CN109902875B - Information processing method and information processing device - Google Patents
Information processing method and information processing device Download PDFInfo
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Abstract
The application provides an information processing method and an information processing device, wherein the method is applied to an electric automobile and comprises the following steps: acquiring actual parameter information of driving behaviors and expected parameter information obtained through a preset algorithm; obtaining an actual health value of a battery of the electric automobile based on the actual parameter information; obtaining an expected health value of the battery based on the expected parameter information; if the expected health value is greater than the actual health value; and adjusting the actual parameter information to the expected parameter information in a preset mode, outputting corresponding prompt information, and guiding the driving behavior of the user through the prompt information so as to achieve the effects of prolonging the service life of the battery, reducing resource waste and improving the service efficiency of the battery.
Description
Technical Field
The present application relates to an information processing method and an information processing apparatus.
Background
With the continuous development and utilization of petroleum resources, on one hand, the petroleum resources are continuously reduced, but the petroleum resources are difficult to regenerate, and on the other hand, certain influence is caused on the environment (such as air). At present, the use of new energy with batteries and the like as cores is advocated by the nation to promote the use of new energy automobiles, but in the prior art, when using new energy automobiles, the use protection of the energy core batteries of the new energy automobiles still has certain defects.
Disclosure of Invention
In view of the above problems in the prior art, the present application provides an information processing method and an information processing apparatus.
The application provides an information processing method, which is applied to an electric automobile and comprises the following steps:
acquiring actual parameter information of driving behaviors and expected parameter information obtained through a preset algorithm;
obtaining an actual health value of a battery of the electric automobile based on the actual parameter information;
obtaining an expected health value of the battery based on the expected parameter information;
if the expected health value is greater than the actual health value;
and adjusting the actual parameter information to the expected parameter information in a preset mode, and outputting corresponding prompt information.
In some embodiments of the present application, the obtaining an actual health value of a battery of the electric vehicle based on the actual parameter information includes:
and inputting the actual parameter information into a health value prediction model trained in advance to obtain an actual health value output by the health value prediction model, wherein the health value prediction model is obtained by training historical parameter information and historical health values.
In some embodiments of the present application, the obtaining of the expected parameter information by using a preset algorithm includes:
obtaining first sub-expected parameter information and second sub-predicted parameter information through a preset algorithm, wherein the first sub-expected parameter information is used for obtaining a first sub-expected health value, the second sub-expected parameter information is used for obtaining a second sub-expected health value, and the preset algorithm is one of simulated annealing, a genetic algorithm or a particle swarm algorithm.
In some embodiments of the present application, the obtaining of the expected health value of the battery based on the expected parameter information includes:
inputting the first sub-expected parameter information into the health value prediction model to obtain the first sub-expected health value output by the health value prediction model;
and inputting the second sub-expected parameter information into the health value prediction model to obtain the second sub-expected health value output by the health value prediction model.
In some embodiments of the present application, the adjusting the actual parameter information to the expected parameter information in a preset manner includes:
adjusting the actual parameter information to the expected parameter information by a predetermined magnitude.
In some embodiments of the present application, said adjusting the actual parameter information to the expected parameter information by a predetermined magnitude comprises:
adjusting the actual parameter information to the first sub-expected parameter information by a predetermined magnitude, or,
and adjusting the actual parameter information to the second sub-expectation parameter information by the predetermined amplitude, wherein the number of times of adjusting the actual parameter information to the predetermined amplitude required by the first sub-expectation parameter information is smaller than the number of times of adjusting the actual parameter information to the predetermined amplitude required by the second sub-expectation parameter information.
An embodiment of the present application further provides an information processing apparatus, including:
the obtaining module is used for obtaining actual parameter information of the driving behavior and expected parameter information obtained through a preset algorithm;
the first processing module is used for obtaining an actual health value of a battery of the electric automobile based on the actual parameter information;
a second processing module for deriving an expected health value of the battery based on the expected parameter information;
an adjustment module for adjusting the expected health value if the expected health value is greater than the actual health value; and adjusting the actual parameter information to the expected parameter information in a preset mode, and outputting corresponding prompt information through an output module.
In some embodiments of the present application, the first processing module is specifically configured to:
and inputting the actual parameter information into a health value prediction model trained in advance to obtain an actual health value output by the health value prediction model, wherein the health value prediction model is obtained by training historical parameter information and historical health values.
In some embodiments of the present application, the obtaining module is specifically configured to:
obtaining first sub-expected parameter information and second sub-predicted parameter information through a preset algorithm, wherein the first sub-expected parameter information is used for obtaining a first sub-expected health value, the second sub-expected parameter information is used for obtaining a second sub-expected health value, and the preset algorithm is one of simulated annealing, a genetic algorithm or a particle swarm algorithm.
In some embodiments of the present application, the second processing module is specifically configured to:
inputting the first sub-expected parameter information into the health value prediction model to obtain the first sub-expected health value output by the health value prediction model;
and inputting the second sub-expected parameter information into the health value prediction model to obtain the second sub-expected health value output by the health value prediction model.
Compared with the prior art, the information processing method and the information processing device provided by the embodiment of the application have the beneficial effects that: the actual health value of the battery of the electric automobile can be obtained by obtaining the actual parameter information of the driving behavior; the expected parameter information is obtained through a preset algorithm, and an expected health value of the battery of the electric automobile can be obtained based on the obtained expected parameter information; and when the expected health value is greater than the actual health value, the actual parameter information can be adjusted to the expected parameter information in a preset mode, and corresponding prompt information is output so as to guide the driving behavior of a user through the prompt information, so that the effects of prolonging the service life of the battery, reducing resource waste and improving the service efficiency of the battery are achieved.
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FIG. 1 is a flow chart of an information processing method in an embodiment of the present application;
fig. 2 is a block diagram of an information processing apparatus in the embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the present application is described in detail below with reference to the accompanying drawings and the detailed description.
Various aspects and features of the present application are described herein with reference to the drawings.
These and other characteristics of the present application will become apparent from the following description of preferred forms of embodiment, given as non-limiting examples, with reference to the attached drawings.
It should also be understood that, although the present application has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of application, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely exemplary of the application and that it may be embodied in various forms. Well-known and/or repeated functions and structures have not been described in detail so as to not unnecessarily obscure the present application with unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present application in virtually any appropriately detailed structure.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the application.
The application provides an information processing method, which is applied to an electric automobile and comprises the following steps:
acquiring actual parameter information of driving behaviors and expected parameter information obtained through a preset algorithm;
obtaining an actual health value of a battery of the electric automobile based on the actual parameter information;
obtaining an expected health value of the battery based on the expected parameter information;
if the expected health value is greater than the actual health value;
and adjusting the actual parameter information to the expected parameter information in a preset mode, and outputting corresponding prompt information.
According to the information processing method, the actual health value of the battery of the electric automobile can be obtained by obtaining the actual parameter information of the driving behavior; the expected parameter information is obtained through a preset algorithm, and an expected health value of the battery of the electric automobile can be obtained based on the obtained expected parameter information; and when the expected health value is greater than the actual health value, the actual parameter information can be adjusted to the expected parameter information in a preset mode, and corresponding prompt information is output so as to guide the driving behavior of a user through the prompt information, so that the effects of prolonging the service life of the battery, reducing resource waste and improving the service efficiency of the battery are achieved.
In order to explain the above technical solutions in more detail, the following describes an information processing method in an embodiment of the present application with a specific embodiment and a drawing.
As shown in fig. 1, fig. 1 is a flowchart of an information processing method in an embodiment of the present application, where the information processing method includes:
step 101: actual parameter information of the driving behavior and expected parameter information obtained through a preset algorithm are obtained. The actual parameter information may be recorded parameter information corresponding to the driving of the electric vehicle by the user within a latest period of time (a preset period of time from the current time, such as a month, a half month, or a week before the current time, which is not explicitly limited herein), and specifically may include one or more of an inside temperature, an outside temperature, a charging frequency, a charging duration, a charging current, a charging voltage, a charging resistance, a remaining amount before charging, a remaining amount after charging, a charging depth, a discharging temperature, a discharging frequency, a discharging duration, a discharging current, a discharging voltage, a remaining amount before discharging, a remaining amount after discharging, a discharging depth, a self-discharging depth, and the like; the expected parameter information can be parameter information randomly generated through a preset algorithm, and the expected parameter information can also comprise one or more of the temperature inside the vehicle, the temperature outside the vehicle, the charging times, the charging time, the charging current, the charging voltage, the charging resistance, the residual electric quantity before charging, the residual electric quantity after charging, the charging depth, the discharging temperature, the discharging times, the discharging time, the discharging current, the discharging voltage, the residual electric quantity before discharging, the residual electric quantity after discharging, the discharging depth, the self-discharging depth and the like; of course, the expected parameter information may also be parameter information generated by the preset algorithm based on the actual parameter information, and when the expected parameter information is parameter information generated based on the actual parameter information, the expected parameter information may be parameter information generated by the preset algorithm with the actual parameter information as a reference value.
In some embodiments of the present application, the obtaining of the expected parameter information by using a preset algorithm includes: obtaining a first sub-expected parameter information and a second sub-expected parameter information by a preset algorithm, wherein the first sub-expected parameter information is used for obtaining a first sub-expected health value, and the first sub-expected parameter information may be parameter information with a smaller deviation from the actual parameter information, so as to facilitate adjusting the actual parameter information to the first sub-expected parameter information, the second sub-expected parameter information is used for obtaining a second sub-expected health value, and the second sub-expected parameter information is parameter information with a larger deviation from the actual parameter information (relative to the deviation between the actual parameter information and the first sub-expected parameter), but the second sub-expected health value obtained based on the second sub-expected parameter information may be higher than the first sub-expected health value obtained based on the first sub-expected parameter information, and characterizing that the second sub-expected parameter information is optimal parameter information, wherein the preset algorithm is one of simulated annealing, genetic algorithm or particle swarm algorithm.
Step 102: and obtaining an actual health value of the battery of the electric automobile based on the actual parameter information. Specifically, the actual health value of the battery of the electric vehicle may be obtained through a corresponding calculation model or algorithm based on the actual parameter information.
In some embodiments of the present application, the obtaining an actual health value of a battery of the electric vehicle based on the actual parameter information includes: and inputting the actual parameter information into a health value prediction model trained in advance to obtain an actual health value output by the health value prediction model, wherein the health value prediction model is obtained by training historical parameter information and historical health values. In this embodiment, the health value prediction model is obtained by training historical parameter information and historical health values, specifically, a health value prediction model is constructed, the historical parameter information of the electric vehicle is used as the input of the health value prediction model, and the historical health value of the electric vehicle is used as the output of the health value prediction model, wherein the historical parameter information can be obtained by extracting statistical characteristics such as total amount, mean value, extreme value, quantile point, segmental distribution and the like, for example, the historical parameter information can be used by the user in each month from 2018 to 1 month, and can be the temperature inside the vehicle, the temperature outside the vehicle, the charging times, the charging duration, the charging current, the charging voltage, the charging resistance, the residual capacity before charging, the residual capacity after charging, the charging depth, One or more of discharge temperature, discharge times, discharge duration, discharge current, discharge voltage, residual capacity before discharge, residual capacity after discharge, discharge depth, self-discharge depth and the like, and accordingly, when the health value prediction model is not trained, the corresponding historical health value can be obtained through a health value algorithm through the corresponding historical parameter information of each month in the 2018 1-10 months, wherein the health value algorithm is as follows:
the health value is current battery capacity/rated battery capacity.
The current battery capacity is obtained in the following manner, specifically referring to the following formula:
the residual capacity represents the ratio of the residual capacity of the battery after the battery is used for a period of time or is left unused for a long time to the capacity of the battery in a fully charged state, the common percentage represents 0-100%, and after conversion, the battery can be completely discharged when the residual capacity is 0, and the battery can be completely fully charged when the residual capacity is 1;
the battery of the electric automobile comprises one or more battery packs, one battery pack comprises a plurality of battery cells, and when the current battery capacity is calculated, the current battery capacity is calculated by using the battery cell with the minimum capacity in the battery cells.
Moreover, when calculating the current battery capacity, the corresponding conditions are also required to be met, including that the current battery capacity can be calculated only after the automobile is in a flameout state for a certain time (such as 30 minutes); after charging, it is necessary to ensure that the difference between the remaining capacity of the battery after charging and the remaining capacity of the battery before charging is greater than a specified threshold (e.g., the difference is greater than or equal to 50%).
After the historical health value corresponding to the historical parameter information is obtained through the historical parameter information, a health value prediction model can be trained based on the historical parameter information and the historical health value. After the training of the health value prediction model is completed, the actual parameter information can be directly input into the health value prediction model to obtain the actual health value of the battery of the electric automobile.
Step 103: obtaining an expected health value of the battery based on the expected parameter information;
wherein, when the expected health value of the battery is obtained based on the expected parameter information, the prediction can be carried out through the following model:
wherein the content of the first and second substances,
is the expected parameter information.
In some embodiments of the present application, if the first sub-prediction parameter information and the second sub-prediction parameter information are obtained by a preset algorithm, respectively, the obtaining of the expected health value of the battery based on the expected parameter information includes: inputting the first sub-expected parameter information into the health value prediction model to obtain the first sub-expected health value output by the health value prediction model; and inputting the second sub-expected parameter information into the health value prediction model to obtain the second sub-expected health value output by the health value prediction model.
Step 104: if the expected health value is greater than the actual health value; and adjusting the actual parameter information to the expected parameter information in a preset mode, and outputting corresponding prompt information. The prompt information is used for guiding a user to perform corresponding operation so as to gradually adjust the acquired actual parameter information of the driving behavior to the expected parameter information corresponding to the expected health value. As an example, the prompt information may suggest that the user turns up or down the temperature in the electric vehicle, suggest that the user charges the electric vehicle when the remaining power is lower than or equal to the first threshold (of course, the user may perform corresponding adjustment on the first threshold), suggest that the user reuses the electric vehicle when the remaining power is higher than or equal to the second threshold (also, the user may perform corresponding adjustment on the second threshold), suggest that the user performs corresponding adjustment on the self-discharge depth, and the like.
In some embodiments of the present application, the adjusting the actual parameter information to the expected parameter information in a preset manner includes: adjusting the actual parameter information to the expected parameter information by a predetermined magnitude. The actual parameter information is adjusted in a preset range step by step, wherein the preset range can be adjusted in a constant value or proportion mode, on one hand, the adjustment range of the actual parameter information is not too large, the influence on the actual use of a user due to the too large adjustment range is avoided, on the other hand, the acceptance of the user on the adjustment can be improved by adjusting the actual parameter information in the preset range step by step, the user can accept the adjustment easily, the use experience of the user is improved, and in addition, certain conditions can be met in the adjustment process, such as keeping the total charging times, the total charging quantity and the discharging depth of the electric automobile, and the like. As an example, according to the actual parameter information of the electric vehicle used by the user in 2018, month 9 and month 10, taking the case of selecting the actual parameter information as the charging time keeping unchanged, for example, the charging current (fast charging) of 30A is performed 15 times in month 9, the corresponding actual health value is 0.85, the charging current (30A) is performed 13 times in month 10, the charging current (slow charging) of 10A is performed 1 time, the corresponding historical health value is 0.88, and in the corresponding expected parameter information obtained by the preset algorithm, the given expected parameter information is the charging current (fast charging) of 30A is performed 2 times, the charging current (slow charging) of 10A is performed 13 times, it can be seen that the deviation between the expected parameter information and the actual parameter information is still relatively large, at this time, the actual parameter information is adjusted to the expected parameter information by a predetermined margin, for example, at 11 months, the prompt message is given to suggest that the user performs charging 11 times at a charging current of 30A (fast charging) and charging 4 times at a charging current of 10A (slow charging), i.e., with a small change width, and the actual parameter information is adjusted to make the user more acceptable.
In some embodiments of the present application, said adjusting the actual parameter information to the expected parameter information by a predetermined magnitude comprises: adjusting the actual parameter information to the first sub-expectation parameter information by a predetermined amplitude, or adjusting the actual parameter information to the second sub-expectation parameter information by the predetermined amplitude, wherein the number of times of adjusting the actual parameter information to the predetermined amplitude required for the first sub-expectation parameter information is smaller than the number of times of adjusting the actual parameter information to the predetermined amplitude required for the second sub-expectation parameter information. In this embodiment, if the expected parameter information is obtained by a particle swarm optimization, a first sub-expected parameter information and a second sub-predicted parameter information may be obtained respectively, where the first sub-expected parameter information may be parameter information with a small relative deviation from the actual parameter information, and the second sub-expected parameter information is parameter information with a large relative deviation from the actual parameter information (relative to the deviation between the actual parameter information and the first sub-expected parameter). As an example, also taking a case that the user uses the actual parameter information of the electric vehicle in 2018 in 9 and 10 months, and taking the selected actual parameter information as the charging frequency to keep unchanged, for example, the charging current (fast charging) of 30A is 15 times in 9 months, the corresponding actual health value is 0.85, the charging current (30A) is 14 times in 10 months, the charging current (slow charging) of 10A is 1 time, the corresponding historical health value is 0.87, and among the corresponding expected parameter information obtained by the particle swarm algorithm, the given first sub-expected parameter information is charging current (fast charging) of 30A for 10 times, the charging current (slow charging) of 10A for 5 times, the second sub-expected parameter information is charging current (fast charging) of 30A for 2 times, and the charging current (slow charging) of 10A for 13 times, wherein the first sub-expectation parameter information is parameter information relatively close to the actual parameter information of the driving behavior, the second sub-expectation parameter information is parameter information relatively deviated from the actual parameter information of the driving behavior, and further, when the actual parameter information is adjusted, the actual parameter information may be adjusted to the first sub-expected parameter information by a predetermined amplitude, for example, in 11 months, the charging current is adjusted to 30A (fast charging) for 12 times, the charging current is adjusted to 10A (slow charging) for 3 times, the charging was conducted 10 times at a charging current of 30A (fast charging) and 5 times at a charging current of 10A (slow charging) after 12 months, i.e. to the first sub-expectation parameter information, after which the actual parameter information, which has been adjusted to the first sub-expectation parameter information, is gradually adjusted to the second sub-expectation parameter information at a predetermined magnitude. Of course, in the actual adjustment process, not only the adjustment is performed according to the charging frequency, but also one or more of the in-vehicle temperature, the charging frequency, the charging time, the charging current, the charging voltage, the charging resistance, the remaining amount before charging, the remaining amount after charging, the charging depth, the discharging temperature, the discharging frequency, the discharging time, the discharging current, the discharging voltage, the remaining amount before discharging, the remaining amount after discharging, the discharging depth, and the like can be adjusted. For example, the charging current is adjusted to 10A step by step in a manner of decreasing in sequence from 32.5A, and so on, which will not be described in detail.
An embodiment of the present application further provides an information processing apparatus, as shown in fig. 2, including:
the system comprises an obtaining module 1, a calculating module and a processing module, wherein the obtaining module is used for obtaining actual parameter information of driving behaviors and expected parameter information obtained through a preset algorithm;
the first processing module 2 is used for obtaining an actual health value of a battery of the electric automobile based on the actual parameter information;
a second processing module 3 for deriving an expected health value of the battery based on the expected parameter information;
an adjustment module 4 for adjusting the expected health value if the expected health value is greater than the actual health value; the actual parameter information is adjusted to the expected parameter information in a preset manner, and a corresponding prompt message is output through the output module 5.
The first processing module 2 and the second processing module 3 may be the same processing module or different processing modules.
In some embodiments of the present application, the first processing module 2 is specifically configured to: and inputting the actual parameter information into a health value prediction model trained in advance to obtain an actual health value output by the health value prediction model, wherein the health value prediction model is obtained by training historical parameter information and historical health values.
In some embodiments of the present application, the obtaining module 1 is specifically configured to: obtaining first sub-expected parameter information and second sub-predicted parameter information through a preset algorithm, wherein the first sub-expected parameter information is used for obtaining a first sub-expected health value, the second sub-expected parameter information is used for obtaining a second sub-expected health value, and the preset algorithm is one of simulated annealing, a genetic algorithm or a particle swarm algorithm.
In some embodiments of the present application, the second processing module 3 is specifically configured to: inputting the first sub-expected parameter information into the health value prediction model to obtain the first sub-expected health value output by the health value prediction model; and inputting the second sub-expected parameter information into the health value prediction model to obtain the second sub-expected health value output by the health value prediction model.
In some embodiments of the present application, the adjusting module 4 is specifically configured to: adjusting the actual parameter information to the expected parameter information by a predetermined magnitude.
In some embodiments of the present application, the adjusting module 4 is specifically configured to: adjusting the actual parameter information to the first sub-expectation parameter information by a predetermined amplitude, or adjusting the actual parameter information to the second sub-expectation parameter information by the predetermined amplitude, wherein the number of times of adjusting the actual parameter information to the predetermined amplitude required for the first sub-expectation parameter information is smaller than the number of times of adjusting the actual parameter information to the predetermined amplitude required for the second sub-expectation parameter information.
Since the information processing apparatus described in this embodiment is an information processing apparatus corresponding to the information processing method in this embodiment, a person skilled in the art can understand the specific implementation of the information processing apparatus in this embodiment and various modifications thereof based on the information processing method in this embodiment, and therefore, the information processing apparatus will not be described in detail here. The information processing apparatus corresponding to the information processing method implemented by the person skilled in the art in the embodiment of the present application is within the scope of the protection of the present application.
As will be appreciated by one skilled in the art, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processing module 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 processing module 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.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (9)
1. An information processing method is applied to an electric automobile and comprises the following steps:
acquiring actual parameter information of driving behaviors and acquiring expected parameter information through a preset algorithm;
obtaining an actual health value of a battery of the electric automobile based on the actual parameter information;
obtaining an expected health value of the battery based on the expected parameter information;
if the expected health value is greater than the actual health value;
adjusting the actual parameter information to the expected parameter information in a preset mode, and outputting corresponding prompt information; wherein the adjusting the actual parameter information to the expected parameter information in a preset manner includes: adjusting the actual parameter information to the expected parameter information by a predetermined amplitude;
wherein, obtaining the expected parameter information through a preset algorithm comprises:
obtaining first sub-expected parameter information and second sub-expected parameter information through a preset algorithm, wherein a second sub-expected health value obtained based on the second sub-expected parameter information is higher than a first sub-expected health value obtained based on the first sub-expected parameter information;
wherein the adjusting the actual parameter information to the expected parameter information by a predetermined magnitude comprises: firstly, the actual parameter information is adjusted to the first sub-expected parameter information by a predetermined amplitude, and then the actual parameter information adjusted to the first sub-expected parameter information is gradually adjusted to the second sub-expected parameter information by a predetermined amplitude, wherein the first sub-expected parameter information is parameter information relatively close to the actual parameter information, and the second sub-expected parameter information is parameter information relatively deviated from the actual parameter information.
2. The information processing method according to claim 1, wherein the deriving an actual health value of a battery of the electric vehicle based on the actual parameter information includes:
and inputting the actual parameter information into a health value prediction model trained in advance to obtain an actual health value output by the health value prediction model, wherein the health value prediction model is obtained by training historical parameter information and historical health values.
3. The information processing method according to claim 2, the preset algorithm is one of simulated annealing, a genetic algorithm, or a particle swarm algorithm.
4. The information processing method according to claim 3, the deriving an expected health value of the battery based on the expected parameter information, comprising:
inputting the first sub-expected parameter information into the health value prediction model to obtain the first sub-expected health value output by the health value prediction model;
and inputting the second sub-expected parameter information into the health value prediction model to obtain the second sub-expected health value output by the health value prediction model.
5. The information processing method according to claim 1, wherein the adjusting the actual parameter information to the expected parameter information by a predetermined magnitude includes:
adjusting the actual parameter information to the first sub-expected parameter information by a predetermined magnitude, or,
and adjusting the actual parameter information to the second sub-expectation parameter information by the predetermined amplitude, wherein the number of times of adjusting the actual parameter information to the predetermined amplitude required by the first sub-expectation parameter information is smaller than the number of times of adjusting the actual parameter information to the predetermined amplitude required by the second sub-expectation parameter information.
6. An information processing apparatus comprising:
the obtaining module is used for obtaining actual parameter information of the driving behavior and obtaining expected parameter information through a preset algorithm;
the first processing module is used for obtaining an actual health value of a battery of the electric automobile based on the actual parameter information;
a second processing module for deriving an expected health value of the battery based on the expected parameter information;
an adjustment module for adjusting the expected health value if the expected health value is greater than the actual health value; adjusting the actual parameter information to the expected parameter information in a preset mode, and outputting corresponding prompt information through an output module; wherein the adjusting the actual parameter information to the expected parameter information in a preset manner includes: adjusting the actual parameter information to the expected parameter information by a predetermined amplitude;
wherein, obtaining the expected parameter information through a preset algorithm comprises:
obtaining first sub-expected parameter information and second sub-expected parameter information through a preset algorithm, wherein a second sub-expected health value obtained based on the second sub-expected parameter information is higher than a first sub-expected health value obtained based on the first sub-expected parameter information;
wherein the adjusting the actual parameter information to the expected parameter information by a predetermined magnitude comprises: firstly, the actual parameter information is adjusted to the first sub-expected parameter information by a predetermined amplitude, and then the actual parameter information adjusted to the first sub-expected parameter information is gradually adjusted to the second sub-expected parameter information by a predetermined amplitude, wherein the first sub-expected parameter information is parameter information relatively close to the actual parameter information, and the second sub-expected parameter information is parameter information relatively deviated from the actual parameter information.
7. The information processing apparatus according to claim 6, wherein the first processing module is specifically configured to:
and inputting the actual parameter information into a health value prediction model trained in advance to obtain an actual health value output by the health value prediction model, wherein the health value prediction model is obtained by training historical parameter information and historical health values.
8. The information processing apparatus according to claim 7, the preset algorithm being one of simulated annealing, a genetic algorithm, or a particle swarm algorithm.
9. The information processing apparatus according to claim 8, wherein the second processing module is specifically configured to:
inputting the first sub-expected parameter information into the health value prediction model to obtain the first sub-expected health value output by the health value prediction model;
and inputting the second sub-expected parameter information into the health value prediction model to obtain the second sub-expected health value output by the health value prediction model.
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