient temperatures or at different charging locations, such as charging stations at different locations, the current charging environment information may include the charging location, such as the location of the charging station and the ambient temperature corresponding to the charging location;
moreover, different charging currents and/or different charging voltages all affect the charging duration of the rechargeable battery, so that the current charging power needs to be obtained.
Obtaining current charging power:
the current charging power may include a demand charging power and an actual charging power corresponding to the rechargeable battery.
The required charging power refers to the charging power requested by the charging battery to the charging equipment, and comprises a requested required charging voltage and a requested charging current; the actual charging power refers to charging power fed back to the charging battery by the charging device, and includes an actual charging voltage and an actual charging current. The required charging power is the same as or similar to the actual charging power.
In specific implementation, for the current electric quantity, the same rechargeable battery may request the same charging power from different rechargeable devices, that is, the required power is the same, but because different rechargeable devices are different in model or manufacturer, the charging powers fed back to the rechargeable battery by different rechargeable devices may be different, that is, the actual powers may be different, but the condition that the required charging power is the same as or similar to the actual charging power is satisfied, so the current charging power may include the combined power of the required charging power and the actual charging power corresponding to the rechargeable battery; alternatively, the current charging power may be any one of the actual charging power and the actual charging power.
It should be noted that, in order to obtain a more accurate remaining charging time, the current charging power includes a combined power of a required charging power and an actual charging power corresponding to the rechargeable battery.
Obtaining the current electric quantity:
in specific implementation, the state of charge (SOC) of a rechargeable battery is obtained; (discharge experiment method, open circuit voltage method, neural network method, etc.); the state of charge SOC represents a ratio of the remaining capacity of the rechargeable battery to the total capacity of the rechargeable battery under the same conditions;
it should be noted that the state of charge SOC of the rechargeable battery may be obtained by using an open-circuit voltage method, a neural network method, and the like, and since the method for detecting the SOC by using the open-circuit voltage method and the neural network method is the prior art, the embodiments of the present application are not described herein again.
And then, determining the product of the total charge of the rechargeable battery and the state of charge SOC of the rechargeable battery as the current charge of the rechargeable battery. And the total electric quantity of the rechargeable battery is the rated electric quantity or the maximum discharge capacity of the rechargeable battery.
In one example, if the rechargeable battery is located in the electric vehicle, that is, the total electric quantity of the rechargeable battery cannot be obtained, a preset mapping relationship between the vehicle type and the total electric quantity of the rechargeable battery can be searched according to the vehicle type of the electric vehicle, and the total electric quantity of the rechargeable battery corresponding to the vehicle type is determined;
the preset mapping relation between the vehicle type and the total electric quantity of the rechargeable battery is counted based on an existing vehicle purchase order, and the vehicle purchase order is mainly selected from the order after 2019.
For example, the preset mapping relationship between the vehicle type and the total charge amount of the rechargeable battery may be as shown in table 1:
TABLE 1
From table 1, when the vehicle type is biddie 200 Pro, the total charge of the corresponding rechargeable battery is 43 kw-hrs; when the vehicle type is BYDBYDE 6, the total charge of the corresponding rechargeable battery is 82 kilowatt hours; when the vehicle type is Tesla Model S, the total electric quantity of the corresponding rechargeable battery is 90 kilowatt-hours; when the vehicle type is the great wall C30EV, the total charge of the corresponding rechargeable battery is 26.57 kilowatt-hours.
In summary, 10 pieces of characteristic information, including a charging city corresponding to the rechargeable battery in the charging state, a charging station location, an ambient temperature, a vehicle type to which the rechargeable battery belongs, a total electric quantity of the rechargeable battery corresponding to the vehicle type, a state of charge SOC, a charging demand voltage, a charging demand current, a charging actual voltage, and a charging actual current, can be obtained.
Step S220, inputting the current charging environment information, the current electric quantity and the current charging power into a preset charging duration prediction model to obtain the charging duration required by the rechargeable battery from the current electric quantity to the total electric quantity of the rechargeable battery.
The charging duration prediction model is obtained by performing iterative training on the neural network according to the charging environment information in the historical charging data, the historical current electric quantity and the charging power. The charging duration prediction model may include a 3-layer network layer: an input layer, a hidden layer, and an output layer.
In one example, the input layer may include 10 neurons (i.e., corresponding to 10 signatures), the hidden layer includes 64 neurons, and employs a Linear rectification function (ReLU) as an activation function, a Mini-batch gradient descent (MBGD) function as a gradient descent optimizer, and the output layer includes 1 neuron, and may employ a Mean Square Error (MSE) algorithm as a loss function of the model.
And then, analyzing the current charging environment information, the current electric quantity and the current charging power through a charging duration prediction model, and outputting the charging duration required by the rechargeable battery from the current electric quantity to the total electric quantity of the rechargeable battery.
In one embodiment, as shown in fig. 3, the training process of the charging duration prediction model includes:
step S31, the history charging data of each rechargeable battery is acquired.
In specific implementation, acquiring candidate historical charging data of each rechargeable battery; the candidate historical charging data may include charging environment information of the rechargeable battery in a charging state, historical current charge amount, charging power, historical charging end charge amount, and actual charging remaining time of the corresponding rechargeable battery. And the actual charging remaining time length is the real time length required for charging from the historical current electric quantity to the total electric quantity of the rechargeable battery.
Screening the candidate historical charging data according to a preset screening condition to obtain the historical charging data meeting the preset screening condition; the preset screening condition may include that the historical charging end electric quantity is the total electric quantity of the corresponding rechargeable battery, and the actual charging remaining time is longer than the preset time.
Specifically, historical charging data that the charging ending electric quantity of the rechargeable battery is the total electric quantity of the rechargeable battery is selected from the candidate historical charging data, namely the first historical charging data of the total electric quantity of the rechargeable battery from the historical current electric quantity to full charge is obtained, and therefore the accuracy of the trained model is guaranteed; then, selecting historical charging data with the charging remaining time length being greater than the preset time length from the first historical charging data, namely deleting shorter charging remaining time length, such as 10 minutes of historical charging data, from the first historical charging data, so as to further ensure the accuracy of the trained model.
It should be noted that, historical charging data with a charging remaining time longer than a preset time may also be selected from the candidate historical charging data, and then the historical charging data with the charging end electric quantity being the total electric quantity of the rechargeable battery is selected.
And S32, taking the charging environment information, the historical current electric quantity and the charging power of each rechargeable battery in a charging state as training samples, taking the actual charging remaining time corresponding to each training sample as a sample label of the training sample, and performing iterative training on the neural network.
In specific implementation, charging environment information, historical current electric quantity and charging power of each rechargeable battery in a charging state are used as training samples, actual charging remaining time corresponding to each training sample is used as a sample label of the training sample, iterative training is carried out on the neural network, and charging time corresponding to each training sample is output. The neural network includes preset model parameters.
In the iterative training process, a preset loss function, such as an MSE algorithm, is adopted to perform loss calculation on the charging duration corresponding to any training sample and the actual charging remaining duration of the corresponding training sample to obtain a loss value of the training sample, and the trained model parameter of the current neural network is updated based on the loss value.
And S33, determining the trained current neural network meeting the preset iteration condition as a charging duration prediction model.
And if the calculated loss value is not greater than a preset loss threshold value or the iteration number reaches a preset number threshold value, determining the current neural network corresponding to the loss value or the iteration number as a charging duration prediction model.
The preset iteration condition may also be another iteration ending condition, and the embodiment of the present application is not limited herein.
And step S230, determining the charging time length output by the charging time length prediction model as the charging remaining time length of the rechargeable battery.
In a possible implementation, after the remaining charging duration is obtained, the obtained remaining charging duration may be sent to a terminal that establishes a communication connection, so that the terminal displays the remaining charging duration for a user to watch.
In another possible implementation, after the remaining charging time period is obtained, a time period prediction log of the rechargeable battery may be generated for on-line effect evaluation, where the time period prediction log may include the charging remaining time period of the current charging of the rechargeable battery.
It can be understood that the duration prediction log may further include information of a charging city, a charging station location, an ambient temperature, a vehicle type to which the rechargeable battery belongs, a total electric quantity of the rechargeable battery corresponding to the vehicle type, a state of charge SOC, a charging demand voltage, a charging demand current, a charging actual voltage, a charging actual current, and the like, which are involved in the charging process of the rechargeable battery.
In an example, as shown in fig. 4, when an electric vehicle is charged at a power station, a comparison diagram of the remaining charging time length and the remaining charging time length reported by the BMS obtained in the embodiment of the present application and an actual remaining charging time length is shown.
As shown in fig. 4, a horizontal axis of a coordinate system represents actual time, and a vertical axis represents remaining charging time at different actual times, where a curve 1 is a curve corresponding to the remaining charging time obtained in the embodiment of the present application; the curve 2 is a curve corresponding to the residual charging time reported by the BMS; curve 3 is a curve corresponding to the actual remaining charging time.
It can be seen that curve 1 is smoother; the curve 2 is not smooth, and has more folding points, namely the charging residual time is subjected to total jumping; the curve 1 is closer to the curve 3, that is, the charging remaining time length obtained in the embodiment of the present application is closer to the actual charging remaining time length.
The method for acquiring the remaining time of battery charging provided by the embodiment of the application comprises the steps of inputting current charging environment information, current electric quantity and current charging power into a preset charging time prediction model after acquiring the current charging environment information, the current electric quantity and the current charging power corresponding to a rechargeable battery in a charging state, analyzing the current charging environment information, the current electric quantity and the current charging power through the charging time prediction model, and outputting the charging time required by the rechargeable battery from the current electric quantity to the total electric quantity of the rechargeable battery; the charging duration prediction model is obtained by performing iterative training on a neural network according to each charging environment information, historical current electric quantity and each charging power in historical charging data; and determines the output charging period as the charging remaining period of the rechargeable battery.
The method analyzes the multi-dimensional characteristic information such as charging environment information, current electric quantity and charging power in the charging big data through the machine learning technology to obtain the charging remaining time of the rechargeable battery, and 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 electric quantity, the method has higher accuracy and improves user experience.
Corresponding to the above method, an embodiment of the present application further provides an apparatus for acquiring a remaining battery charging time, as shown in fig. 5, where the apparatus for acquiring a remaining battery charging time may include: an acquisition unit 510, an input unit 520, and a determination unit 530;
an obtaining unit 510, configured to obtain current charging environment information, current electric quantity, and current charging power corresponding to the rechargeable battery in the charging state;
an input unit 520, configured to input the current charging environment information, the current electric quantity, and the current charging power into a preset charging duration prediction model, analyze the current charging environment information, the current electric quantity, and the current charging power through the charging duration prediction model, and output a charging duration required by the rechargeable battery from the current electric quantity to a total electric quantity of the rechargeable battery; the charging duration prediction model is obtained by performing iterative training on a neural network according to each charging environment information, historical current electric quantity and each charging power in historical charging data;
a determining unit 530 for determining the output charging time period as the charging remaining time period of the rechargeable battery.
In one possible embodiment, the apparatus further comprises a training unit 540;
a training unit 540, configured to perform the following steps:
acquiring historical charging data of each rechargeable battery, wherein the historical charging data comprises charging environment information of the rechargeable battery in a charging state, historical current electric quantity, charging power and actual charging remaining time of the corresponding rechargeable battery; the actual charging remaining time length is the real time length required for charging from the historical current electric quantity to the total electric quantity of the rechargeable battery;
and taking the charging environment information, the historical current electric quantity and the charging power of each rechargeable battery in a charging state as training samples, taking the actual charging remaining time corresponding to each training sample as a sample label of the training sample, performing iterative training on the neural network, and determining the trained current neural network meeting preset iterative conditions as a charging time prediction model.
In one possible embodiment, the historical charging data further includes a historical end-of-charge; a training unit 540, further configured to:
acquiring candidate historical charging data of each rechargeable battery;
screening the candidate historical charging data to obtain historical charging data meeting preset screening conditions; the preset screening condition comprises that the historical charging ending electric quantity is the total electric quantity of the corresponding rechargeable battery, and the actual charging remaining time is longer than the preset time.
In one possible embodiment, the charging environment information includes a charging location and a corresponding ambient temperature;
the charging power comprises required charging power and actual charging power corresponding to the rechargeable battery.
In a possible embodiment, the obtaining unit 510 is further configured to obtain a state of charge SOC of the rechargeable battery; the state of charge (SOC) represents the ratio of the residual capacity of the rechargeable battery to the total capacity of the rechargeable battery under the same condition;
the determining unit 530 is further configured to, if the rechargeable battery is located in an electric vehicle, find a mapping relationship between a preset vehicle type and a total electric quantity of the rechargeable battery according to a vehicle type of the electric vehicle, and determine the total electric quantity of the rechargeable battery corresponding to the vehicle type;
and determining the product of the determined total electric quantity of the rechargeable battery and the SOC of the rechargeable battery as the current electric quantity of the rechargeable battery.
In one possible embodiment, the apparatus further comprises a transmitting unit 550;
a sending unit 550, 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 one possible embodiment, the apparatus further comprises a log generation unit 560;
a log generating unit 560, configured to generate a duration prediction log of the rechargeable battery, where the duration prediction log includes a remaining charging duration of the rechargeable battery in the current charging.
The functions of the functional units of the apparatus for acquiring remaining battery charging time provided in the above embodiments of the present application may be implemented by the above method steps, and therefore, detailed working processes and beneficial effects of the units in the apparatus for acquiring remaining battery charging time provided in the embodiments of the present application are not repeated herein.
An electronic device is further provided in the embodiments of the present application, as shown in fig. 6, and includes a processor 610, a communication interface 620, a memory 630, and a communication bus 640, where the processor 610, the communication interface 620, and the memory 630 complete communication with each other through the communication bus 640.
A memory 630 for storing computer programs;
the processor 610, when executing the program stored in the memory 630, implements the following steps:
acquiring current charging environment information, current electric quantity and current charging power corresponding to a rechargeable battery in a charging state;
inputting the current charging environment information, the current electric quantity and the current charging power into a preset charging duration prediction model, analyzing the current charging environment information, the current electric quantity and the current charging power through the charging duration prediction model, and outputting the charging duration required by the rechargeable battery from the current electric quantity to the total electric quantity of the rechargeable battery; the charging duration prediction model is obtained by performing iterative training on a neural network according to each charging environment information, historical current electric quantity and each charging power in historical charging data;
and determining the output charging time period as the charging remaining time period of the rechargeable battery.
In one possible embodiment, the training process of the charging duration prediction model includes:
acquiring historical charging data of each rechargeable battery, wherein the historical charging data comprises charging environment information of the rechargeable battery in a charging state, historical current electric quantity, charging power and actual charging remaining time of the corresponding rechargeable battery; the actual charging remaining time length is the real time length required for charging from the historical current electric quantity to the total electric quantity of the rechargeable battery;
and taking the charging environment information, the historical current electric quantity and the charging power of each rechargeable battery in a charging state as training samples, taking the actual charging remaining time corresponding to each training sample as a sample label of the training sample, performing iterative training on the neural network, and determining the trained current neural network meeting preset iterative conditions as a charging time prediction model.
In one possible embodiment, the historical charging data further includes a historical end-of-charge;
acquiring historical charging data of each rechargeable battery, wherein the historical charging data comprises the following steps:
acquiring candidate historical charging data of each rechargeable battery;
screening the candidate historical charging data to obtain historical charging data meeting preset screening conditions; the preset screening condition comprises that the historical charging ending electric quantity is the total electric quantity of the corresponding rechargeable battery, and the actual charging remaining time is longer than the preset time.
In one possible embodiment, the charging environment information includes a charging location and a corresponding ambient temperature;
the charging power comprises required charging power and actual charging power corresponding to the rechargeable battery.
In one possible embodiment, the determining of the current amount of power includes:
acquiring the state of charge (SOC) of the rechargeable battery; the state of charge (SOC) represents the ratio of the residual capacity of the rechargeable battery to the total capacity of the rechargeable battery under the same condition;
if the rechargeable battery is located in the electric vehicle, searching a preset mapping relation between the vehicle type and the total electric quantity of the rechargeable battery according to the vehicle type of the electric vehicle, and determining the total electric quantity of the rechargeable battery corresponding to the vehicle type;
and determining the product of the determined total electric quantity of the rechargeable battery and the SOC of the rechargeable battery as the current electric quantity of the rechargeable battery.
In one possible embodiment, after determining the output charging period as the remaining charging period of the rechargeable battery, the method further includes:
and sending the residual charging time length to a terminal establishing communication connection so that the terminal displays the residual charging time length.
In one possible embodiment, after determining the output charging period as the remaining charging period of the rechargeable battery, the method further includes:
and generating a duration prediction log of the rechargeable battery, wherein the duration prediction log comprises the charging remaining duration of the current charging of the rechargeable battery.
The aforementioned communication bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this is not intended to represent only one bus or type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
Since the implementation manner and the beneficial effects of the problem solving of each device of the electronic device in the foregoing embodiment can be implemented by referring to each step in the embodiment shown in fig. 2, detailed working processes and beneficial effects of the electronic device provided in the embodiment of the present application are not repeated herein.
In another embodiment provided by the present application, a computer-readable storage medium is further provided, in which instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method for acquiring the remaining battery charging time period according to any one of the above embodiments.
In another embodiment provided by the present application, there is also provided a computer program product containing instructions, which when run on a computer, causes the computer to execute the method for acquiring the remaining battery charging time according to any one of the above embodiments.
As will be appreciated by one of skill in the art, the embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, 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 embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the true scope of the embodiments of the present application.
It is apparent that those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the embodiments of the present application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims of the embodiments of the present application and their equivalents, the embodiments of the present application are also intended to include such modifications and variations.
Claims (10)
1. A method for acquiring a remaining time of battery charging is characterized by comprising the following steps:
acquiring current charging environment information, current electric quantity and current charging power corresponding to a rechargeable battery in a charging state;
inputting the current charging environment information, the current electric quantity and the current charging power into a preset charging duration prediction model, analyzing the current charging environment information, the current electric quantity and the current charging power through the charging duration prediction model, and outputting the charging duration required by the rechargeable battery from the current electric quantity to the total electric quantity of the rechargeable battery; the charging duration prediction model is obtained by performing iterative training on a neural network according to each charging environment information, historical current electric quantity and each charging power in historical charging data;
and determining the output charging time length as the charging remaining time length of the rechargeable battery.
2. The method of claim 1, wherein the training process of the charging duration prediction model comprises:
acquiring historical charging data of each rechargeable battery, wherein the historical charging data comprises charging environment information of the rechargeable battery in a charging state, historical current electric quantity, charging power and actual charging remaining time of the corresponding rechargeable battery; the actual charging remaining time length is the real time length required for charging from the historical current electric quantity to the total electric quantity of the rechargeable battery;
and taking the charging environment information, the historical current electric quantity and the charging power of each rechargeable battery in a charging state as training samples, taking the actual charging remaining time corresponding to each training sample as a sample label of the training sample, performing iterative training on the neural network, and determining the trained current neural network meeting preset iterative conditions as a charging time prediction model.
3. The method of claim 2, wherein the historical charging data further comprises a historical end-of-charge;
acquiring historical charging data of each rechargeable battery, wherein the historical charging data comprises the following steps:
acquiring candidate historical charging data of each rechargeable battery;
screening the candidate historical charging data to obtain historical charging data meeting preset screening conditions; the preset screening condition comprises that the historical charging ending electric quantity is the total electric quantity of the corresponding rechargeable battery, and the actual charging remaining time is longer than the preset time.
4. The method of claim 1 or 2, wherein the charging environment information comprises a charging location and a corresponding ambient temperature;
the charging power comprises required charging power and actual charging power corresponding to the rechargeable battery.
5. The method of claim 1 or 2, wherein the determination of the current amount of power comprises:
acquiring the state of charge (SOC) of the rechargeable battery; the state of charge (SOC) represents the ratio of the residual capacity of the rechargeable battery to the total capacity of the rechargeable battery under the same condition;
if the rechargeable battery is located in the electric vehicle, searching a preset mapping relation between the vehicle type and the total electric quantity of the rechargeable battery according to the vehicle type of the electric vehicle, and determining the total electric quantity of the rechargeable battery corresponding to the vehicle type;
and determining the product of the determined total electric quantity of the rechargeable battery and the SOC of the rechargeable battery as the current electric quantity of the rechargeable battery.
6. The method of claim 1, wherein after determining the outputted charging period as the remaining period of charging time of the rechargeable battery, the method further comprises:
and sending the residual charging time length to a terminal establishing communication connection so that the terminal displays the residual charging time length.
7. The method of claim 1, wherein after determining the outputted charging period as the remaining period of charging time of the rechargeable battery, the method further comprises:
and generating a duration prediction log of the rechargeable battery, wherein the duration prediction log comprises the charging remaining duration of the current charging of the rechargeable battery.
8. An apparatus for obtaining a remaining charging time of a battery, the apparatus comprising:
the device comprises an acquisition unit, a charging unit and a control unit, wherein the acquisition unit is used for acquiring current charging environment information, current electric quantity and current charging power corresponding to a rechargeable battery in a charging state;
the input unit is used for inputting the current charging environment information, the current electric quantity and the current charging power into a preset charging duration prediction model, analyzing the current charging environment information, the current electric quantity and the current charging power through the charging duration prediction model, and outputting the charging duration required by the rechargeable battery from the current electric quantity to the total electric quantity of the rechargeable battery; the charging duration prediction model is obtained by performing iterative training on a neural network according to each charging environment information, historical current electric quantity and each charging power in historical charging data;
a determination unit configured to determine the output charging time period as a charging remaining time period 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 are communicated with each other through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1-7 when executing a program stored on a memory.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117110895A (en) * | 2023-10-19 | 2023-11-24 | 武汉船用电力推进装置研究所(中国船舶集团有限公司第七一二研究所) | Marine lithium ion power battery residual energy estimation method, equipment and medium |
| CN117277519A (en) * | 2023-11-21 | 2023-12-22 | 深圳鹏城新能科技有限公司 | Method, system and medium for protecting energy storage inverter battery from overcharge |
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2021
- 2021-07-22 CN CN202110831077.3A patent/CN115224751A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117110895A (en) * | 2023-10-19 | 2023-11-24 | 武汉船用电力推进装置研究所(中国船舶集团有限公司第七一二研究所) | Marine lithium ion power battery residual energy estimation method, equipment and medium |
| CN117110895B (en) * | 2023-10-19 | 2024-01-05 | 武汉船用电力推进装置研究所(中国船舶集团有限公司第七一二研究所) | Marine lithium ion power battery residual energy estimation method, equipment and medium |
| CN117277519A (en) * | 2023-11-21 | 2023-12-22 | 深圳鹏城新能科技有限公司 | Method, system and medium for protecting energy storage inverter battery from overcharge |
| CN117277519B (en) * | 2023-11-21 | 2024-02-02 | 深圳鹏城新能科技有限公司 | Method, system and medium for protecting energy storage inverter battery from overcharge |
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