CN110542867A - Battery state of health evaluation method, device and storage medium - Google Patents

Abstract

The present invention is applicable to the technical field of battery detection, and provides a battery health status evaluation method, device and storage medium. The method includes: acquiring the voltage parameters of a sample battery at each preset aging node, and according to each preset aging node obtain the target transfer parameter of the sample battery; obtain the voltage parameter of the target battery, and obtain the aging characteristic parameter of the target battery according to the voltage parameter of the target battery and the target transfer parameter; according to the aging characteristic The parameters are calculated to obtain the health status evaluation value of the target battery. The present invention acquires a common feature of the same brand and the same batch of batteries by acquiring the target transmission parameters of the sample battery, and then combines the feature with the voltage parameters of the target battery to evaluate the health status of the target battery, which can accurately track and evaluate the health of the battery state, with higher evaluation accuracy.

Description

Battery state of health evaluation method, device and storage medium

technical field

The invention belongs to the technical field of battery detection, and in particular relates to a battery health status evaluation method, device and storage medium.

Background technique

In recent years, with the development of electric vehicles, lithium-ion batteries have been widely used due to their advantages such as high energy density, high charging efficiency, and strong safety and stability. The state of health of the battery (SOH, State of Health) reflects the degree of life decay of the battery. Accurate evaluation of the state of health of the battery can provide a reference for the decision-making of preventive maintenance and maintenance of the battery in the electric vehicle in a timely manner, reducing maintenance costs, Reduce the probability of vehicle failure and ensure the safe and efficient operation of the vehicle.

The current SOH evaluation method has insufficient precision and large errors, and there are certain problems in evaluating the battery health status.

Contents of the invention

In view of this, the embodiments of the present invention provide a method, device, and storage medium for evaluating the state of health of a battery to solve the problems of insufficient accuracy and large errors in the evaluation of the state of health of a battery in the prior art.

The first aspect of the embodiments of the present invention provides a battery state of health evaluation method, including:

Acquiring voltage parameters of the sample battery at each preset aging node, and obtaining target delivery parameters of the sample battery according to the voltage parameters of each preset aging node;

Acquiring voltage parameters of the target battery, and obtaining aging characteristic parameters of the target battery according to the voltage parameters of the target battery and the target transfer parameters;

The health state evaluation value of the target battery is calculated according to the aging characteristic parameter.

The second aspect of the embodiments of the present invention provides a battery state of health assessment device, including:

A target delivery parameter acquisition module, configured to acquire the voltage parameters of the sample battery at each preset aging node, and obtain the target delivery parameter of the sample battery according to the voltage parameters of each preset aging node;

An aging characteristic parameter acquisition module, configured to acquire a voltage parameter of a target battery, and obtain an aging characteristic parameter of the target battery according to the voltage parameter of the target battery and the target transfer parameter;

A health status evaluation module, configured to calculate a health status evaluation value of the target battery according to the aging characteristic parameters.

A third aspect of the embodiments of the present invention provides a terminal device, including: a memory, a processor, and a computer program stored in the memory and operable on the processor, and the processor executes the computer program When implementing the steps of the method for evaluating the state of health of the battery as described in the first aspect of the embodiment of the present invention.

The fourth aspect of the embodiment of the present invention provides a computer-readable storage medium, including: the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program described in the first aspect of the embodiment of the present invention is implemented. The steps of the battery state of health assessment method.

The embodiment of the present invention obtains the voltage parameters of the sample battery at each preset aging node, and obtains the target transfer parameter of the sample battery according to the voltage parameters of each preset aging node, and the target transfer parameter reflects the same batch and the same brand Common characteristics of batteries. Then obtain the voltage parameter of the target battery, and obtain the aging characteristic parameter of the target battery according to the voltage parameter of the target battery and the target transfer parameter, that is, the aging characteristic parameter is obtained by combining the common characteristics with the voltage parameter of the target battery itself, and finally according to The aging characteristic parameter is calculated to obtain the health status evaluation value of the target battery. By extracting the common characteristics of the same brand and the same batch of batteries and combining the individual characteristics of the target battery to evaluate the health status of the target battery, the health status of the target battery can be more accurately reflected, and the evaluation results are highly accurate.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the descriptions of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only of the present invention. For some embodiments, those of ordinary skill in the art can also obtain other drawings based on these drawings without paying creative efforts.

Fig. 1 is a schematic diagram of the implementation flow of a method for evaluating the state of health of a battery provided by an embodiment of the present invention;

Fig. 2 is the voltage frequency curve diagram of the constant current charging stage under the standard test provided by the embodiment of the present invention;

Fig. 3 is a voltage parameter diagram and a voltage frequency curve diagram of each preset aging node in the constant current charging stage of the sample battery provided by the embodiment of the present invention;

Fig. 4 is a graph of weight values of sample batteries in each voltage range provided by an embodiment of the present invention;

Fig. 5 is a schematic diagram of a battery state of health assessment device provided by an embodiment of the present invention;

Fig. 6 is a schematic diagram of a terminal device provided by an embodiment of the present invention.

Detailed ways

In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

In order to illustrate the technical solutions of the present invention, specific examples are used below to illustrate.

FIG. 1 is a schematic diagram of the implementation flow of a method for evaluating the state of health of a battery provided by an embodiment of the present invention, which is described in detail as follows.

Step S101, obtaining voltage parameters of the sample battery at each preset aging node, and obtaining target transfer parameters of the sample battery according to the voltage parameters of each preset aging node.

The State of Health (SOH) of a lithium-ion battery represents the remaining available capacity of the battery. SOH usually characterizes the state of the battery from the beginning of life to the end of life in percentage form. Generally, when the battery is new, the SOH of the battery is greater than or equal to 100%, and when it is completely scrapped, the SOH of the battery is equal to 0. When the SOH of the battery is less than or equal to 80%, the life of the battery can be considered to be terminated. The state of health of the battery is expressed as the ratio of the current remaining available capacity of the battery to the nominal capacity of the battery when it leaves the factory. The expression of its SOH is:

Among them, Q _age represents the actual available capacity of the battery in the current state, and Q _all represents the nominal capacity of the battery.

As the number of times of use of a lithium-ion battery increases, its capacity continues to decay. During the use of the battery, the active material on the internal electrode sheet of the battery will continue to decrease, and the battery will gradually age, resulting in a gradual decline in its ability to store electricity.

According to the characteristics of the battery, the preset aging node of the sample battery is obtained, and the aging node is the node of the charging and discharging times of the battery. For example, the aging node can be 10 times, 20 times, or 30 times. A full-life aging test is carried out on the battery, the voltage parameters of the sample battery at each preset aging node are obtained, and the target transfer parameters of the sample battery are obtained according to the voltage parameters of each preset aging node. The sample battery and the target battery to be evaluated are all batteries of the same brand and batch purchased in batches. The sample batteries are randomly selected from the battery packs of the same brand and batch purchased in batches. Due to the raw materials of the same brand and the same batch of batteries The selected sample batteries are generally representative, so the target transfer parameters of the sample batteries can represent the characteristics of the same brand and the same batch of batteries in the entire life cycle.

Optionally, step S101 may include:

In step S1011, the voltage parameters of the sample battery at each preset aging node are obtained, and the voltage parameters of each preset aging node are respectively counted to obtain a voltage probability density model of the sample battery.

Optionally, obtaining the voltage parameters of the sample battery at each preset node may include:

1. Carry out cycle aging test on the sample battery in a constant current mode;

The aging process of the sample battery under normal use is simulated by the cycle aging test.

2. When reaching each preset aging node, conduct a standard test on the sample battery in a constant current mode to obtain the voltage parameters of the sample battery at each preset aging node.

Optionally, during the standard test process of the sample battery, the real-time sample battery charging voltage data is recorded at equal intervals, so as to obtain the voltage parameters of the sample battery at each preset aging node. Optionally, to ensure the accuracy of data analysis, the amount of data collected for each charge is at least 1×10 ³ . Generally, the sampling interval is 0.2s to 2s. For example, the sampling interval may be 1s.

Specifically, the above experimental process can be:

The experimental platform is composed of DT50W-16 lithium ion detector and its upper computer. The experimental platform can set a variety of charging and discharging modes, and can monitor and save parameters such as voltage, current, and capacity during battery charging and discharging. The sample battery uses a domestic brand 18650 lithium iron phosphate lithium ion battery.

At room temperature, the sample battery is placed in the tester, and the battery is subjected to a cycle aging test and a standard test charge and discharge experiment according to the constant current constant voltage (CCCV) charge and discharge mechanism. This experiment is mainly divided into two stages: standard test and cyclic aging test. The cycle aging test is to shorten the experimental time, and the sample battery is charged and discharged at 4 times the rate of current. The standard test is to obtain the voltage parameters of the sample battery under the standard test. By alternately performing these two tests, the voltage, current and power of the battery under different aging nodes are obtained. See Table 1 for parameter settings. Each sample battery must undergo the experimental process of SOH decay from 100% to below 50%.

Table 1 Parameter setting of the whole life aging experiment

The charging mechanism of the standard test and the cycle aging test adopts the constant current and constant voltage method, and the discharge mechanism adopts the constant current discharge method. The standard test method is: at room temperature, discharge with a constant current of 0.5C to a cut-off voltage of 2.5V, let it stand for 3600s, and then charge it with a standard charging method to a cut-off current of 0.1C. The cycle aging test method is: at room temperature, discharge with a constant current of 4C to a cut-off voltage of 1.8V, let it stand for 3600s, and then charge it to a cut-off current of 0.1C with a constant current of 4C and a voltage of 3.75V. Every 10 cycles of aging test is regarded as an aging node, and a standard test is performed to record the current, voltage and charging power during the charging process, so as to obtain the voltage parameters of the sample battery at each preset aging node.

It can be seen from the above that the voltage parameters of the sample battery at each preset aging node have discrete characteristics, and the discrete voltage values of the sample battery at each preset aging node are counted separately to obtain the probability density model of the sample battery.

Optionally, step S1011 may include:

1. Obtain the voltage frequency curve of each preset aging node according to the voltage parameters of each preset aging node.

The discrete voltage values of the sample batteries at each preset aging node are counted to obtain a voltage frequency curve of the sample battery at each preset aging node. The horizontal axis of the curve is the voltage value, and the vertical axis is the frequency of occurrence of the corresponding voltage value, refer to FIG. 2 .

2. Divide the preset first characteristic voltage interval into a plurality of second characteristic voltage intervals according to preset intervals.

Optionally, the first characteristic voltage range may be 3364mV-3499mV; the preset interval may be 5mV; for example, the first characteristic voltage range 3364mV-3499mV may be divided into 27 second characteristic voltages at a preset interval of 5mV interval.

3. Counting the number of voltage points at which the voltage frequency curves of each preset aging node are located in each second characteristic voltage interval to obtain a voltage probability density model of the sample battery.

The voltage frequency curve is divided according to the above steps, and the number of voltage points at which the voltage frequency curve of each preset aging node is located in each second characteristic voltage interval is counted, that is, the voltage frequency, so as to obtain the voltage probability density model of the sample battery. It can be seen from the battery characteristics and experiments that the characteristics of the battery cannot be accurately reflected when the battery voltage is too high or too low. When the battery voltage is within a specific voltage range, the characteristics are stable and can better reflect the characteristics of the battery. The voltage within the battery is counted, which improves the accuracy of battery health status assessment.

Step S1012, according to the voltage probability density model, obtain the target transfer parameter.

Optionally, step S1012 may include:

1. Construct an evaluation index matrix of the sample battery according to the voltage probability density model.

Evaluate the m aging nodes of the sample battery, the evaluation index is the voltage frequency in n second characteristic intervals, and the evaluation index matrix D is:

Among them, D _ij is the index value of the jth evaluation index of the ith preset aging node, i=1,2,...m, j=1,2,...n; m is the number of preset aging nodes, n is the number of second characteristic voltage intervals; the jth evaluation index of the ith preset aging node is the voltage frequency in the jth second characteristic interval of the ith preset aging node.

For example, in one embodiment of the present invention, the preset number of aging nodes is 8, and the number of second feature intervals is 27.

2. Calculate and obtain the weights of each evaluation index corresponding to the evaluation index matrix according to the entropy weight method, and the weights of each evaluation index are the target transfer parameters.

The entropy weight method is an objective value assignment method. In the process of specific use, the entropy weight method uses information entropy to calculate the entropy weight of each index according to the variation degree of each index, and then corrects the weight of each index through the entropy weight, so as to obtain a more objective index weight. Generally speaking, if the entropy weight method of the information entropy index weight determination method of a certain index is smaller, it indicates that the index value is more variable, the amount of information provided is more, and the role it can play in the comprehensive evaluation is also greater. , the greater its weight. On the contrary, if the entropy weight method of the information entropy index weight determination method of a certain index is larger, it indicates that the index value has a smaller degree of variation, the less information it provides, and the smaller the role it plays in the comprehensive evaluation. The weight is also smaller.

1) Normalize the data in the evaluation index matrix D to construct a standardized matrix.

The standardized matrix R=(R _ij ) _m×n is:

Wherein, R _ij is the normalized value of the jth evaluation index of the ith preset aging node.

2) Calculate the entropy value H j of the j-th evaluation index according to the proportion of the j-th evaluation index of the i-th preset aging node, and the entropy value H _j of the _j -th evaluation index is:

Wherein, f _ij is the proportion of the index value of the i-th preset aging node under the j-th evaluation index; when f _ij =0, f _ij lnf _ij =0.

3) Calculate the weight of each evaluation index according to the entropy value of the j evaluation index, and the weight w _j of the j evaluation index is:

4) Obtain the target transfer parameter W of the sample battery according to the weight w _j of the jth evaluation index, and the target transfer parameter W of the sample battery = (w ₁ , w ₂ ... w _j , ... w _n ).

Considering the inconsistency of each battery of the same brand, the number of sample batteries can be multiple, and selecting multiple sample batteries to obtain the target transfer function can improve the accuracy of health status assessment.

Optionally, step S101 may include:

1. Obtain the quantity of the sample batteries.

2. Obtain the voltage parameters of each sample battery at each preset aging node, and obtain the sample transfer parameters corresponding to each sample battery according to the voltage parameters of each sample battery at each preset quantization node.

Wherein, the sample delivery parameters corresponding to each sample battery are consistent with the calculation method of the target delivery parameters of the sample batteries in the above steps.

3. Obtain the target transfer parameter according to the sample transfer parameter corresponding to each sample cell and the quantity of the sample cells. Optionally, average the sample transfer parameters corresponding to each sample battery to obtain a target transfer parameter.

By selecting multiple sample batteries and calculating the sample transfer parameters of each sample battery respectively, and then performing mean value processing on each sample transfer parameter, the final target transfer parameters are obtained, eliminating the impact of the inconsistency of the battery samples of the same brand, Improved accuracy of battery state of health assessment.

Step S102, acquiring voltage parameters of the target battery, and obtaining aging characteristic parameters of the target battery according to the voltage parameters of the target battery and the target transfer parameters.

Carry out a charge and discharge standard test on the target battery to obtain the voltage parameters of the target battery in the constant current charging phase. The target transfer parameters can reflect the common characteristics of the same brand and the same batch of batteries. According to the target transfer parameters that reflect the common characteristics of the same brand and the same batch of batteries combined with the voltage parameters that reflect the individual characteristics of the target battery, the aging characteristic parameters of the target battery can be calculated.

Optionally, step S102 may include:

Step S1021, determining index values of various evaluation indicators corresponding to the target battery according to the voltage parameters of the target battery.

It can be seen from the above steps that the target transfer parameter may be the weight value of each evaluation index of the sample battery determined by the entropy weight method.

Based on the above, the step S1021 may include:

1. Obtain the voltage parameters of the target battery and form the voltage frequency curve of the target battery;

2. Count the number of voltage points in each second characteristic voltage interval of the voltage frequency curve of the target battery, and use the number of voltage points in each second characteristic interval as the index value of each evaluation index.

In step S1022, the weighted sum of the index values of the various evaluation indexes and the weights of the various evaluation indexes is obtained to obtain the aging characteristic parameter of the target battery.

The aging characteristic parameter P _f of the target battery is:

Among them, w _j is the weight of the j-th evaluation index, and E _j is the index value of the j-th evaluation index;

Step S103 , calculating the health status evaluation value of the target battery according to the aging characteristic parameters.

Optionally, the health evaluation value of the target battery is:

SOH＝0.0000658P _f ² +0.049P _f +25.62 (9)

Among them, SOH is the evaluation value of the state of health of the target battery, and P _f is the aging characteristic parameter of the battery.

In the above-mentioned embodiment, the voltage parameters of the sample battery at each preset aging node are obtained, and the target delivery parameters of the sample battery are obtained according to the voltage parameters of each preset aging node; the target delivery parameters can reflect the same brand. The common characteristics of the whole life cycle of batch batteries. Then obtain the voltage parameter of the target battery, and obtain the aging characteristic parameter of the target battery according to the voltage parameter reflecting the individual characteristics of the target battery and the target transfer parameter reflecting the common characteristics of the same brand and the same batch of batteries; The aging characteristic parameter is calculated to obtain the health status evaluation value of the target battery. The health status evaluation method of the battery in the present invention only needs to carry out a cycle aging test on the sample battery to obtain the common characteristics of the same brand and the same batch of batteries, and only needs to charge and discharge the target battery once to calculate the health status of the target battery Evaluation value, the method is simple and easy to use, can accurately track and evaluate the health status of the battery, and has higher evaluation accuracy.

The embodiment of the present invention is further described by taking the probability density model combined with the entropy weight method to evaluate the health state of the battery as an example.

1. Carry out a cycle aging test on the sample battery. The preset aging nodes are set to four aging nodes of 20 times, 40 times, 60 times and 80 times respectively, and the voltage frequency curve of each preset aging node is obtained. Refer to Figure 3.

2. Establish a voltage probability density model according to the voltage frequency curve of each preset aging node, and obtain the weight value of each voltage section through the entropy weight method according to the voltage probability density model. As shown in FIG. 4 , the first characteristic voltage interval is a voltage from 3364mV to 3499mV, and each section is 5mV, which is divided into 27 sections in total. The first point on the coordinate axis represents the weight of the first voltage range 3364-3369mV. For example, the weight of the fifth voltage range 3384mV-3389mV is at most 0.06564, and the weight of the 25th voltage range 3484mV-3389mV is at least 0.02727.

3. Obtain the voltage parameters of the target battery, and calculate the health status evaluation value of the target battery according to the weight value obtained above.

It should be understood that the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined by its functions and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present invention.

Corresponding to a battery state of health assessment method described in the above embodiments, FIG. 5 shows an example diagram of a battery state of health assessment apparatus 500 provided in an embodiment of the present invention. As shown in Figure 5, the device may include:

A target delivery parameter acquisition module 501, configured to acquire the voltage parameters of the sample battery at each preset aging node, and obtain the target delivery parameter of the sample battery according to the voltage parameters of each preset aging node;

An aging characteristic parameter acquisition module 502, configured to acquire a voltage parameter of a target battery, and obtain an aging characteristic parameter of the target battery according to the voltage parameter of the target battery and the target transfer parameter;

A health status evaluation module 503, configured to calculate a health status evaluation value of the target battery according to the aging characteristic parameters.

Optionally, the target delivery parameter acquisition module 501 may include:

A voltage probability density model building unit 5011, configured to obtain voltage parameters of the sample battery at each preset aging node, and perform statistics on the voltage parameters of each preset aging node to obtain a voltage probability density model of the sample battery;

The target transfer parameter acquisition unit 5012 is configured to obtain the target transfer parameter according to the voltage probability density model.

Optionally, the voltage probability density model building unit 5011 may include:

The voltage frequency curve establishment subunit is used to obtain the voltage frequency curve of each preset aging node according to the voltage parameters of each preset aging node;

The characteristic voltage interval division subunit is used to divide the preset first characteristic voltage interval into a plurality of second characteristic voltage intervals according to preset intervals;

The voltage probability density model establishing subunit is used to count the number of voltage points whose voltage frequency curves of each preset aging node are in each second characteristic voltage interval, and obtain the voltage probability density model of the sample battery.

Optionally, the target delivery parameter acquisition unit 5012 may include:

An evaluation index matrix construction subunit, configured to construct an evaluation index matrix of the sample battery according to the voltage probability density model;

The target transfer parameter acquisition subunit is configured to calculate and obtain the weights of each evaluation index corresponding to the evaluation index matrix according to the entropy weight method, and the weights of each evaluation index are the target transfer parameters.

Optionally, the aging characteristic parameter acquisition module 502 may include:

An index value determining unit, configured to determine index values of various evaluation indexes corresponding to the target battery according to the voltage parameters of the target battery;

The aging characteristic parameter acquisition unit is configured to obtain the aging characteristic parameter of the target battery by summing the index values of the various evaluation indexes and the weighted sums of the various evaluation indexes.

Optionally, the target delivery parameter acquisition module 501 may include:

a sample battery quantity acquisition unit, configured to acquire the sample battery quantity;

A sample transfer parameter acquisition unit, configured to respectively acquire the voltage parameters of each sample battery at each preset aging node, and obtain the sample transfer parameters corresponding to each sample battery according to the voltage parameters of each sample battery at each preset quantization node;

The target transfer parameter acquisition unit is configured to obtain the target transfer parameter according to the sample transfer parameter corresponding to each sample battery and the number of the sample batteries.

Optionally, the health state evaluation module 503 calculates the health state evaluation value of the target battery according to the aging characteristic parameters, which may include

SOH＝0.0000658P _f ² +0.049P _f +25.62

Among them, SOH is the evaluation value of the state of health of the target battery, and P _f is the aging characteristic parameter of the battery.

Fig. 6 is a schematic diagram of a terminal device provided by an embodiment of the present invention. As shown in FIG. 6 , the terminal device 600 of this embodiment includes: a processor 601, a memory 602, and a computer program 603 stored in the memory 602 and operable on the processor 601, such as a method for evaluating the state of health of a battery program of. When the processor 601 executes the computer program 603, it implements the steps in the embodiment of the above-mentioned method for evaluating the state of health of a battery, such as steps S101 to S103 shown in FIG. 1 , when the processor 601 executes the computer program 603 The functions of the modules in the above-mentioned device embodiments are implemented, for example, the functions of the modules 501 to 503 shown in FIG. 5 .

Exemplarily, the computer program 603 can be divided into one or more program modules, and the one or more program modules are stored in the memory 602 and executed by the processor 601 to complete the present invention . The one or more program modules may be a series of computer program instruction segments capable of accomplishing specific functions, and the instruction segments are used to describe the execution process of the computer program 603 in the battery health status evaluation apparatus 500 or the terminal device 600 . For example, the computer program 603 can be divided into a target delivery parameter acquisition module 501, an aging characteristic parameter acquisition module 502, and a health status evaluation module 503, and the specific functions of each module will not be repeated here.

The terminal device 600 may be computing devices such as desktop computers, notebooks, palmtop computers, and cloud servers. The terminal device may include, but not limited to, a processor 601 and a memory 602 . Those skilled in the art can understand that FIG. 6 is only an example of a terminal device 600, and does not constitute a limitation to the terminal device 600. It may include more or less components than those shown in the figure, or combine some components, or different components. , for example, the terminal device may also include an input and output device, a network access device, a bus, and the like.

The so-called processor 601 may be a central processing unit (Central Processing Unit, CPU), and may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, and the like.

The storage 602 may be an internal storage unit of the terminal device 600 , for example, a hard disk or memory of the terminal device 600 . The memory 602 may also be an external storage device of the terminal device 600, such as a plug-in hard disk equipped on the terminal device 600, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card , Flash Card (Flash Card) and so on. Further, the memory 602 may also include both an internal storage unit of the terminal device 600 and an external storage device. The memory 602 is used to store the computer program and other programs and data required by the terminal device 600 . The memory 602 can also be used to temporarily store data that has been output or will be output.

Those skilled in the art can clearly understand that for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used for illustration. In practical applications, the above-mentioned functions can be assigned to different functional units, Completion of modules means that the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated into one processing unit, or each unit may exist separately physically, or two or more units may be integrated into one unit, and the above-mentioned integrated units may adopt hardware It can also be implemented in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the present application. For the specific working process of the units and modules in the above system, reference may be made to the corresponding process in the foregoing method embodiments, and details will not be repeated here.

In the above-mentioned embodiments, the descriptions of each embodiment have their own emphases, and for parts that are not detailed or recorded in a certain embodiment, refer to the relevant descriptions of other embodiments.

Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal equipment and method may be implemented in other ways. For example, the device/terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units Or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

If the integrated module/unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention realizes all or part of the processes in the methods of the above embodiments, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer When the program is executed by the processor, the steps in the above-mentioned various method embodiments can be realized. . Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable Excludes electrical carrier signals and telecommunication signals.

The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still carry out the foregoing embodiments Modifications to the technical solutions recorded in the examples, or equivalent replacement of some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should be included in within the protection scope of the present invention.

Claims (10)

1. a battery state of health assessment method, comprising:

Acquiring voltage parameters of a sample battery at each preset aging node, and acquiring target transmission parameters of the sample battery according to the voltage parameters of each preset aging node;

Acquiring a voltage parameter of a target battery, and acquiring an aging characteristic parameter of the target battery according to the voltage parameter of the target battery and the target transmission parameter;

And calculating the health state evaluation value of the target battery according to the aging characteristic parameters.

2. the method for estimating the state of health of a battery according to claim 1, wherein the obtaining the voltage parameters of the sample battery at each preset aging node and obtaining the target transfer parameters of the sample battery according to the voltage parameters of each preset aging node comprises:

Acquiring voltage parameters of a sample battery at each preset aging node, and respectively counting the voltage parameters of each preset aging node to obtain a voltage probability density model of the sample battery;

and obtaining the target transmission parameter according to the voltage probability density model.

3. The method for evaluating the state of health of a battery according to claim 2, wherein the step of performing statistics on the voltage parameters of the preset aging nodes to obtain a voltage probability density model of the sample battery comprises:

Obtaining a voltage frequency curve of each preset aging node according to the voltage parameter of each preset aging node;

Dividing a preset first characteristic voltage interval into a plurality of second characteristic voltage intervals according to a preset interval;

and respectively counting the number of voltage points of the voltage frequency curve of each preset aging node in each second characteristic voltage interval to obtain a voltage probability density model of the sample battery.

4. the battery state of health assessment method of claim 2, wherein said obtaining the target transfer parameter from the voltage probability density model comprises:

Constructing an evaluation index matrix of the sample battery according to the voltage probability density model;

And calculating the weight of each evaluation index corresponding to the evaluation index matrix according to an entropy weight method, wherein the weight of each evaluation index is the target transmission parameter.

5. the method of claim 4, wherein obtaining the aging characteristic parameter of the target battery according to the voltage parameter of the target battery and the target transfer parameter comprises:

determining index values of various evaluation indexes corresponding to the target battery according to the voltage parameters of the target battery;

And weighting and summing the index values of the evaluation indexes and the weights of the evaluation indexes to obtain the aging characteristic parameters of the target battery.

6. the method for estimating the state of health of a battery according to claim 1, wherein the obtaining the voltage parameters of the sample battery at each preset aging node and obtaining the target transfer parameters of the sample battery according to the voltage parameters of each preset aging node comprises:

Acquiring the number of the sample batteries;

respectively obtaining voltage parameters of each sample battery at each preset aging node, and obtaining sample transmission parameters corresponding to each sample battery according to the voltage parameters of each sample battery at each preset quantization node;

And obtaining the target transmission parameters according to the sample transmission parameters corresponding to the sample batteries and the number of the sample batteries.

7. The battery state of health estimation method according to any one of claims 1 to 6, wherein the calculating the state of health estimation value of the target battery from the aging characteristic parameter includes:

the state of health evaluation value of the target battery is as follows:

SOH＝0.0000658P+0.049P+25.62

Where SOH is the state of health estimation value of the target battery, and Pf is the aging characteristic parameter of the battery.

8. a battery state of health assessment apparatus, comprising:

The target transmission parameter acquisition module is used for acquiring voltage parameters of the sample battery at each preset aging node and acquiring the target transmission parameters of the sample battery according to the voltage parameters of each preset aging node;

the aging characteristic parameter acquisition module is used for acquiring a voltage parameter of a target battery and acquiring the aging characteristic parameter of the target battery according to the voltage parameter of the target battery and the target transmission parameter;

And the health state evaluation module is used for calculating the health state evaluation value of the target battery according to the aging characteristic parameters.

9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the battery state of health assessment method according to any one of claims 1 to 7 when executing the computer program.

10. a computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the battery state of health assessment method according to any one of claims 1 to 7.