CN113306449B - Battery health reminding method and system for new energy automobile - Google Patents
Battery health reminding method and system for new energy automobile Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- 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
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- 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
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Tests Of Electric Status Of Batteries (AREA)
Abstract
The embodiment of the application provides a battery health reminding method and system for a new energy automobile, and belongs to the technical field of new energy automobiles. Comprising the following steps: acquiring operation parameters and charging parameters related to the health of a battery of the new energy automobile; determining a first influence parameter which corresponds to the current charging parameter and shows the influence degree of the battery health and a first sub-influence degree parameter which corresponds to the current charging parameter, a second influence parameter and a second sub-influence degree parameter which corresponds to the first influence degree parameter and the second influence degree parameter; obtaining estimated battery health of the new energy automobile; and when the estimated battery health is smaller than the target health threshold, controlling the new energy automobile to send out a battery excessive loss prompt, and comparing and displaying the higher one of the first and second sub-influence degree parameters. The application combines the behavior habit problem of the battery of the new energy automobile in the use process, evaluates the health condition and the service life of the battery, and increases the service life of the battery.
Description
Technical Field
The application relates to the technical field of new energy automobiles, in particular to a battery health reminding method and system of a new energy automobile.
Background
In the current stage, the new energy automobile mainly adopts DCDC to charge the battery, so that the battery of the new energy automobile is the most important part of the electric automobile. The service life of the battery at the current stage is generally 2-4 years, and for some users with poor habit, the battery is used up very quickly, if the battery is not replaced, the condition that the duration of the automobile is reduced, other parts of the automobile are failed and the like occurs, and if the battery is replaced, the price is higher. Therefore, the service life of the battery at the present stage is a key technology for influencing the popularization of new energy automobiles.
Disclosure of Invention
The application aims to provide a battery health reminding method and a system for a new energy automobile, which can be used for evaluating the health condition and the service life of a battery by combining the behavior habit problem of the battery of the new energy automobile in the use process, so that a user can protect the battery and the service life of the battery can be prolonged.
In order to achieve the above object, an embodiment of the present application provides a method for reminding a new energy automobile of battery health, which is characterized in that the method for reminding the new energy automobile of battery health includes: acquiring operation parameters and charging parameters related to the health of a battery of a new energy automobile, wherein the operation parameters comprise the pedal degree condition of an automobile accelerator and the service condition of automobile functional equipment, and the charging parameters comprise the charging times, the charging power and the automobile working mode during charging; determining a first influence parameter corresponding to the current operation parameter and showing the influence degree of the battery health and a first sub-influence degree parameter corresponding to the first influence degree parameter; determining a second influence parameter corresponding to the current charging parameter and showing the influence degree of the battery health and a second sub-influence degree parameter corresponding to the second influence degree parameter respectively based on the second battery health evaluation model; acquiring estimated battery health of the new energy automobile under the influence of the first influence parameter and the second influence parameter; and acquiring a target health degree threshold value of the new energy automobile in the current use time, and controlling the new energy automobile to send out a battery excessive loss reminding when the estimated battery health degree is smaller than the target health degree threshold value, and comparing and displaying the higher one of the first sub-influence degree parameters and the higher one of the second sub-influence degree parameters.
Preferably, the method for acquiring the first battery health evaluation model includes: establishing an untrained first battery health evaluation model, wherein the input of the first battery health evaluation model is an operation parameter, and the output is a first influence parameter and a first sub-influence degree parameter corresponding to the first influence parameter; acquiring historical data of an operation parameter, a first influence parameter and a first sub-influence degree parameter corresponding to the operation parameter and the first influence parameter; and training based on the historical data to obtain the trained first battery health assessment model.
Preferably, the obtaining the estimated battery health of the new energy automobile under the influence of the first influence parameter and the second influence parameter includes: acquiring current model information of an automobile; determining the current battery health degree weight corresponding to the first influence parameter and the second influence parameter of the current vehicle type information according to the weight corresponding relation between the preset vehicle type information and the battery health degree weight; and calculating the estimated battery health of the new energy automobile based on the current battery health weight, the first influence parameter and the second influence parameter.
Preferably, the calculating the estimated battery health SoH of the new energy automobile based on the current battery health weights a and b, the first influence parameter p and the second influence parameter q includes:
calculating the estimated battery health SoH of the new energy automobile by the following formula:
SoH＝a*p+b*q。
preferably, the obtaining the target health degree threshold value of the new energy automobile at the current use time includes: cong Yun platform obtains the curve correspondence of vehicle type information and a health threshold curve, wherein the health threshold curve is configured as a correlation curve of changing use time and a target health threshold; determining a current health threshold curve corresponding to the current vehicle type information based on the curve corresponding relation; and determining a target health degree threshold corresponding to the current use time based on the current health threshold curve.
Preferably, the method for reminding the health of the battery of the new energy automobile further comprises the following steps: acquiring the change rate of the current estimated battery health and the last estimated battery health; and when the change rate is larger than a preset change threshold, controlling the new energy automobile to send out abnormal use reminding of the battery.
In addition, the application also provides a battery health reminding system of the new energy automobile, which comprises: the system comprises a parameter acquisition unit, a battery management unit and a charging unit, wherein the parameter acquisition unit is used for acquiring operation parameters and charging parameters related to the health of a battery of a new energy automobile, the operation parameters comprise the pedal degree condition of an automobile accelerator and the service condition of automobile functional equipment, and the charging parameters comprise the charging times, the charging power and the working mode of the automobile during charging; a first parameter determining unit, configured to determine, based on a first battery health evaluation model, a first influence parameter corresponding to a current operation parameter and showing a battery health influence degree, and a first sub-influence degree parameter corresponding to the first influence parameter; a second parameter determining unit for determining a second influence parameter corresponding to the current charging parameter and showing the influence degree of the battery health and a second sub-influence degree parameter corresponding to the current charging parameter based on a second battery health evaluation model; the health degree acquisition unit is used for acquiring the estimated battery health degree of the new energy automobile under the influence of the first influence parameter and the second influence parameter; and the reminding display unit is used for acquiring a target health degree threshold value of the new energy automobile in the current use time, controlling the new energy automobile to send out a battery excessive loss reminding when the estimated battery health degree is smaller than the target health degree threshold value, and comparing and displaying one of the higher first sub-influence degree parameters and one of the higher second sub-influence degree parameters.
Preferably, the first parameter determination unit includes: the model building module is used for building an untrained first battery health evaluation model, wherein the input of the first battery health evaluation model is an operation parameter, and the output is a first influence parameter and a first sub-influence degree parameter corresponding to the first influence parameter; the data acquisition module is used for acquiring historical data of the operation parameters, the first influence parameters and the corresponding first sub-influence degree parameters; and a model acquisition module for training based on the historical data to obtain the trained first battery health assessment model.
In addition, the application also provides a machine-readable storage medium, and the machine-readable storage medium is stored with instructions for causing a machine to execute the method for reminding the battery health of the new energy automobile.
In addition, the present application also provides a processor for executing a program, wherein the program is executed to execute: the battery health reminding method of the new energy automobile is as described above.
According to the technical scheme, the battery health reminding method of the new energy automobile combines the operation parameters and the charging parameters related to battery health to realize health assessment of the new energy automobile, predicts the current battery health degree under the influence of the operation parameters and the charging parameters, controls the new energy automobile to send out loss reminding when the predicted battery health degree is smaller than the target health degree threshold, and displays one of the higher first sub-influence degree parameters and one of the higher second sub-influence degree parameters for reminding the automobile of loss.
Additional features and advantages of embodiments of the application will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the embodiments of the application. In the drawings:
fig. 1 is a flowchart illustrating a battery health reminding method of a new energy automobile according to the present application;
FIG. 2 is a flow chart illustrating the method of the present application for obtaining estimated battery health of the new energy vehicle under the influence of the first influencing parameter and the second influencing parameter;
FIG. 3 is a flow chart illustrating the acquisition of the target health threshold of the new energy automobile at the current use time according to the present application; and
fig. 4 is a block diagram illustrating a battery health reminding system of the new energy automobile according to the present application.
Detailed Description
The following describes the detailed implementation of the embodiments of the present application with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the application, are not intended to limit the application.
Fig. 1 is a flowchart illustrating a battery health reminding method of a new energy automobile according to the present application, and as shown in fig. 1, the battery health reminding method of a new energy automobile includes:
s101, acquiring operation parameters and charging parameters related to the health of a battery of a new energy automobile, wherein the operation parameters comprise the pedal degree condition of an automobile accelerator and the service condition of automobile functional equipment, and the charging parameters comprise the charging times, the charging power and the automobile working mode during charging; the automobile accelerator pedal degree is mainly obtained through an accelerator sensor, the pedal degree is mainly represented by pedal degrees, such as pedal 20%, pedal 30%, and the like, the automobile function equipment can be used by an air conditioner, the playing equipment is used, the more the equipment is used, the larger the power is, the larger the loss of a battery of an automobile is, the more the charging times are, the larger the battery loss is, the working mode of the automobile can enable the mode of waiting for charging to be closed or the mode of starting video and audio playing to be started, and the different modes are different from each other in the loss of the battery.
S102, determining a first influence parameter which corresponds to the current operation parameter and shows the influence degree of the battery health and a first sub-influence degree parameter which corresponds to the first influence degree parameter respectively based on the first battery health evaluation model. The first battery health evaluation model is used for evaluating first influence parameters, and the influence degree of each operation parameter forms a first sub-influence degree parameter together due to the fact that the operation parameters are more in variety, and the first sub-influence degree parameter mainly reflects the influence degree of each operation parameter.
S103, determining second influence parameters which correspond to the current charging parameters and show the influence degree of the battery health and second sub-influence degree parameters which correspond to the second influence degree parameters respectively based on the second battery health evaluation model. The second battery health evaluation model and the first health evaluation model are both used for evaluating the battery health degree, and the influence parameters aimed at by the second battery health evaluation model and the first health evaluation model are different.
S104, obtaining the estimated battery health of the new energy automobile under the influence of the first influence parameter and the second influence parameter.
S105, acquiring a target health degree threshold value of the new energy automobile in the current use time, and controlling the new energy automobile to send out a battery excessive loss reminding when the estimated battery health degree is smaller than the target health degree threshold value, and comparing and displaying the higher one of the first sub-influence degree parameters and the higher one of the second sub-influence degree parameters. The higher one of the above shows the most dominant bad habit or the most dominant cause affecting the battery, which is convenient for the user to find out the problem, and further realizes the protection of the battery.
Preferably, the method for obtaining the first battery health assessment model in S102 may include:
establishing an untrained first battery health evaluation model, wherein the input of the first battery health evaluation model is an operation parameter, and the output is a first influence parameter and a first sub-influence degree parameter corresponding to the first influence parameter;
acquiring historical data of an operation parameter, a first influence parameter and a first sub-influence degree parameter corresponding to the operation parameter and the first influence parameter; and
training based on the historical data to obtain the trained first battery health assessment model.
The second battery health evaluation model in S103 is established in the same manner as the first battery health evaluation model, and will not be described again.
Preferably, as shown in fig. 2, the obtaining the estimated battery health of the new energy automobile under the influence of the first influence parameter and the second influence parameter in S104 includes:
s201, acquiring current vehicle type information of an automobile;
s202, determining the current battery health degree weight corresponding to the first influence parameter and the second influence parameter of the current vehicle type information according to the weight corresponding relation between the preset vehicle type information and the battery health degree weight; and
and S203, calculating the estimated battery health of the new energy automobile based on the current battery health weight, the first influence parameter and the second influence parameter.
Preferably, the calculating the estimated battery health SoH of the new energy automobile based on the current battery health weights a and b, the first influence parameter p and the second influence parameter q includes:
calculating the estimated battery health SoH of the new energy automobile by the following formula:
SoH＝a*p+b*q。
for example, if the battery health degree weight a is 4 and the weight b is 6, the second influence parameter q has a higher influence on the model of the vehicle, and if the second influence parameter q has a larger influence on the estimated battery health degree SoH, the larger the parameter change, the larger the influence on the battery health degree.
Preferably, fig. 3 is a flowchart of the obtaining the target health threshold of the new energy automobile at the current use time, as shown in fig. 3, where the step S105 may include:
s301, cong Yun platform obtains curve correspondence of vehicle type information and a health threshold curve, wherein the health threshold curve is configured as a correlation curve of changing use time and a target health degree threshold;
s302, determining a current health threshold curve corresponding to the current vehicle type information based on the curve correspondence; and
s303, determining a target health degree threshold corresponding to the current use time based on the current health threshold curve.
Preferably, the method for reminding the health of the battery of the new energy automobile further comprises the following steps:
acquiring the change rate of the current estimated battery health and the last estimated battery health; and
and when the change rate is larger than a preset change threshold, controlling the new energy automobile to send out abnormal reminding of battery use.
In addition, the application also provides a battery health reminding system of the new energy automobile, as shown in fig. 4, the battery health reminding system of the new energy automobile can comprise:
the system comprises a parameter acquisition unit, a battery management unit and a charging unit, wherein the parameter acquisition unit is used for acquiring operation parameters and charging parameters related to the health of a battery of a new energy automobile, the operation parameters comprise the pedal degree condition of an automobile accelerator and the service condition of automobile functional equipment, and the charging parameters comprise the charging times, the charging power and the working mode of the automobile during charging;
a first parameter determining unit, configured to determine, based on a first battery health evaluation model, a first influence parameter corresponding to a current operation parameter and showing a battery health influence degree, and a first sub-influence degree parameter corresponding to the first influence parameter;
a second parameter determining unit for determining a second influence parameter corresponding to the current charging parameter and showing the influence degree of the battery health and a second sub-influence degree parameter corresponding to the current charging parameter based on a second battery health evaluation model;
the health degree acquisition unit is used for acquiring the estimated battery health degree of the new energy automobile under the influence of the first influence parameter and the second influence parameter; and
the reminding display unit is used for acquiring a target health degree threshold value of the new energy automobile in the current use time, controlling the new energy automobile to send out a battery excessive loss reminding when the estimated battery health degree is smaller than the target health degree threshold value, and comparing and displaying the higher one of the first sub-influence degree parameters and the higher one of the second sub-influence degree parameters.
Preferably, the first parameter determination unit includes:
the model building module is used for building an untrained first battery health evaluation model, wherein the input of the first battery health evaluation model is an operation parameter, and the output is a first influence parameter and a first sub-influence degree parameter corresponding to the first influence parameter;
the data acquisition module is used for acquiring historical data of the operation parameters, the first influence parameters and the corresponding first sub-influence degree parameters; and
and the model acquisition module is used for training based on the historical data to obtain the trained first battery health evaluation model.
In addition, the application also provides a machine-readable storage medium, wherein the machine-readable storage medium is stored with instructions for causing a machine to execute the method for reminding the battery health of the new energy automobile according to any one of the above methods.
In addition, the present application also provides a processor for executing a program, wherein the program is executed to execute: the battery health reminding method of the new energy automobile is as described above.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, 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, 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.
The present application is 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to 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.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. 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 apparatus that comprises an element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, 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, 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.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (5)
1. The battery health reminding method of the new energy automobile is characterized by comprising the following steps of:
acquiring operation parameters and charging parameters related to the health of a battery of a new energy automobile, wherein the operation parameters comprise the pedal degree condition of an automobile accelerator and the service condition of automobile functional equipment, and the charging parameters comprise the charging times, the charging power and the automobile working mode during charging;
determining a first influence parameter corresponding to the current operation parameter and showing the influence degree of the battery health and a first sub-influence degree parameter corresponding to the first influence degree parameter;
determining a second influence parameter corresponding to the current charging parameter and showing the influence degree of the battery health and a second sub-influence degree parameter corresponding to the second influence degree parameter respectively based on the second battery health evaluation model;
acquiring estimated battery health of the new energy automobile under the influence of the first influence parameter and the second influence parameter; and
acquiring a target health degree threshold value of the new energy automobile in the current use time, and controlling the new energy automobile to send out a battery excessive loss reminding when the estimated battery health degree is smaller than the target health degree threshold value, and comparing and displaying the higher one of the first sub-influence degree parameters and the higher one of the second sub-influence degree parameters;
the method for acquiring the first battery health evaluation model comprises the following steps:
establishing an untrained first battery health evaluation model, wherein the input of the first battery health evaluation model is an operation parameter, and the output is a first influence parameter and a first sub-influence degree parameter corresponding to the first influence parameter;
acquiring historical data of an operation parameter, a first influence parameter and a first sub-influence degree parameter corresponding to the operation parameter and the first influence parameter; and
training based on the historical data to obtain a trained first battery health assessment model;
the obtaining the estimated battery health of the new energy automobile under the influence of the first influence parameter and the second influence parameter includes:
acquiring current model information of an automobile;
determining the current battery health degree weight corresponding to the first influence parameter and the second influence parameter of the current vehicle type information according to the weight corresponding relation between the preset vehicle type information and the battery health degree weight; and
calculating estimated battery health of the new energy automobile based on the current battery health weight, the first influence parameter and the second influence parameter;
the calculating the estimated battery health SoH of the new energy automobile based on the current battery health weights a and b, the first influence parameter p and the second influence parameter q includes:
calculating the estimated battery health SoH of the new energy automobile by the following formula:
SoH=a*p+b*q；
the obtaining the target health degree threshold value of the new energy automobile in the current use time comprises the following steps:
acquiring a curve corresponding relation between vehicle type information and a health threshold curve from a cloud platform, wherein the health threshold curve is configured as a correlation curve of changing service time and a target health threshold;
determining a current health threshold curve corresponding to the current vehicle type information based on the curve corresponding relation; and
and determining a target health degree threshold corresponding to the current use time based on the current health threshold curve.
2. The battery health reminding method of the new energy automobile according to claim 1, characterized in that the battery health reminding method of the new energy automobile further comprises:
acquiring the change rate of the current estimated battery health and the last estimated battery health; and
and when the change rate is larger than a preset change threshold, controlling the new energy automobile to send out abnormal reminding of battery use.
3. The utility model provides a battery health warning system of new energy automobile which characterized in that, the battery health warning system of new energy automobile includes:
the system comprises a parameter acquisition unit, a battery management unit and a charging unit, wherein the parameter acquisition unit is used for acquiring operation parameters and charging parameters related to the health of a battery of a new energy automobile, the operation parameters comprise the pedal degree condition of an automobile accelerator and the service condition of automobile functional equipment, and the charging parameters comprise the charging times, the charging power and the working mode of the automobile during charging;
a first parameter determining unit, configured to determine, based on a first battery health evaluation model, a first influence parameter corresponding to a current operation parameter and showing a battery health influence degree, and a first sub-influence degree parameter corresponding to the first influence parameter;
a second parameter determining unit for determining a second influence parameter corresponding to the current charging parameter and showing the influence degree of the battery health and a second sub-influence degree parameter corresponding to the current charging parameter based on a second battery health evaluation model;
the health degree acquisition unit is used for acquiring the estimated battery health degree of the new energy automobile under the influence of the first influence parameter and the second influence parameter; and
the reminding display unit is used for acquiring a target health degree threshold value of the new energy automobile in the current use time, controlling the new energy automobile to send out a battery excessive loss reminding when the estimated battery health degree is smaller than the target health degree threshold value, and comparing and displaying the higher one of the first sub-influence degree parameters and the higher one of the second sub-influence degree parameters;
the first parameter determination unit includes:
the model building module is used for building an untrained first battery health evaluation model, wherein the input of the first battery health evaluation model is an operation parameter, and the output is a first influence parameter and a first sub-influence degree parameter corresponding to the first influence parameter;
the data acquisition module is used for acquiring historical data of the operation parameters, the first influence parameters and the corresponding first sub-influence degree parameters; and
the model acquisition module is used for training based on the historical data to obtain the trained first battery health evaluation model;
the obtaining the estimated battery health of the new energy automobile under the influence of the first influence parameter and the second influence parameter includes:
acquiring current model information of an automobile;
determining the current battery health degree weight corresponding to the first influence parameter and the second influence parameter of the current vehicle type information according to the weight corresponding relation between the preset vehicle type information and the battery health degree weight; and
calculating estimated battery health of the new energy automobile based on the current battery health weight, the first influence parameter and the second influence parameter;
the calculating the estimated battery health SoH of the new energy automobile based on the current battery health weights a and b, the first influence parameter p and the second influence parameter q includes:
calculating the estimated battery health SoH of the new energy automobile by the following formula:
SoH=a*p+b*q；
the obtaining the target health degree threshold value of the new energy automobile in the current use time comprises the following steps:
acquiring a curve corresponding relation between vehicle type information and a health threshold curve from a cloud platform, wherein the health threshold curve is configured as a correlation curve of changing service time and a target health threshold;
determining a current health threshold curve corresponding to the current vehicle type information based on the curve corresponding relation; and
and determining a target health degree threshold corresponding to the current use time based on the current health threshold curve.
4. A machine-readable storage medium having instructions stored thereon for causing a machine to perform the method of battery health alert for a new energy vehicle of any of claims 1-2.
5. A processor configured to execute a program, wherein the program is configured to, when executed, perform: the method for battery health alert of a new energy automobile according to any one of claims 1-2.
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| CN114047693B (en) * | 2021-10-22 | 2023-12-22 | 合肥工业大学 | Self-adaptive prevention method and system for fire disaster of automobile battery facing charging process |
| CN114295997A (en) * | 2021-12-20 | 2022-04-08 | 江苏纵帆微电子有限公司 | Method for monitoring health state of low-voltage storage battery of automobile by using TBOX |
| CN114154252B (en) * | 2022-02-09 | 2022-04-19 | 北京航空航天大学 | Risk assessment method and device for failure mode of power battery system of new energy automobile |
| CN114839554A (en) * | 2022-05-06 | 2022-08-02 | 苏州琞能能源科技有限公司 | Battery alarm method and device, electronic equipment and storage medium |
| CN115598556A (en) * | 2022-05-16 | 2023-01-13 | 深圳市道通合创新能源有限公司（Cn） | Method for determining state of health of power battery of electric vehicle and server |
| CN115117981B (en) * | 2022-08-26 | 2022-11-18 | 深圳市高健实业股份有限公司 | Chip for programmable control of charging of new energy automobile battery |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108254696A (en) * | 2017-12-29 | 2018-07-06 | 上海电气集团股份有限公司 | The health state evaluation method and system of battery |
| CN110244238A (en) * | 2018-03-09 | 2019-09-17 | 宁德时代新能源科技股份有限公司 | Battery health state detection method, system and computer readable storage medium |
| CN112034350A (en) * | 2020-08-28 | 2020-12-04 | 厦门科灿信息技术有限公司 | Battery pack health state prediction method and terminal equipment |
| CN112433169A (en) * | 2020-11-25 | 2021-03-02 | 北京理工新源信息科技有限公司 | Cloud power battery health degree evaluation system and method |
| US10942223B1 (en) * | 2019-07-31 | 2021-03-09 | Cox Automotive, Inc. | Systems and methods for determining vehicle battery health |
| CN112666464A (en) * | 2021-01-27 | 2021-04-16 | 北京嘀嘀无限科技发展有限公司 | Battery health state prediction method and device, electronic equipment and readable storage medium |
-
2021
- 2021-06-15 CN CN202110658837.5A patent/CN113306449B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108254696A (en) * | 2017-12-29 | 2018-07-06 | 上海电气集团股份有限公司 | The health state evaluation method and system of battery |
| CN110244238A (en) * | 2018-03-09 | 2019-09-17 | 宁德时代新能源科技股份有限公司 | Battery health state detection method, system and computer readable storage medium |
| US10942223B1 (en) * | 2019-07-31 | 2021-03-09 | Cox Automotive, Inc. | Systems and methods for determining vehicle battery health |
| CN112034350A (en) * | 2020-08-28 | 2020-12-04 | 厦门科灿信息技术有限公司 | Battery pack health state prediction method and terminal equipment |
| CN112433169A (en) * | 2020-11-25 | 2021-03-02 | 北京理工新源信息科技有限公司 | Cloud power battery health degree evaluation system and method |
| CN112666464A (en) * | 2021-01-27 | 2021-04-16 | 北京嘀嘀无限科技发展有限公司 | Battery health state prediction method and device, electronic equipment and readable storage medium |
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