WO2024120502A1

WO2024120502A1 - Method and device for testing state of health of battery, and storage medium

Info

Publication number: WO2024120502A1
Application number: PCT/CN2023/137298
Authority: WO
Inventors: 翁耿达
Original assignee: 湖北亿纬动力有限公司
Priority date: 2022-12-08
Filing date: 2023-12-07
Publication date: 2024-06-13
Also published as: CN115808624A

Abstract

A method and device for testing a state of health (SOH) of a battery, and a storage medium. The method for testing an SOH comprises the following steps: testing a battery on the basis of an SOC-OCV curve and a charging working condition, so as to acquire a first SOH of the battery (202); then testing the battery on the basis of the service time of the battery, so as to acquire a second SOH of the battery (204); and finally, according to the first SOH, acquiring an accurate SOH of the battery, or according to the first SOH and the second SOH, acquiring the accurate SOH of the battery (206).

Description

A battery health detection method, device and storage medium

This application claims the priority of the Chinese patent application filed with the China Patent Office on December 8, 2022, with application number 202211573227.6. The entire contents of the above application are incorporated by reference into this application.

Technical Field

The present application relates to the field of battery technology, for example, to a battery health detection method, device and storage medium.

Background technique

Vehicles, especially commercial vehicles and engineering vehicles, generally do not pay enough attention to the health of batteries. The health of batteries is usually judged by the battery life or the endurance of a single charge.

Although the detection method of related technology is easy to implement, due to the limitations of battery test data and the single calculation method, the health status calculated by this method is likely to deviate greatly from the actual health status of the battery system, resulting in excessive use of the battery, thereby accelerating battery degradation and even causing safety problems.

technical problem

The present application provides a battery health detection method, device and storage medium, which can accurately detect the health of the battery and avoid large deviations in the detected health.

Technical Solutions

In a first aspect, an embodiment of the present application provides a method for detecting the health of a battery. The method comprises:

Detecting the battery based on a SOC-OCV curve and a charging condition to obtain a first health status of the battery;

Detecting the battery based on the usage time of the battery to obtain a second health status of the battery;

The accurate health of the battery is obtained according to the first health, or the accurate health of the battery is obtained according to the first health and the second health.

In a second aspect, an embodiment of the present application provides a battery health detection device. and a processor, the memory storing a computer program, and the processor implementing the steps of the method described above when executing the computer program.

In a third aspect, an embodiment of the present application provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method described above are implemented.

Beneficial Effects

Beneficial effects of the present application: The present application provides a battery health detection method, device and storage medium, and the health detection method includes the following steps: detecting the battery based on the SOC-OCV curve and the charging condition to obtain the first health of the battery; detecting the battery based on the battery usage time to obtain the second health of the battery; obtaining the precise health of the battery according to the first health, or obtaining the precise health of the battery according to the first health and the second health. Therefore, a first health with high accuracy is obtained based on the SOC-OCV curve and the charging condition, and the precise health of the battery is obtained according to the first health, because the first health is obtained in combination with the SOC-OCV curve and the charging condition, so a precise health with high accuracy can be obtained. In addition, the second health is obtained based on the battery usage time, and then the first health and the second health are combined to obtain the precise health of the battery, which improves the accuracy of the battery health, avoids the excessive use of the battery, thereby improving the battery safety, and ensuring or even extending the battery system life cycle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a diagram showing an application environment of a battery health detection method according to an embodiment;

FIG2 is a schematic diagram of a flow chart of a battery health detection method according to an embodiment;

FIG3 is a schematic diagram of a flow chart of a battery health detection method according to an embodiment;

FIG4 is a schematic flow chart of a method for detecting battery health in one embodiment;

FIG5 is a structural block diagram of a battery health detection device in one embodiment;

FIG. 6 is a diagram showing the internal structure of a battery health detection device according to an embodiment.

Embodiments of the present invention

A battery health detection method provided in an embodiment of the present application can be applied in an application environment as shown in FIG. 1 . Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 detects the battery based on the SOC-OCV curve and the charging condition to obtain the first health of the battery, and further detects the battery based on the battery usage time to obtain the second health of the battery, and finally obtains the precise health of the battery according to the first health, or obtains the precise health of the battery according to the first health and the second health. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

In one embodiment, as shown in FIG. 2 , a battery health detection method is provided, and the method is applied to the terminal 102 in FIG. 1 as an example for description, including the following steps:

Step 202: Detect the battery based on the SOC-OCV curve and the charging condition to obtain a first health status of the battery.

Among them, the SOC-OCV curve is a very important curve in the battery SOC (State Of Charge, the current remaining power of the battery or the state of charge of the battery) calibration process. Usually, after the electric vehicle has been running for a period of time, before the vehicle is stopped and restarted, the curve will be called to correct the SOC value and obtain an updated SOC value through a preset algorithm and other correction coefficients.

When the battery system is discharged to a certain depth and has been standing for more than a period of time, the SOC-OCV curve can be used to calibrate the SOC value to its true value. There are two types of lithium batteries that are currently mainstream in the market: one is a ternary lithium battery and the other is a lithium iron phosphate battery. The linearity of the charge and discharge voltage curve of the ternary lithium battery is good, and it can be corrected by standing still within the use range to obtain a more accurate SOC value; the linearity of the charge and discharge voltage curve of the lithium iron phosphate is poor, and there is a platform range. The voltage values in the range of 25%-90% are slightly different, and the SOC-OCV curve cannot be used for correction. Therefore, the SOC_OCV range of lithium iron phosphate should be selected below 25% (3.2V). Based on the above SOC-OCV range selection, during the use of the vehicle, after the battery has been left to stand (2H), the true value of the SOC can be calculated through the SOC-OCV curve.

That is, before this step, the rest time of the battery is further calculated, and the SOC-OCV calibration is started only after the rest time reaches a preset time threshold, such as 2 hours.

Furthermore, after starting the SOC-OCV calibration, the battery is marked for charging and the charging condition is recorded in real time.

Step 204: Detect the battery based on the battery usage time to obtain a second health status of the battery.

Specifically, the battery usage time is first obtained, and then the second health is obtained in the usage time and health mapping table according to the usage time. It can be understood that the usage time and health mapping table is preset, and the mapping relationship is different for each specification of battery. Therefore, in actual applications, for batteries of the same specification, a usage time and health mapping table needs to be stored.

The usage time can be calculated through the battery cycle data, that is, the cumulative discharge ampere-hour data.

Step 206: Obtain the accurate health of the battery according to the first health, or obtain the accurate health of the battery according to the first health and the second health.

Therefore, the SOC-OCV curve and the charging condition obtain a first health with high accuracy, and the accurate health of the battery is obtained according to the first health, because the first health is obtained in combination with the SOC-OCV curve and the charging condition, so a high-precision accurate health can be obtained. In addition, the second health is obtained based on the battery usage time, and then the first health and the second health are combined to obtain the accurate health of the battery, that is, the battery cycle parameters, SOC-OCV correction and charging condition are combined to calculate the battery health, which improves the accuracy of the battery health, avoids the excessive use of the battery, thereby improving the battery safety, ensuring or even extending the battery system life cycle.

In one embodiment, as shown in FIG3 , the above step S202 further includes:

Step 302: trigger the SOC-OCV curve to calibrate the voltage of the battery.

Step 304 , when it is detected that the battery charging condition meets a preset condition, the current voltage is obtained based on the voltage calibrated by the SOC-OCV and the charging capacity.

As mentioned above, the linearity of the lithium iron phosphate charge and discharge voltage curve is poor, and there is a platform range. The voltage values in the range of 25%-90% are slightly different, and the SOC-OCV curve cannot be used for correction. Therefore, the charging condition needs to meet the preset conditions. In one embodiment, the charging condition can be a condition where charging meets 90% of the conditions, or a condition where charging meets full charge.

Therefore, this step can specifically be to detect that the battery charging condition meets the full charging condition, and obtain the current voltage based on the voltage calibrated by the SOC-OCV and the charging capacity. Specifically, the SOC of the battery is first calibrated by the SOC-OCV, and then when charging, the cumulative charging ampere-hours are recorded, and then the value after the SOC calibration is added to obtain the current voltage value.

Step 306: Obtain the first health status according to the current voltage and the reference voltage.

In one embodiment, this step may obtain the first health level according to the current voltage and the rated voltage capacity. Specifically, the first health level may be the ratio of the current voltage to the rated voltage capacity.

In this embodiment, the SOC of the battery is first calibrated through SOC-OCV to obtain an accurate SOC value, and then charged to a preset charging condition, and the charged power value is obtained according to the charging ampere-hours. The current voltage is further obtained based on the SOC calibration value and the charged power value, and then the first health degree is obtained by comparing the current voltage with the rated voltage capacity, which can improve the accuracy of the first health degree.

In one embodiment, as shown in FIG. 4 , the above step S206 further includes:

Step 402: Determine whether the battery usage time reaches a preset time threshold.

This step specifically determines whether the cycle data of the battery reaches a preset number, such as 2000, which means that the usage time reaches a preset time threshold.

When it is determined that the time reaches the preset time threshold, step S404 is executed; when it is determined that the time does not reach the preset time threshold, step S406 is executed.

Step 404: Obtain the accurate health of the battery according to the first health and the second health.

This step specifically obtains the precise health of the battery based on the weight ratio of the first health and the second health. That is, after the battery has been calibrated with SOC-OCV and calculated under full charge conditions to obtain the first health, and the battery usage time reaches a preset time threshold, the weight ratio of the first health and the second health is obtained, and the first health weight value is obtained based on the weight ratio and the first health, and the second health weight value is obtained based on the weight ratio and the second health, and then the first health weight value and the second health weight value are added to obtain the precise health of the battery.

Specifically, the first health, the second health, and the precise health of the battery satisfy the following relationship:

SOH＝SOH-01*(SOH-01/(SOH-01+SOH-02))+SOH-02*(SOH-02/(SOH-01+S OH-02)), wherein the SOH is the precise health of the battery, the SOH-01 is the first health, and the SOH-02 is the second health.

Step 406: Obtain the accurate health of the battery according to the first health.

That is to say, after the battery has been calibrated with SOC-OCV and calculated under full charge conditions to obtain a first health level, but the battery usage time has not reached a preset time threshold, the first health level is directly used as the precise health level of the battery.

In this embodiment, different health calculation methods are selected by judging the battery usage time to obtain the value closest to the actual health of the battery under different conditions (ie, usage time), thereby improving the accuracy of health detection.

It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the present application also provides a battery health detection device for implementing the battery health detection method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more battery health detection devices provided below can refer to the limitations of the battery health detection method above, and will not be repeated here.

In one embodiment, as shown in FIG5 , a battery health detection device 500 is provided, comprising: a first health acquisition module 501 , a second health acquisition module 502 and an accurate health acquisition module 503 , wherein:

The first health acquisition module 501 is configured to detect the battery based on the SOC-OCV curve and the charging condition to acquire the first health of the battery;

The second health acquisition module 502 is configured to check the battery based on the battery usage time. measuring, obtaining a second health status of the battery;

The precise health acquisition module 503 is configured to acquire the precise health of the battery according to the first health, or to acquire the precise health of the battery according to the first health and the second health.

In one embodiment, the first health status acquisition module 501 further triggers the SOC-OCV curve to calibrate the voltage of the battery, and then when it detects that the battery charging condition meets the preset conditions, the current voltage is obtained based on the SOC-OCV calibrated voltage and the charging capacity, and finally the first health status is obtained according to the current voltage and the reference voltage.

In one embodiment, when the first health acquisition module 501 further detects that the battery charging condition meets the full charging condition, the current voltage is obtained based on the SOC-OCV calibrated voltage and charging capacity, and the first health is further obtained based on the current voltage and rated voltage capacity.

In one embodiment, the first health level is a ratio of the current voltage to the rated voltage capacity.

In one embodiment, the second health degree acquisition module 502 acquires the usage time of the battery, and then acquires the second health degree from a usage time and health degree mapping table.

In one embodiment, the precise health acquisition module 503 further determines whether the usage time of the battery reaches a preset time threshold, and when it is determined that the time reaches the preset time threshold, the precise health of the battery is acquired according to the first health and the second health; when it is determined that the time does not reach the preset time threshold, the precise health of the battery is acquired according to the first health.

In one embodiment, the accurate health acquisition module 503 further uses the first health as the accurate health of the battery;

Alternatively, the precise health of the battery is obtained according to a weight ratio of the first health and the second health.

In one embodiment, the first health, the second health, and the precise health of the battery satisfy the following relationship:
SOH＝SOH-01*(SOH-01/(SOH-01+SOH-02))+SOH-02*(SOH-02/(SOH-01+S
OH-02);

Among them, the SOH is the precise health of the battery, the SOH-01 is the first health, and the SOH-02 is the second health.

Each module in the battery health detection device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

In one embodiment, a battery health detection device is provided, which may be a server, and its internal structure diagram may be as shown in FIG6. The device includes a processor, a memory, and a network interface connected via a system bus. Among them, the processor of the device is configured to provide computing and control capabilities. The memory of the device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the device is configured to store data for battery health detection. The network interface of the device is configured to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the battery health detection method described above is implemented.

Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: detecting the battery based on the SOC-OCV curve and the charging condition to obtain the first health of the battery, and further comprising:

Triggering the SOC-OCV curve to calibrate the voltage of the battery;

When detecting that the battery charging condition meets a preset condition, obtaining a current voltage based on the voltage calibrated by the SOC-OCV and the charging capacity;

The first health level is obtained according to the current voltage and the reference voltage.

In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: when the detection of the battery charging condition meets the preset condition, the current voltage is obtained based on the voltage and charging capacity of the SOC-OCV calibration, and further comprising:

When detecting that the battery charging condition meets the full charging condition, obtaining the current voltage based on the voltage calibrated by the SOC-OCV and the charging capacity;

The obtaining the first health status according to the current voltage and the reference voltage also includes:

The first health level is obtained according to the current voltage and the rated voltage capacity.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

The obtaining the first health degree according to the current voltage and the rated voltage capacity also includes:

The first health level is a ratio of the current voltage to the rated voltage capacity.

The detecting the battery based on the usage time of the battery to obtain the second health of the battery includes:

Obtaining the battery usage time;

The second health degree is obtained from the usage time and health degree mapping table.

The obtaining of the accurate health of the battery according to the first health, or obtaining of the accurate health of the battery according to the first health and the second health, further includes:

Determining whether the battery usage time reaches a preset time threshold;

When it is determined that the time reaches a preset time threshold, obtaining the accurate health of the battery according to the first health and the second health;

When it is determined that the time does not reach the preset time threshold, the accurate health of the battery is obtained according to the first health.

The obtaining the accurate health of the battery according to the first health includes: using the first health as the accurate health of the battery;

The obtaining the accurate health of the battery according to the first health and the second health also includes:

The accurate health of the battery is obtained according to a weight ratio of the first health and the second health.

The obtaining the accurate health of the battery according to the weight ratio of the first health and the second health also includes:

The first health, the second health, and the precise health of the battery satisfy the following relationship:
SOH＝SOH-01*(SOH-01/(SOH-01+SOH-02))+SOH-02*(SOH-02/(SOH-01+S
OH-02);

Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). DRAM), etc. The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

Claims

A method for detecting the health of a battery, the method comprising:

Detecting the battery based on a SOC-OCV curve and a charging condition to obtain a first health status of the battery;

Detecting the battery based on the usage time of the battery to obtain a second health status of the battery;

The accurate health of the battery is obtained according to the first health, or the accurate health of the battery is obtained according to the first health and the second health.
The method according to claim 1, wherein the detecting the battery based on the SOC-OCV curve and the charging condition to obtain the first health status of the battery further comprises:

Triggering the SOC-OCV curve to calibrate the voltage of the battery;

When detecting that the battery charging condition meets a preset condition, obtaining a current voltage based on the voltage calibrated by the SOC-OCV and the charging capacity;

The first health level is obtained according to the current voltage and the reference voltage.
The method according to claim 2, wherein when the detecting that the battery charging condition meets a preset condition, obtaining the current voltage based on the SOC-OCV calibrated voltage and the charging capacity, further comprises:

When detecting that the battery charging condition meets the full charging condition, obtaining the current voltage based on the voltage calibrated by the SOC-OCV and the charging capacity;

The obtaining the first health degree according to the current voltage and the reference voltage also includes:

The first health level is obtained according to the current voltage and the rated voltage capacity.
The method according to claim 3, wherein the obtaining the first health level according to the current voltage and the rated voltage capacity further comprises:

The first health level is a ratio of the current voltage to the rated voltage capacity.
The method according to claim 1, wherein the detecting the battery based on the battery usage time to obtain the second health of the battery comprises:

Obtaining the battery usage time;

The second health degree is acquired according to the usage time and the usage time and health degree mapping table.
The method according to claim 5, wherein the obtaining the accurate health of the battery according to the first health or the first health and the second health further comprises:

Determining whether the battery usage time reaches a preset time threshold;

When it is determined that the time reaches a preset time threshold, obtaining the accurate health of the battery according to the first health and the second health;

When it is determined that the time does not reach the preset time threshold, the accurate health of the battery is obtained according to the first health.
The method according to claim 6, wherein the obtaining the accurate health of the battery according to the first health further comprises: using the first health as the accurate health of the battery;

The obtaining the accurate health of the battery according to the first health and the second health also includes:

The accurate health of the battery is obtained according to a weight ratio of the first health and the second health.
The method according to claim 7, wherein the obtaining the accurate health of the battery according to the weight ratio of the first health and the second health further comprises:

The first health, the second health, and the precise health of the battery satisfy the following relationship:
SOH＝SOH-01*(SOH-01/(SOH-01+SOH-02))+SOH-02*(SOH-02/(SOH-01+S
OH-02);

Among them, the SOH is the precise health of the battery, the SOH-01 is the first health, and the SOH-02 is the second health.
A battery health detection device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods of claims 1 to 8 when executing the computer program.
A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 8.