CN116027201A

CN116027201A - Estimation method of battery state of health and establishment method of capacity estimation model

Info

Publication number: CN116027201A
Application number: CN202310020601.8A
Authority: CN
Inventors: 刘飞龙; 孙帅; 任杰
Original assignee: Sany Heavy Industry Co Ltd
Current assignee: Sany Heavy Industry Co Ltd
Priority date: 2023-01-06
Filing date: 2023-01-06
Publication date: 2023-04-28

Abstract

The application discloses a method and a device for estimating a battery state of health, a method for establishing a capacity estimation model, a vehicle and a computer readable storage medium, wherein the method for estimating the battery state of health uses a curve similarity between a battery state curve and an initial state curve as a health indicator for essentially stably representing the degradation degree of the battery state of health, and calculates the battery state of health, so that the battery state of health obtained based on the calculation of the method is more accurate. In addition, the method for estimating the state of health of the battery considers that the IC curve of the complete charge and discharge process of the battery is difficult to obtain under some application scenes, and the method for estimating the state of health of the battery improves the adaptability and the robustness of the method for estimating the state of health of the battery by acquiring the similarity of the curve based on the curve of the state of the battery and the initial state of the curve representing the corresponding relation between the increment capacity and the voltage of the battery in the process of at least partial charge or at least partial discharge of the battery.

Description

Estimation method of battery state of health and establishment method of capacity estimation model

Technical Field

The present invention relates to the field of computer technology, and in particular, to a battery state of health estimation technique in the field of computer technology, and more particularly, to a battery state of health estimation method and a capacity estimation model establishment method.

Background

The State of Health (SOH) is a parameter indicating the Health of a battery in various application scenarios (e.g., vehicles, personal electronic devices, network storage, etc.). For many battery management systems (Battery Management System, BMS), battery health status is a key information for the battery management system to manage the battery operating status.

Therefore, in some usage situations, it is necessary to estimate the state of health of the battery, and it is necessary to provide a method for estimating the state of health of the battery with high adaptability.

Disclosure of Invention

In order to solve the technical problems, the application provides a battery state of health estimation method and a capacity estimation model establishment method, so as to realize the characteristic of improving the adaptability of the battery state of health estimation method.

In order to achieve the technical purpose, the embodiment of the application provides the following technical scheme:

in a first aspect, a method for estimating a state of health of a battery is provided, including:

acquiring a battery state curve, wherein the battery state curve represents the corresponding relation between the increment capacity and the voltage of the battery in the process of at least partially charging or at least partially discharging the battery;

obtaining the curve similarity between the battery state curve and an initial state curve, wherein the initial state curve represents the corresponding relation between the increment capacity and the voltage of the battery in the process of at least partial charging or at least partial discharging in an initial charging and discharging cycle, and the curve similarity represents the similarity difference between the battery state curve and the initial state curve;

And calculating the battery health state of the battery by taking the curve similarity as a health indicator.

In some embodiments, the calculating the battery state of health of the battery using the curve similarity as the health indicator comprises:

obtaining the current maximum available capacity of the battery according to the curve similarity and a capacity estimation model, wherein the capacity estimation model characterizes the calculation relation between the curve similarity and the current maximum available capacity of the battery, the capacity estimation model is obtained according to battery historical data in a fitting mode, and the battery historical data comprises the corresponding relation between the historical battery capacity and the historical curve similarity;

and taking the ratio of the current maximum available capacity of the battery to the rated capacity of the battery as the battery health state of the battery.

In some embodiments, the acquiring the curve similarity between the battery state curve and the historical state curve further comprises:

and interpolating and filtering the battery state curve to reduce noise of the battery state curve and improve smoothness of the battery state curve.

In some embodiments, the interpolating and filtering the battery state curve includes:

Cubic spline interpolation and Savitzky-Golay filtering are performed on the battery state curves.

In some embodiments, the battery state curve and the initial state curve each include a correspondence between battery delta capacity and voltage when the battery is within a preset SOC interval; the preset SOC interval is an SOC interval including a peak section of a battery increment capacity and voltage curve of the battery.

In some embodiments, the acquiring a battery state curve includes:

acquiring battery working condition data, wherein the battery working condition data comprises voltage, current and SOC in a battery charging and discharging cycle process;

extracting target data of the battery working condition data, wherein the target data is discharge section data or charge section data of the battery working condition data;

and when the discharge segment data meets a preset SOC section, calculating the battery state curve, wherein the preset SOC section is an SOC section comprising the battery increment capacity of the battery and the peak value section of the voltage curve.

In some embodiments, the extracting the target data of the battery condition data includes:

taking the data when the current in the battery working condition data meets a first preset condition as charging section data of the battery working condition data; the first preset condition comprises that the current in the battery working condition data is smaller than or equal to a preset current value and the SOC has an ascending trend;

Or (b)

Taking the data when the current in the battery working condition data meets a second preset condition as discharge segment data of the battery working condition data; the second preset condition includes that the current in the battery working condition data is greater than or equal to a preset current value and the SOC has a descending trend.

In some embodiments, the obtaining the curve similarity between the battery state curve and the historical state curve comprises:

and calculating the curve similarity between the battery state curve and the historical state curve by using a dynamic time warping algorithm.

In a second aspect, a method for establishing a capacity estimation model is provided, which is used for estimating a current maximum available capacity of a battery, and the method for establishing the capacity estimation model includes:

acquiring a historical battery state curve, wherein the historical state curve represents the corresponding relation between the increment capacity and the voltage of the battery in the process of at least partially charging or at least partially discharging the battery in the history;

extracting, from the historical battery state curves, a curve similarity between each of the historical battery state curves and an initial battery state curve, the curve similarity characterizing a similarity difference between the historical battery state curves and the selected historical state curve;

Taking the curve similarity as a health indicator, and establishing a capacity estimation model, wherein the capacity estimation model characterizes the calculation relation between the curve similarity and the current maximum available capacity of the battery;

and fitting the capacity estimation model by using battery history data to determine model parameters in the capacity estimation model, wherein the battery history data comprises a corresponding relation between the capacity of a historical battery and the similarity of a historical curve.

In a third aspect, there is provided an estimation apparatus of a state of health of a battery, comprising:

the curve acquisition module is used for acquiring a battery state curve, wherein the battery state curve represents the corresponding relation between the increment capacity and the voltage of the battery in the process of at least partially charging or at least partially discharging the battery;

the similarity difference module is used for obtaining the similarity of the battery state curve and an initial state curve, wherein the initial state curve represents the corresponding relation between the increment capacity and the voltage of the battery in the process of at least partial charging or at least partial discharging in an initial charging and discharging cycle, and the similarity of the battery state curve and the initial state curve represents the similarity difference between the battery state curve and the initial state curve;

And the health state module is used for calculating the battery health state of the battery by taking the curve similarity as a health indicator.

In a fourth aspect, there is provided a vehicle comprising: a controller and a memory; the memory is connected with the controller, the memory is used for storing a computer program, and the controller is used for realizing the method for estimating the battery health state according to any one of the above by running the computer program stored in the memory.

In a fifth aspect, a computer readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, implements a method for estimating a state of health of a battery as described in any of the above or a method for establishing a capacity estimation model as described above.

In a sixth aspect, there is provided a computer program product or computer program, the computer program product comprising a computer program stored in a computer readable storage medium; the processor of the computer device reads the computer program from the computer readable storage medium, and the processor implements the above-described battery state of health estimation method or the above-described capacity estimation model establishment method when executing the computer program.

As can be seen from the above technical solution, the embodiments of the present application provide a method and an apparatus for estimating a battery state of health, a method for establishing a capacity estimation model, a vehicle, and a computer readable storage medium, where the method for estimating a battery state of health uses a curve similarity between a battery state curve and an initial state curve as a health indicator that essentially stably characterizes a degradation degree of a battery state of health, and performs calculation of a battery state of health, so that a battery state of health obtained based on the calculation of the method is more accurate. In addition, the method for estimating the battery state of health considers that an IC curve (namely a curve representing the corresponding relation between the increment capacity and the voltage of the battery in the process of charging and discharging the battery) of the whole battery is difficult to obtain in some application scenes, and the method for estimating the battery state of health improves the adaptability and the robustness of the method for estimating the battery state of health by obtaining the curve similarity based on the battery state curve representing the corresponding relation between the increment capacity and the voltage of the battery in the process of at least partially charging or at least partially discharging the battery and the initial state curve.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings may be obtained according to the provided drawings without inventive effort to a person skilled in the art.

Fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present application;

fig. 2 is a schematic diagram of another application scenario provided in an embodiment of the present application;

fig. 3 is a flowchart of a method for estimating a battery state of health according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a battery status curve provided in one embodiment of the present application;

FIG. 5 is a graph showing a comparison of battery status curves for different charge and discharge cycles according to one embodiment of the present application;

FIG. 6 is a schematic diagram illustrating a distinction between charge segment and discharge segment data according to one embodiment of the present application;

FIG. 7 is a flow chart of a method for creating a capacity estimation model according to an embodiment of the present application;

FIG. 8 is a schematic diagram of a verification result provided in an embodiment of the present application;

fig. 9 is a schematic structural diagram of a device for estimating a battery state of health according to an embodiment of the present application;

FIG. 10 is a schematic view of an automobile according to an embodiment of the present application;

fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Unless defined otherwise, technical or scientific terms used in the embodiments of the present specification should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present specification belongs. The terms "first," "second," and the like, as used in the embodiments of the present disclosure, do not denote any order, quantity, or importance, but rather are used to avoid intermixing of the components.

Throughout the specification, unless the context requires otherwise, the word "plurality" means "at least two", and the word "comprising" is to be construed as open, inclusive meaning, i.e. as "comprising, but not limited to. In the description of the present specification, the terms "one embodiment," "some embodiments," "example embodiments," "examples," "particular examples," or "some examples," etc., are intended to indicate that a particular feature, structure, material, or characteristic associated with the embodiment or example is included in at least one embodiment or example of the present specification. The schematic representations of the above terms do not necessarily refer to the same embodiment or example.

The technical solutions of the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is apparent that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.

SUMMARY

At present, some methods for estimating the state of health of a battery acquire corrected SOH based on the relation between a charging capacity value and an initial capacity value in the charging process, and the methods do not consider the influence of conditions such as charging multiplying power, temperature and the like on the charging capacity value, so that SOH calculation errors are larger and robustness is poor. Other methods for estimating the state of health of the battery require measurement of the internal resistance of the battery, but the measurement of the internal resistance of the battery is difficult, and the measurement cost is high and the applicability is poor.

In order to solve the above problems, the inventor provides a novel health indicator for essentially stably representing the degradation degree of a battery, namely, the similarity of the battery state curve and the initial state curve by stretching into a research on an incremental capacity analysis method, and calculates the battery state of health of the battery based on the health indicator, thereby being beneficial to improving the accuracy of calculating the battery state of health, reducing the calculation error of the battery state of health and improving the robustness of an estimation method of the battery state of health. In addition, the inventor considers that in some usage situations (such as a power battery of a vehicle), the battery generally does not perform a complete charge-discharge cycle (i.e. from 0% of the remaining power to 100% of the remaining power or from 100% of the remaining power to 0% of the remaining power), that is, it is difficult to obtain a battery increment capacity-voltage (IC) curve representing the complete charge-discharge cycle of the battery, so that in the estimation process of the battery health state, the similarity between the battery state curve representing the corresponding relationship between the battery increment capacity and the voltage during at least partial charge or at least partial discharge of the battery and the initial state curve is considered as a health indicator, on one hand, the increment capacity (Incremental Capacity, IC) can be calculated by differentiating the charge/discharge capacity to the complete voltage change, so that the calculation cost is low, the calculation of the internal resistance of the battery is not required, and on the other hand, compared with the estimation method of the IC curve requiring the complete charge-discharge cycle of the battery.

Based on the above-mentioned ideas, the embodiment of the application provides a method for estimating the state of health of a battery, and the following will describe exemplary possible application scenarios and possible implementation manners of the method for estimating the state of health of a battery with reference to the accompanying drawings.

Exemplary scenario

Estimation of battery state of health is of practical interest in many application scenarios, such as mobile storage (e.g., electric car) applications, fixed storage (e.g., network storage) applications, and portable storage (e.g., personal electronic device) applications, where battery state of health is an important parameter for device self-management or operation and maintenance personnel management. Referring to fig. 1 and 2, fig. 1 shows an application example of estimating a battery state of health in an electric vehicle, in which the battery state of health of a power battery is a key parameter for determining a duration of the electric vehicle, an acceleration capability of the electric vehicle, and the like, so that a controller (e.g., a whole vehicle controller) of the electric vehicle can collect various parameters of the power battery to estimate the battery state of health of the power battery, and when a value of the battery state of health is low or the battery state of health drops rapidly in a period of time, graphic prompt information including "abnormal battery state" can be pushed to a user through a device such as a central control screen and an instrument panel, so that the user can take measures such as replacing the power battery, changing driving habits, and the like in time, thereby improving a worsening trend of the battery state of health of the power battery.

Fig. 2 shows an application example of estimating a battery state of health in a personal electronic device (e.g., a mobile phone, a tablet computer), in which the personal electronic device may perform estimation of a battery state of health, and when the battery state of health is less than a certain value, a graphic prompt including "battery state of health is pushed to a user, and attention is paid to the battery state of health", so that the user can take measures such as replacing the battery, maintaining, adjusting the usage habit, and the like in time, and improve the trend of deterioration of the battery state of health.

It should be noted that fig. 1 and fig. 2 only show two possible application scenarios by way of example, and should not be taken as a limitation on the application scenarios of the battery state of health estimation method provided in the embodiments of the present application. The method for estimating the battery state of health can be applied to other various application scenes in which battery state of health detection is required.

An exemplary description will be made below of a method for estimating a state of health of a battery provided in an embodiment of the present application, with reference to the accompanying drawings.

Exemplary method

An embodiment of the present application provides a method for estimating a state of health of a battery, and referring to fig. 3, the method includes:

S101: a battery state curve is obtained, the battery state curve representing a correspondence between the incremental capacity and the voltage of the battery during at least partial charging or at least partial discharging of the battery.

Referring to fig. 4, fig. 4 shows a battery state curve representing the correspondence between the battery delta capacity and voltage during discharge, and the curve shown in fig. 4 may also be referred to as an IC (delta capacity) curve, in which the abscissa is the discharge voltage (Discharge voltage) in volts (V), the ordinate is the delta capacity (Incremental Capacity) in amperes/volts (Ah/V). The battery State curve shown in fig. 4 may be an IC curve corresponding to a State of Charge (SOC) of the battery changing from 80% to 30%, and in the application scenario shown in fig. 1, SOC (State of Charge) data of the battery generally changes between 80% and 30%, so that the IC curve corresponding to a SOC of the battery changing between 80% and 30% is taken as the battery State curve. Of course, the battery state curve shown in fig. 4 may also be an IC curve corresponding to the SOC of the battery changing from 70% to 40%, and in other embodiments of the present application, the battery state curve may not be an IC curve, as long as the battery state curve can be made to represent the correspondence between the incremental capacity and the voltage of the battery during at least part of the charging process or at least part of the discharging process, which is not limited in this application, and the present application is specific to the actual situation.

S102: and obtaining the curve similarity between the battery state curve and an initial state curve, wherein the initial state curve represents the corresponding relation between the increment capacity and the voltage of the battery in the process of at least partial charging or at least partial discharging in an initial charging and discharging cycle, and the curve similarity represents the similarity difference between the battery state curve and the initial state curve.

Referring to fig. 5, fig. 5 is an incremental capacity curve (IC Curves of Different Cycles) of different charge and discharge cycles, specifically showing battery state curves obtained in the 1 st charge and discharge cycle (Cycel 1), the 201 st charge and discharge cycle (Cycel 201), the 401 st charge and discharge cycle (Cycel 401), the 601 st charge and discharge cycle (Cycel 601) and the 801 st charge and discharge cycle (Cycel 801), in fig. 5, voltage (voltage) is plotted on the abscissa in volts (V), incremental capacity (Incremental Capacity) is plotted on the ordinate in amperes/volts (Ah/V).

By studying the battery state curves during different battery charging cycles, the peak height of the battery state curves is reduced along with the increase of the battery charging cycles, and the tendency of the peak position to move towards low voltage is obvious. Therefore, the degradation degree of the battery can be well represented by the curve similarity between different battery state curves, and considering that the battery of the vehicle cannot always undergo a complete charge-discharge process (in general, the power battery of the vehicle cannot be discharged to 0% for recharging due to the purposes of a power battery protection mechanism of the vehicle, user charging convenience and the like), that is, the SOC of the battery of the vehicle cannot be discharged from 100% to 0% under normal conditions, the curve similarity between the battery state curve representing the correspondence between the incremental capacity and the voltage of the battery during at least partial charging or at least partial discharging of the battery and the initial state curve is considered as a health indicator during the estimation process of the battery state, on the one hand, the incremental capacity can be calculated by differentiating the charge/discharge capacity to the whole voltage variation, the calculation cost is low, the calculation of the internal resistance of the battery is not required, and on the other hand, compared with the estimation method of the IC curve requiring the acquisition of the complete charge-discharge cycle of the battery, the adaptability is good.

S103: and calculating the battery health state of the battery by taking the curve similarity as a health indicator.

In step S103, after the health indicator is obtained, the health indicator may be substituted into a mapping function of the health indicator and the battery health status obtained by using a linear regression method, etc., to calculate the battery health status of the battery. In some embodiments, the health indicator may also be used as an input to a pre-trained neural network, through which predictions of the pre-trained neural network an estimate of the battery state of health may be made. The specific calculation mode is not limited in this application.

In some embodiments, a feasible way of calculating the battery health status of the battery is provided, specifically, the calculating the battery health status of the battery using the curve similarity as the health indicator includes:

s1031: and obtaining the current maximum available capacity of the battery according to the curve similarity and a capacity estimation model, wherein the capacity estimation model characterizes the calculation relation between the curve similarity and the current maximum available capacity of the battery, the capacity estimation model is obtained according to battery historical data in a fitting mode, and the battery historical data comprises the corresponding relation between the historical battery capacity and the historical curve similarity.

S1032: and taking the ratio of the current maximum available capacity of the battery to the rated capacity of the battery as the battery health state of the battery.

In the embodiment, a capacity estimation model is obtained based on historical data fitting of the battery, the curve similarity is used as model input and substituted into the capacity estimation model, and the current maximum available capacity of the battery can be obtained through calculation.

Referring to equation (1) below, equation (1) gives a feasible capacity estimation model to build.

In the formula (1), Q _i For the current maximum available capacity of the battery, A, B, C is the model parameter of the capacity estimation model, AD _i And (3) defining the current maximum available capacity of the battery and the rated capacity of the battery according to the SOH definition of the battery as the curve similarity (i is the charge and discharge cycle times), and obtaining the SOH of the battery.

In order to obtain a battery state curve with high smoothness and low noise, in an embodiment of the present application, before obtaining the curve similarity between the battery state curve and the initial state curve, the method further includes:

The purpose of interpolating the battery state curve is to supplement missing data points in the battery state curve and improve the smoothness of the battery state curve. The purpose of filtering the battery state curve is to reduce noise of the battery state curve.

Since the voltage and current data are recorded at equal time intervals, the battery capacity increment and the sampling precision at both ends of the discharge voltage curve are low, which may result in a large calculation error of the battery state curve. Therefore, in order to improve the calculation accuracy and smoothness of the battery state curve, interpolation and filtering processing are performed on the battery state curve in the present embodiment.

After interpolation and filtering are carried out on the battery state curve, the accuracy of the subsequent curve similarity acquisition is increased, and therefore the estimation accuracy of the whole battery state of health estimation method on the battery state of health is improved.

Optionally, in an embodiment of the present application, the interpolating and filtering the battery state curve includes:

cubic spline interpolation and Savitzky-Golay filtering (abbreviated as SG filtering) are performed on the battery state curve.

The cubic spline interpolation is to divide the known data into a plurality of segments, each segment constructs a cubic function, and ensures that the joint of the segment functions has the properties of 0-order continuity, continuous first-order derivative and continuous second-order derivative. The interpolation method is used for interpolating the battery state curve, so that the trade-off between interpolation calculation and retaining of all original data is realized.

The original battery state curve may have large noise, and is not suitable to be directly used as input data for estimating the battery state of health, and filtering is needed to remove the noise. Compared with other filtering methods, the SG filtering tends to filter less high-frequency information, the basic shape and width of the filtered data are kept unchanged, and when the data change rapidly, the method is more effective than other methods, so that the data characteristics of the battery state curve are considered, and noise in the battery state curve is eliminated by adopting the SG filtering.

In order to improve the estimation accuracy of the battery state of health, in one embodiment of the present application, the battery state curve and the initial state curve each include a correspondence between a battery increment capacity and a voltage when the battery is within a preset SOC interval; the preset SOC interval is an SOC interval including a peak section of a battery increment capacity and voltage curve of the battery.

Still referring to fig. 5, when the peak value distribution of the battery state curve at different charge cycles is between 3.8V and 3.4V, the SOC interval may be 30% to 80%, and the preset SOC interval may be set to 30% to 80% at this time, so as to ensure that the peak value sections of the battery state curve and the initial state curve may be included. When the peak segment of the battery delta capacity versus voltage curve of the battery is included in the battery state curve and the initial state curve, it is easier to calculate the curve similarity between the two curves, thereby improving the estimation accuracy of the battery state of health.

Optionally, an embodiment of the present application proposes a feasible curve similarity calculation method, and specifically the obtaining the curve similarity between the battery state curve and the historical state curve includes:

The dynamic time warping algorithm (Dynamic Time Warping, DTW) is an algorithm for measuring the similarity between two sequences, which may differ in voltage or delta capacity, such as a battery state curve and a history state curve, by comparing two sets of asynchronous signals to find the best matching path between them to measure the similarity. In the SOH estimation problem we focus mainly on measuring the differences in IC amplitude, rather than its distribution on the voltage axis. Thus, the DTW algorithm was introduced to be well suited for measuring differences in shape characteristics of two IC curves.

In one embodiment of the present application, a feasible method for acquiring a battery state curve is provided, i.e. the acquiring a battery state curve includes:

and acquiring battery working condition data, wherein the battery working condition data comprises voltage, current and state of charge (SOC) in a battery charging and discharging cycle process.

And extracting target data of the battery working condition data, wherein the target data is discharge section data or charge section data of the battery working condition data. Referring to fig. 6, fig. 6 shows a schematic diagram of distinguishing between the charging section and the discharging section data, for example, the charging section and the discharging section may be distinguished according to a change in current, SOC, or the like. In fig. 6, the abscissa is time and the ordinate is SOC.

Optionally, the extracting the target data of the battery condition data includes:

or (b)

In this embodiment, by setting the first preset condition and the second preset condition, the occasional current fluctuation and other conditions in the battery charging and discharging process are eliminated, and the abnormal division of the discharging section and the charging section caused by the coupled current fluctuation is avoided.

Correspondingly, the embodiment of the application also provides a method for establishing a capacity estimation model, as shown in fig. 7, for estimating the current maximum available capacity of the battery, where the method for establishing the capacity estimation model includes:

s701: acquiring a historical battery state curve, wherein the historical state curve represents the corresponding relation between the increment capacity and the voltage of the battery in the process of at least partially charging or at least partially discharging the battery in the history;

s702: extracting, from the historical battery state curves, a curve similarity between each of the historical battery state curves and an initial battery state curve, the curve similarity characterizing a similarity difference between the historical battery state curves and the selected historical state curve;

s703: taking the curve similarity as a health indicator, and establishing a capacity estimation model, wherein the capacity estimation model characterizes the calculation relation between the curve similarity and the current maximum available capacity of the battery;

S704: and fitting the capacity estimation model by using battery history data to determine model parameters in the capacity estimation model, wherein the battery history data comprises a corresponding relation between the capacity of a historical battery and the similarity of a historical curve. Taking formula (1) as an example, in step S704, determining the model parameters in the capacity estimation model is determining A, B, C parameters in formula (1).

The method for establishing the capacity estimation model provided in the embodiment belongs to the same application conception as the method for estimating the battery state of health provided in the embodiment of the present application, and the established capacity estimation model can be applied to the method for estimating the battery state of health. For details of the battery state curve, the health indicator, etc., which are not described in detail in the present embodiment, reference may be made to the relevant content of the method for estimating the battery state according to the foregoing embodiment of the present application, and the details are not described herein.

In one embodiment of the present application, the example analysis is performed using maryland disclosure data, a capacity estimation model shown in formula (1) is established through steps S701 to S703, and three model parameters A, B and C are fitted through battery history data in the maryland disclosure data, so as to obtain the capacity estimation model shown in formula (2).

Based on the Capacity estimation model shown in formula (2), the estimation method of the battery state of health of the above embodiment is verified by verification data, so as to obtain a verification result shown in fig. 8, wherein the abscissa of the left graph and the right graph of fig. 8 is the Cycle number (Cycle), the ordinate of the left graph of fig. 8 is the battery Capacity (Capacity) and the unit is the ampere hour (Ah), and different data points respectively represent the measured actual battery Capacity (Measured Capacity) and the battery Capacity (Estimated Capacity) estimated by the estimation method of the battery state of health provided by the embodiment of the present application. The right graph of fig. 8 is the statistics of the left graph of fig. 8, and the ordinate of the right graph of fig. 8 is the error rate (Estimated SOH Error) of the estimated battery state of health in%. The data in the right graph of fig. 8 represent Root Mean Square Error (RMSE), mean Absolute Error (MAE), and erroneous data points (error), respectively.

As can be seen from the right graph of fig. 8, the Root Mean Square Error (RMSE) of the battery state of health estimation method provided by the embodiment of the present application is 2.16%, and the Mean Absolute Error (MAE) is 2.98%, which indicates that the battery state of health estimation method provided by the embodiment of the present application has higher accuracy and robustness.

Exemplary apparatus

The embodiment of the application also provides a device for estimating the state of health of a battery, as shown in fig. 9, including:

a curve obtaining module 100, configured to obtain a battery state curve, where the battery state curve represents a correspondence between an incremental capacity and a voltage of the battery during at least partial charging or at least partial discharging of the battery;

the similarity difference module 200 is configured to obtain a similarity between the battery state curve and an initial state curve, where the initial state curve represents a correspondence between an incremental capacity of the battery and a voltage during at least partial charging or at least partial discharging of the battery in an initial charge-discharge cycle, and the similarity represents a similarity difference between the battery state curve and the initial state curve;

the health status module 300 is configured to calculate a battery health status of the battery using the curve similarity as a health indicator.

The device for estimating the state of health of the battery provided in this embodiment belongs to the same application conception as the method for estimating the state of health of the battery provided in the above embodiment of the present application, and the method for estimating the state of health of the battery provided in any of the above embodiments of the present application may be executed, and the device has a functional module and beneficial effects corresponding to the execution of the method for estimating the state of health of the battery. Technical details not described in detail in the present embodiment may be referred to the specific processing content of the method for estimating the battery state of health provided in the above embodiment of the present application, and will not be described herein.

Exemplary vehicle

The embodiment of the application also provides a vehicle, referring to fig. 10, including: a controller 400 and a memory 500; wherein the memory 500 is connected to the controller 400, the memory 500 is used for storing a computer program, and the controller 400 is used for implementing the method for estimating the battery state of health according to any one of the above by running the computer program stored in the memory 50.

The controller 400 may be a vehicle controller of a vehicle, or may be a battery management system controller, which is not limited in this application. The controller 400 and the memory 500 may establish a communication connection through a CAN (Controller Area Network ) bus of the vehicle.

Exemplary electronic device

Another embodiment of the present application further provides an electronic device, referring to fig. 11, and an exemplary embodiment of the present specification further provides an electronic device, including: the battery state of health estimation method includes a memory storing a computer program, and a processor executing steps in the battery state of health estimation method or the capacity estimation model establishment method according to various embodiments of the present specification described in the above embodiments of the present specification when the processor executes the computer program.

The internal structure of the electronic device may be as shown in fig. 11, and the electronic device includes a processor, a memory, a network interface, and an input device connected through a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile 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 network interface of the electronic device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, performs the steps in the estimation method of the battery state of health or the establishment method of the capacity estimation model according to the various embodiments of the present specification described in the above embodiments of the present specification.

The processor may include a host processor, and may also include a baseband chip, modem, and the like.

The memory stores programs for executing the technical scheme of the invention, and can also store an operating system and other key services. In particular, the program may include program code including computer-operating instructions. More specifically, the memory may include read-only memory (ROM), other types of static storage devices that may store static information and instructions, random access memory (random access memory, RAM), other types of dynamic storage devices that may store information and instructions, disk storage, flash, and the like.

The processor may be a general-purpose processor, such as a general-purpose Central Processing Unit (CPU), microprocessor, etc., or may be an Application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present invention. But may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.

The input device may include means for receiving data and information entered by a user, such as a keyboard, mouse, camera, scanner, light pen, voice input device, touch screen, pedometer or gravity sensor, etc.

The output device may include means, such as a display screen, printer, speakers, etc., that allow information to be output to the user.

The communication interface may include means, such as any transceiver, for communicating with other devices or communication networks, such as ethernet, radio Access Network (RAN), wireless Local Area Network (WLAN), etc.

The processor executes the program stored in the memory and invokes other devices, which can be used to implement the steps of any of the methods for estimating the state of health of the battery or the method for establishing the capacity estimation model provided in the embodiments of the present application.

The electronic equipment can also comprise a display component and a voice component, wherein the display component can be a liquid crystal display screen or an electronic ink display screen, an input device of the electronic equipment can be a touch layer covered on the display component, can also be a key, a track ball or a touch pad arranged on a shell of the electronic 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. 11 is merely a block diagram of a portion of the structure associated with the present description and does not constitute a limitation of the electronic device to which the present description is applied, and that a particular electronic device may include more or less components than those shown in the drawings, or may combine certain components, or have a different arrangement of components.

Exemplary computer program product and storage Medium

In addition to the methods and apparatus described above, the methods of estimating battery state of health or establishing a capacity estimation model provided by the embodiments of the present specification may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in the methods of estimating battery state of health or establishing a capacity estimation model according to the various embodiments of the present specification described in the "exemplary methods" section of the present specification.

The computer program product may write program code for performing the operations of embodiments of the present description in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.

Further, the embodiments of the present specification also provide a computer-readable storage medium having stored thereon a computer program that is executed by a processor to perform the steps in the estimation method of the battery state of health or the establishment method of the capacity estimation model according to the various embodiments of the present specification described in the above-described "exemplary method" section of the present specification.

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, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

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 merely represent a few implementations of the present description, which are described in more detail and are not to be construed as limiting the scope of the solutions provided by the examples of the present description. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the present description, which is within the scope of the present description. Accordingly, the protection scope of the patent should be determined by the appended claims.

Claims

1. A method for estimating a state of health of a battery, comprising:

2. The method of claim 1, wherein calculating a battery state of health of the battery using the curve similarity as a health indicator comprises:

3. The method of claim 1, wherein prior to the obtaining the curve similarity between the battery state curve and the initial state curve further comprises:

4. The method of claim 3, wherein said interpolating and filtering said battery state curve comprises:

5. The method of claim 1, wherein the battery state curve and the initial state curve each include a correspondence between battery delta capacity and voltage when the battery is within a preset SOC interval; the preset SOC interval is an SOC interval including a peak section of a battery increment capacity and voltage curve of the battery.

6. The method of any one of claims 1-5, wherein the obtaining a battery state curve comprises:

acquiring battery working condition data, wherein the battery working condition data comprises voltage, current and state of charge (SOC) in a battery charging and discharging cycle process;

7. The method of claim 6, wherein the extracting target data for the battery condition data comprises:

or (b)

8. The method of any one of claims 1-5, wherein said obtaining a curve similarity between the battery state curve and a historical state curve comprises:

9. A method for establishing a capacity estimation model for estimating a current maximum available capacity of a battery, the method comprising:

10. An estimation device of a state of health of a battery, comprising:

11. A vehicle, characterized by comprising: a controller and a memory; wherein the memory is connected to the controller, the memory is used for storing a computer program, and the controller is used for realizing the method for estimating the state of health of the battery according to any one of claims 1 to 8 by running the computer program stored in the memory.

12. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method of estimating the state of health of a battery according to any one of claims 1 to 8 or the method of establishing a capacity estimation model according to claim 9.