CN116243168A - Battery health determination method, device, equipment, medium and product - Google Patents
Battery health determination method, device, equipment, medium and product Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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Abstract
The application relates to a battery health determination method, apparatus, computer device, storage medium and computer program product. The method comprises the following steps: firstly, acquiring battery accumulated data, wherein the battery accumulated data comprises vehicle accumulated driving mileage, vehicle accumulated driving area, vehicle accumulated driving time, battery accumulated charging capacity, battery accumulated discharging capacity, charging modes and accumulated charging capacity corresponding to each charging mode, collecting battery charging data, the battery charging data comprises battery codes, charging current, charging time, charging initial charge state and charging end charge state, then calculating initial health degree of a battery according to the battery charging data, then performing first curve fitting according to the initial health degree and the battery accumulated charging capacity to obtain a target model, and finally determining target health degree according to the target model and the battery accumulated data. The method provided by the application can improve the accuracy of the battery health degree.
Description
Technical Field
The present application relates to the field of vehicle battery technology, and in particular, to a battery health determination method, apparatus, computer device, storage medium, and computer program product.
Background
With the development of electric vehicles, the research on vehicle batteries is more and more advanced, and the battery health is an important research content.
At present, a commonly used battery health degree prediction method is to use the ratio of the charge and discharge capacity of a battery to the standard capacity and the internal circulation resistance to perform machine learning so as to predict the battery health degree, but when the number of samples is limited, the machine learning cannot thoroughly learn all the characteristics, and is limited by the influence of complex working conditions, discontinuous use and other various factors of the battery in the use process, the calculation and prediction precision of the battery health degree are poor, the requirement of the full life cycle of the battery on the precision of the health degree cannot be met, the battery cannot be used and the price value is increased at a vehicle end as much as possible, and the residual use value of the battery is objectively wasted.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a battery health degree determination method, apparatus, computer device, computer readable storage medium, and computer program product that can improve the accuracy of battery health degree prediction.
In a first aspect, the present application provides a method for determining a battery health, the method comprising:
Acquiring battery accumulated data, wherein the battery accumulated data comprises vehicle accumulated driving mileage, vehicle accumulated driving area, vehicle accumulated driving time, battery accumulated charging capacity, battery accumulated discharging capacity, charging mode and accumulated charging capacity corresponding to each charging mode;
collecting battery charging data, wherein the battery charging data comprises a battery code, a charging current, a charging time, a charging initial charge state and a charging end charge state;
calculating the initial health degree of the battery according to the battery charging data;
performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model;
and determining the target health degree according to the target model and the battery accumulated data.
In one embodiment, the charging current is periodically collected, and the calculating the initial health of the battery according to the battery charging data includes:
obtaining the rated capacity of a battery;
determining an average current value according to the charging currents acquired in two adjacent preset periods;
determining a state of charge difference value according to the charge start state of charge and the charge end state of charge;
and calculating the initial health according to the rated capacity of the battery, the average current value, the preset period and the state of charge difference value.
In one embodiment, the calculating the initial health from the battery rated capacity, the average current value, the preset period, and the state of charge difference value includes:
determining a first ratio according to the average current value, the preset period and the state of charge difference value;
determining a second ratio based on the first ratio and the battery rated capacity;
and determining the initial health degree according to the second proportion.
In one embodiment, the performing a first curve fitting according to the initial health and the accumulated charge capacity of the battery to obtain a target model includes:
acquiring a first accumulated charge capacity corresponding to a preset charge mode;
determining a charge ratio according to the first accumulated charge capacity and the accumulated charge capacity of the battery;
grouping a plurality of batteries according to the charging proportion and the vehicle accumulated running area;
determining a plurality of average healthiness degrees in each group according to the grouping result of the grouping and a preset charging capacity;
and performing first curve fitting according to the average health degree and the accumulated charging capacity of the battery to obtain the target model.
In one embodiment, the performing a first curve fitting according to the initial health and the accumulated charge capacity of the battery to obtain a target model includes:
Obtaining a plurality of fitting degrees of different initial fitting modes;
comparing the fitting degree, and determining the initial fitting mode corresponding to the maximum fitting degree as a target fitting mode;
and determining the target model according to the target fitting mode, the initial health degree and the accumulated charging capacity of the battery.
In one embodiment, before the first curve fitting is performed according to the initial health and the accumulated charge capacity of the battery to obtain the target model, the method further includes:
performing a second curve fitting according to the initial health degree and the accumulated charging capacity of the battery to obtain a fitting health degree;
calculating a target difference value between the fitting health degree and the corresponding initial health degree;
and if the target difference value is smaller than or equal to a preset difference value, executing the step of performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model.
In a second aspect, the present application provides a battery health determination apparatus, the apparatus comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring battery accumulated data, and the battery accumulated data comprises vehicle accumulated driving mileage, vehicle accumulated driving area, vehicle accumulated driving time, battery accumulated charging capacity, battery accumulated discharging capacity, charging modes and accumulated charging capacity corresponding to each charging mode;
The system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring battery charging data, and the battery charging data comprises battery codes, charging current, charging time, charging initial charge state and charging end charge state;
the calculating module is used for calculating the initial health degree of the battery according to the battery charging data;
the curve fitting module is used for performing first curve fitting according to the initial health degree and the accumulated charging capacity of the battery to obtain a target model;
and the determining module is used for determining the target health degree according to the target model and the battery accumulated data.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method of any of the embodiments described above when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the embodiments described above.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, implements the steps of the method of any of the embodiments described above.
The method, the device, the computer equipment, the storage medium and the computer program product for determining the battery health degree comprise the steps of firstly obtaining battery accumulated data, wherein the battery accumulated data comprises vehicle accumulated driving mileage, vehicle accumulated driving area, vehicle accumulated driving time, battery accumulated charging capacity, battery accumulated discharging capacity, charging mode and accumulated charging capacity corresponding to each charging mode, collecting battery charging data, wherein the battery charging data comprises battery codes, charging current, charging time, charging initial charge state and charging end charge state, then calculating the initial health degree of a battery according to the battery charging data, then performing first curve fitting according to the initial health degree and the battery accumulated charging capacity to obtain a target model, and finally determining the target health degree according to the target model and the battery accumulated data. According to the method, the initial health degree and the accumulated battery charging capacity are subjected to curve fitting to obtain the target model, and the target health degree is determined according to the target model and the accumulated battery data, so that the accuracy of the battery health degree can be improved.
Drawings
FIG. 1 is a flow chart of a method of determining battery health in one embodiment;
FIG. 2 is a flow chart of an initial health determination method in one embodiment;
FIG. 3 is a flow chart diagram of a method of determining battery health in another embodiment;
FIG. 4 is a first fitted plot containing scatter points in another embodiment;
FIG. 5 is a first fitted plot of a deleted scatter plot in another embodiment;
FIG. 6 is a schematic diagram of a second fitted curve in another embodiment;
FIG. 7 is a block diagram showing the structure of a battery health determination apparatus in one embodiment;
fig. 8 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a method for determining the health of a battery is provided, where the method is applied to a terminal for illustrating, it is understood that the method may also be applied to a server, and may also be applied to a system including the terminal and the server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the steps of:
S202, acquiring battery accumulated data, wherein the battery accumulated data comprises vehicle accumulated driving mileage, vehicle accumulated driving area, vehicle accumulated driving time, battery accumulated charging capacity, battery accumulated discharging capacity, charging modes and accumulated charging capacity corresponding to each charging mode.
The vehicle cumulative travel area refers to all areas where the vehicle arrives from the factory, for example, the vehicle cumulative travel area includes Hubei province, hunan province, and Henan province, the battery cumulative charge capacity refers to the total amount of charge of the battery from the factory, the battery cumulative discharge capacity refers to the total amount of discharge of the battery from the factory, and the charging manner includes quick charging and slow charging, wherein the quick charging and the slow charging are distinguished according to the magnitude of the charging current.
Specifically, the terminal continuously acquires battery accumulated data according to the running condition of the vehicle and the charging condition of the battery.
S204, collecting battery charging data, wherein the battery charging data comprise battery codes, charging current, charging time, charging start charge state and charging end charge state.
The charging time includes a date of charging and a time period spent in the charging process, and the state of charge is used to reflect the remaining capacity of the battery, and is defined as a ratio of the remaining capacity to the battery capacity, and is generally expressed as a percentage.
Specifically, the terminal periodically collects charging current when the battery is charged, and records the battery code, charging time, charging start charge state and charging end charge state.
S206, calculating the initial health degree of the battery according to the battery charging data.
The health degree refers to the percentage of the current capacity and the delivery capacity of the battery, and generally the health degree of a new delivery battery is 100%, and the health degree of an retired battery is about 80%.
Specifically, the terminal calculates the initial health according to the charge start charge state, the charge end charge state and the periodically collected charge current in the battery charge data.
And S208, performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model.
Specifically, the batteries are grouped according to the accumulated running area and the charging mode of the vehicle, the data of a plurality of groups of batteries are respectively input into a plurality of rectangular coordinate systems taking the accumulated charging capacity of the batteries as independent variables and the initial health degree as dependent variable, then the data in each coordinate system is preprocessed, for example, the preprocessing comprises the steps of deleting scattered points in the coordinate system and simplifying the data according to the preset charging capacity, then the points in each coordinate system are fitted by utilizing a plurality of fitting modes to obtain a plurality of fitting curves of each group of data, for example, the fitting modes comprise first polynomial fitting, second polynomial fitting and third polynomial fitting, finally the fitting degree of each fitting curve is obtained, and the fitting curve corresponding to the maximum fitting degree is determined as the target model of the group of data.
S210, determining the target health degree according to the target model and the battery accumulated data.
The target health refers to the current health of the battery as determined from the target model.
Specifically, a target model corresponding to the battery is determined according to the charging mode of the battery and the accumulated running area of the vehicle, and then the target health degree is determined according to the current accumulated charging capacity of the battery and the target model.
In the above method for determining the health degree of the battery, firstly, battery accumulated data is obtained, the battery accumulated data comprises vehicle accumulated driving mileage, vehicle accumulated driving area, vehicle accumulated driving time, battery accumulated charging capacity, battery accumulated discharging capacity, charging mode and accumulated charging capacity corresponding to each charging mode, battery charging data is collected, the battery charging data comprises battery codes, charging current, charging time, charging initial charging state and charging end charging state, then initial health degree of the battery is calculated according to the battery charging data, then first curve fitting is carried out according to the initial health degree and the battery accumulated charging capacity to obtain a target model, and finally the target health degree is determined according to the target model and the battery accumulated data. According to the method, the initial health degree and the accumulated battery charging capacity are subjected to curve fitting to obtain the target model, and the target health degree is determined according to the target model and the accumulated battery data, so that the accuracy of the battery health degree can be improved.
In some embodiments, the charging current is periodically collected, and calculating the initial health of the battery from the battery charging data comprises: obtaining the rated capacity of a battery; determining an average current value according to the charging currents acquired in two adjacent preset periods; determining a state of charge difference value according to the charge start state of charge and the charge end state of charge; and calculating the initial health according to the rated capacity of the battery, the average current value, the preset period and the state of charge difference value.
In the step, the rated capacity of the battery refers to the maximum capacity calibrated on a nameplate of the battery, and the average current value refers to the average value of charging currents acquired in two adjacent preset periods.
Specifically, the terminal inputs the rated capacity of the battery, the average current value, the preset period and the state of charge difference value into a calculation formula to calculate and obtain the initial health degree.
According to the method provided by the step, the initial health degree is determined according to the plurality of battery data, and the calculation accuracy of the initial health degree can be improved.
In some embodiments, as shown in fig. 2, fig. 2 is a flowchart of an initial health determining method in one embodiment, calculating an initial health according to a battery rated capacity, an average current value, a preset period, and a state of charge difference value, including: determining a first proportion according to the average current value, the preset period and the state of charge difference value; determining a second ratio based on the first ratio and the battery rated capacity; the initial health is determined based on the second ratio.
In this step, the calculation formula of the initial health degree is as follows:
wherein I is an average current value, delta t is a preset period, delta SOC is a state of charge difference value, and Q is the rated capacity of the battery.
According to the method provided by the step, the initial health degree is directly calculated by using the calculation formula, so that the efficiency of determining the initial health degree can be improved.
In some embodiments, performing a first curve fit based on the initial health and the accumulated charge capacity of the battery to obtain a target model includes: acquiring a first accumulated charge capacity corresponding to a preset charge mode; determining a charging proportion according to the first accumulated charging capacity and the accumulated charging capacity of the battery; grouping a plurality of batteries according to the charging proportion and the accumulated running area of the vehicle; determining a plurality of average healthiness degrees in each group according to the grouping result of the grouping and a preset charging capacity; and performing first curve fitting according to the average health degree and the accumulated charge capacity of the battery to obtain a target model.
In this step, the charging ratio refers to a ratio between the first accumulated charging capacity and the accumulated charging capacity of the battery, for example, the preset charging mode is quick charging, and then the charging ratio is a ratio between the accumulated quick charging capacity and the accumulated charging capacity of the battery.
Specifically, the terminal calculates the charging proportion according to a preset charging mode, and groups the calculated charging proportion, for example, the charging proportion is divided into 4 groups: 0 to 0.25, 0.25 to 0.5, 0.5 to 0.75 and 0.75 to 1, then grouping the plurality of batteries according to the grouping result of the vehicle cumulative running area and the charging ratio, for example, the vehicle cumulative running area comprises Hubei province and Hunan province, then the plurality of batteries can be divided into 12 groups, then calculating the average value of battery data in each group according to the preset charging capacity, for example, the preset charging capacity is 500Ah, for the batteries grouped into the charging ratio of 0 to 0.25 and the cumulative running area is Hubei province, dividing the battery cumulative charging capacity into a plurality of sections by taking 500Ah as a scale, calculating the average value of all initial health degrees corresponding to the battery cumulative charging capacity in each section, and finally performing curve fitting according to the calculated average values and the battery cumulative charging capacity to obtain a target model of each group of batteries.
According to the method provided by the step, the batteries are grouped to obtain a plurality of target models, so that the accuracy of health degree prediction can be improved.
In some embodiments, performing a first curve fit based on the initial health and the accumulated charge capacity of the battery to obtain a target model includes: obtaining a plurality of fitting degrees of different initial fitting modes; comparing the fitting degrees, and determining an initial fitting mode corresponding to the maximum fitting degree as a target fitting mode; and determining a target model according to the target fitting mode, the initial health degree and the accumulated charging capacity of the battery.
In the step, the preprocessed initial health degree and the battery accumulated charge capacity are fitted by utilizing a plurality of initial fitting modes, then a target fitting mode is determined by comparing the fitting degrees, and finally a target model is determined according to the target fitting mode, the average health degree and the battery accumulated charge capacity.
According to the method provided by the step, curve fitting is carried out by utilizing a plurality of fitting modes, and finally, the most suitable fitting mode is selected according to the fitting degree, so that the accuracy of a fitted curve can be improved.
In some embodiments, before the first curve fitting is performed according to the initial health and the accumulated charge capacity of the battery to obtain the target model, the method further comprises: performing second curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain fitted health degree; calculating a target difference value between the fitting health degree and the corresponding initial health degree; and if the target difference value is smaller than or equal to the preset difference value, executing a step of performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model.
In the step, before performing the first curve fitting, data of each battery is input into a rectangular coordinate system with initial health degree as a dependent variable and accumulated battery charging capacity as an independent variable, a second curve fitting is performed by using a quadratic polynomial fitting method, a fitting health degree corresponding to the accumulated battery charging capacity of each battery on a fitting curve in the coordinate system and a target difference value of the corresponding initial health degree are calculated, if the target difference value is greater than a preset difference value, a point corresponding to the initial health degree in the coordinate system is deleted from the coordinate system, and after deleting all points corresponding to the target difference value greater than the preset difference value, the first curve fitting is performed according to battery data corresponding to the remaining points in the coordinate system.
According to the method provided by the step, the scattered points are deleted by using the curve fitting method, so that the accuracy of the first curve fitting can be improved.
In one embodiment, as shown in fig. 3, fig. 3 is a flowchart of a method for determining the health of a battery in another embodiment, which includes the following:
(1) Vehicle data is collected, consisting essentially of two parts:
1) Charging high frequency raw data (typically 10 s-frame): including battery code, date, charge current, time stamp, charge start state of charge, charge stop state of charge, etc.
2) Battery accumulation information: the method comprises the following fields of driving mileage, main driving regions of the vehicle, calendar days, date, accumulated charge capacity, accumulated discharge capacity, charging mode, accumulated charge capacity corresponding to the charging mode and the like.
(2) Dividing charging data obtained in the step (1) into different charging events according to time and battery codes, and calculating the initial health degree of the charging event after integrating each charging event, wherein the calculation formula of the initial health degree is as follows:
in the formula, the obtained health degree is a percentage (such as 98%), I is an average current of current data of a front frame and a rear frame, Δt is a time difference between the front frame and the rear frame, Δsoc=soc1 (current charge cut-off state of charge) -SOC0 (current charge start state of charge), and Q is a rated capacity of the battery.
(3) The fast charge ratio is calculated in the following manner:
fast charge ratio = accumulated fast charge capacity/accumulated charge capacity, accumulated charge capacity including fast charge and slow charge.
All data are divided into 4 groups according to the current fast-fill ratio: 0 to 0.25, 0.25 to 0.5, 0.5 to 0.75 and 0.75 to 1.
(4) The initial health is taken as the ordinate and the accumulated charge is taken as the abscissa, resulting in fig. 4, which contains a large number of scattered points. The reason for the occurrence of scattered points is that Δsoc is inaccurate due to the too short charging time in the initial health degree calculation process, i×Δt is increased due to the loss of data, and the initial health degree and the actual health degree are greatly different. Such outliers need to be deleted prior to modeling.
The scattered point deleting mode comprises the following steps: the data of each battery is fitted to the initial health of the battery and the accumulated charge amount by using 2 times of curves to obtain a first fitted curve, and when the first fitted health-initial health is more than 0.5% (within 0.5 percent, the first fitted health can be regarded as capacity fluctuation), the point is deleted. The method traverses all the batteries, and the graph after the scattered points are deleted is shown in fig. 5.
(5) All data are further grouped according to different regions and quick charge proportion groups, N groups of data are obtained, and the value of N depends on the conditions of the province of vehicle operation and the quick charge proportion. Such as: group 1: anhui province-0-0.25.
(6) In order to perform the post-fitting, the situation that the fitting line deviates from the trend due to excessive data in the early period is avoided, data simplification is performed on each subdivided group in the step 7, the data in 500Ah are averaged by taking 500Ah as a scale, and a new average point is obtained.
(7) And (3) respectively performing polynomial fitting for 1,2 and 3 times according to the grouping in the step (5), obtaining a plurality of second fitting curves, observing a linear result, and calculating a fitting degree R2 of the fitting. Fig. 6 is an example of a fitting situation and a result of a set of data, wherein fig. 6 is an initial health degree on an ordinate and an accumulated charge amount on an abscissa.
(8) According to experience, the battery attenuation can become faster at the later stage, and a graph is combined, 3 times polynomial is selected to fit the health degree in each sub-division group, and the relationship between the health degree of each group and the accumulated charge amount is obtained. And 99% within the group, the initial health of the battery is within + -1% of the second fit health
As described above, any one battery can find a calculation formula of the corresponding health degree and accumulated charge capacity according to the key information such as the region, accumulated charge-discharge capacity, fast charge proportion and the like, the current health degree of the battery can be calculated, and the accumulated charge amount of the battery attenuated to 80% can be calculated according to the formula.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a battery health degree determining device for realizing the above related battery health degree determining method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the device for determining the health of a battery provided below may be referred to the limitation of the method for determining the health of a battery hereinabove, and will not be described herein.
In one embodiment, as shown in fig. 7, there is provided a battery health determining apparatus 700 including: an acquisition module 701, an acquisition module 702, a calculation module 703, a curve fitting module 704 and a determination module 705, wherein:
the acquiring module 701 is configured to acquire battery accumulated data, where the battery accumulated data includes a vehicle accumulated driving distance, a vehicle accumulated driving area, a vehicle accumulated driving time, a battery accumulated charging capacity, a battery accumulated discharging capacity, a charging mode, and an accumulated charging capacity corresponding to each charging mode.
The collection module 702 is configured to collect battery charging data, where the battery charging data includes a battery code, a charging current, a charging time, a charging start state of charge, and a charging end state of charge.
A calculating module 703, configured to calculate an initial health of the battery according to the battery charging data.
And the curve fitting module 704 is configured to perform a first curve fitting according to the initial health and the accumulated charge capacity of the battery to obtain a target model.
A determining module 705, configured to determine a target health according to the target model and the battery accumulated data.
In some embodiments, the computing module 703 includes:
And the acquisition unit is used for acquiring the rated capacity of the battery.
And the first determining unit is used for determining an average current value according to the charging currents acquired in two adjacent preset periods.
And the second determining unit is used for determining a state of charge difference value according to the charge start state of charge and the charge end state of charge.
And the calculating unit is used for calculating the initial health according to the rated capacity of the battery, the average current value, the preset period and the state of charge difference value.
In some embodiments, the computing unit is further configured to: determining a first ratio according to the average current value, the preset period and the state of charge difference value; determining a second ratio based on the first ratio and the battery rated capacity; and determining the initial health degree according to the second proportion.
In some embodiments, curve fitting module 704 is further configured to: acquiring a first accumulated charge capacity corresponding to a preset charge mode; determining a charge ratio according to the first accumulated charge capacity and the accumulated charge capacity of the battery; grouping a plurality of batteries according to the charging proportion and the vehicle accumulated running area; determining a plurality of average healthiness degrees in each group according to the grouping result of the grouping and a preset charging capacity; and performing first curve fitting according to the average health degree and the accumulated charging capacity of the battery to obtain the target model.
In some embodiments, curve fitting module 704 is further configured to: obtaining a plurality of fitting degrees of different initial fitting modes; comparing the fitting degree, and determining the initial fitting mode corresponding to the maximum fitting degree as a target fitting mode; and determining the target model according to the target fitting mode, the initial health degree and the accumulated charging capacity of the battery.
In some embodiments, the battery health determination apparatus 700 is specifically configured to: performing a second curve fitting according to the initial health degree and the accumulated charging capacity of the battery to obtain a fitting health degree; calculating a target difference value between the fitting health degree and the corresponding initial health degree; and if the target difference value is smaller than or equal to a preset difference value, executing the step of performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model.
The respective modules in the above-described battery health determination apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 8. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of determining battery health. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 8 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of: acquiring battery accumulated data, wherein the battery accumulated data comprises vehicle accumulated driving mileage, vehicle accumulated driving area, vehicle accumulated driving time, battery accumulated charging capacity, battery accumulated discharging capacity, charging mode and accumulated charging capacity corresponding to each charging mode; collecting battery charging data, wherein the battery charging data comprises a battery code, a charging current, a charging time, a charging initial charge state and a charging end charge state; calculating the initial health degree of the battery according to the battery charging data; performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model; and determining the target health degree according to the target model and the battery accumulated data.
In one embodiment, the computing of the initial health of the battery from the battery charge data, implemented when the processor executes the computer program, comprises: obtaining the rated capacity of a battery; determining an average current value according to the charging currents acquired in two adjacent preset periods; determining a state of charge difference value according to the charge start state of charge and the charge end state of charge; and calculating the initial health according to the rated capacity of the battery, the average current value, the preset period and the state of charge difference value.
In one embodiment, calculating the initial health from the battery rated capacity, the average current value, the preset period, and the state of charge difference, as implemented when the processor executes the computer program, comprises: determining a first ratio according to the average current value, the preset period and the state of charge difference value; determining a second ratio based on the first ratio and the battery rated capacity; and determining the initial health degree according to the second proportion.
In one embodiment, performing a first curve fit according to the initial health and the accumulated charge capacity of the battery implemented when the processor executes the computer program to obtain a target model includes: acquiring a first accumulated charge capacity corresponding to a preset charge mode; determining a charge ratio according to the first accumulated charge capacity and the accumulated charge capacity of the battery; grouping a plurality of batteries according to the charging proportion and the vehicle accumulated running area; determining a plurality of average healthiness degrees in each group according to the grouping result of the grouping and a preset charging capacity; and performing first curve fitting according to the average health degree and the accumulated charging capacity of the battery to obtain the target model.
In one embodiment, performing a first curve fit according to the initial health and the accumulated charge capacity of the battery implemented when the processor executes the computer program to obtain a target model includes: obtaining a plurality of fitting degrees of different initial fitting modes; comparing the fitting degree, and determining the initial fitting mode corresponding to the maximum fitting degree as a target fitting mode; and determining the target model according to the target fitting mode, the initial health degree and the accumulated charging capacity of the battery.
In one embodiment, before the first curve fitting according to the initial health and the accumulated charge capacity of the battery implemented when the processor executes the computer program to obtain the target model, the method further includes: performing a second curve fitting according to the initial health degree and the accumulated charging capacity of the battery to obtain a fitting health degree; calculating a target difference value between the fitting health degree and the corresponding initial health degree; and if the target difference value is smaller than or equal to a preset difference value, executing the step of performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring battery accumulated data, wherein the battery accumulated data comprises vehicle accumulated driving mileage, vehicle accumulated driving area, vehicle accumulated driving time, battery accumulated charging capacity, battery accumulated discharging capacity, charging mode and accumulated charging capacity corresponding to each charging mode; collecting battery charging data, wherein the battery charging data comprises a battery code, a charging current, a charging time, a charging initial charge state and a charging end charge state; calculating the initial health degree of the battery according to the battery charging data; performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model; and determining the target health degree according to the target model and the battery accumulated data.
In one embodiment, a computer program, when executed by a processor, calculates an initial health of a battery from the battery charge data, comprising: obtaining the rated capacity of a battery; determining an average current value according to the charging currents acquired in two adjacent preset periods; determining a state of charge difference value according to the charge start state of charge and the charge end state of charge; and calculating the initial health according to the rated capacity of the battery, the average current value, the preset period and the state of charge difference value.
In one embodiment, the calculating of the initial health from the battery rated capacity, the average current value, the preset period, and the state of charge difference, as implemented when the computer program is executed by the processor, comprises: determining a first ratio according to the average current value, the preset period and the state of charge difference value; determining a second ratio based on the first ratio and the battery rated capacity; and determining the initial health degree according to the second proportion.
In one embodiment, a first curve fitting based on the initial health and the battery accumulated charge capacity implemented when the computer program is executed by the processor results in a target model comprising: acquiring a first accumulated charge capacity corresponding to a preset charge mode; determining a charge ratio according to the first accumulated charge capacity and the accumulated charge capacity of the battery; grouping a plurality of batteries according to the charging proportion and the vehicle accumulated running area; determining a plurality of average healthiness degrees in each group according to the grouping result of the grouping and a preset charging capacity; and performing first curve fitting according to the average health degree and the accumulated charging capacity of the battery to obtain the target model.
In one embodiment, a first curve fitting based on the initial health and the battery accumulated charge capacity implemented when the computer program is executed by the processor results in a target model comprising: obtaining a plurality of fitting degrees of different initial fitting modes; comparing the fitting degree, and determining the initial fitting mode corresponding to the maximum fitting degree as a target fitting mode; and determining the target model according to the target fitting mode, the initial health degree and the accumulated charging capacity of the battery.
In one embodiment, before the first curve fitting according to the initial health and the accumulated charge capacity of the battery to obtain the target model, the computer program when executed by the processor further comprises: performing a second curve fitting according to the initial health degree and the accumulated charging capacity of the battery to obtain a fitting health degree; calculating a target difference value between the fitting health degree and the corresponding initial health degree; and if the target difference value is smaller than or equal to a preset difference value, executing the step of performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of: acquiring battery accumulated data, wherein the battery accumulated data comprises vehicle accumulated driving mileage, vehicle accumulated driving area, vehicle accumulated driving time, battery accumulated charging capacity, battery accumulated discharging capacity, charging mode and accumulated charging capacity corresponding to each charging mode; collecting battery charging data, wherein the battery charging data comprises a battery code, a charging current, a charging time, a charging initial charge state and a charging end charge state; calculating the initial health degree of the battery according to the battery charging data; performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model; and determining the target health degree according to the target model and the battery accumulated data.
In one embodiment, a computer program, when executed by a processor, calculates an initial health of a battery from the battery charge data, comprising: obtaining the rated capacity of a battery; determining an average current value according to the charging currents acquired in two adjacent preset periods; determining a state of charge difference value according to the charge start state of charge and the charge end state of charge; and calculating the initial health according to the rated capacity of the battery, the average current value, the preset period and the state of charge difference value.
In one embodiment, the calculating of the initial health from the battery rated capacity, the average current value, the preset period, and the state of charge difference, as implemented when the computer program is executed by the processor, comprises: determining a first ratio according to the average current value, the preset period and the state of charge difference value; determining a second ratio based on the first ratio and the battery rated capacity; and determining the initial health degree according to the second proportion.
In one embodiment, a first curve fitting based on the initial health and the battery accumulated charge capacity implemented when the computer program is executed by the processor results in a target model comprising: acquiring a first accumulated charge capacity corresponding to a preset charge mode; determining a charge ratio according to the first accumulated charge capacity and the accumulated charge capacity of the battery; grouping a plurality of batteries according to the charging proportion and the vehicle accumulated running area; determining a plurality of average healthiness degrees in each group according to the grouping result of the grouping and a preset charging capacity; and performing first curve fitting according to the average health degree and the accumulated charging capacity of the battery to obtain the target model.
In one embodiment, a first curve fitting based on the initial health and the battery accumulated charge capacity implemented when the computer program is executed by the processor results in a target model comprising: obtaining a plurality of fitting degrees of different initial fitting modes; comparing the fitting degree, and determining the initial fitting mode corresponding to the maximum fitting degree as a target fitting mode; and determining the target model according to the target fitting mode, the initial health degree and the accumulated charging capacity of the battery.
In one embodiment, before the first curve fitting according to the initial health and the accumulated charge capacity of the battery to obtain the target model, the computer program when executed by the processor further comprises: performing a second curve fitting according to the initial health degree and the accumulated charging capacity of the battery to obtain a fitting health degree; calculating a target difference value between the fitting health degree and the corresponding initial health degree; and if the target difference value is smaller than or equal to a preset difference value, executing the step of performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to comply with the related laws and regulations and standards of the related countries and regions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of determining battery health, the method comprising:
acquiring battery accumulated data, wherein the battery accumulated data comprises vehicle accumulated driving mileage, vehicle accumulated driving area, vehicle accumulated driving time, battery accumulated charging capacity, battery accumulated discharging capacity, charging mode and accumulated charging capacity corresponding to each charging mode;
Collecting battery charging data, wherein the battery charging data comprises a battery code, a charging current, a charging time, a charging initial charge state and a charging end charge state;
calculating the initial health degree of the battery according to the battery charging data;
performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model;
and determining the target health degree according to the target model and the battery accumulated data.
2. The method of claim 1, wherein the charging current is periodically collected, and wherein calculating the initial health of the battery based on the battery charging data comprises:
obtaining the rated capacity of a battery;
determining an average current value according to the charging currents acquired in two adjacent preset periods;
determining a state of charge difference value according to the charge start state of charge and the charge end state of charge;
and calculating the initial health according to the rated capacity of the battery, the average current value, the preset period and the state of charge difference value.
3. The method of claim 2, wherein said calculating said initial health from said battery rated capacity, said average current value, said preset period, and said state of charge difference value comprises:
Determining a first ratio according to the average current value, the preset period and the state of charge difference value;
determining a second ratio based on the first ratio and the battery rated capacity;
and determining the initial health degree according to the second proportion.
4. The method of claim 1, wherein said performing a first curve fit based on said initial health and said battery accumulated charge capacity to obtain a target model comprises:
acquiring a first accumulated charge capacity corresponding to a preset charge mode;
determining a charge ratio according to the first accumulated charge capacity and the accumulated charge capacity of the battery;
grouping a plurality of batteries according to the charging proportion and the vehicle accumulated running area;
determining a plurality of average healthiness degrees in each group according to the grouping result of the grouping and a preset charging capacity;
and performing first curve fitting according to the average health degree and the accumulated charging capacity of the battery to obtain the target model.
5. The method of claim 1, wherein said performing a first curve fit based on said initial health and said battery accumulated charge capacity to obtain a target model comprises:
Obtaining a plurality of fitting degrees of different initial fitting modes;
comparing the fitting degree, and determining the initial fitting mode corresponding to the maximum fitting degree as a target fitting mode;
and determining the target model according to the target fitting mode, the initial health degree and the accumulated charging capacity of the battery.
6. The method of claim 1, wherein prior to performing a first curve fit to obtain a target model based on the initial health and the battery accumulated charge capacity, further comprising:
performing a second curve fitting according to the initial health degree and the accumulated charging capacity of the battery to obtain a fitting health degree;
calculating a target difference value between the fitting health degree and the corresponding initial health degree;
and if the target difference value is smaller than or equal to a preset difference value, executing the step of performing first curve fitting according to the initial health degree and the accumulated charge capacity of the battery to obtain a target model.
7. A battery health determination apparatus, the apparatus comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring battery accumulated data, and the battery accumulated data comprises vehicle accumulated driving mileage, vehicle accumulated driving area, vehicle accumulated driving time, battery accumulated charging capacity, battery accumulated discharging capacity, charging modes and accumulated charging capacity corresponding to each charging mode;
The system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring battery charging data, and the battery charging data comprises battery codes, charging current, charging time, charging initial charge state and charging end charge state;
the calculating module is used for calculating the initial health degree of the battery according to the battery charging data;
the curve fitting module is used for performing first curve fitting according to the initial health degree and the accumulated charging capacity of the battery to obtain a target model;
and the determining module is used for determining the target health degree according to the target model and the battery accumulated data.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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