CN112668852A - Method and device for evaluating influence of user usage behavior on battery pack aging - Google Patents
Method and device for evaluating influence of user usage behavior on battery pack aging Download PDFInfo
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Abstract
The embodiment of the application discloses a method and a device for evaluating the influence of user usage behaviors on battery pack aging, wherein the user usage behavior data in the current period of time is obtained, a battery pack service life attenuation coefficient corresponding to the user usage behavior data is obtained according to the user usage behavior data, and the battery pack attenuation coefficient represents the influence of the user usage behaviors on the battery pack service life attenuation, so that the influence degree of the user usage behaviors on the battery pack performance can be quantitatively measured. In addition, after the life attenuation coefficient of the battery pack is obtained, prompt information can be sent according to the life attenuation coefficient of the battery pack to prompt a user to keep the current using behavior or avoid the current using behavior, so that the working performance of the battery pack is ensured.
Description
Technical Field
The application relates to the technical field of automatic control, in particular to a method and a device for evaluating the influence of user usage behaviors on battery pack aging.
Background
With the continuous development of electric vehicle technology and the advantage that the influence on the environment is smaller than that of the traditional vehicle, the electric vehicle is more and more concerned by the public. The electric automobile is driven by a motor to run by taking a vehicle-mounted power supply as power. Specifically, the electric vehicle drives the vehicle to run using electricity stored in the battery pack, and thus the service life of the battery pack determines the service life of the electric vehicle. The service life of the battery pack is limited by many factors, such as different driving habits of users, temperature conditions of the use environment, charging and discharging modes, and the like. That is, the usage behavior of the user seriously affects the performance of the battery pack, and how to obtain the effect of the usage behavior of the user on the performance of the battery pack is an urgent problem to be solved.
Disclosure of Invention
In view of this, embodiments of the present application provide a method and an apparatus for evaluating an influence of a user usage behavior on aging of a battery pack, so as to achieve more reasonable and effective obtaining of an influence degree of the user usage behavior on performance degradation of the battery pack.
In order to solve the above problem, the technical solution provided by the embodiment of the present application is as follows:
in a first aspect of the embodiments of the present application, a method for evaluating an influence of a user usage behavior on aging of a battery pack is provided, where the method includes:
acquiring user usage behavior data, wherein the user usage behavior data represents usage behavior of the battery pack by a user;
and determining a life attenuation coefficient of the battery pack according to the user usage behavior data, wherein the battery pack attenuation coefficient represents the influence degree of the user usage behavior data on the life attenuation of the battery pack.
In one possible implementation, the method further includes:
and sending a prompt message according to the service life attenuation coefficient of the battery pack, wherein the prompt message is used for prompting a user to keep or avoid the use behavior of the battery pack.
In one possible implementation, the method further includes:
and obtaining a use behavior evaluation result according to the life attenuation coefficient of the battery pack and the user use behavior data, wherein the prompt message comprises the use behavior evaluation result.
In one possible implementation manner, the obtaining of the usage behavior evaluation result according to the battery pack life decay coefficient and the user usage behavior data includes:
obtaining an evaluation result corresponding to the use behavior data of each dimension;
and obtaining a use behavior evaluation result according to the evaluation result corresponding to each dimension use behavior data and a weight coefficient, wherein the weight coefficient is used for indicating the contribution strength of each dimension use behavior data to the life attenuation coefficient of the battery pack.
In one possible implementation, the method further includes:
and sending the target user usage behavior data with the usage behavior evaluation result meeting the preset conditions to a client to prompt a user to use the vehicle battery pack according to the target user usage behavior data.
In a possible implementation manner, the obtaining an evaluation result corresponding to the usage behavior data of each dimension includes:
inputting the user using behavior data and the battery pack attenuation coefficient into a using behavior evaluation model, and obtaining an evaluation result corresponding to each dimension using behavior data, wherein the using behavior evaluation model is generated according to training of using behavior data of a training user and a label corresponding to each dimension using behavior data in the training using user behavior data.
In a possible implementation manner, the obtaining a battery pack life attenuation coefficient according to the user usage behavior data includes:
and inputting the user use behavior data into an attenuation correlation model to obtain a life attenuation coefficient of the battery pack, wherein the attenuation correlation model is generated according to training user use behavior data and labels corresponding to the training user use behavior data.
In a second aspect of the embodiments of the present application, there is provided an apparatus for evaluating an influence of usage behavior of a user on aging of a battery pack, the apparatus including:
the acquisition unit is used for acquiring user usage behavior data which represents the usage behavior of the battery pack by a user;
the determining unit is used for determining a life attenuation coefficient of the battery pack according to the user usage behavior data, and the battery pack attenuation coefficient represents the influence degree of the user usage behavior data on the life attenuation of the battery pack.
In one possible implementation, the apparatus may further include: a transmitting unit;
and the sending unit is used for sending a prompt message according to the life attenuation coefficient of the battery pack, wherein the prompt message is used for prompting a user to keep or avoid the use behavior of the battery pack.
In a possible implementation manner, the obtaining unit is further configured to obtain a usage behavior evaluation result according to the life decay coefficient of the battery pack and the user usage behavior data, and the prompt message includes the usage behavior evaluation result.
In a possible implementation manner, the obtaining unit is specifically configured to obtain an evaluation result corresponding to the usage behavior data of each dimension; and obtaining a use behavior evaluation result according to the evaluation result corresponding to each dimension use behavior data and a weight coefficient, wherein the weight coefficient is used for indicating the contribution strength of each dimension use behavior data to the life attenuation coefficient of the battery pack.
In one possible implementation, the apparatus further includes: a transmitting unit;
and the sending unit is used for sending the target user use behavior data of which the use behavior evaluation result meets the preset condition to a client so as to prompt a user to use the vehicle according to the target user use behavior data.
In a possible implementation manner, the obtaining unit is specifically configured to input the user usage behavior data and the battery pack attenuation coefficient into a usage behavior evaluation model, and obtain an evaluation result corresponding to each dimension usage behavior data, where the usage behavior evaluation model is generated according to training of the user usage behavior data and a label corresponding to the training usage behavior data.
In a possible implementation manner, the obtaining unit is specifically configured to input the user usage behavior data into an attenuation correlation model to obtain a life attenuation coefficient of the battery pack, where the attenuation correlation model is generated according to training of the user usage behavior data and a label corresponding to the training user usage behavior data.
In a third aspect of embodiments of the present application, a computer-readable storage medium is provided, where instructions are stored in the computer-readable storage medium, and when the instructions are executed on a terminal device, the instructions cause the terminal device to perform the method for evaluating the influence of usage behavior of a user on aging of a battery pack according to the first aspect.
In a fourth aspect of embodiments of the present application, there is provided an apparatus, including: the battery pack aging evaluation method comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program to execute the method for evaluating the influence of the user using behaviors on the aging of the battery pack.
Therefore, the embodiment of the application has the following beneficial effects:
according to the embodiment of the application, the user usage behavior data in the current period of time is firstly acquired, and the user usage behavior data can represent the usage behavior of the user on the battery pack in the latest period of time. And obtaining a battery pack service life attenuation coefficient corresponding to the user use behavior data according to the user use behavior data, wherein the battery pack attenuation coefficient represents the influence of the use behavior of the user on the attenuation of the battery pack service life, and further the influence degree of the use behavior of the user on the battery pack performance can be quantitatively measured. In addition, after the life attenuation coefficient of the battery pack is obtained, prompt information can be sent according to the life attenuation coefficient of the battery pack to prompt a user to keep the current using behavior or avoid the current using behavior. For example, when the life decay coefficient of the battery pack is large, it indicates that the current usage behavior will cause the battery life to decay greatly, so that the battery life is shortened, and the usage behavior should be avoided as much as possible in future use. Or when the life attenuation coefficient of the battery pack is smaller, the current use behavior is not harmful to the life attenuation of the battery, and the user can be prompted to keep the use behavior, so that the working performance of the battery pack is ensured.
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Fig. 1 is a flowchart of a method for evaluating an influence of a user usage behavior on aging of a battery pack according to an embodiment of the present application;
fig. 2 is a structural diagram of an evaluation of an influence of a user usage behavior on battery pack aging according to an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the drawings are described in detail below.
The inventor finds that, in the conventional battery pack life prediction research, the conventional prediction scheme only gives the corresponding battery pack life according to the user usage behavior data, and cannot reflect the influence degree of the user usage behavior data on the battery life decay.
Based on this, the embodiment of the application provides an evaluation method for influence of user usage on battery pack aging, and specifically, for a target vehicle, usage behavior data of a user on the target vehicle is acquired, and the usage behavior data of the user can reflect the usage situation of the user on the battery pack. And determining a life attenuation coefficient of the battery pack according to the user using behavior data, wherein the battery pack attenuation coefficient can represent the influence degree of the using behavior of the user on the life attenuation of the battery pack. When the attenuation coefficient of the battery pack is larger, the damage of the current user using behavior to the battery pack is larger, and the user can be prompted to reduce similar using behaviors as much as possible; when the attenuation coefficient of the battery pack is small, the damage of the current user's use behavior to the battery is small, and then the user can be prompted to keep similar use behavior as much as possible, so that the service life of the battery pack is ensured.
The target vehicle may be an electric vehicle, and the electric vehicle may be a pure electric vehicle, a hybrid electric vehicle, or a fuel cell vehicle.
In order to facilitate understanding of the technical solutions provided in the embodiments of the present application, the technical solutions will be described below with reference to the accompanying drawings.
Referring to fig. 1, which is a flowchart of a method for evaluating an influence of a user usage behavior on aging of a battery pack according to an embodiment of the present application, the method may include:
s101: and acquiring user use behavior data, wherein the user use behavior data represents the use behavior of the battery pack by the user.
In the present embodiment, to determine the degree of influence of the usage behavior of the user on the life decay of the battery pack, the usage behavior data of the user is first acquired. The user use behavior data mainly comprises the driving modes of the user, such as the depth of an accelerator pedal and the depth of a brake pedal; charge and discharge habits, charge and discharge modes, vehicle use frequency and the like. The charging and discharging habit may reflect the charging and discharging operations of the battery pack by the user, such as recharging after the battery runs out, charging when the battery is turned upside down, plugging the battery terminal before the battery terminal is plugged in during charging, and the like. For some bad charging and discharging habits, the service life of the battery pack is greatly reduced. The charging and discharging modes can comprise a charging mode and a discharging mode, and the charging mode can comprise direct current fast charging and alternating current slow charging.
S102: and determining a life attenuation coefficient of the battery pack according to the user usage behavior data, wherein the battery pack attenuation coefficient represents the influence degree of the user usage behavior data on the life attenuation of the battery pack.
In a specific implementation, the life coefficient of the battery pack corresponding to the user usage behavior data may be determined according to the following two ways:
in an example, a functional relationship between the user usage behavior data and the life attenuation coefficient of the battery pack may be pre-constructed, and after the user usage behavior data is obtained, the corresponding life attenuation coefficient of the battery pack is determined by using the functional relationship.
In another example, the user usage behavior data is input into the decay correlation model to obtain a battery pack life decay factor. That is, a decay correlation model may be obtained in advance, and the user usage behavior data may be input into the decay correlation model as input data to obtain the life decay coefficient of the battery pack output by the decay correlation model.
The attenuation correlation model is generated by training according to the training user use behavior data and the classification labels corresponding to the training user use behavior data. The attenuation correlation model can be obtained specifically by:
1) acquiring the use behavior data of a training user and a label corresponding to the use behavior data of the training user.
For training, a decay correlation model is generated, and a large amount of training data, namely training user usage behavior data and a decay coefficient corresponding to the training user usage behavior data, can be obtained in advance. The training user usage behavior data can include various usage behavior data to ensure that the decay correlation model generated by training can identify various user usage behavior data and various application scenarios.
2) And training the parameters of the initial deep learning model according to the using behavior data of the training user and the labels corresponding to the using behavior data of the training user to obtain the attenuation correlation model.
When training is carried out, a corresponding label exists in each kind of training user use behavior data in the obtained training user use behavior data, the label is an attenuation coefficient, parameters of the initial deep learning model are trained by using the training user use data and the corresponding attenuation coefficient until the parameters are converged, and an attenuation correlation model is obtained.
In particular, the deep learning model can be trained in a supervised learning manner. In the actual training process, the deep learning model can calculate the contribution strength of the use behavior of each dimension in the use behavior data of the training user to the current corresponding attenuation coefficient, so as to train the weight coefficient in the deep learning model. For example, the calculation of the contribution strength may be performed using a model having a feature selection function.
In practical application, after the user usage behavior data is input into the attenuation correlation model, not only the attenuation coefficient corresponding to the user usage behavior data can be obtained, but also the contribution strength corresponding to the usage behavior of each dimension in the user usage behavior data can be obtained. For example, the user usage behavior has three dimensions of a, B, and C, the training generates an attenuation correlation model of a + B + C ═ Y, and the attenuation coefficient is Y, and the coefficients a, B, and C are the contribution strengths of the three feature dimensions. That is, the input of the obtained attenuation correlation model is the user usage behavior data, and the output is the life attenuation coefficient and the corresponding contribution strength of each dimension.
After the battery pack attenuation coefficient corresponding to the user usage behavior data is obtained in the above manner, a prompt message for prompting the user to maintain or avoid the usage behavior of the battery pack may be sent according to the battery pack attenuation coefficient. Specifically, when the attenuation coefficient of the battery pack is greater than or equal to a first preset threshold, prompting is performed to avoid similar use behaviors, so that damage to the battery pack is reduced; when the attenuation coefficient of the battery is less than a first preset threshold, the prompt is used to maintain similar usage behavior.
In a specific implementation manner, a usage behavior evaluation result may be obtained according to the life decay coefficient of the battery pack and the user usage behavior data, and the sent prompt message may include the usage behavior evaluation result, where the usage behavior evaluation result is used to reflect whether the user usage behavior corresponding to the user usage behavior data is normative or not, so that the user may intuitively know whether the current usage behavior of the user is normative or not.
Wherein the usage behavior evaluation result can be obtained by:
1) and obtaining an evaluation result corresponding to the use behavior data of each dimension.
In this embodiment, the user usage behavior data includes multiple dimensions of usage behavior data, for example, different dimensions such as a charging and discharging manner and a charging and discharging habit, and an evaluation result corresponding to each dimension of usage behavior data is obtained.
The evaluation result corresponding to each dimension use behavior data can be obtained by using a use behavior evaluation model generated by pre-training. Namely, the user usage behavior data and the battery pack attenuation coefficient are used as input data to be input into the usage behavior evaluation model, and the evaluation result corresponding to each dimension usage behavior data is obtained according to the output result.
The using behavior evaluation model is generated by pre-training according to the using behavior data of the training user and the label corresponding to the using behavior data of each dimension in the using behavior data of the training user. The training user usage behavior data comprises multi-dimensional user usage behavior data, and the label is an attenuation coefficient corresponding to the training user usage behavior and an evaluation result corresponding to each dimensional user usage behavior data in the training user usage behavior data. Specifically, a large amount of training data may be obtained, where the training data includes training user usage behavior data, an attenuation coefficient corresponding to the training user usage behavior data, and an evaluation result corresponding to each dimension usage behavior data in the training user usage behavior data, and the training data is used to train parameters of the initial model to generate a usage behavior evaluation model. The obtained training data may include various use behavior data and attenuation coefficients, so as to ensure that the user use behavior evaluation model generated by training may be applicable to various scenarios.
In particular, the deep learning model can be trained in a supervised learning manner. In the actual training process, the deep learning model can calculate the evaluation result of the current corresponding attenuation coefficient of the use behavior of each dimension in the use behavior data of the training user, so as to train and generate the use behavior evaluation model.
2) And obtaining a use behavior evaluation result according to the evaluation result corresponding to the use behavior data of each dimension and the weight coefficient.
After the evaluation result and the weight coefficient corresponding to the usage behavior data of each dimension are obtained, the evaluation result and the weight coefficient of the usage behavior data of each dimension can be weighted and summed to obtain the usage behavior evaluation result corresponding to the usage behavior data of the user. Wherein the weight coefficient is used for indicating the contribution strength of the usage behavior data of each dimension to the life decay coefficient of the battery pack. When the weight coefficient corresponding to the use behavior data of a certain dimension is larger, the influence of the use behavior data of the dimension on the service life attenuation of the battery pack is larger. For example, the user usage behavior data includes 3 dimensions of usage behavior data, the evaluation result corresponding to the first dimension of usage behavior data is a, and the weight coefficient is 0.6; the evaluation result corresponding to the second dimension use behavior data is b, and the weight coefficient is 0.3; the evaluation result corresponding to the third-dimension usage behavior data is c, the weight coefficient is 0.1, and the usage behavior evaluation result is 0.6 a +0.3 b +0.1 c. The evaluation result may be a specific evaluation score.
In some implementation manners, in order to improve the service life of the battery pack, a standard use behavior may be recommended to the user, specifically, the target user use behavior data whose use behavior evaluation result meets a preset condition is sent to the client, so as to prompt the user to use the vehicle according to the target user use behavior data.
According to the method, in order to detect the influence degree of the use behavior of the user on the life attenuation of the battery pack, the use behavior data of the user in the current period of time is obtained, and the use behavior data of the user can represent the use behavior of the user on the battery pack in the latest period of time. And obtaining a battery pack service life attenuation coefficient corresponding to the user use behavior data according to the user use behavior data, wherein the battery pack attenuation coefficient represents the influence of the use behavior of the user on the attenuation of the battery pack service life, and further the influence degree of the use behavior of the user on the battery pack performance can be quantitatively measured.
In addition, after the life attenuation coefficient of the battery pack is obtained, prompt information can be sent according to the life attenuation coefficient of the battery pack to prompt a user to keep the current using behavior or avoid the current using behavior. For example, when the life decay coefficient of the battery pack is large, it indicates that the current usage behavior will cause the battery life to decay greatly, so that the battery life is shortened, and the usage behavior should be avoided as much as possible in future use. Or when the life attenuation coefficient of the battery pack is smaller, the current use behavior is not harmful to the life attenuation of the battery, and the user can be prompted to keep the use behavior, so that the working performance of the battery pack is ensured.
Based on the foregoing method embodiment, an embodiment of the present application further provides an apparatus for evaluating an influence of a user usage behavior on battery pack aging, referring to fig. 2, which is a structural diagram of an apparatus for evaluating an influence of a user usage behavior on battery pack aging according to an embodiment of the present application, and as shown in fig. 2, the apparatus 200 may include: an acquisition unit 201 and a determination unit 202.
The obtaining unit 201 obtains user usage behavior data, which represents usage behavior of the battery pack by a user. For a specific implementation of the obtaining unit 201, reference may be made to the related description of S101.
A determining unit 202, configured to determine a battery pack life attenuation coefficient according to the user usage behavior data, where the battery pack attenuation coefficient represents a degree of influence of the user usage behavior data on life attenuation of the battery pack. For a specific implementation of the determining unit 202, reference may be made to the related description of S102.
In one possible implementation, the apparatus may further include: a transmitting unit (not shown in the figure);
and the sending unit is used for sending a prompt message according to the life attenuation coefficient of the battery pack, wherein the prompt message is used for prompting a user to keep or avoid the use behavior of the battery pack. For a specific implementation of the sending unit, see the related description of S102.
In a possible implementation manner, the obtaining unit 201 is further configured to obtain a usage behavior evaluation result according to the battery pack life decay coefficient and the user usage behavior data, where the prompt message includes the usage behavior evaluation result. For a specific implementation of the obtaining unit 201, reference may be made to the related description of S102.
In a possible implementation manner, the obtaining unit 201 is specifically configured to obtain an evaluation result corresponding to the usage behavior data of each dimension; and obtaining a use behavior evaluation result according to the evaluation result corresponding to each dimension use behavior data and a weight coefficient, wherein the weight coefficient is used for indicating the contribution strength of each dimension use behavior data to the life attenuation coefficient of the battery pack. For a specific implementation of the obtaining unit 201, reference may be made to the related description of S102.
In one possible implementation, the apparatus further includes: a transmitting unit (not shown in the figure);
and the sending unit is used for sending the target user use behavior data of which the use behavior evaluation result meets the preset condition to a client so as to prompt a user to use the vehicle according to the target user use behavior data. For a specific implementation of the sending unit, see the related description of S102.
In a possible implementation manner, the obtaining unit 201 is specifically configured to input the user usage behavior data and the battery pack attenuation coefficient into a usage behavior evaluation model, and obtain an evaluation result corresponding to each dimension usage behavior data, where the usage behavior evaluation model is generated according to training of the usage behavior data of a training user and a label corresponding to the training usage behavior data. For a specific implementation of the obtaining unit 201, reference may be made to the related description of S102.
In a possible implementation manner, the obtaining unit 201 is specifically configured to input the user usage behavior data into an attenuation correlation model, and obtain a life attenuation coefficient of the battery pack, where the attenuation correlation model is generated according to training of the training user usage behavior data and a label corresponding to the training user usage behavior data.
It should be noted that, for implementation of each unit in this embodiment, reference may be made to related descriptions of the above method embodiments, and details of this embodiment are not described herein again.
In addition, a computer-readable storage medium is further provided, where instructions are stored, and when the instructions are run on a terminal device, the terminal device is caused to execute the method for evaluating the influence of the user usage behavior on the aging of the battery pack.
The embodiment of the application provides a device for realizing evaluation of influence of user use behaviors on aging of a battery pack, which comprises: the processor executes the computer program to realize the method for evaluating the influence of the user using behaviors on the aging of the battery pack.
It should be noted that, in the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. For the system or the device disclosed by the embodiment, the description is simple because the system or the device corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for assessing the effect of user usage behavior on battery pack aging, the method comprising:
acquiring user usage behavior data, wherein the user usage behavior data represents usage behavior of the battery pack by a user;
and determining a life attenuation coefficient of the battery pack according to the user usage behavior data, wherein the battery pack attenuation coefficient represents the influence degree of the user usage behavior data on the life attenuation of the battery pack.
2. The method of claim 1, further comprising:
and sending a prompt message according to the service life attenuation coefficient of the battery pack, wherein the prompt message is used for prompting a user to keep or avoid the use behavior of the battery pack.
3. The method according to claim 1 or 2, characterized in that the method further comprises:
and obtaining a use behavior evaluation result according to the life attenuation coefficient of the battery pack and the user use behavior data, wherein the prompt message comprises the use behavior evaluation result.
4. The method of claim 3, wherein the user usage behavior data comprises a plurality of dimensions of usage behavior data, and wherein obtaining usage behavior assessment results based on the battery pack life decay factor and the user usage behavior data comprises:
obtaining an evaluation result corresponding to the use behavior data of each dimension;
and obtaining a use behavior evaluation result according to the evaluation result corresponding to each dimension use behavior data and a weight coefficient, wherein the weight coefficient is used for indicating the contribution strength of each dimension use behavior data to the life attenuation coefficient of the battery pack.
5. The method according to claim 3 or 4, characterized in that the method further comprises:
and sending the target user use behavior data of which the use behavior evaluation result meets the preset condition to a client so as to prompt a user to use the vehicle according to the target user use behavior data.
6. The method according to claim 3 or 4, wherein the obtaining of the evaluation result corresponding to the usage behavior data of each dimension comprises:
inputting the user using behavior data and the battery pack attenuation coefficient into a using behavior evaluation model, and obtaining an evaluation result corresponding to each dimension using behavior data, wherein the using behavior evaluation model is generated according to training of using behavior data of a training user and a label corresponding to each dimension using behavior data in the training using user behavior data.
7. The method of claim 1, wherein obtaining a battery pack life decay factor from the user usage behavior data comprises:
and inputting the user use behavior data into an attenuation correlation model to obtain a life attenuation coefficient of the battery pack, wherein the attenuation correlation model is generated according to training user use behavior data and labels corresponding to the training user use behavior data.
8. An apparatus for evaluating an effect of usage behavior of a user on aging of a battery pack, the apparatus comprising:
the acquisition unit is used for acquiring user usage behavior data which represents the usage behavior of the battery pack by a user;
the determining unit is used for determining a life attenuation coefficient of the battery pack according to the user usage behavior data, and the battery pack attenuation coefficient represents the influence degree of the user usage behavior data on the life attenuation of the battery pack.
9. A computer-readable storage medium having stored therein instructions that, when run on a terminal device, cause the terminal device to perform the method of assessing the effect of user usage behavior on battery pack aging of any of claims 1-7.
10. An apparatus, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the computer program to perform the method for assessing the effect of user usage behavior on battery pack aging according to any one of claims 1 to 7.
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