Lithium ion battery aging detection method for electrochemical energy storage power station

Abstract

The invention provides an aging detection method for a lithium ion battery of an electrochemical energy storage power station. The aging detection method comprises the following steps: selecting an aged lithium ion battery for charging and discharging test; dividing the charging and discharging related data of the lithium ion battery according to the proportion of the state of charge; performing multivariate sample entropy calculation on the charge and discharge related data of each charge state interval; a multivariate sample entropy difference value of different charge state intervals is obtained through an equation (1); collecting charge and discharge related data of the lithium ion battery in the working process in real time to obtain a multivariate sample entropy difference value of the lithium ion battery in the working process, and calculating an aging deviation value through an equation (2); and extracting aging deviation values of the plurality of charge state intervals, and judging that the corresponding lithium ion battery is aged when a mean value of the aging deviation values is greater than zero. The method has the beneficial effects that the multivariate sample entropy can comprehensively reflect the complexity and confusion degree of the battery under different working conditions, and the problem that the aging characteristics of the battery cannot be comprehensively captured by a single physical quantity is avoided.

Description

Technical Field

[0001] The invention belongs to the technical field of lithium ion battery aging detection, and in particular relates to an aging detection method for lithium ion batteries in electrochemical energy storage power stations. Background Art

[0002] In order to achieve the "dual carbon" goal, renewable energy is continuously connected to the power grid. However, the intermittent and uncertain nature of the connection of new energy to the power grid will lead to instability of the power grid. Therefore, electrochemical energy storage power stations are built in large numbers to support the voltage stability of the power system, suppress power fluctuations and improve the quality of electric energy. However, accidents in electrochemical energy storage power stations occur frequently, and battery aging is one of the causes of accidents in electrochemical energy storage power stations. Due to the differences in the quality of lithium-ion batteries during the production process, and the different stresses that each lithium-ion battery can withstand during the operation of the energy storage power station, the degree of aging of lithium-ion batteries in the same energy storage power station is different. The capacity of the aged battery is lower than that of the healthy battery, which will lead to an unbalanced battery capacity within a cluster, and then cause some batteries to be overcharged and over-discharged during operation. Lithium-ion batteries will cause internal short circuit failures during overcharging and over-discharging. Severe internal short circuits will cause thermal runaway of lithium-ion batteries, thereby causing fires in energy storage power stations, which seriously endangers the safety of energy storage power stations. Therefore, inventing a fast and effective online aging detection method for lithium-ion batteries is of great significance to ensure the safe operation of electrochemical energy storage power stations.

[0003] Lithium-ion battery aging is generally detected through a complete charge and discharge curve. However, it is usually difficult to have a complete charge and discharge curve in an energy storage power station, and generally only a fragmentary charge and discharge curve is available. Therefore, the present application proposes a lithium-ion battery aging detection method based on a lithium-ion battery charge and discharge fragment. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for detecting aging of lithium-ion batteries in an electrochemical energy storage power station, which is particularly suitable for online detection of lithium-ion batteries working in an electrochemical energy storage power station.

[0005] The technical solution adopted by the present invention is: in the first aspect, a method for detecting aging of lithium-ion batteries in an electrochemical energy storage power station is provided, comprising:

[0006] Select aged lithium-ion batteries for charge and discharge tests;

[0007] The charging and discharging related data of the lithium-ion battery are divided according to the proportion of the state of charge;

[0008] Perform multivariate sample entropy calculation on charge and discharge related data in each state of charge interval;

[0009] The multivariate sample entropy difference ΔMSE(i) of different state of charge intervals is obtained by the equation ΔMSE(i)=MSE(i)-MSE(i+1), i=1,2,3,…,L-1, where L is the number of state of charge intervals;

[0010] Real-time collection of charging and discharging data of lithium-ion batteries during operation, and obtaining the multivariate sample entropy difference of lithium-ion batteries during operation, through the equation Δ(i) = ΔMSE online (i)-ΔMSE offline (i), i = 1, 2, 3, ..., L to calculate the aging deviation value Δ(i), where ΔMSE online ΔMSE is the multivariate sample entropy difference of lithium-ion batteries in different charge state ranges during operation. offline is the multivariate sample entropy difference under different state of charge intervals of the aged lithium-ion battery, and L is the number of state of charge intervals;

[0011] Extract the aging deviation value Δ(i) of multiple state of charge intervals, when the mean value of Δ(i) When the corresponding lithium-ion battery is aged, it is judged that L is the number of charge state intervals.

[0012] Furthermore, selecting an aged lithium-ion battery for charge and discharge testing includes the following steps:

[0013] Discharge the aged lithium-ion battery to the lower cut-off voltage and then leave it to stand;

[0014] The aged lithium-ion battery is charged by constant current charging to the upper cut-off voltage and then left to stand;

[0015] The aged lithium-ion battery is discharged by constant power discharge to the lower cut-off voltage;

[0016] Collect voltage, current, and temperature of aged lithium-ion batteries during cyclic charge and discharge.

[0017] Furthermore, performing multivariate sample entropy calculation on the charge-discharge related data of each state of charge interval includes the following steps:

[0018] The lithium battery data time series is embedded in the space of different dimensions. Assume that the voltage, current and temperature time series are {x k (i), i = 1, 2, 3, ..., N}, the voltage, current and temperature data are respectively organized into a set of m-dimensional vectors X according to the sequence number. k (i) = {x k (i),x k (i+τ),…,x k(i + N - (m - 1)τ);}, where k is the number of channels, m is the dimension of the phase space embedded in each channel, and τ is the embedding length of each channel;

[0019] Concatenate the time series of multiple channels of the voltage, current, and temperature of the lithium battery, i.e., X(i) = {X1(i); X2(i); X3(i)};

[0020] Calculate the infinity norm d[X(i), X(j)] between each point of the m-dimensional vector and X(i) through the equation Calculate the tolerable correlation r k , for each i value, count the number of d[X(i), X(j)] < r, i.e., the number of similar point pairs and i ≠ j, through the equation Calculate its ratio to the distance N - m, i.e., the similarity Through the equation Calculate the average similarity C m (r);

[0021] Increase the dimension by one, i.e., become m + 1, and calculate the average similarity C corresponding to m + 1 dimensions m+1 (r);

[0022] Through the equation Calculate the multivariate sample entropy MSE(i) of the lithium battery under different state of charge intervals.

[0023] In a second aspect, an electronic device is provided, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the lithium-ion battery aging detection method provided by the present disclosure.

[0024] In a third aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the lithium-ion battery aging detection method provided by the present disclosure.

[0025] In a fourth aspect, a computer program product is provided, including computer programs / instructions, and the computer programs / instructions execute the lithium-ion battery aging detection method provided by the present disclosure when being executed by a processor.

[0026] The advantages and positive effects of the present invention are as follows: due to the adoption of the above technical scheme, by means of the segmented state of charge interval method, aging detection can be performed using fragmented data, which is suitable for the actual operating environment of a distributed electrochemical energy storage power station; the multivariate sample entropy can comprehensively reflect the complexity and chaos of the battery under different operating conditions, avoiding the problem that a single physical quantity cannot fully capture the battery aging characteristics, and improving the accuracy and reliability of detection; the introduction of an aging deviation value can effectively distinguish between normal batteries and aged batteries, which is particularly suitable for online monitoring scenarios, and by comparing with the entropy difference of offline aged batteries, the aged batteries can be quickly and accurately identified to ensure the safe operation of the energy storage power station; the online monitoring method can timely detect and process aging batteries without affecting the normal operation of the energy storage power station, avoid safety accidents caused by battery aging, and ensure the stability and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the lithium-ion battery aging detection method according to an embodiment of the present invention DETAILED DESCRIPTION

[0028] The present disclosure is described more fully below with reference to the accompanying drawings, in which exemplary embodiments of the present disclosure are described. The technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0029] like Figure 1 As shown, the present invention provides a method for detecting aging of lithium-ion batteries in an electrochemical energy storage power station, comprising:

[0030] S100, select an aged lithium-ion battery for charge and discharge test;

[0031] Select a group of lithium-ion batteries with known aging as experimental objects. These batteries should have different degrees of aging to ensure the diversity of experimental data. Prepare charging and discharging test equipment, temperature sensors, data acquisition systems, etc. to ensure that the battery voltage, current, temperature and other data can be accurately recorded.

[0032] S200, dividing the charge and discharge related data of the lithium-ion battery according to the proportion of the state of charge;

[0033] Preferably, the collected charge and discharge data is divided into 10 segments at intervals of 10% of the state of charge, that is, SOC from 0% to 100%, each segment is 10%. By using the method of segmented state of charge intervals, aging detection can be performed using segmented data, which is suitable for the actual operating environment of distributed electrochemical energy storage power stations.

[0034] S300, performing multivariate sample entropy calculation on charge and discharge related data of each state of charge interval;

[0035] S400, obtaining a multivariate sample entropy difference ΔMSE(i) of different state of charge intervals by using the equation ΔMSE(i)=MSE(i)-MSE(i+1), i=1,2,3,…,L-1(1), where L is the number of state of charge intervals;

[0036] Multivariate sample entropy can comprehensively reflect the complexity and chaos of the battery under different working conditions, avoiding the problem that a single physical quantity cannot fully capture the battery aging characteristics, and improving the accuracy and reliability of detection.

[0037] S500, real-time collection of charging and discharging data of the lithium-ion battery during operation, obtaining the multivariate sample entropy difference of the lithium-ion battery during operation, and using the equation Δ(i)=ΔMSE online (i)-ΔMSE offline (i), i = 1, 2, 3, ..., L (2) to calculate the aging deviation value Δ(i), where ΔMSE online is the multivariate sample entropy difference of lithium-ion batteries in different charge state ranges during operation, ΔMSE offline is the multivariate sample entropy difference under different state of charge intervals of the aged lithium-ion battery, and L is the number of state of charge intervals;

[0038] The online monitoring method can detect and handle aging batteries in a timely manner without affecting the normal operation of the energy storage power station, avoid safety accidents caused by battery aging, and ensure the stability and safety of the system. The introduction of aging deviation values ​​can effectively distinguish between normal batteries and aging batteries, which is especially suitable for online monitoring scenarios. By comparing with the entropy difference of offline aging batteries, the aged batteries can be quickly and accurately identified to ensure the safe operation of the energy storage power station.

[0039] S600, extracting aging deviation values ​​Δ(i) of multiple state of charge intervals, when the average value of Δ(i) When the corresponding lithium-ion battery is aged, it is judged that L is the number of charge state intervals.

[0040] Extract the aging deviation value Δ(i) of multiple state of charge intervals. When the mean value of Δ(i) is greater than zero, the battery is considered to have aged; otherwise, the battery is considered to have not aged. Once battery aging is detected, the system should immediately issue an alarm to notify the operation and maintenance personnel. The alarm information should include detailed information such as the specific location of the aging battery and the degree of aging.

[0041] In one embodiment, selecting an aged lithium-ion battery for charge and discharge testing includes the following steps:

[0042] S101, discharging the aged lithium-ion battery to a lower cut-off voltage, and then leaving it to stand;

[0043] The aged lithium-ion battery is discharged to the lower cut-off voltage, and preferably left to stand for 1 hour to ensure that the battery is in a stable state.

[0044] S102, charging the aged lithium-ion battery by a constant current charging method, charging to an upper limit cut-off voltage, and then leaving it to stand;

[0045] The battery is charged by constant current charging to an upper cut-off voltage, and preferably left to stand for 1 hour.

[0046] S103, discharging the aged lithium-ion battery in a constant power discharge mode to a lower cut-off voltage;

[0047] The battery is discharged in a constant power discharge mode until it reaches the lower cut-off voltage, completing a complete charge and discharge cycle.

[0048] S104, collecting the voltage, current and temperature of the aged lithium-ion battery during the cyclic charge and discharge process.

[0049] In one embodiment, performing multivariate sample entropy calculation on charge-discharge related data of each state of charge interval includes the following steps:

[0050] S301, embed the lithium battery data time series into spaces of different dimensions, assuming that the voltage, current and temperature time series are {x k (i), i=1,2,3,…,N}, the voltage, current and temperature data are respectively organized into a set of m-dimensional vector equations X according to the sequence number k (i) = {x k (i),x k (i+τ),…,x k (i+N-(m-1)τ);}(3), where k is the number of channels, m is the dimension of the phase space in which each channel is embedded, and τ is the embedding length of each channel;

[0051] Taking the collected voltage, current, and temperature time series data of the lithium battery as inputs, where k is the number of channels, that is, X1(i) represents the time series of voltage, X2(i) represents the time series of current, and X3(i) represents the time series of temperature. The embedding dimension m represents the dimension of each channel embedded in the phase space, and the embedding length τ represents the embedding length of each channel. The voltage, current, and temperature data are respectively grouped into a set of m-dimensional vector equations (3) according to the serial numbers.

[0052] S302. Concatenate the time series of multiple channels of voltage, current, and temperature of the lithium battery, that is, Equation X(i) = {X1(i); X2(i); X3(i)} (4);

[0053] Concatenate the multi-dimensional vectors of the voltage, current, and temperature of the lithium battery through Equation (4) to form a comprehensive multi-dimensional vector. The data at each time point is converted into a high-dimensional vector, which contains the information of the voltage, current, and temperature of the lithium battery.

[0054] S303. Calculate the infinity norm d[X(i), X(j)] between each pair of points of the m-dimensional vector X(i) through the equation Calculate the tolerable correlation r k , for each i value, count the number of d[X(i), X(j)] < r, that is, the number of similar point pairs and i ≠ j, calculate its ratio to the distance N - m through the equation that is, the similarity Through the equation Calculate the average similarity C m (r);

[0055] S304. Increase the dimension by one, that is, become m + 1, and calculate the average similarity C m+1 (r);

[0056] S305. Through the equation Calculate the multi-sample entropy MSE(i) of the lithium battery under different state of charge intervals.

[0057] Based on the embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0058] An electronic device includes at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the lithium-ion battery aging detection method provided by the present disclosure.

[0059] Electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0060] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the lithium-ion battery aging detection method provided by the present disclosure.

[0061] Various embodiments of the present disclosure may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0062] A computer program product includes a computer program / instruction. When the computer program / instruction is executed by a processor, the lithium-ion battery aging detection method provided in the present disclosure is implemented.

[0063] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0064] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0065] The embodiments of the present invention are described in detail above, but the contents described are only preferred embodiments of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of application of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for detecting aging of lithium-ion batteries in electrochemical energy storage power stations, characterized in that: include: Select aged lithium-ion batteries for charge and discharge tests; The charge and discharge related data of the lithium-ion battery are divided according to the proportion of the state of charge; Perform multivariate sample entropy calculation on the charge and discharge related data in each state of charge interval; The multivariate sample entropy difference ΔMSE(i) of different state of charge intervals is obtained by the equation ΔMSE(i)=MSE(i)-MSE(i+1), i=1,2,3,…,L-1, where L is the number of state of charge intervals; Real-time collection of charging and discharging data of lithium-ion batteries during operation to obtain the multivariate sample entropy difference ΔMSE of lithium-ion batteries during operation online , through the equation Δ(i) = ΔMSE online (i)-ΔMSE offline (i), i = 1, 2, 3, ..., L to calculate the aging deviation value Δ(i), where ΔMSE offline is the multivariate sample entropy difference under different state of charge intervals of the aged lithium-ion battery, and L is the number of state of charge intervals; Extract the aging deviation value Δ(i) of multiple state of charge intervals, when the mean value of Δ(i) When the corresponding lithium-ion battery is aged, it is judged that L is the number of charge state intervals.

2. The detection method according to claim 1, characterized in that: Selecting an aged lithium-ion battery for charge and discharge testing includes the following steps: Discharge the aged lithium-ion battery to the lower cut-off voltage and then leave it to stand; The aged lithium-ion battery is charged by constant current charging to the upper cut-off voltage and then left to stand; The aged lithium-ion battery is discharged by constant power discharge to the lower cut-off voltage; Collect voltage, current, and temperature of aged lithium-ion batteries during cyclic charge and discharge.

3. The detection method according to claim 1, characterized in that: The multivariate sample entropy calculation for the charge and discharge related data of each state of charge interval includes the following steps: The lithium battery data time series is embedded in the space of different dimensions. Assume that the voltage, current and temperature time series are {x k (i), i = 1, 2, 3, ..., N}, the voltage, current and temperature data are respectively organized into a set of m-dimensional vectors X according to the sequence number. k (i) = {x k (i),x k (i+τ),…,x k (i+N-(m-1)τ);}, where k is the number of channels, m is the dimension of the phase space in which each channel is embedded, and τ is the embedding length of each channel; The time series of multiple channels of voltage, current and temperature of the lithium battery are spliced, that is, X(i) = {X1(i); X2(i); X3(i)}; Calculate the infinity norm d[X(i), X(j)] between each pair of points of the m-dimensional vector through the equation Calculate the tolerable correlation r k , for each i value, count the number of d[X(i), X(j)] < r, that is, the number of similar point pairs and i ≠ j, through the equation Calculate its ratio to the distance N - m, that is, the similarity Through the equation Calculate the average similarity C m (r); Increase the dimension by one, that is, to m+1, and calculate the average similarity C corresponding to the m+1 dimension m+1 (r); By equation i=1,2,3,…,L calculates the multivariate sample entropy MSE(i) of the lithium battery in different state of charge intervals, where L is the number of state of charge intervals.

4. An electronic device comprising: at least one processor; as well as a memory communicatively connected to at least one processor; wherein, The memory stores instructions that can be executed by at least one processor. The instructions are executed by at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 3.

5. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to make a computer execute the method according to any one of claims 1 to 3.

6. A computer program product comprising a computer program / instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 3.

Images