CN117007973A - Battery state prediction method, device, equipment and storage medium - Google Patents
Battery state prediction method, device, equipment and storage medium Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The application belongs to the technical field of automobiles, and discloses a battery state prediction method, a device, equipment and a storage medium; the method comprises the following steps: acquiring battery voltage and battery current, and acquiring battery electric quantity according to the battery voltage and the battery current; acquiring a total driving mileage, a total driving time, a current driving speed and a historical charging record; obtaining battery use parameters according to the battery electric quantity, the total driving mileage, the total driving time, the current driving speed and the historical charging record; predicting the battery state according to the battery use parameters to obtain a predicted battery state; the application determines the battery electric quantity through the voltage and the current, determines the battery use parameter according to the battery electric quantity, the total driving mileage, the total driving time, the current driving speed and the historical charging record, and obtains the use condition of the battery at a plurality of angles based on the use parameter, thereby feeding back the battery state more accurately through the use parameter, so that a user can perform battery replacement or charging operation according to the battery state, and driving safety is ensured.
Description
Technical Field
The present application relates to the field of automotive technologies, and in particular, to a method, an apparatus, a device, and a storage medium for predicting a battery state.
Background
The automobile battery electric quantity monitoring technology is an important technology, and aims to accurately judge the electric quantity state of a vehicle storage battery so as to take measures in time to avoid the problems of incapability of starting, unstable running and the like caused by insufficient electric quantity.
The voltage detection method is one of the most commonly used methods for monitoring the electric quantity of an automobile battery at present, and utilizes potential difference generated by chemical reaction inside the battery to calculate the electric quantity of the battery. However, the voltage detection method can only provide the value of the battery voltage, but cannot accurately determine the actual electric quantity of the battery, because the battery is affected by various factors, such as temperature, load, service life, and the like, during the use. Therefore, the voltage detection method is prone to errors in practical use. The SOC value detection method is another common method for monitoring the electric quantity of the automobile battery, and calculates the SOC value of the battery by using the energy stored in the battery. The SOC value detection method can provide a more accurate state of charge of the battery than the voltage detection method, but it requires a dedicated electronic device to measure and calculate the SOC value, and is also prone to errors due to the SOC value being affected by factors such as battery life, charge current, discharge current, and the like. The time detection method is a simpler method for monitoring the electric quantity of the battery of the automobile, and estimates the electric quantity state of the battery by recording the running time of the automobile and the charging time of the battery. Although the time detection method is easy to realize, the calculation accuracy is low, and more accurate electric quantity state information cannot be provided.
Disclosure of Invention
The application mainly aims to provide a battery state prediction method, device, equipment and storage medium, and aims to solve the technical problem that potential safety hazards exist in driving caused by inaccurate detection of battery electric quantity of an automobile in the prior art.
In order to achieve the above object, the present application provides a battery state prediction method, comprising the steps of:
acquiring battery voltage and battery current, and obtaining battery electric quantity according to the battery voltage and the battery current;
acquiring a total driving mileage, a total driving time, a current driving speed and a historical charging record;
obtaining battery use parameters according to the battery electric quantity, the total driving mileage, the total driving time, the current driving speed and the historical charging record;
and predicting the battery state according to the battery use parameters to obtain a predicted battery state.
Optionally, the battery usage parameters include a battery remaining power, a battery remaining usage time, and a battery safety capacity;
and obtaining battery usage parameters according to the battery power, the total driving mileage, the total driving time, the current driving speed and the historical charging record, wherein the battery usage parameters comprise:
determining the remaining battery power according to the battery power, the total driving mileage, the current driving speed and the historical charging record;
obtaining the residual service life of the battery according to the total running time and the preset service life of the battery;
and obtaining the battery safety capacity according to the preset minimum safety electric quantity and the battery capacity.
Optionally, the determining the remaining battery power according to the battery power, the total driving range, the current driving speed and the historical charging record includes:
acquiring current running time, and acquiring current running mileage according to the current running speed and the current running time;
obtaining a mileage energy consumption value according to the current mileage, the total mileage and a preset battery energy consumption coefficient;
obtaining a reference residual electric quantity according to the battery electric quantity, the mileage energy consumption value and the historical charging record;
and correcting the reference residual electric quantity through preset correction parameters to obtain the residual electric quantity of the battery.
Optionally, the preset correction parameters include a time correction parameter, a mileage correction parameter, and a speed correction parameter;
the step of correcting the reference residual electric quantity by presetting correction parameters, before obtaining the residual electric quantity of the battery, comprises the following steps:
obtaining a time correction parameter according to the total running time and the service life of the preset battery;
obtaining mileage correction parameters according to the total driving mileage;
and obtaining a speed correction parameter according to the current running speed.
Optionally, the correcting the reference remaining power by the preset correction parameter to obtain the remaining power of the battery includes:
adjusting the reference residual electric quantity according to the time correction parameter to obtain an initial battery residual electric quantity;
adjusting the initial residual electric quantity according to the mileage correction parameter to obtain an adjusted initial battery residual electric quantity;
and adjusting the adjusted initial battery residual capacity according to the speed correction parameters to obtain the battery residual capacity.
Optionally, the battery usage parameter predicts a battery state to obtain a predicted battery state, including:
judging whether the residual electric quantity of the battery in the use condition of the battery is smaller than or equal to the safe capacity of the battery;
if the battery residual capacity in the battery service condition is smaller than or equal to the battery safety capacity, predicting the current battery state as a state to be charged, and if the battery residual capacity in the battery service condition is larger than the battery safety capacity, predicting the current battery state as a normal use state;
judging whether the residual service time of the battery is less than or equal to a preset time threshold value;
and if the remaining battery service time is smaller than or equal to a preset time threshold, predicting the current battery state as a dangerous state, and if the remaining battery service time is larger than the preset time threshold, predicting the current battery state as a normal service state.
Optionally, the predicting the battery state according to the battery usage parameter, after obtaining the predicted battery state, further includes:
judging whether the predicted battery state meets an alarm condition or not;
when the predicted battery state meets an alarm condition, an alarm grade of the predicted battery state is obtained;
and determining alarm content according to the alarm level, and reminding according to the alarm content.
In addition, in order to achieve the above object, the present application also proposes a battery state prediction apparatus including:
the parameter acquisition module is used for acquiring battery voltage and battery current and obtaining battery electric quantity according to the battery voltage and the battery current;
the parameter acquisition module is also used for acquiring total driving mileage, total driving time, current driving speed and historical charging record;
the state prediction module is used for obtaining battery use parameters according to the battery electric quantity, the total driving mileage, the total driving time, the current driving speed and the historical charging record;
and the state prediction module is also used for predicting the battery state according to the battery use parameters to obtain a predicted battery state.
In addition, in order to achieve the above object, the present application also proposes a battery state prediction apparatus including: a memory, a processor, and a battery state prediction program stored on the memory and executable on the processor, the battery state prediction program configured to implement the steps of the battery state prediction method as described above.
In addition, in order to achieve the above object, the present application also proposes a storage medium having stored thereon a battery state prediction program which, when executed by a processor, implements the steps of the battery state prediction method as described above.
The application determines the battery electric quantity through the voltage and the current, determines the use parameters of the battery according to the battery electric quantity, the total driving mileage, the total driving time, the current driving speed and the historical charging record, and can obtain the use conditions of the battery at a plurality of angles based on the use parameters, thereby being capable of feeding back the battery state more accurately when predicting the battery state according to the use parameters, so that a user can perform battery replacement or charging operation according to the battery state, and driving safety is ensured.
Drawings
Fig. 1 is a schematic structural diagram of a battery state prediction apparatus of a hardware running environment according to an embodiment of the present application;
FIG. 2 is a flowchart of a battery state prediction method according to a first embodiment of the present application;
FIG. 3 is a flowchart of a battery state prediction method according to a second embodiment of the present application;
FIG. 4 is a schematic flow chart of a battery state prediction method according to an embodiment of the present application;
fig. 5 is a block diagram showing the structure of a first embodiment of the battery state predicting apparatus according to the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Referring to fig. 1, fig. 1 is a schematic diagram of a battery state prediction device in a hardware running environment according to an embodiment of the present application.
As shown in fig. 1, the battery state prediction apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the battery state prediction apparatus, and may include more or fewer components than shown, or may combine certain components, or may have a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a battery state prediction program may be included in the memory 1005 as one type of storage medium.
In the battery state prediction apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the battery state prediction apparatus of the present application may be provided in the battery state prediction apparatus, which invokes the battery state prediction program stored in the memory 1005 through the processor 1001 and executes the battery state prediction method provided by the embodiment of the present application.
An embodiment of the present application provides a battery state prediction method, and referring to fig. 2, fig. 2 is a flowchart of a first embodiment of a battery state prediction method according to the present application.
In this embodiment, the battery state prediction method includes the following steps:
step S10: and obtaining battery voltage and battery current, and obtaining battery electric quantity according to the battery voltage and the battery current.
It should be noted that, the execution body of the embodiment is a battery state prediction device, where the battery state prediction device has functions of data processing, data communication, program running, and the like, and the battery state prediction may be an integrated controller, a control computer, and other devices with similar functions, and the embodiment is not limited to this.
It is understood that the battery voltage may be a value that is provided by a voltage detection method and the battery current may be obtained by direct measurement.
It should be understood that the target battery charge may be calculated from the potential difference generated by the chemical reaction within the battery, or may be calculated from ohm's law.
It should be noted that, the current common battery power of the automobile can be obtained through strategies such as voltage detection and SOC value method, but the battery is affected by various factors such as temperature, load, service life and the like in the use process, so that the time power of the battery cannot be accurately judged.
Step S20: and acquiring the total driving mileage, the total driving time, the current driving speed and the historical charging record.
It is understood that the total mileage of the vehicle can be stored in the vehicle, and the total distance of the vehicle from the start of driving to the present can be also understood.
It is understood that the total travel time can be understood as how long the vehicle has been in use, and also simply as the time difference from the date of production to the present.
It is understood that the current running speed may be derived from the current engine speed of the vehicle.
It is understood that the history charging record may be recorded at each charging, and may be obtained directly when the history charging record needs to be checked.
It should be appreciated that the historical charge record may include a historical charge number and a historical total charge time.
Step S30: and obtaining battery use parameters according to the battery electric quantity, the total driving mileage, the total driving time, the current driving speed and the historical charging record.
It is understood that the battery usage parameters may include a remaining battery power, a remaining battery usage time, and a battery safety capacity.
It should be understood that the remaining battery power may be a value that can more accurately represent the battery power obtained by correcting the battery power.
It will be appreciated that the battery may be produced with a corresponding battery life, the battery life being different from one battery to another, and the remaining battery life may be derived from the battery life and the total travel time of the vehicle.
It should be understood that the maximum electric quantity of each battery is fixed, and based on the maximum electric quantity and the consumption speed of the electric quantity, a minimum safe electric quantity can be obtained in advance, and the minimum safe electric quantity can be set manually according to the actual situation, which is not limited in this embodiment.
Step S40: and predicting the battery state according to the battery use parameters to obtain a predicted battery state.
It is understood that battery conditions may include normal use conditions, to-be-charged conditions, and dangerous conditions.
It should be understood that the dangerous state may be a state determined according to the service life of the battery, and when the service life of the battery is too long, the remaining service life of the battery is too low, which may generate a safety hazard to driving of the automobile and may cause life hazard to a driver.
It should be appreciated that the predicted battery state may be a normal use state or a state to be charged or a dangerous state. Or can be a composite of a normal use state and a charged state.
It should be noted that, the predicting the battery state by using the battery usage parameter to obtain a predicted battery state includes:
judging whether the battery residual capacity in the battery use condition is smaller than or equal to the battery safety capacity, wherein the battery safety capacity can be obtained according to the following formula:
A＝C·S
wherein A represents the safe electric capacity of the battery, S represents the minimum safe electric capacity, and C represents the electric capacity of the battery;
if the battery residual capacity in the battery service condition is smaller than or equal to the battery safety capacity, predicting the current battery state as a state to be charged, and if the battery residual capacity in the battery service condition is larger than the battery safety capacity, predicting the current battery state as a normal use state;
judging whether the remaining battery service time is less than or equal to a preset time threshold, wherein the remaining battery service time is as follows:
T2＝T0-T1
wherein T2 represents the remaining battery life, T1 represents the total travel time, and T0 represents the battery life.
If the remaining service time of the battery is less than or equal to a preset time threshold, predicting the current battery state as a dangerous state; the preset time threshold can be determined according to the service life of the big data vehicle battery, and can be adjusted according to actual conditions, which is not limited by the embodiment;
and if the remaining service time of the battery is greater than a preset time threshold, predicting that the current battery state is a normal service state.
It should be emphasized that, after predicting the battery state according to the battery usage parameter to obtain the predicted battery state, the method further includes: judging whether the predicted battery state meets an alarm condition, wherein when the predicted battery state is in an abnormal use state, the predicted battery state is considered to meet the alarm condition; when the predicted battery state meets the alarm condition, the alarm grade can be divided into a plurality of categories according to the predicted battery state, each category can also be classified, the state to be charged is one category, and the dangerous state is another category, wherein the state to be charged can also be divided into more categories according to the quantity of the residual electric quantity, for example, the battery safety capacity is 10000, when the battery electric quantity is less than 10000, the alarm condition is met, the safety battery capacity 10000 is equally divided into two portions of 0-5000,5001-10000, and the two categories; and determining alarm content according to the alarm level, and reminding according to the alarm content, wherein it is understood that when the alarm level is in a dangerous level, a user is correspondingly reminded of replacing a battery, when the alarm level is in a to-be-charged type, different reminding is carried out according to different levels of the residual electric quantity, and the alarm has to be charged and reminds of the residual driving mileage similar to the situation that the electric quantity is very small.
According to the embodiment, the battery electric quantity is determined through the voltage and the current, the use parameters of the battery are determined according to the battery electric quantity, the total driving mileage, the total driving time, the current driving speed and the historical charging record, and the use conditions of the battery at a plurality of angles can be obtained based on the use parameters, so that the battery state can be fed back more accurately when the battery state is predicted according to the use parameters, a user can replace or charge the battery according to the battery state, and driving safety is ensured.
Referring to FIG. 3, FIG. 3 is a flow chart of a second embodiment of the XX method of the present application.
Based on the first embodiment, the XX method of this embodiment includes, at step S20:
step S21: and determining the remaining battery capacity according to the battery capacity, the total driving mileage, the current driving speed and the historical charging record.
It is understood that the battery usage parameters include a remaining battery power, a remaining battery usage time, and a battery safety capacity.
It should be noted that the determining the remaining battery power according to the battery power, the total driving distance, the current driving speed and the historical charging record includes: acquiring current running time, and acquiring current running mileage according to the current running speed and the current running time; obtaining a mileage energy consumption value according to the current mileage, the total mileage and a preset battery energy consumption coefficient, wherein the calculation of the mileage energy consumption value can refer to a series of formulas:
E＝(D-D1)k
wherein E represents a mileage energy consumption value, D represents a total driving mileage, D1 represents a current total driving mileage, k represents a preset battery energy consumption coefficient, and further, the calculation of the current total driving mileage can refer to a series of formulas:
D1＝V1·t1
wherein V1 represents the current vehicle speed, t1 represents the current vehicle running time, and t1 can be recorded when the automobile is started each time;
obtaining a reference residual capacity according to the battery capacity, the mileage energy consumption value and the historical charging record, wherein the calculation mode of the reference residual capacity can refer to the following formula:
B＝C-H1+E
wherein H1 is a history charging record, and C represents a battery capacitance;
and correcting the reference residual electric quantity through preset correction parameters to obtain the residual electric quantity of the battery.
It should be noted that the preset correction parameters include a time correction parameter, a mileage correction parameter, and a speed correction parameter.
It should be further noted that, before the correcting the reference remaining power by the preset correction parameter to obtain the remaining power of the battery, the method includes:
the time correction parameter is obtained from the total travel time and the preset battery life, wherein it is understood that the battery life is also related to time, and if the battery has been used for a long time, a shortage of electric power may occur even if its capacity is large. Therefore, it is also very important to judge the service condition of the battery according to the service time of the battery, so that the time correction parameter can be obtained according to the collected sample data, namely, the strict cost data of the service time of the battery and the battery power of a plurality of vehicles, the linear relation between the service time of the battery and the battery power can be obtained according to the sample data, and the time correction parameter can be obtained according to the total running time of the battery and the preset service life of the battery according to the linear relation.
And obtaining mileage correction parameters according to the total mileage, wherein the mileage of the vehicle is combined with information such as battery capacity, historical charging record and the like, and the residual electric quantity of the battery can be estimated, but as the mileage of the vehicle is increased, the more the battery is lost, and obtaining mileage correction parameters according to the data relationship between the acquired sample vehicle mileage and the electric quantity of the battery and the total mileage of the vehicle.
The speed correction parameter is obtained according to the current running speed, and it can be understood that the energy consumption speed of the battery is completely different when the vehicle runs at a high speed and when the vehicle runs at a slow speed, for example, if the vehicle runs on a highway frequently, the energy consumption speed of the battery is relatively high, and the state of charge of the battery needs to be more paid attention to; if the vehicle is driven at a high speed, the battery power may be rapidly changed while the battery power is predicted, and the relationship between different vehicle speeds and battery power consumption between sample vehicles may be collected, so that the time correction parameter may be obtained according to the consumption relationship and the current running speed.
It should be further noted that the correcting the reference remaining power by the preset correction parameter to obtain the remaining power of the battery includes:
adjusting the reference residual electric quantity according to the time correction parameter to obtain an initial battery residual electric quantity; adjusting the initial residual electric quantity according to the mileage correction parameter to obtain an adjusted initial battery residual electric quantity; and adjusting the adjusted initial battery residual capacity according to the speed correction parameters to obtain the battery residual capacity.
Step S22: and obtaining the residual service life of the battery according to the total running time and the preset service life of the battery.
It will be appreciated that subtracting the total travel time from the preset battery life may result in a remaining battery life.
Step S23: and obtaining the battery safety capacity according to the preset minimum safety electric quantity and the battery capacity.
It will be appreciated that the battery safe capacity can be obtained by multiplying the preset minimum safe charge by the battery capacity.
It should be appreciated that the battery safety capacity may be a power standard that may be used to compare with the current battery power to determine the state of the battery.
In an implementation, a generalized flow of vehicle state prediction may refer to FIG. 4.
According to the embodiment, parameters which can influence the accuracy of the battery electric quantity of the vehicle are acquired through sample data, wherein the parameters comprise the total running time of the vehicle, the total running mileage of the vehicle and the current running speed of the vehicle, corresponding time correction parameters, mileage correction parameters and speed correction parameters are obtained according to the relation between the parameters and the battery electric quantity, and the battery electric quantity is corrected according to the time correction parameters, the mileage correction parameters and the speed correction parameters, so that more accurate battery electric quantity is obtained, and further, the battery state can be predicted more accurately.
In addition, the embodiment of the application also provides a storage medium, wherein the storage medium stores a battery state prediction program, and the battery state prediction program realizes the steps of the battery state prediction method when being executed by a processor.
Referring to fig. 5, fig. 5 is a block diagram showing the structure of a first embodiment of the battery state predicting apparatus according to the present application.
As shown in fig. 5, a battery state prediction apparatus according to an embodiment of the present application includes:
the parameter acquisition module 10 is used for acquiring battery voltage and battery current, and obtaining battery electric quantity according to the battery voltage and the battery current;
the parameter obtaining module 10 is further configured to obtain a total driving mileage, a total driving time, a current driving speed, and a historical charging record;
the state prediction module 20 is configured to obtain a battery usage parameter according to the battery power, the total driving distance, the total driving time, the current driving speed, and the historical charging record;
the state prediction module 20 is further configured to predict a battery state according to the battery usage parameter, so as to obtain a predicted battery state.
According to the embodiment, the battery electric quantity is determined through the voltage and the current, the use parameters of the battery are determined according to the battery electric quantity, the total driving mileage, the total driving time, the current driving speed and the historical charging record, and the use conditions of the battery at a plurality of angles can be obtained based on the use parameters, so that the battery state can be fed back more accurately when the battery state is predicted according to the use parameters, a user can replace or charge the battery according to the battery state, and driving safety is ensured.
In an embodiment, the parameter obtaining module 20 is further configured to determine a remaining battery power according to the battery power, the total driving range, the current driving speed and the historical charging record;
obtaining the residual service life of the battery according to the total running time and the preset service life of the battery;
and obtaining the battery safety capacity according to the preset minimum safety electric quantity and the battery capacity.
In an embodiment, the parameter obtaining module 20 is further configured to obtain a current running time, and obtain a current running mileage according to the current running speed and the current running time;
obtaining a mileage energy consumption value according to the current mileage, the total mileage and a preset battery energy consumption coefficient;
obtaining a reference residual electric quantity according to the battery electric quantity, the mileage energy consumption value and the historical charging record;
and correcting the reference residual electric quantity through preset correction parameters to obtain the residual electric quantity of the battery.
In an embodiment, the parameter obtaining module 20 is further configured to obtain a time correction parameter according to the total running time and the preset battery service life;
obtaining mileage correction parameters according to the total driving mileage;
and obtaining a speed correction parameter according to the current running speed.
In an embodiment, the parameter obtaining module 20 is further configured to adjust the reference remaining power according to the time correction parameter to obtain an initial remaining power of the battery;
adjusting the initial residual electric quantity according to the mileage correction parameter to obtain an adjusted initial battery residual electric quantity;
and adjusting the adjusted initial battery residual capacity according to the speed correction parameters to obtain the battery residual capacity.
In an embodiment, the parameter obtaining module 20 is further configured to determine whether a remaining battery power in the battery usage situation is less than or equal to a battery safety capacity;
if the battery residual capacity in the battery service condition is smaller than or equal to the battery safety capacity, predicting the current battery state as a state to be charged, and if the battery residual capacity in the battery service condition is larger than the battery safety capacity, predicting the current battery state as a normal use state;
judging whether the residual service time of the battery is less than or equal to a preset time threshold value;
and if the remaining battery service time is smaller than or equal to a preset time threshold, predicting the current battery state as a dangerous state, and if the remaining battery service time is larger than the preset time threshold, predicting the current battery state as a normal service state.
In an embodiment, the parameter obtaining module 20 is further configured to determine whether the predicted battery state meets an alarm condition;
when the predicted battery state meets an alarm condition, an alarm grade of the predicted battery state is obtained;
and determining alarm content according to the alarm level, and reminding according to the alarm content.
It should be understood that the foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the application as desired, and the application is not limited thereto.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present application, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
Furthermore, it should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) and including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present application.
It should be understood that, although the steps in the flowcharts in the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the figures may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily occurring in sequence, but may be performed alternately or alternately with other steps or at least a portion of the other steps or stages.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the application, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A battery state prediction method, characterized in that the battery state prediction method comprises:
acquiring battery voltage and battery current, and obtaining battery electric quantity according to the battery voltage and the battery current;
acquiring a total driving mileage, a total driving time, a current driving speed and a historical charging record;
obtaining battery use parameters according to the battery electric quantity, the total driving mileage, the total driving time, the current driving speed and the historical charging record;
and predicting the battery state according to the battery use parameters to obtain a predicted battery state.
2. The battery state prediction method according to claim 1, wherein the battery usage parameters include a battery remaining amount, a battery remaining usage time, a battery safety capacity;
and obtaining battery usage parameters according to the battery power, the total driving mileage, the total driving time, the current driving speed and the historical charging record, wherein the battery usage parameters comprise:
determining the remaining battery power according to the battery power, the total driving mileage, the current driving speed and the historical charging record;
obtaining the residual service life of the battery according to the total running time and the preset service life of the battery;
and obtaining the battery safety capacity according to the preset minimum safety electric quantity and the battery capacity.
3. The battery state prediction method according to claim 2, wherein the determining the remaining battery power from the battery power, the total travel distance, the current travel speed, and the history of charging includes:
acquiring current running time, and acquiring current running mileage according to the current running speed and the current running time;
obtaining a mileage energy consumption value according to the current mileage, the total mileage and a preset battery energy consumption coefficient;
obtaining a reference residual electric quantity according to the battery electric quantity, the mileage energy consumption value and the historical charging record;
and correcting the reference residual electric quantity through preset correction parameters to obtain the residual electric quantity of the battery.
4. The battery state prediction method of claim 3, wherein the preset correction parameters include a time correction parameter, a mileage correction parameter, and a speed correction parameter;
the step of correcting the reference residual electric quantity by presetting correction parameters, before obtaining the residual electric quantity of the battery, comprises the following steps:
obtaining a time correction parameter according to the total running time and the service life of the preset battery;
obtaining mileage correction parameters according to the total driving mileage;
and obtaining a speed correction parameter according to the current running speed.
5. The battery state prediction method according to claim 4, wherein the correcting the reference remaining power by the preset correction parameter to obtain the remaining power of the battery includes:
adjusting the reference residual electric quantity according to the time correction parameter to obtain an initial battery residual electric quantity;
adjusting the initial residual electric quantity according to the mileage correction parameter to obtain an adjusted initial battery residual electric quantity;
and adjusting the adjusted initial battery residual capacity according to the speed correction parameters to obtain the battery residual capacity.
6. The battery state prediction method according to claim 1, wherein the predicting the battery state using the parameter to obtain the predicted battery state includes:
judging whether the residual electric quantity of the battery in the use condition of the battery is smaller than or equal to the safe capacity of the battery;
if the battery residual capacity in the battery service condition is smaller than or equal to the battery safety capacity, predicting the current battery state as a state to be charged, and if the battery residual capacity in the battery service condition is larger than the battery safety capacity, predicting the current battery state as a normal use state;
judging whether the residual service time of the battery is less than or equal to a preset time threshold value;
and if the remaining battery service time is smaller than or equal to a preset time threshold, predicting the current battery state as a dangerous state, and if the remaining battery service time is larger than the preset time threshold, predicting the current battery state as a normal service state.
7. The battery state prediction method according to claim 6, wherein the predicting the battery state according to the battery usage parameter, after obtaining the predicted battery state, further comprises:
judging whether the predicted battery state meets an alarm condition or not;
when the predicted battery state meets an alarm condition, an alarm grade of the predicted battery state is obtained;
and determining alarm content according to the alarm level, and reminding according to the alarm content.
8. A battery state prediction apparatus, characterized by comprising:
the parameter acquisition module is used for acquiring battery voltage and battery current and obtaining battery electric quantity according to the battery voltage and the battery current;
the parameter acquisition module is also used for acquiring total driving mileage, total driving time, current driving speed and historical charging record;
the state prediction module is used for obtaining battery use parameters according to the battery electric quantity, the total driving mileage, the total driving time, the current driving speed and the historical charging record;
and the state prediction module is also used for predicting the battery state according to the battery use parameters to obtain a predicted battery state.
9. A battery state prediction apparatus, characterized in that the apparatus comprises: a memory, a processor, and a battery state prediction program stored on the memory and executable on the processor, the battery state prediction program configured to implement the battery state prediction method of any one of claims 1 to 7.
10. A storage medium having stored thereon a battery state prediction program which, when executed by a processor, implements the battery state prediction method according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117556589A (en) * | 2024-01-04 | 2024-02-13 | 江阴飞阳电子科技有限公司 | Intelligent calibration method and system for electric quantity of instrument |
| CN117676685A (en) * | 2023-12-14 | 2024-03-08 | 深圳市华芯物联科技有限公司 | Power supply state monitoring and early warning system and method for 5G communication equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117676685A (en) * | 2023-12-14 | 2024-03-08 | 深圳市华芯物联科技有限公司 | Power supply state monitoring and early warning system and method for 5G communication equipment |
| CN117556589A (en) * | 2024-01-04 | 2024-02-13 | 江阴飞阳电子科技有限公司 | Intelligent calibration method and system for electric quantity of instrument |
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