CN114896779A - Service life optimization method and device of power battery, vehicle and storage medium - Google Patents

Service life optimization method and device of power battery, vehicle and storage medium Download PDF

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Publication number
CN114896779A
CN114896779A CN202210482232.XA CN202210482232A CN114896779A CN 114896779 A CN114896779 A CN 114896779A CN 202210482232 A CN202210482232 A CN 202210482232A CN 114896779 A CN114896779 A CN 114896779A
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service life
current
power battery
optimal
parameters
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李旭影
周炳伟
熊建
邵赓华
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Beiqi Foton Motor Co Ltd
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Beiqi Foton Motor Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/04—Ageing analysis or optimisation against ageing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • 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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Abstract

The application relates to the technical field of automobiles, in particular to a service life optimization method and device of a power battery, a vehicle and a storage medium, wherein the method comprises the following steps: when the vehicle is detected to be in a service life mode, acquiring current parameters of a power battery; identifying the current control intention of a user, and matching target control parameters of the power battery corresponding to the current control intention; and optimizing the target control parameters of the power battery according to the current parameters to obtain the current optimal control parameters of the power battery, and controlling the power battery to execute a charging action and/or a discharging action based on the current optimal control parameters so that the power battery is in the best service life. Therefore, the problems that the optimal service life of the power battery of the vehicle cannot be guaranteed, the service life is short, the optimal service performance of the battery is reduced, the cost performance of the vehicle is low and the like in the related technology are solved.

Description

Service life optimization method and device of power battery, vehicle and storage medium
Technical Field
The application relates to the technical field of automobiles, in particular to a method and a device for optimizing the service life of a power battery, a vehicle and a storage medium.
Background
For a vehicle equipped with a power battery, for example, the driving mode of an electric vehicle is generally followed by the driving mode of a fuel vehicle, such as an economy mode, a standard mode, a sport mode, and the like, and the driving performance and the comfort level of different modes are different.
However, in the related art, the power battery is usually controlled from the viewpoint of power and comfort of the electric vehicle, and the influence of the battery life on the power battery is ignored, so that the control mode for the power battery in the related art cannot ensure the best service life, the service life of the power battery is greatly shortened, and the cost performance of the electric vehicle and the best service performance of the power battery are reduced.
Disclosure of Invention
The application provides a service life optimization method and device for a power battery, a vehicle and a storage medium, and aims to solve the problems that the service life of the power battery of the vehicle cannot be guaranteed to be optimal, the service life is short, the optimal service performance of the battery is reduced, the cost performance of the vehicle is low and the like in the related technology.
An embodiment of a first aspect of the present application provides a method for optimizing a service life of a power battery, including the following steps: when the vehicle is detected to be in a service life mode, acquiring current parameters of a power battery; identifying the current control intention of a user, and matching the target control parameters of the power battery corresponding to the current control intention; and optimizing the target control parameters of the power battery according to the current parameters to obtain the current optimal control parameters of the power battery, and controlling the power battery to execute a charging action and/or a discharging action based on the current optimal control parameters so that the power battery has the optimal service life.
Further, the optimizing the target control parameter of the power battery according to the current parameter to obtain the current optimal control parameter of the power battery includes: matching an optimal service life model of the power battery based on the current parameters; and inputting the target control parameters into the optimal service life model to generate the current optimal control parameters.
Further, the inputting the target control parameter into the optimal service life model and generating the current optimal control parameter include: inputting the target control parameters into the optimal service life model to generate at least one reference control parameter; and identifying the selection intention of the user, and taking the reference control parameter corresponding to the selection intention as the current optimal control parameter.
Optionally, the matching the optimal service life model of the power battery based on the current parameters includes: collecting current environmental information of the vehicle; matching the optimal service life model based on the current parameters and the current environmental information.
Optionally, the matching the optimal service life model of the power battery based on the current parameters further includes: identifying identity information of a user, and matching the driving habit of the user according to the identity information; matching the optimal service life model based on the current parameters, the current environmental information, and the driving habits.
The embodiment of the second aspect of the present application provides a service life optimization device for a power battery, including: the acquisition module is used for acquiring the current parameters of the power battery when the vehicle is detected to be in the service life mode; the matching module is used for identifying the current control intention of a user and matching the target control parameters of the power battery corresponding to the current control intention; and the optimization module is used for optimizing the target control parameters of the power battery according to the current parameters to obtain the current optimal control parameters of the power battery, and controlling the power battery to execute a charging action and/or a discharging action based on the current optimal control parameters, so that the power battery is in the best service life.
Further, the optimization module is to: matching an optimal service life model of the power battery based on the current parameters; and inputting the target control parameters into the optimal service life model to generate the current optimal control parameters.
Further, the optimization module is further configured to input the target control parameter into the optimal service life model, and generate at least one reference control parameter; and identifying the selection intention of the user, and taking the reference control parameter corresponding to the selection intention as the current optimal control parameter.
An embodiment of a third aspect of the present application provides a vehicle, comprising: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the service life optimization method of the power battery according to the embodiment.
A fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method for optimizing the service life of a power battery according to the foregoing embodiment.
Therefore, the application has at least the following beneficial effects:
the service life mode of the power battery is increased, the power battery can be controlled to work based on the optimal service life, the service life of the power battery is guaranteed to be optimal, the service life of the power battery can be effectively prolonged, the cost performance of a vehicle is improved, and the optimal use performance of the battery is improved. Therefore, the technical problems that the optimal service performance of the power battery of the vehicle is reduced, the cost performance of the vehicle is low and the like due to the fact that the optimal service life and the short service life of the power battery of the vehicle cannot be guaranteed in the related technology are solved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a diagram illustrating the effect of charge cutoff on battery life in the related art;
FIG. 2 is a diagram illustrating the effect of different discharge depths on battery life in the related art;
FIG. 3 is a graph showing an exemplary effect of temperature on cycle life in the related art;
FIG. 4 is a graph showing an exemplary effect of temperature on a storage capacity in the related art;
FIG. 5 is a schematic flowchart of a method for optimizing service life of a power battery according to an embodiment of the present application;
FIG. 6 is a block diagram of a system for optimizing service life of a power battery according to an embodiment of the present application;
FIG. 7 is a schematic flowchart of a method for optimizing service life of a power battery according to an embodiment of the application;
fig. 8 is a block diagram of a service life optimizing device for a power battery according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of a vehicle according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
For a vehicle equipped with a power battery, for example, a driving mode of an electric vehicle generally follows a driving mode of a fuel vehicle, and generally includes an economy mode, a standard mode, a sport mode, and the like, and the power performance and the comfort level of different modes are different. For example, the power output of the economy mode is relatively gentle and continuous; after the mode is switched to the standard mode, the speed is increased to be stronger most obviously; the exercise mode is suitable for being started at high speed, the output power is increased, and the power of lightly stepping on the accelerator is transmitted to quickly increase the speed. In addition, the power saving, comfort and exercise modes correspond to the energy recovery settings of strong, medium, light and three gears respectively, that is, the difference between the intervention time and the intervention intensity of the energy recovery system. Earlier the energy recovery system intervenes, more power is saved, but the cost of power saving is that the shortage of power and the recovery of kinetic energy affect a part of the ride experience. The greater the energy recovery intervention intensity, the more the sacrificial ride experience, but the more power efficient.
However, in the related art, no matter which driving mode is adopted, only the power output and whether power is saved are considered, and the key index of the service life of the battery is ignored, which affects the service life of the electric vehicle, and the specific steps are as follows:
although rechargeable batteries have a much longer life than primary batteries, their useful life is not unlimited, rechargeable lithium ion batteries typically can be charged up to 1000 and up to 20000 or more times, with the cycle times depending on a number of other factors, in addition to the electrochemical type, including: 1) charging rate, such as comparison of slow charging and fast charging; 2) a charge level, such as 75% full, full or overcharge; 3) a level of discharge before charging; 4) no battery and the temperature of the battery.
As shown in fig. 1 and 2, full charge and full discharge are disadvantageous for prolonging the service life of the battery, wherein, as shown in fig. 1, the influence of the charge cut-off voltage on the service life of the battery is as follows: after 200 cycles, the capacity of the battery has obvious attenuation (different attenuation degrees of batteries of different manufacturers), and a solid line is 4.15V, and a dotted line is 4.10V; as shown in fig. 2, the effect of different depth of discharge on the life of the battery is: the frequent discharge depth exceeds 70-80%, the discharge capacity of the battery is obviously attenuated, and the cycle life of the battery is reduced (the attenuation degree of the batteries of different manufacturers is different).
In addition, temperature also has a certain effect on the life of the mobile battery, and as shown in fig. 3, the effect of temperature on cycle life is: as cycle life is extended, lowering the temperature can cause the battery to contain a higher capacity; as shown in fig. 4, the effect of temperature on storage capacity is: as the storage temperature increases, the capacity decreases dramatically.
Accordingly, both electric and hybrid vehicles present many engineering challenges, primarily because such vehicles require power batteries to meet consumer expectations regarding performance, range, reliability, life, and cost. In order to solve the above problems, embodiments of the present application add a battery life optimization mode for a finished vehicle, which may be configured to select an operating parameter and a charging parameter to emphasize the health of the battery and the battery life in terms of mileage and life.
A service life optimization method, device, vehicle, and storage medium of a power battery according to an embodiment of the present application will be described below with reference to the drawings. Aiming at the problems that the service life of a power battery of a vehicle cannot be guaranteed to be optimal, the service life is short, the optimal service performance of the battery is reduced, the cost performance of the vehicle is low and the like in the related technology mentioned in the background technology, the application provides a service life optimization method of the power battery. Therefore, the technical problems that the optimal service performance of the power battery of the vehicle is reduced, the cost performance of the vehicle is low and the like due to the fact that the optimal service life and the short service life of the power battery of the vehicle cannot be guaranteed in the related technology are solved.
Specifically, fig. 5 is a schematic flowchart of a method for optimizing the service life of a power battery according to an embodiment of the present disclosure. It should be noted that the method of the embodiment of the present application may be applied to a vehicle equipped with a power battery, such as a pure electric vehicle or a hybrid electric vehicle, and is not particularly limited in this regard.
As shown in fig. 5, the method for optimizing the service life of the power battery includes the following steps:
in step S101, upon detecting that the vehicle is in the life mode, the current parameters of the power battery are acquired.
The life mode refers to a mode capable of maximally prolonging the service life of the battery, and in the embodiment of the present application, the vehicle may be controlled to enter the life mode in multiple ways, for example, the vehicle may be controlled to enter the life mode in a key triggering manner or a voice triggering manner, which is not specifically limited.
It can be understood that, when the vehicle enters the life mode, the vehicle is controlled to execute the optimization method of steps S101 to S103, and first, the current parameters of the power battery are obtained, where the current parameters may include information of operating conditions, mileage, usage of the power battery, and the like, and parameters of temperature, current, voltage, and the like.
In step S102, the current control intention of the user is identified, and the target control parameter of the power battery corresponding to the current control intention is matched.
The current control intention refers to an intention of a user for controlling the power battery to discharge or charge, namely, the intention can include a charging control intention and a discharging control intention, for example, when the trigger of an accelerator pedal is detected, the current control intention of the user can be identified as the discharging control intention; when the charging gun connection is detected, it may be recognized that the current control intention of the user is a charging intention.
It is understood that, when a specific control intention of a user is detected, the embodiment of the application may match a control parameter corresponding to the control intention, for example, a discharge parameter or a charge parameter, etc.
In step S103, the target control parameter of the power battery is optimized according to the current parameter to obtain the current optimal control parameter of the power battery, and the power battery is controlled to perform a charging action and/or a discharging action based on the current optimal control parameter, so that the power battery has the optimal service life.
The current optimal control parameter refers to an optimal control parameter corresponding to the optimal service life of the power battery.
It can be understood that the power battery can be controlled to work based on the optimal service life, the service life of the power battery is guaranteed to be optimal, therefore, the purpose of prolonging the service life of the battery is achieved by increasing the optimal service life mode of the battery, the optimal use performance of the battery is improved while the cost performance of a vehicle is improved, the battery can be more reasonably and effectively protected, abuse of the battery is prevented, and safety is improved.
In the embodiment of the present application, optimizing the target control parameter of the power battery according to the current parameter to obtain the current optimal control parameter of the power battery includes: matching an optimal service life model of the power battery based on the current parameters; and inputting the target control parameters into the optimal service life model to generate current optimal control parameters.
It can be understood that, the embodiment of the application can estimate the vehicle usage model based on the current parameters, optimize the key parameters of various vehicle batteries according to the specific information provided by the vehicle usage model of the user, and customize the battery optimal service life model suitable for the vehicle user, so as to meet the requirements of the user on use, comfort, reliability and long service life of the battery.
It should be noted that, the embodiment of the present application may not only match the optimal service life model based on the current parameters, but also match in other various manners, which is not limited in particular. The matching of the optimal service life model in other various ways in the embodiment of the present application may include the following implementation manners:
as a possible implementation manner, matching the optimal service life model of the power battery based on the current parameters includes: collecting current environmental information of a vehicle; the optimal service life model is matched based on current parameters and current environmental information.
In some embodiments, the present environment information may include a geographic position, temperature information, and the like, and in some embodiments, the driving environment of the user, such as the geographic position, and the temperature information of the whole year may be known by using GPS (Global Positioning System) Positioning, big data analysis, and the like.
It can be understood that the environment where the vehicle is located can be considered when the optimal service model is determined, and the optimal service life model is determined through the current parameters and the current environment information, so that the accuracy of matching the optimal service life model is improved, the adaptability of the vehicle to the environment is improved, and the service life optimization effect of the power battery is improved.
Specifically, according to the embodiment of the application, the position information of a user can be mastered through GPS data, some related information of battery parameters can be mastered through big data, a vehicle use model of the user is obtained, key parameters of various batteries used by the vehicle are optimized according to specific information provided by the vehicle use model of the user, and a battery optimal service life model suitable for the vehicle user is customized, so that the requirements of the user are met, and meanwhile, the purpose of prolonging the service life of the battery is achieved.
As another possible implementation manner, matching the optimal service life model of the power battery based on the current parameters further includes: identifying identity information of a user, and matching driving habits of the user according to the identity information; the optimal service life model is matched based on current parameters, current environmental information and driving habits.
The identity information may include fingerprint information, face information, and the like of the user to identify the specific identity of the user.
It can be understood that the driving habit of the user can be considered when the optimal use model is determined, the optimal service life model is determined through the current parameters, the current environment information and the driving habit, the accuracy of model matching is improved, the adaptability of the vehicle to the environment is improved, the use requirement of the user can be effectively met through the optimal use model, and the use experience of the user is improved.
Specifically, the embodiment of the application can combine the GPS and the big data information to more clearly know the use environment, the driving habit and other information of the user and more accurately obtain the use model of the vehicle, so that the use mode is continuously optimized for relevant parameters such as the optimal life mode and the like, the optimal life mode is more suitable for each user, the use environment, the driving habit and other information of the user can be more clearly obtained through the GPS and the big data information, the optimal life mode is more customized, intelligent and personalized, even if a driver is replaced, the use model of the vehicle can be continuously updated through the GPS, the big data information and other information, and the key parameters of the optimal life modes are continuously optimized and corrected.
After obtaining the optimal service life model according to any of the above embodiments, the embodiment of the present application may not only output the current optimal control parameter based on the optimal service life model, but also obtain the current optimal control parameter according to the selection of the user, and in some embodiments, the target control parameter is input to the optimal service life model to generate the current optimal control parameter, including: inputting the target control parameters into the optimal service life model to generate at least one reference control parameter; and identifying the selection intention of the user, and taking the reference control parameter corresponding to the selection intention as the current optimal control parameter.
Wherein a reference control parameter may be understood as a suggested control parameter to be recommended to a user.
It can be understood that the current optimal control parameter can be determined according to the selection of the user, so that the intention of the user can be obtained through information interaction with the user, the use requirements of different users can be met, the purpose of prolonging the service life of the power battery is achieved, and the use experience of the user is improved.
In some embodiments, the Application program (APP) can customize the best life mode of the power battery, so as to prolong the service life of the power battery.
It should be noted that after the life mode is selected, the panel or APP of the vehicle provides some different operating parameters or charging parameters, and provides a plurality of preset parameter values for selection. Wherein the preset parameter values are also allowed to be modified by the car supplier and/or the repair shop or the user of the car by means of the suggested values confirmed by the car supplier. The life model may vary depending on the specifications of the vehicle, such as the chemical architecture of the battery, the capabilities of the thermal management system, the charging configuration, etc., and typically the system will select one or more parameters based on knowing that it is a battery, such as a lithium-ion chemical architecture.
In the best battery life mode, the embodiment of the present application may set the following parameters:
1) setting a maximum upper limit value and a minimum lower limit value of an SOC (State of Charge), which can be specifically set by a person skilled in the art according to an actual situation, and is not specifically limited; for example, a minimum SOC level of 35% and a maximum SOC level of 80% are set, the minimum value of SOC may be 5% higher than the standard mode, and the minimum value of SOC may be 10% lower than the maximum value of SOC.
2) A minimum cell voltage value, such as 3.0V, etc., is set, which is not particularly limited.
3) The temperature of the battery at the time of discharge, for example, the temperature of the battery pack may be maintained between 30 to 35 c, or between 25 to 30 c, etc., which is not particularly limited.
4) The battery temperature to be maintained during charging is set, and the suitable battery temperature can reduce the decay of the service life, for example, the battery temperature can be set to a preset value, such as any value between 35 ℃ and 40 ℃, and the like, which is not particularly limited; after the battery temperature setting is completed, the temperature management of the battery may be performed by a thermal management system of the battery.
5) The storage temperature of the battery is set, and the storage temperature may be maintained between 15-20 ℃, or between 20-25 ℃, etc., which is not particularly limited.
6) The SOC value when stored is set and when the vehicle enters the storage phase, the storage time is set by the user through the interface, for example, the SOC value may be set to 50%, 40%, 30%, etc., while the vehicle has the ability to be charged to a higher SOC value (60%) for the user's convenience. For example, if the user sets a storage date, a driving time, etc. through the interface, the controller may charge to the set SOC value after the storage date is finished.
7) The maximum charging rate is set, in the service life mode, the charging rate is relatively low, the appropriate charging rate is configured according to the fast and slow charging conditions and the charging time required by a user, the more abundant the time is, the lower the charging rate is, the better the battery is kept in a healthy state, and meanwhile, the charging temperature is also considered, for example, 20-25 ℃. The charging is carried out by adopting 1/3C, 15-20 ℃, 1/4C, 1/5C at 10-15 ℃, 1/10C at 0-10 ℃, and meanwhile, the user can input the time of the next driving and can be used for distributing the charging time.
8) Setting the thermal balance in the discharging process, and monitoring to keep the thermal balance within a preset range, for example, the thermal balance is set to be less than or equal to 20 ℃, and the thermal balance of the battery pack can not exceed 20 ℃.
9) The maximum discharge rate is set and provided for a user to select the discharge rates of the high gear, the medium gear and the low gear so as to ensure that the power is enough to carry out safe driving, for example, in a conventional discharge process, the maximum allowable discharge rate is between 1C and 2C, and under the condition that the maximum service life mode meets the safe driving standard of the user, the maximum allowable discharge rate is below 1C, for example, 0.8C.
10) When the user charging time leaves, the charging time is prolonged, specifically: because the vehicle is connected with the charging power supply for a long time, the SOC of the battery can be kept to be higher in a standard mode, for example, about 90 percent, and the battery is easy to damage; therefore, in the life mode, the SOC value during self-discharge can be set to be a lower value, so that when the charging cycle is started, the frequency of repeated charging is reduced, for example, 30% or 20%, the battery is prevented from being damaged, and the purpose of prolonging the service life of the power battery is achieved.
In some embodiments, as the user uses the vehicle for a period of time, such as at intervals of one month, three months, half a year, etc., the latest vehicle use model of the user is obtained by continuous updating periodically according to the GPS and big data information, the key parameters of the battery are optimized, the optimal service life model of the battery of the vehicle user is continuously optimized, and then the model is applied to the actual use of the user, and in the actual use, the use data of the user is continuously collected based on the GPS and the big data platform, and is imported into the use model of the vehicle for optimization and continuous updating.
The method for optimizing the service life of the power battery is explained by a specific embodiment, wherein, as shown in fig. 6, the system for optimizing the service life of the power battery comprises: the system comprises a controller, a sensor, a GPS, a whole vehicle control panel/application APP and a big data platform, wherein the sensor can comprise a temperature sensor, a voltage sensor, a current sensor and the like, and the controller can output a control instruction according to acquired information, such as a discharging instruction, a charging instruction, a heat management instruction and the like.
Based on the system of fig. 6, as shown in fig. 7, the method for optimizing the service life of the power battery comprises the following steps:
first, a parameter value is preset. The parameter value has an initial default value of optimal use, and a user can perform corresponding setting through a whole vehicle panel or a mobile phone application APP according to requirements, wherein the parameter is a relevant key parameter value of the embodiment.
And secondly, transmitting preset parameter values into a battery management system Controller through a CAN (Controller Area Network) Network or WiFi (Wireless Fidelity) or other modes, and storing the parameter values according to the requirements of a specified format by a vehicle use model in the Controller. The vehicle use model can regularly obtain vehicle use data information of a large data platform through a T-BOX (Telematics BOX), and filter, analyze and extract key parameters of the vehicle use information, such as SOC, voltage, current, temperature, output power, charging information, thermal management and other information, such as the SOC use range is analyzed, so that the electric quantity requirement of a user can be known and the lowest and highest values of the SOC can be configured; for another example, the discharge power requirement actually used by the user can be known by analyzing the vehicle speed information, and the power limit of the battery can be configured reasonably with the required power; for another example, by analyzing the temperature information of the battery, the temperature environment of the battery in use can be known, some key parameters for setting the thermal management of the battery, such as configuration of heating power, heating time or whether cooling is on, and the like, and then the key parameter threshold of the user is updated on the basis.
And thirdly, the service life model in the controller can call the key parameter thresholds, the key parameter thresholds are configured according to different requirements of charging, discharging or storing and the like, parameters such as temperature, current, voltage and the like are acquired in real time, output is controlled, such as output power, heating or cooling starting and the like, the vehicle battery is ensured to be used within a set key parameter threshold range, and the purpose of prolonging the use of the battery is achieved.
And fourthly, in the process that a user actually uses the vehicle, the vehicle needs to be provided with a T-box and a GPS device, vehicle data information such as vehicle speed, current, voltage, temperature, SOC, heating or cooling and the like is uploaded to the big data platform through the T-box, and meanwhile, the GPS collects the position information of the whole vehicle in real time and transmits the position information to the big data platform in real time.
According to the method for optimizing the service life of the power battery, the purpose of prolonging the service life of the battery is achieved by increasing the optimal service life mode of the battery, the cost performance of a vehicle is improved, the optimal use performance of the battery is also improved, the battery is protected more reasonably and effectively, abuse of the battery is prevented, and the safety of the power battery is improved; meanwhile, the GPS and the big data information are combined, the information such as the use environment and the driving habit of the user can be accurately acquired, the optimal service life mode is customized, intelligent and personalized, and the use experience of the user is improved.
Next, a service life optimization device of a power battery according to an embodiment of the present application will be described with reference to the drawings.
Fig. 8 is a block diagram schematically illustrating a service life optimizing apparatus for a power battery according to an embodiment of the present application.
As shown in fig. 8, the service life optimizing device 10 for a power battery includes: an acquisition module 100, a matching module 200 and an optimization module 300.
The obtaining module 100 is configured to obtain a current parameter of the power battery when it is detected that the vehicle is in a life mode; the matching module 200 is used for identifying the current control intention of the user and matching the target control parameters of the power battery corresponding to the current control intention; the optimization module 300 is configured to optimize a target control parameter of the power battery according to the current parameter, obtain a current optimal control parameter of the power battery, and control the power battery to perform a charging action and/or a discharging action based on the current optimal control parameter, so that the power battery is in an optimal service life.
In an embodiment of the present application, the optimization module 300 is configured to: matching an optimal service life model of the power battery based on the current parameters; and inputting the target control parameters into the optimal service life model to generate current optimal control parameters.
In the embodiment of the present application, the optimization module 300 is further configured to input the target control parameter into the optimal service life model, and generate at least one reference control parameter; and identifying the selection intention of the user, and taking the reference control parameter corresponding to the selection intention as the current optimal control parameter.
It should be noted that the foregoing explanation of the embodiment of the method for optimizing the service life of a power battery is also applicable to the device for optimizing the service life of a power battery of this embodiment, and will not be described herein again.
According to the service life optimizing device of the power battery, the purpose of prolonging the service life of the battery is achieved by increasing the optimal service life mode of the battery, the cost performance of a vehicle is improved, the optimal service performance of the battery is improved, the battery is protected more reasonably and effectively, abuse of the battery is prevented, and the safety of the power battery is improved; meanwhile, the GPS and the big data information are combined, the information such as the use environment and the driving habit of the user can be accurately acquired, the optimal service life mode is customized, intelligent and personalized, and the use experience of the user is improved.
Fig. 9 is a schematic structural diagram of a vehicle according to an embodiment of the present application. The vehicle may include:
a memory 901, a processor 902 and a computer program stored on the memory 901 and executable on the processor 902.
The processor 902, when executing the program, implements the method for optimizing the service life of the power battery provided in the above-described embodiments.
Further, the vehicle further includes:
a communication interface 903 for communication between the memory 901 and the processor 902.
A memory 901 for storing computer programs executable on the processor 902.
The Memory 901 may include a high-speed RAM (Random Access Memory) Memory, and may also include a nonvolatile Memory, such as at least one disk Memory.
If the memory 901, the processor 902, and the communication interface 903 are implemented independently, the communication interface 903, the memory 901, and the processor 902 may be connected to each other through a bus and perform communication with each other. The bus may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 9, but this does not indicate only one bus or one type of bus.
Optionally, in a specific implementation, if the memory 901, the processor 902, and the communication interface 903 are integrated on a chip, the memory 901, the processor 902, and the communication interface 903 may complete mutual communication through an internal interface.
The processor 902 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
Embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the service life optimization method for a power battery as above.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a programmable gate array, a field programmable gate array, or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims (10)

1. A service life optimization method of a power battery is characterized by comprising the following steps:
when the vehicle is detected to be in a service life mode, acquiring current parameters of a power battery;
identifying the current control intention of a user, and matching the target control parameters of the power battery corresponding to the current control intention; and
and optimizing the target control parameters of the power battery according to the current parameters to obtain the current optimal control parameters of the power battery, and controlling the power battery to execute a charging action and/or a discharging action based on the current optimal control parameters so that the power battery has the optimal service life.
2. The method according to claim 1, wherein the optimizing the target control parameter of the power battery according to the current parameter to obtain the current optimal control parameter of the power battery comprises:
matching an optimal service life model of the power battery based on the current parameters;
and inputting the target control parameters into the optimal service life model to generate the current optimal control parameters.
3. The method of claim 2, wherein said inputting said target control parameters into said optimal service life model, generating said current optimal control parameters, comprises:
inputting the target control parameters into the optimal service life model to generate at least one reference control parameter;
and identifying the selection intention of the user, and taking the reference control parameter corresponding to the selection intention as the current optimal control parameter.
4. The method of claim 2, wherein said matching the optimal service life model of the power cell based on the current parameters comprises:
collecting current environmental information of the vehicle;
matching the optimal service life model based on the current parameters and the current environmental information.
5. The method of claim 4, wherein said matching the optimal service life model of the power cell based on the current parameters further comprises:
identifying identity information of a user, and matching the driving habit of the user according to the identity information;
matching the optimal service life model based on the current parameters, the current environmental information, and the driving habits.
6. A service life optimization device for a power battery, comprising:
the acquisition module is used for acquiring the current parameters of the power battery when the vehicle is detected to be in the service life mode;
the matching module is used for identifying the current control intention of a user and matching the target control parameters of the power battery corresponding to the current control intention; and
and the optimization module is used for optimizing the target control parameters of the power battery according to the current parameters to obtain the current optimal control parameters of the power battery, and controlling the power battery to execute a charging action and/or a discharging action based on the current optimal control parameters, so that the power battery is in the best service life.
7. The apparatus of claim 6, wherein the optimization module is configured to: matching an optimal service life model of the power battery based on the current parameters; and inputting the target control parameters into the optimal service life model to generate the current optimal control parameters.
8. The apparatus of claim 7, wherein the optimization module is further configured to input the target control parameter into the optimal service life model, generating at least one reference control parameter; and identifying the selection intention of the user, and taking the reference control parameter corresponding to the selection intention as the current optimal control parameter.
9. A vehicle, characterized by comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method for optimizing the service life of a power battery according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor for implementing the method for optimizing the service life of a power cell according to any one of claims 1 to 7.
CN202210482232.XA 2022-05-05 2022-05-05 Service life optimization method and device of power battery, vehicle and storage medium Pending CN114896779A (en)

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