CN111766524B - Method, device, equipment and storage medium for determining battery health state - Google Patents
Method, device, equipment and storage medium for determining battery health state Download PDFInfo
- Publication number
- CN111766524B CN111766524B CN202010687758.2A CN202010687758A CN111766524B CN 111766524 B CN111766524 B CN 111766524B CN 202010687758 A CN202010687758 A CN 202010687758A CN 111766524 B CN111766524 B CN 111766524B
- Authority
- CN
- China
- Prior art keywords
- charging
- capacity
- battery
- voltage
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Abstract
The application relates to a method, a device, equipment and a storage medium for determining the state of health of a battery, wherein the method comprises the following steps: determining a charging capacity and charging voltage corresponding relation based on the acquired battery charging data; acquiring a first charging capacity; the first charging capacity is determined based on a capacity-voltage differential curve; determining a first charging voltage corresponding to the first charging capacity from the charging capacity-charging voltage correspondence; determining the time corresponding to the first charging voltage and the time corresponding to the preset full-charge voltage based on the battery charging data; determining a second charging capacity according to the time corresponding to the first charging voltage, the time corresponding to the preset full-charge voltage and the charging current corresponding to different times; and determining the health state of the battery based on the second charging capacity, the first charging capacity and the obtained factory capacity of the battery. Therefore, the safety monitoring of the battery in the service period can be realized, the calculation accuracy of the SOH of the battery can be improved, the calculation data volume is small, and the error accumulation can be reduced.
Description
Technical Field
The present disclosure relates to the field of power battery technologies, and in particular, to a method, an apparatus, a device, and a storage medium for determining a battery health status.
Background
In recent years, the market of Chinese electric automobiles has been rapidly increased, and by 2020, the market of up to 310 thousands of electric automobiles keeps a lot of attention to the safety problem of power batteries in the industry. Automobile enterprises begin to build a big data platform, and safety monitoring on power batteries in a service period is realized by screening and analyzing data uploaded to the platform by electric automobiles.
The State of Health (SOH) of a battery represents the percentage of the full charge capacity of the battery relative to the rated capacity, and describes the size of the attenuation degree of the capacity of the battery, which is one of the key indexes for judging whether a power battery meets the elimination standard, so the calculation accuracy of the SOH is particularly important.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a storage medium for determining the state of health of a battery, which can realize the safety monitoring of the battery in a service period, can improve the calculation accuracy of the state of health (SOH) of the battery, have small calculation data amount, and can reduce the error accumulation.
In one aspect, an embodiment of the present application provides a method for determining a state of health of a battery, including:
determining a charging capacity and charging voltage corresponding relation based on the acquired battery charging data; the battery charging data comprises charging voltage and charging current corresponding to different moments in the charging process;
acquiring a first charging capacity; the first charge capacity is determined based on a capacity-voltage differential curve; the first charging capacity corresponds to a peak-to-valley position of a capacity-voltage differential curve; the capacity-voltage differential curve is determined based on battery first-charge data;
determining a first charging voltage corresponding to the first charging capacity from the charging capacity-charging voltage correspondence;
determining the time corresponding to the first charging voltage and the time corresponding to the preset full-charge voltage based on the battery charging data;
determining a second charging capacity according to the moment corresponding to the first charging voltage, the moment corresponding to the preset full-charge voltage and the charging current corresponding to different moments;
and determining the health state of the battery based on the second charging capacity, the first charging capacity and the acquired factory capacity of the battery.
Optionally, obtaining the first charging capacity includes: acquiring first charging data of a battery; determining a capacity-voltage differential curve based on the battery first-charge data; determining the capacity corresponding to the peak-valley position in the capacity-voltage differential curve as a reference charging capacity to obtain a reference charging capacity set; the number of reference charge capacities in the reference charge capacity set is the same as the number of peaks and valleys in the capacity-voltage differential curve; a first charging capacity is determined from a set of reference charging capacities.
Optionally, the charging current corresponding to different moments is equal to the preset discharging rate of the factory capacity of the battery; the battery charging data also comprises battery temperatures corresponding to different moments; the battery temperature corresponding to different moments is greater than the preset temperature.
Optionally, determining the second charging capacity according to the time corresponding to the first charging voltage, the time corresponding to the preset full-charge voltage, and the charging currents corresponding to different times includes: and integrating the charging current from the moment corresponding to the first charging voltage to the moment corresponding to the preset full-charge voltage to obtain a second charging capacity.
Optionally, determining the health state of the battery based on the second charging capacity, the first charging capacity, and the obtained factory capacity of the battery includes: determining the actual total capacity of the battery according to the second charging capacity and the first charging capacity; and determining the health state of the battery according to the actual total capacity of the battery and the obtained factory capacity of the battery.
On the other hand, an embodiment of the present application provides an apparatus for determining a state of health of a battery, including:
the first determining module is used for determining a charging capacity and charging voltage corresponding relation based on the acquired battery charging data; the battery charging data comprises charging voltage and charging current corresponding to different moments in the charging process;
the acquisition module is used for acquiring a first charging capacity; the first charge capacity is determined based on a capacity-voltage differential curve; the first charging capacity corresponds to a peak-to-valley position of a capacity-voltage differential curve; the capacity-voltage differential curve is determined based on battery first-charge data;
the second determining module is used for determining a first charging voltage corresponding to the first charging capacity from the charging capacity and charging voltage corresponding relation;
the third determining module is used for determining the time corresponding to the first charging voltage and the time corresponding to the preset full-electricity voltage based on the battery charging data;
the fourth determining module is used for determining a second charging capacity according to the moment corresponding to the first charging voltage, the moment corresponding to the preset full-charge voltage and the charging current corresponding to different moments;
and the fifth determining module is used for determining the health state of the battery based on the second charging capacity, the first charging capacity and the acquired factory battery capacity.
In another aspect, an embodiment of the present application provides an apparatus, where the apparatus includes a processor and a memory, where the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded by the processor and executes the method for determining the state of health of the battery.
In another aspect, an embodiment of the present application provides a computer storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the above method for determining the state of health of a battery.
The method, the device, the equipment and the storage medium for determining the health state of the battery have the following beneficial effects:
determining a charging capacity and charging voltage corresponding relation based on the acquired battery charging data; the battery charging data comprises charging voltage and charging current corresponding to different moments in the charging process; acquiring a first charging capacity; the first charging capacity is determined based on a capacity-voltage differential curve; the first charge capacity corresponds to a peak-to-valley position of a capacity-voltage differential curve; the capacity-voltage differential curve is determined based on battery first-charge data; determining a first charging voltage corresponding to the first charging capacity from the charging capacity and charging voltage corresponding relation; determining the time corresponding to the first charging voltage and the time corresponding to the preset full-charge voltage based on the battery charging data; determining a second charging capacity according to the moment corresponding to the first charging voltage, the moment corresponding to the preset full-charge voltage and the charging current corresponding to different moments; and determining the health state of the battery based on the second charging capacity, the first charging capacity and the acquired factory capacity of the battery. Therefore, the safety monitoring of the battery in the service period can be realized, the calculation accuracy of the health state of the battery can be improved, the calculation data volume is small, and the error accumulation can be reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of a method for determining a state of health of a battery according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a dQ/dV-Q curve provided by an embodiment of the present application;
FIG. 4 is a diagram illustrating a dQ/dV-V curve provided by an embodiment of the present application;
FIG. 5 is a schematic illustration of another dQ/dV-Q curve provided by an embodiment of the present application;
FIG. 6 is a graph illustrating a relationship between a charging capacity and a charging voltage according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a battery state of health determination apparatus according to an embodiment of the present application;
fig. 8 is a hardware block diagram of a server according to a method for determining a battery health state provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the prior art, the method for calculating the state of health (SOH) of the battery mainly comprises two methods:
the accumulated electric quantity swallowing-spitting method is obtained by recording the current accumulated charge-discharge capacity of the battery and the total charge-discharge capacity of the full life cycle of the battery. Take the following formula as an example:
wherein SOH represents battery state of health; q p The unit of the accumulated charge-discharge capacity is Ah, and the accumulated charge-discharge capacity is obtained by performing ampere-hour integration on current and time in the charge-discharge process by a Battery Management System (BMS); q t The total charge-discharge capacity of the battery in the whole life cycle is Ah and is calibrated by a charge-discharge cycle test.
The disadvantage of the accumulated electric quantity swallowing-spitting method is that only the capacity loss in the battery charging and discharging process, namely the capacity attenuation caused by the cycle life, is considered, the capacity attenuation caused by the calendar life, namely the capacity loss under the standing working condition, is not considered, the applicability to the lithium iron phosphate battery with the calendar life attenuation leading is poor, and the SOH calculated according to the accumulated electric quantity swallowing-spitting method is low in accuracy;
and (3) working condition accumulation, namely, overlapping the capacity loss caused by different working conditions by building a capacity attenuation prediction model, wherein the SOH calculation method takes the following formula as an example:
wherein, F c 、F s Represents the capacity attenuation,%, under each condition; n represents the number of operating conditions. F c And F s The calendar fade and the cycle fade represent the capacity fade during the rest and operation of the battery, respectively.
The disadvantage of the operating condition additive method is that, although both calendar decay and cycle decay are considered, the stationary decay is strongly correlated with the shelf time, battery temperature and state of charge; the cycle decay is related to factors such as discharge depth, battery temperature, discharge rate and electric quantity state; therefore, the working condition accumulation method has the disadvantages that a large amount of test data under different working conditions are needed to build an analysis model, the working condition factors comprise discharge rate, discharge depth, battery temperature and the like, the dependence on data accuracy is high, and the analysis cost is high.
The two existing SOH calculation methods are based on all current historical use data of the battery, and error accumulation exists in big data analysis with low data uploading frequency, so that accuracy is reduced.
In order to solve the defects in the prior art, the embodiment of the application provides a method for determining the state of health of a battery, which can realize the safety monitoring of the battery in a service period, improve the calculation accuracy of the state of health (SOH) of the battery, and reduce the error accumulation due to small calculation data amount. Referring to fig. 1, fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present application, including a server 101 and a vehicle 102; the vehicle 102 uploads the battery use data in the battery charging process to the server 101 through the internet of vehicles; the server 101 performs screening and calculation based on the battery usage data to obtain the state of health SOH of the battery in the vehicle 102.
Firstly, in the process of charging a battery, a vehicle 102 uploads battery use data to a server 101 through the internet of vehicles, the server 101 performs screening based on the battery use data to obtain battery charging data, and the battery charging data comprises charging voltage and charging current corresponding to different moments in the charging process; secondly, the server 101 determines a charging capacity and charging voltage correspondence based on the battery charging data; secondly, the server 101 acquires a first charging capacity, which is determined by the server 101 from a capacity-voltage differential curve determined based on battery first-time charging data, the first charging capacity corresponding to a peak-to-valley position of the capacity-voltage differential curve; secondly, the server 101 determines a first charging voltage corresponding to the first charging capacity from the charging capacity and charging voltage corresponding relationship, and then determines a time corresponding to the first charging voltage and a time corresponding to a preset full-charge voltage based on the battery charging data; secondly, the server 101 determines a second charging capacity according to the time corresponding to the first charging voltage, the time corresponding to the preset full-power voltage and charging currents corresponding to different times; next, the server 101 determines the state of health SOH of the battery based on the second charging capacity, the first charging capacity, and the acquired factory battery capacity.
Alternatively, the server 101 may be a server of a large data platform.
Optionally, the battery 102 may be a lithium iron phosphate battery, and may also be a nickel-cobalt-manganese ternary lithium battery.
Optionally, in other application scenarios, the battery is taken out of the vehicle 102 and is placed in the charging and replacing station for charging, so that the server 101 may also be a server of the charging and replacing station, the server 101 is wirelessly connected to the battery of the vehicle 102, and the battery directly sends battery charging data to the server of the charging and replacing station through a wireless signal.
A specific embodiment of a method for determining a state of health of a battery according to the present application is described below, and fig. 2 is a schematic flow chart of the method for determining a state of health of a battery according to the embodiment of the present application, and the present specification provides the method operation steps as in the embodiment or the flow chart, but may include more or less operation steps based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of sequences, and does not represent a unique order of performance. In practice, the system or server product may be implemented in a sequential or parallel manner (e.g., parallel processor or multi-threaded environment) according to the embodiments or methods shown in the figures. Specifically, as shown in fig. 2, the method may include:
s201: determining a charging capacity and charging voltage corresponding relation based on the acquired battery charging data; the battery charging data includes charging voltage and charging current corresponding to different moments in the charging process.
In the embodiment of the application, in the charging process of a battery of a vehicle, battery use data are uploaded to a server of a big data platform through the Internet of vehicles, and the battery use data comprise the current electric quantity percentage SOC, charging voltage and charging current corresponding to different moments, temperature, charging power and other data; the server screens out battery charging data from the battery use data, the battery charging data mainly comprise charging voltage and charging current corresponding to different moments in the charging process, and the server can determine the charging capacity and charging voltage corresponding relation according to the charging voltage and the charging current corresponding to different moments in the charging process.
In an optional implementation manner, the charging current corresponding to different times is equal to a preset discharging rate of the factory capacity of the battery, and the battery charging data further includes battery temperatures corresponding to different times; the battery temperatures corresponding to different moments are all higher than the preset temperature; specifically, when the current electric quantity percentage SOC is 30% -80%, the battery is in a constant current charging state, the charging current corresponding to different moments is in the range of 0.1C-0.33C, and the battery temperature corresponding to different moments is larger than 25 ℃.
S203: acquiring a first charging capacity; the first charging capacity is determined based on a capacity-voltage differential curve; the first charging capacity corresponds to a peak-to-valley position of a capacity-voltage differential curve; the capacity-voltage differential curve is determined based on battery first-charge data.
In the embodiment of the present application, a lithium iron phosphate battery is taken as an example, and a schematic diagram of a capacity-voltage differential curve determined by a server based on battery first charging data is provided, as shown in fig. 3, a dQ/dV-Q curve is plotted with a capacity-voltage differential value as a vertical axis and a charging capacity value as a horizontal axis, where dQ/dV is a differential of a capacity Q and a voltage V, and reflects a capacity increase of a battery in a unit voltage; along with the aging of the battery, the capacity Q value corresponding to the position of the dQ/dV peak valley (namely, the point A and the point B in the graph 3) basically keeps unchanged, the charging data under the specific working condition is screened and obtained through the server based on the charging characteristic, the actual capacity of the current battery is determined, and then the actual capacity is compared with the factory capacity of the battery, so that the purpose of calculating the SOH can be achieved, and each calculation has independence, error-free accumulation and high accuracy. Based on this, in step S203, the server determines a capacity-voltage differential curve based on the first charge data of the battery, and determines a first charge capacity corresponding to the peak-to-valley position of the capacity-voltage differential curve from the capacity-voltage differential curve. That is, the first charge capacity is a calibration capacity, and the capacity values at the peak-valley positions are all equal to the first charge capacity based on the capacity-voltage differential curve determined based on the charge data generated each time the battery is charged.
In the method for determining the peak-valley position of the capacity-voltage differential curve in the embodiment of the application, when the corresponding dQ/dV value at the moment after the current moment is greater than the corresponding dQ/dV value at the moment, the corresponding dQ/dV at the moment is the peak-valley position of the capacity-voltage differential curve; alternatively, the position corresponding to the value of the smallest value of dQ/dV in a certain period of time is determined as the peak-to-valley position of the capacity voltage differential curve.
Considering that the dQ/dV-Q curve may have a plurality of peak-to-valley positions, an alternative embodiment for obtaining the first charging capacity includes: acquiring first charging data of a battery; determining a capacity-voltage differential curve based on the battery first-charge data; determining the capacity corresponding to the peak-valley position in the capacity-voltage differential curve as a reference charging capacity to obtain a reference charging capacity set; the number of reference charge capacities in the reference charge capacity set is the same as the number of peaks and valleys in the capacity-voltage differential curve; a first charging capacity is determined from a set of reference charging capacities. Specifically, the reference charging capacity value in the reference charging capacity set is deleted as a larger value and the reference charging capacity value in the reference charging capacity set is deleted as a smaller value, and then an appropriate reference charging capacity is selected from the remaining reference charging capacities in combination with the actual situation as the first charging capacity. The reason for this is that, in the actual charging situation, the situation that the battery is charged from the empty amount of electricity or the full amount of electricity is small, and therefore, selecting the reference charging capacity with a large value as the first charging capacitor will result in insufficient amount of charging data to be obtained, and selecting the reference charging capacity with a small value as the first charging capacitor will result in a large calculation amount.
S205: a first charging voltage corresponding to the first charging capacity is determined from the charging capacity-charging voltage correspondence relationship.
S207: and determining the time corresponding to the first charging voltage and the time corresponding to the preset full-charge voltage based on the battery charging data.
S209: and determining the second charging capacity according to the moment corresponding to the first charging voltage, the moment corresponding to the preset full-electricity voltage and the charging current corresponding to different moments.
In an embodiment of the present application, the server may also determine the corresponding capacity-voltage differential curve based on the battery charging data, and due to the above-mentioned battery charging characteristic, the server determines that the capacitance value of the corresponding capacity-voltage differential curve at the peak-valley position is equal to the first charging capacitance based on the battery charging data. If the actual capacity of the current battery needs to be calculated, only the capacity value increased from the first charging capacitor to the full capacity needs to be determined, and the sum of the first charging capacitor and the increased capacity value is the actual capacity of the current battery.
Therefore, the server determines a first charging voltage corresponding to the first charging capacity from the charging capacity and charging voltage corresponding relation, and then determines a time corresponding to the first charging voltage and a time corresponding to a preset full-charge voltage based on the battery charging data; the server calculates a second charging capacity, that is, an increased capacity value, based on the time corresponding to the first charging voltage, the time corresponding to the preset full-charge voltage, and the charging current corresponding to different times.
In an optional embodiment, determining the second charging capacity according to a time corresponding to the first charging voltage, a time corresponding to a preset full-charge voltage, and a charging current corresponding to a different time includes: and integrating the charging current from the moment corresponding to the first charging voltage to the moment corresponding to the preset full-charge voltage to obtain a second charging capacity. Wherein the preset full electric voltage may be 4.25V. Specifically, the second charge capacity is calculated according to the formula (1):
Q 2 ＝∫I·dt (1)
wherein Q 2 Represents a second charge capacity; i represents a charging current from a time corresponding to the first charging voltage to a time corresponding to a preset full-charge voltage.
S211: and determining the health state of the battery based on the second charging capacity, the first charging capacity and the acquired factory capacity of the battery.
In the embodiment of the application, after the server determines the first charging capacity and the second charging capacity, the server obtains the factory capacity of the battery, and then the state of health (SOH) of the battery can be calculated.
An optional embodiment of determining the state of health of the battery based on the second charging capacity, the first charging capacity, and the obtained factory capacity of the battery includes: determining the actual total capacity of the battery according to the second charging capacity and the first charging capacity; and determining the health state of the battery according to the actual total capacity of the battery and the obtained factory capacity of the battery. Specifically, the battery state of health is determined according to equation (2):
wherein SOH represents a battery state of health; q 1 Representing a first charge capacity; q 2 Indicating the second charge capacity.
In other embodiments of the present application, the magnitude of the current during the charging process is kept unchanged, so in step S203, the purpose of obtaining the calibrated first charging capacity to calculate the SOH can be achieved based on the dt/dV curve. In addition, it should be noted that the capacity-voltage differential curves mentioned in the embodiments of the present application refer to curves that are plotted with the capacity-voltage differential value as the vertical axis and the charge capacity value as the horizontal axis, that is, dQ/dV-Q curves; in other embodiments of the present application, the capacity-voltage differential curve may further plot a dQ/dV-V curve with the capacity-voltage differential value as a vertical axis and the charging voltage value as a horizontal axis, as shown in fig. 4, where fig. 4 is a diagram of the dQ/dV-V curve provided in the embodiments of the present application; since the peak-to-valley position of the dQ/dV-V curve obtained based on the same battery charging data is the same as the dQ/dV value corresponding to the peak-to-valley position of the dQ/dV-V curve, any method for calculating the state of health SOH of the battery based on the principle that the capacity corresponding to the peak-to-valley position in the capacity-voltage differential curve is not changed falls within the scope of the present application.
The above steps S201 to S211 and their optional embodiments are described below by a specific example. Referring to fig. 3, 5 and 6, in this example, taking a lithium iron phosphate battery with a factory capacity of 315Ah as an example, as shown in fig. 3, a server of a big data platform draws a dQ/dV-Q curve based on first charging data uploaded by the battery; the server determines a Q value corresponding to the peak-valley position B point as a first charging capacitor, obtains the moment of the peak-valley position B point through software, and obtains the calibration capacity at the moment by carrying out ampere-hour integration on the current and the time, namely the first charging capacitor Q 1 =182Ah; similarly, when the subsequent battery is charged for the nth time, the current battery charging data can be uploaded to the server, as shown in fig. 5, the server can draw a dQ/dV-Q curve according to the obtained current battery charging data, and based on the battery charging characteristics, the capacity value corresponding to the peak-valley position a in fig. 5 and the capacity value corresponding to the peak-valley position B in fig. 3 are both equal to the first charging capacitor Q 1 Thus, by determining from Q 1 And obtaining the actual capacity of the current battery by the capacitance value increased from the moment to the current full-charge moment of the battery. Specifically, the server first determines the charging capacity and charging voltage corresponding relationship according to the current battery charging data, as shown in fig. 6The charging capacity and charging voltage corresponding relation can be a relation curve of the charging capacity and charging voltage determined according to the charging voltage and the charging current corresponding to different moments in the current nth charging process; the server then determines a first charge capacity Q from the charge capacity versus charge voltage curve 1 Corresponding first charging voltage V 1 (ii) a Secondly, the server determines a first charging voltage V based on the battery charging data 1 Corresponding time t 1 And a time t corresponding to the preset full electric voltage 4.25V 2 Obtaining a second charging capacity Q according to the formula (1) 2 If =120Ah, the actual current battery capacitance is Q 1 +Q 2 =302Ah; next, the server determines the battery state of health SOH =302/315 × 100% =95.87% according to formula (2).
It should be noted that, in an optional implementation manner of the step S203, after the reference charge capacity set is obtained, the first charge capacity is determined from the reference charge capacity set and is used as the calibration capacity, so that the calculation of the SOH may be performed; in other embodiments of the present application, each reference charging capacity in the reference charging capacity set may be used as a calibration capacity, multiple SOH calculations are performed based on subsequent steps, and then a determination is performed according to the results of the multiple calculations, so as to obtain a final SOH; for example, the results of the plurality of calculations are averaged, the difference between each of the results of the plurality of calculations and the average value is calculated, the calculation results having a difference of 2% or more are deleted, and the remaining calculation results are corrected to determine the final SOH.
In summary, according to the method for determining the state of health of the battery provided by the embodiment of the application, the battery charging data under a specific working condition can be obtained through the server screening of the big data platform, and the calculation is independent every time.
An embodiment of the present application further provides a device for determining a state of health of a battery, fig. 7 is a schematic structural diagram of the device for determining a state of health of a battery provided in the embodiment of the present application, and as shown in fig. 7, the device includes:
a first determining module 701, configured to determine a charging capacity and charging voltage correspondence relationship based on the acquired battery charging data; the battery charging data comprises charging voltage and charging current corresponding to different moments in the charging process;
an obtaining module 702, configured to obtain a first charging capacity; the first charging capacity is determined based on a capacity-voltage differential curve; the first charge capacity corresponds to a peak-to-valley position of a capacity-voltage differential curve; the capacity-voltage differential curve is determined based on battery first-charge data;
a second determining module 703, configured to determine a first charging voltage corresponding to the first charging capacity from the charging capacity-charging voltage correspondence;
a third determining module 704, configured to determine, based on the battery charging data, a time corresponding to the first charging voltage and a time corresponding to a preset full-charge voltage;
a fourth determining module 705, configured to determine a second charging capacity according to a time corresponding to the first charging voltage, a time corresponding to a preset full-power voltage, and a charging current corresponding to different times;
a fifth determining module 706, configured to determine a state of health of the battery based on the second charging capacity, the first charging capacity, and the obtained factory battery capacity.
In an optional implementation manner, the obtaining module 702 is specifically configured to:
acquiring first charging data of a battery; determining a capacity-voltage differential curve based on the battery first charge data; determining the capacity corresponding to the peak-valley position in the capacity-voltage differential curve as a reference charging capacity to obtain a reference charging capacity set; the number of reference charge capacities in the reference charge capacity set is the same as the number of peaks and valleys in the capacity-voltage differential curve; a first charging capacity is determined from a set of reference charging capacities.
In an alternative embodiment, the charging current corresponding to different times is equal to a preset discharging rate of the factory capacity of the battery.
In an optional implementation manner, the fourth determining module 705 is specifically configured to:
and integrating the charging current from the moment corresponding to the first charging voltage to the moment corresponding to the preset full-charge voltage to obtain a second charging capacity.
In an optional implementation manner, the fifth determining module 706 is specifically configured to:
determining the actual total capacity of the battery according to the second charging capacity and the first charging capacity; and determining the health state of the battery according to the actual total capacity of the battery and the obtained factory capacity of the battery.
In the embodiment of the application, the determination device of the battery health state can be arranged in a server of a big data platform, and the determination device of the battery health state is used for receiving battery charging data uploaded through a vehicle network and calculating the health state of the current battery, so that the safety monitoring of the battery in a service period can be realized, the calculation accuracy of the SOH of the battery health state can be improved, the calculation data volume is small, and the error accumulation can be reduced. In addition, the determination device for the health state of the battery can be further arranged in the charging and replacing station, the battery which is taken out of the vehicle and placed in the charging and replacing station for charging can upload the use data in the charging process to the determination device for the health state of the battery in the charging and replacing station through establishing wireless connection, and the health state of the battery is monitored through the determination device for the health state of the battery in the charging and replacing station, so that the charging effectiveness and the charging safety can be improved.
The device and method embodiments in the embodiments of the present application are based on the same application concept.
The method embodiments provided in the embodiments of the present application may be executed in a computer terminal, a server, or a similar computing device. Taking the operation on a server as an example, fig. 8 is a hardware structure block diagram of the server of the method for determining the battery health state provided in the embodiment of the present application. As shown in fig. 8, the server 800 may have a relatively large difference due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 810 (the processor 810 may include but is not limited to a Processing device such as a microprocessor NCU or a programmable logic device FPGA), a memory 830 for storing data, one or more storage media 820 (e.g., one or more mass storage devices) for storing applications 823 or data 822. Memory 830 and storage medium 820 may be, among other things, transient or persistent storage. The program stored in storage medium 820 may include one or more modules, each of which may include a series of instruction operations for a server. Still further, the central processor 810 may be configured to communicate with the storage medium 820 to execute a series of instruction operations in the storage medium 820 on the server 800. The server 800 may also include one or more power supplies 860, one or more wired or wireless network interfaces 850, one or more input-output interfaces 840, and/or one or more operating systems 821, such as Windows, mac OS, unix, linux, freeBSD, and the like.
The input-output interface 840 may be used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the server 800. In one example, i/o Interface 840 includes a Network adapter (NIC) that may be coupled to other Network devices via a base station to communicate with the internet. In one example, the input/output interface 840 can be a Radio Frequency (RF) module, which is used to communicate with the internet via wireless.
It will be understood by those skilled in the art that the structure shown in fig. 8 is only an illustration and is not intended to limit the structure of the electronic device. For example, server 800 may also include more or fewer components than shown in FIG. 8, or have a different configuration than shown in FIG. 8.
Embodiments of the present application further provide a storage medium that can be disposed in a server to store at least one instruction, at least one program, a set of codes, or a set of instructions related to implementing a method for determining a battery health status in the method embodiments, where the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by the processor to implement the method for determining a battery health status.
Alternatively, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
As can be seen from the above embodiments of a method, an apparatus, a device, or a storage medium for determining a state of health of a battery provided by the present application, in the present application, a charging capacity and charging voltage correspondence is determined based on acquired battery charging data; the battery charging data comprises charging voltage and charging current corresponding to different moments in the charging process; acquiring a first charging capacity; the first charging capacity is determined based on a capacity-voltage differential curve; the first charging capacity corresponds to a peak-to-valley position of a capacity-voltage differential curve; the capacity-voltage differential curve is determined based on battery first-charge data; determining a first charging voltage corresponding to the first charging capacity from the charging capacity-charging voltage correspondence; determining the time corresponding to the first charging voltage and the time corresponding to the preset full-charge voltage based on the battery charging data; determining a second charging capacity according to the time corresponding to the first charging voltage, the time corresponding to the preset full-charge voltage and the charging current corresponding to different times; and determining the health state of the battery based on the second charging capacity, the first charging capacity and the obtained factory capacity of the battery. Therefore, the safety monitoring of the battery in the service period can be realized, the calculation accuracy of the SOH of the battery can be improved, the calculation data volume is small, and the error accumulation can be reduced.
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only a preferred embodiment of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements and the like that are made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (8)
1. A method of determining a state of health of a battery, comprising:
determining a charging capacity and charging voltage corresponding relation based on the acquired battery charging data; the battery charging data comprises charging voltage and charging current corresponding to different moments in the charging process;
acquiring first charging data of the battery;
determining the capacity-voltage differential curve based on the battery first charge data;
determining the capacity corresponding to the position of the wave valley in the capacity voltage differential curve as reference charging capacity to obtain a reference charging capacity set; the number of reference charge capacities in the reference charge capacity set is the same as the number of peaks and valleys in the capacity-voltage differential curve;
deleting the larger reference charging capacity value and the smaller reference charging capacity value in the reference charging capacity set, and selecting a proper reference charging capacity as a first charging capacity according to the actual situation from the rest reference charging capacities;
determining a first charging voltage corresponding to the first charging capacity from the charging capacity and charging voltage corresponding relation;
determining the time corresponding to the first charging voltage and the time corresponding to a preset full-charge voltage based on the battery charging data;
determining a second charging capacity according to the moment corresponding to the first charging voltage, the moment corresponding to the preset full-electricity voltage and the charging currents corresponding to different moments;
and determining the health state of the battery based on the second charging capacity, the first charging capacity and the acquired factory capacity of the battery.
2. The method according to claim 1, wherein the charging current corresponding to different moments is equal to a preset discharging rate of the factory capacity of the battery;
the battery charging data also comprises battery temperatures corresponding to different moments; and the battery temperatures corresponding to different moments are all higher than the preset temperature.
3. The method of claim 1, wherein determining the second charging capacity according to the time corresponding to the first charging voltage, the time corresponding to the preset full-power voltage, and the charging current corresponding to the different time comprises:
and integrating the charging current from the moment corresponding to the first charging voltage to the moment corresponding to the preset full-charge voltage to obtain the second charging capacity.
4. The method of claim 1, wherein determining the battery health status based on the second charging capacity, the first charging capacity, and the obtained factory battery capacity comprises:
determining the actual total capacity of the battery according to the second charging capacity and the first charging capacity;
and determining the health state of the battery according to the actual total capacity of the battery and the acquired factory capacity of the battery.
5. A battery state of health determination apparatus, comprising:
the first determining module is used for determining a charging capacity and charging voltage corresponding relation based on the acquired battery charging data; the battery charging data comprises charging voltage and charging current corresponding to different moments in the charging process;
the acquisition module is used for acquiring the first charging data of the battery; determining the capacity-voltage differential curve based on the battery first charge data; determining the capacity corresponding to the position of the wave valley in the capacity voltage differential curve as reference charging capacity to obtain a reference charging capacity set; the number of reference charge capacities in the reference charge capacity set is the same as the number of peaks and valleys in the capacity-voltage differential curve; deleting the larger reference charging capacity value and the smaller reference charging capacity value in the reference charging capacity set, and selecting a proper reference charging capacity as a first charging capacity according to the actual situation from the rest reference charging capacities;
a second determining module, configured to determine a first charging voltage corresponding to the first charging capacity from the charging capacity-charging voltage correspondence;
the third determining module is used for determining the time corresponding to the first charging voltage and the time corresponding to a preset full-electricity voltage based on the battery charging data;
the fourth determining module is used for determining a second charging capacity according to the moment corresponding to the first charging voltage, the moment corresponding to the preset full-electricity voltage and the charging currents corresponding to different moments;
and the fifth determining module is used for determining the health state of the battery based on the second charging capacity, the first charging capacity and the acquired factory battery capacity.
6. The apparatus according to claim 5, wherein the charging current corresponding to the different time instants is equal to a preset discharge rate of the factory-delivered battery capacity;
the battery charging data also comprises battery temperatures corresponding to different moments; and the battery temperatures corresponding to different moments are all greater than the preset temperature.
7. An electronic device, characterized in that the device comprises a processor and a memory, in which at least one instruction or at least one program is stored, which is loaded by the processor and which performs the method of battery state of health determination according to any of claims 1-4.
8. A computer storage medium having at least one instruction or at least one program stored therein, the at least one instruction or the at least one program being loaded and executed by a processor to perform the method of battery state of health determination as claimed in any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010687758.2A CN111766524B (en) | 2020-07-16 | 2020-07-16 | Method, device, equipment and storage medium for determining battery health state |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010687758.2A CN111766524B (en) | 2020-07-16 | 2020-07-16 | Method, device, equipment and storage medium for determining battery health state |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111766524A CN111766524A (en) | 2020-10-13 |
| CN111766524B true CN111766524B (en) | 2023-03-10 |
Family
ID=72728160
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010687758.2A Active CN111766524B (en) | 2020-07-16 | 2020-07-16 | Method, device, equipment and storage medium for determining battery health state |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111766524B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112731187A (en) * | 2020-12-25 | 2021-04-30 | 广西宁达汽车科技有限公司 | Battery capacity correction method and battery management system |
| CN114966447A (en) * | 2021-02-20 | 2022-08-30 | 青岛特来电新能源科技有限公司 | New energy equipment health condition evaluation method, device, medium and prompt terminal |
| CN113608132B (en) * | 2021-08-02 | 2024-02-27 | 北京芯虹科技有限责任公司 | Method, system and storage medium for determining residual capacity of lithium ion battery |
| CN114062953B (en) * | 2021-10-26 | 2024-02-13 | 三一汽车起重机械有限公司 | Method and device for determining health state of storage battery and working machine |
| CN117388721A (en) * | 2022-07-04 | 2024-01-12 | 蔚来汽车科技(安徽)有限公司 | Battery system, SOH estimation method for the same, and storage medium |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004361313A (en) * | 2003-06-06 | 2004-12-24 | Panasonic Ev Energy Co Ltd | Remaining capacity arithmetic unit and remaining capacity computing method of secondary battery |
| CN103267950A (en) * | 2012-12-14 | 2013-08-28 | 惠州市亿能电子有限公司 | SOH (State Of Health) value evaluation method of electric car battery pack |
| CN104730468A (en) * | 2015-04-07 | 2015-06-24 | 阳光电源股份有限公司 | Battery SOC estimation method and device and battery management system |
| JP2016053564A (en) * | 2014-09-01 | 2016-04-14 | 横河電機株式会社 | Secondary battery capacity measurement system and secondary battery capacity measurement method |
| CN106646262A (en) * | 2017-01-03 | 2017-05-10 | 重庆长安汽车股份有限公司 | Power battery capacity estimation method, power battery capacity estimation system, and electric vehicle |
| JP2017228389A (en) * | 2016-06-21 | 2017-12-28 | マツダ株式会社 | Diagnostic apparatus of lithium ion battery |
| CN108445406A (en) * | 2018-03-13 | 2018-08-24 | 桂林电子科技大学 | A kind of power battery health status method of estimation |
| CN109061514A (en) * | 2018-09-30 | 2018-12-21 | 北京理工大学 | A kind of power battery decline situation analysis method and system based on big data |
| JP2018206612A (en) * | 2017-06-05 | 2018-12-27 | 三菱自動車工業株式会社 | Secondary batter system |
| CN110967644A (en) * | 2019-05-16 | 2020-04-07 | 宁德时代新能源科技股份有限公司 | Battery pack SOC correction method, battery management system and vehicle |
| JP2020068606A (en) * | 2018-10-25 | 2020-04-30 | 三菱自動車工業株式会社 | Vehicular secondary battery system |
| CN111323719A (en) * | 2020-03-18 | 2020-06-23 | 北京理工大学 | Method and system for online determination of health state of power battery pack of electric automobile |
| CN111381178A (en) * | 2018-12-28 | 2020-07-07 | 奥动新能源汽车科技有限公司 | Method and system for estimating health state of lithium ion battery |
-
2020
- 2020-07-16 CN CN202010687758.2A patent/CN111766524B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004361313A (en) * | 2003-06-06 | 2004-12-24 | Panasonic Ev Energy Co Ltd | Remaining capacity arithmetic unit and remaining capacity computing method of secondary battery |
| CN103267950A (en) * | 2012-12-14 | 2013-08-28 | 惠州市亿能电子有限公司 | SOH (State Of Health) value evaluation method of electric car battery pack |
| JP2016053564A (en) * | 2014-09-01 | 2016-04-14 | 横河電機株式会社 | Secondary battery capacity measurement system and secondary battery capacity measurement method |
| CN104730468A (en) * | 2015-04-07 | 2015-06-24 | 阳光电源股份有限公司 | Battery SOC estimation method and device and battery management system |
| JP2017228389A (en) * | 2016-06-21 | 2017-12-28 | マツダ株式会社 | Diagnostic apparatus of lithium ion battery |
| CN106646262A (en) * | 2017-01-03 | 2017-05-10 | 重庆长安汽车股份有限公司 | Power battery capacity estimation method, power battery capacity estimation system, and electric vehicle |
| JP2018206612A (en) * | 2017-06-05 | 2018-12-27 | 三菱自動車工業株式会社 | Secondary batter system |
| CN108445406A (en) * | 2018-03-13 | 2018-08-24 | 桂林电子科技大学 | A kind of power battery health status method of estimation |
| CN109061514A (en) * | 2018-09-30 | 2018-12-21 | 北京理工大学 | A kind of power battery decline situation analysis method and system based on big data |
| JP2020068606A (en) * | 2018-10-25 | 2020-04-30 | 三菱自動車工業株式会社 | Vehicular secondary battery system |
| CN111381178A (en) * | 2018-12-28 | 2020-07-07 | 奥动新能源汽车科技有限公司 | Method and system for estimating health state of lithium ion battery |
| CN110967644A (en) * | 2019-05-16 | 2020-04-07 | 宁德时代新能源科技股份有限公司 | Battery pack SOC correction method, battery management system and vehicle |
| CN111323719A (en) * | 2020-03-18 | 2020-06-23 | 北京理工大学 | Method and system for online determination of health state of power battery pack of electric automobile |
Non-Patent Citations (1)
| Title |
|---|
| 基于衰退机理的三元锂离子电池ＳＯＨ的诊断与估算;王振新 等;《电子测量技术》;20200531;第7-13页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111766524A (en) | 2020-10-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111766524B (en) | Method, device, equipment and storage medium for determining battery health state | |
| CN108287312B (en) | Sorting method, system and device for retired batteries | |
| CN107533105B (en) | Lithium ion battery state of charge estimation method and device | |
| EP3021127A1 (en) | Method for estimating state of electricity storage device | |
| US20040006440A1 (en) | Method for evaluating capacity of secondary battery using mathematical calculation of specific resistance components of equivalent circuit model fitted from impedance spectrum | |
| CN111812531A (en) | Battery state detection method, device and storage medium | |
| CN109471040B (en) | Capacity determination method, capacity determination device, electronic device, and computer-readable storage medium | |
| CN113219351B (en) | Monitoring method and device for power battery | |
| CN113109729B (en) | Vehicle power battery SOH evaluation method based on accelerated aging test and real vehicle working condition | |
| CN113111508A (en) | Battery cell consistency evaluation method and device and server | |
| CN110806540B (en) | Battery cell test data processing method, device and system and storage medium | |
| CN104335057A (en) | Method and device for determining the actual capacity of a battery | |
| CN111308380A (en) | Echelon battery re-retirement detection method and device and computer equipment | |
| CN111896882A (en) | Battery capacity determination method and device and vehicle | |
| CN109061482B (en) | Battery health degree prediction method and device | |
| CN112051512A (en) | Echelon utilization sorting method and energy storage system | |
| CN112630661A (en) | Battery state of charge (SOC) estimation method and device | |
| CN108020788A (en) | A kind of lithium ion battery internal resistance rapid screening method | |
| CN114264969A (en) | Method and device for evaluating self-discharge performance of battery cell | |
| Wu et al. | State-of-charge and state-of-health estimating method for lithium-ion batteries | |
| CN111199104A (en) | Battery residual performance analysis method, device, equipment and storage medium | |
| CN116224127A (en) | Battery state of health estimation method based on big data analysis | |
| CN115102248A (en) | Battery equalization method and device, terminal equipment and storage medium | |
| CN114103729B (en) | Method and device for balancing battery pack and electric automobile | |
| CN114636936A (en) | Correction method and device for SOC prediction curve of lead-acid battery in charging stage |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230824 Address after: 310051 No. 1760, Jiangling Road, Hangzhou, Zhejiang, Binjiang District Patentee after: ZHEJIANG GEELY HOLDING GROUP Co.,Ltd. Patentee after: Chongqing Ruilan Automobile Research Institute Co.,Ltd. Address before: No.3366 Weiwu Road, dajiangdong Industrial Cluster District, Hangzhou, Zhejiang Province Patentee before: ZHEJIANG JIZHI NEW ENERGY AUTOMOBILE TECHNOLOGY Co.,Ltd. Patentee before: ZHEJIANG GEELY HOLDING GROUP Co.,Ltd. |
|
| TR01 | Transfer of patent right |