Method and system for detecting and quantitatively evaluating micro short circuit in series lithium ion battery pack

Abstract

The invention belongs to the field of lithium ion battery micro short circuit fault diagnosis, and discloses a method and system for detecting and quantitatively evaluating micro short circuit in a series lithium ion battery pack, and the method comprises the steps: carrying out the micro short circuit detection of the lithium ion battery pack based on a dynamic time warping algorithm; after the position of the micro-short-circuit battery is detected, the micro-short-circuit degree of the lithium ion battery is quantitatively evaluated based on a charge-discharge cycle voltage characteristic and charge quantity difference method. The problems that an existing micro short circuit fault diagnosis method based on a lithium ion battery model is complex in calculation, and battery parameters after the battery is aged need to be updated are solved; and moreover, the consumption of computing resources is reduced by using a method of detecting the position of the fault battery by using a dynamic time warping algorithm and then quantitatively evaluating the micro-short-circuit degree of the lithium ion battery.

Description

Technical Field

[0001] The present invention relates to a method for diagnosing micro-short circuit faults in lithium-ion batteries, belongs to the application of lithium-ion battery management system (BMS), and specifically relates to a method and system for detecting and quantitatively evaluating micro-short circuits in series-connected lithium-ion battery packs. Background Art

[0002] Lithium-ion batteries have the advantages of high power density and energy density, long cycle life, and high voltage platform, and have become core energy storage components. However, with the rapid development of electric vehicles, there have been more and more reports of fire accidents related to them in recent years, among which the proportion of electric vehicle accidents caused by power battery failure is as high as 90%; there are many fire accidents in energy storage power station facilities, and the energy storage units that have caught fire are mainly lithium-ion batteries.

[0003] A large part of the cause of battery thermal runaway is internal short circuit. The evolution process of internal short circuit is divided into early, middle and late stages. In the early stage of internal short circuit (i.e. micro short circuit), the battery's electrical and thermal parameters do not change significantly, and it is highly concealed. The late stage of internal short circuit fault is manifested as a sudden drop in voltage and a sharp rise in temperature in a short period of time. At this time, short circuit detection and early warning are relatively easy to achieve. However, the evolution time scale from short circuit to thermal runaway in the late stage is in milliseconds, which means that after the late short circuit fault is detected, it is too late to control and manage it. Therefore, it is necessary to diagnose micro short circuit faults in a timely manner at the early stage of short circuit development.

[0004] The current micro-short circuit fault diagnosis method based on the lithium-ion battery model is computationally complex and requires updating the battery parameters after battery aging; moreover, the current method of quantitatively evaluating each battery cell in the battery pack consumes more computing resources. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention provides a method and system for detecting and quantitatively evaluating micro-short circuits in a series-connected lithium-ion battery pack, discloses a lithium-ion battery pack micro-short circuit detection method based on a dynamic time warping algorithm and a lithium-ion battery micro-short circuit quantitative evaluation method based on charge and discharge cycle voltage characteristics and charge amount differences.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for detecting and quantitatively evaluating micro short circuits in a series lithium-ion battery pack, the method comprising:

[0008] Micro short circuit detection of lithium-ion battery packs based on dynamic time warping algorithm;

[0009] After detecting the location of the micro-short-circuited battery, the degree of micro-short-circuit of the lithium-ion battery is quantitatively evaluated based on the charge and discharge cycle voltage characteristics and charge difference method.

[0010] Preferably, the method for performing micro-short circuit detection on a lithium-ion battery pack based on a dynamic time warping algorithm includes:

[0011] The dynamic time warping algorithm is used to calculate the median of the normalized voltage of the series battery group and the warping path between the voltages of each cell in the series battery group;

[0012] The position of the faulty cell in the lithium-ion battery pack is detected according to the calculation result, and the micro-short circuit detection of the lithium-ion battery pack is completed.

[0013] Preferably, the method of using the dynamic time warping algorithm to calculate the normalized median of the voltage of the series battery group and the distance between the voltages of each cell in the series battery group includes:

[0014] The two charging voltage curves within a preset time interval are represented as time series L x (x 1 ,x 2 ,…,x n ) and L y (y 1 ,y 2 ,…y n );

[0015] Build the distance matrix D to calculate the time series L x (x 1 ,x 2 ,…,x n ) and L y (y 1 ,y 2 ,…y n ), namely:

[0016]

[0017] Among them, the value of each element in the n-order distance matrix is ​​expressed as: x i is the voltage curve L x At time t i The voltage value, y i is the voltage curve L y At time t j Voltage value;

[0018] Based on the values ​​of each element in the n-order distance matrix, the regularized path W is calculated, that is,

[0019] Calculate the normalized median voltage of the series battery pack and the regular path W between the voltages of each cell in the series battery pack, and compare them with a preset threshold;

[0020] If the distance between the normalized median voltage of the series battery pack and the voltage of each cell in the series battery pack is greater than a preset threshold, the corresponding cell is determined to be a fault location in the lithium-ion battery pack.

[0021] Preferably, the method for quantitatively evaluating the micro-short circuit degree of a lithium-ion battery based on the charge-discharge cycle voltage characteristics and the charge amount difference includes:

[0022] V t,cell (Cap,SOC,T,I L )=V OCV (Cap,SOC,T)+V impedance (Cap,SOC,T,I L )

[0023] Among them, Cap represents the capacity of the battery, T represents the temperature of the battery, and I L Indicates the battery charge and discharge current, V OCV is the open circuit voltage, V impedance is the internal impedance voltage of the battery.

[0024] Preferably, if the same voltage point t is specified 1 and t 2 When t 1 Time and t 2 The charge difference of the lithium-ion battery between the times ΔQ cell (t 1 ,t 2 )=0;

[0025] When ΔQ cell (t 1 ,t 2 )=0, calculate the 1 Time and t 2 The charge ΔQ leaked from the battery due to a micro short circuit between the times ISC , the calculation formula is:

[0026] ΔQ ISC (t 1 ,t 2 )=ΔQ load (t 1 ,t 2 )

[0027]

[0028] Among them, T sampleIndicates the sampling time interval, I L is the current of the series battery pack, ΔQ load The amount of charge that the battery obtains or releases from the outside;

[0029] The short-circuit current is calculated as:

[0030]

[0031] Where Δt represents t 1 Time and t 2 The time interval between moments;

[0032] The calculation formula of micro short circuit resistance is:

[0033]

[0034] The present invention also provides a system for detecting and quantitatively evaluating micro-short circuits in a series-connected lithium-ion battery pack, the system being used to implement any one of the methods described, the system comprising: a detection module and an evaluation module;

[0035] The detection module is used to perform micro-short circuit detection on the lithium-ion battery pack based on a dynamic time warping algorithm;

[0036] The evaluation module is used to quantitatively evaluate the micro-short circuit degree of the lithium-ion battery based on the charge-discharge cycle voltage characteristics and the charge amount difference after detecting the position of the micro-short circuit battery.

[0037] Preferably, the detection module comprises: a calculation unit and a detection unit;

[0038] The calculation unit is used to calculate the median of the normalized voltage of the series battery group and the warping path between the voltages of each cell in the series battery group using a dynamic time warping algorithm;

[0039] The detection unit is used to detect the position of the faulty cell in the lithium-ion battery pack according to the calculation result, and complete the micro-short circuit detection of the lithium-ion battery pack.

[0040] Preferably, the process of using the dynamic time warping algorithm to calculate the normalized median of the voltage of the series battery group and the distance between the voltages of each cell in the series battery group includes:

[0041] The two charging voltage curves within a preset time interval are represented as time series L x (x 1 ,x 2 ,…,x n ) and L y (y 1 ,y 2 ,…y n );

[0042] Build the distance matrix D to calculate the time series L x (x 1 ,x 2 ,…,x n ) and L y (y 1 ,y 2 ,…y n ), namely:

[0043]

[0044] Among them, the value of each element in the n-order distance matrix is ​​expressed as: x i is the voltage curve L x At time t i The voltage value, y i is the voltage curve L y At time t j Voltage value;

[0045] Based on the values ​​of each element in the n-order distance matrix, the regularized path W is calculated, that is,

[0046] Calculate the normalized median voltage of the series battery pack and the regular path W between the voltages of each cell in the series battery pack, and compare them with a preset threshold;

[0047] If the distance between the normalized median voltage of the series battery pack and the voltage of each cell in the series battery pack is greater than a preset threshold, the corresponding cell is determined to be a fault location in the lithium-ion battery pack.

[0048] Preferably, the process of quantitatively evaluating the micro-short circuit degree of a lithium-ion battery based on the charge-discharge cycle voltage characteristics and the charge amount difference method includes:

[0049] V t,cell (Cap,SOC,T,I L )=V OCV (Cap,SOC,T)+V impedance (Cap,SOC,T,I L )

[0050] Among them, Cap represents the capacity of the battery, T represents the temperature of the battery, and I L Indicates the battery charge and discharge current, V OCV is the open circuit voltage, V impedance is the internal impedance voltage of the battery.

[0051] Preferably, if the same voltage point t is specified 1 and t2 When t 1 Time and t 2 The charge difference of the lithium-ion battery between the times ΔQ cell (t 1 ,t 2 )=0;

[0052] When ΔQ cell (t 1 ,t 2 )=0, calculate the 1 Time and t 2 The charge ΔQ leaked from the battery due to a micro short circuit between the times ISC , the calculation formula is:

[0053] ΔQ ISC (t 1 ,t 2 )=ΔQ load (t 1 ,t 2 )

[0054]

[0055] Among them, T sample Indicates the sampling time interval, I L is the current of the series battery pack, ΔQ load The amount of charge that the battery obtains or releases from the outside;

[0056] The short-circuit current is calculated as:

[0057]

[0058] Where Δt represents t 1 Time and t 2 The time interval between moments;

[0059] The calculation formula of micro short circuit resistance is:

[0060]

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The present invention provides a method and system for detecting and quantitatively evaluating micro-short circuits in a series-connected lithium-ion battery pack, and specifically discloses a lithium-ion battery pack micro-short circuit detection method based on a dynamic time warping algorithm and a lithium-ion battery micro-short circuit quantitative evaluation method based on charge-discharge cycle voltage characteristics and charge amount differences, which overcomes the problem that the current micro-short circuit fault diagnosis method based on a lithium-ion battery model is relatively complex in calculation and needs to update battery parameters after battery aging; and, the method of using a dynamic time warping algorithm to detect the location of a faulty battery and then quantitatively evaluating the degree of micro-short circuit of a lithium-ion battery reduces the consumption of computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0064] Figure 1 Schematic diagram of the calculation process of DTW according to an embodiment of the present invention;

[0065] Figure 2 A schematic diagram of a voltage and charge curve of two adjacent charge and discharge cycles of a lithium-ion battery according to an embodiment of the present invention;

[0066] Figure 3 It is a schematic diagram of the structure of the experimental platform of the embodiment of the present invention;

[0067] Figure 4 Schematic diagram of charge and discharge voltage curves of 12 batteries in an embodiment of the present invention, with a parallel resistance of 100Ω;

[0068] Figure 5 Schematic diagram of charge and discharge voltage curves of 12 batteries in an embodiment of the present invention, with a parallel resistance of 50Ω;

[0069] Figure 6 Schematic diagram of charge and discharge voltage curves of 12 batteries in an embodiment of the present invention, with a parallel resistance of 10Ω;

[0070] Figure 7 This is a schematic diagram of the diagnostic results of an embodiment of the present invention, where the micro short circuit resistance is 100Ω;

[0071] Figure 8 This is a schematic diagram of the diagnostic results of an embodiment of the present invention, where the micro short circuit resistance is 50Ω;

[0072] Fig. 9 This is a schematic diagram of the diagnostic results of an embodiment of the present invention, where the micro short circuit resistance is 10Ω;

[0073] Fig.10The present invention is a flowchart of a method for detecting and quantitatively evaluating micro short circuits in a series-connected lithium-ion battery pack according to an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0076] Embodiment 1

[0077] like Fig.10 As shown, the present invention provides a method for detecting and quantitatively evaluating micro short circuits in a series lithium-ion battery pack, the method comprising:

[0078] Micro short circuit detection of lithium-ion battery packs based on dynamic time warping algorithm;

[0079] After detecting the location of the micro-short-circuited battery, the degree of micro-short-circuit of the lithium-ion battery is quantitatively evaluated based on the charge and discharge cycle voltage characteristics and charge difference method.

[0080] In this embodiment, a lithium-ion battery pack micro-short circuit detection method based on a dynamic time warping algorithm is

[0081] The Dynamic Time Warping (DTW) algorithm is widely used in artificial intelligence fields such as speech recognition and image extraction to calculate the similarity between two time series. Based on the above analysis of the voltage curve, the present invention uses the DTW algorithm to calculate the similarity relationship of the battery voltage curve. The calculation process of DTW is illustrated by two charging voltage curves within a certain time interval, such as Figure 1 shown.

[0082] The two voltage curves can be expressed as time series L x (x 1 ,x 2 ,…,x n ) and L y (y 1 ,y 2 ,…y n ). Establish the distance matrix D to calculate their similarity. That is:

[0083]

[0084] The value of each element in the n-order distance matrix can be expressed as:

[0085]

[0086] Among them, x i is the voltage curve L x At time t i The voltage value, y i is the voltage curve L y At time t j Voltage value.

[0087] The basic idea of ​​DTW is to make the distance matrix D n×n The upper right corner element d 1n Start and reach the lower left corner element d n1 The sum of the elements of the path is the shortest. Mathematically, the shortest path is defined as a regular path W, and the calculation formula is shown in formula (3).

[0088]

[0089] Among them, w k represents the kth element of the regularized path W; i th Represents the time series L x The index of the i-th point; j th Represents the time series L y The index of the jth point; k is the number of steps in the regular path W, indicating the sequence number of the kth matching point on the path.

[0090] The process of calculating the regularized path should be subject to the following three constraints:

[0091] Boundary conditions: w 1 =(1,1),w k =(n,n), that is, the beginning and end of the two sequences must match;

[0092] Continuity condition: If w k =(a,b),w k-1 =(a',b'), a-a'≤1 and b-b'≤1 must be satisfied;

[0093] Monotonicity condition: If w k =(a,b),w k-1 =(a',b'), a-a'≥0 and b-b'≥0 must be satisfied.

[0094] Among them, a is the kth step of the path, L x The index of L; b is the kth step of the path, y The index of a' is the k-1th step of the path, Lx The index of b' is the k-1th step of the path, L y The index of .

[0095] The shortest path can be expressed as:

[0096]

[0097] After a micro-short circuit occurs in a battery, the voltage change trend changes compared to a normal battery, which is shown as an increase in the DTW distance from a healthy battery; and in a series battery pack, the median voltage can represent the voltage of a normal battery to a certain extent. Therefore, the present invention uses the DTW algorithm to calculate the normalized median voltage of the series battery pack and the regular path W between the voltages of each cell in the series battery pack, i.e., the DTW distance. If the calculation result of a battery exceeds a preset threshold, the battery is marked as a micro-short circuit.

[0098] In this embodiment, a quantitative evaluation method for lithium-ion battery micro-short circuit based on charge-discharge cycle voltage characteristics and charge amount difference is as follows:

[0099] After the location of the micro-short-circuited battery is detected, the following method is used to quantitatively evaluate the micro-short-circuit degree of the battery.

[0100] Specifically, in a lithium-ion battery, the amount of charge ΔQ that the battery receives or releases from the outside is load , the amount of charge stored in the battery ΔQ cell , the charge ΔQ leaked by the battery due to micro short circuit ISC The following relationship is satisfied:

[0101] ΔQ ISC =ΔQ load -ΔQ cell (5)

[0102] This method uses the voltage characteristics of adjacent charge and discharge cycles, so it is necessary to first explain the principle of battery voltage characteristics. The battery voltage can be expressed as the open circuit voltage (OCV) V OCV and the battery internal impedance voltage V impedance It is expressed as shown in formula (6).

[0103] V t,cell (Cap,SOC,T,I L )=V OCV (Cap,SOC,T)+V impedance (Cap,SOC,T,I L ) (6)

[0104] Among them, V t,cellIndicates the terminal voltage of the battery; Cap indicates the capacity of the battery; T indicates the temperature of the battery; I L Indicates the charge and discharge current of the battery.

[0105] From formula (6), we know that the battery voltage changes with the battery capacity, temperature, current and SOC, and can be expressed as a function of these state quantities. If we assume that the battery capacity, temperature and charge and discharge current remain fixed, the battery terminal voltage V t,cell It is just a function of SOC. In the adjacent charge and discharge cycles of lithium-ion battery packs, the capacity of the battery will not change significantly; when the voltage is the same, the current corresponding to the two charging stages is equal; if the battery temperature does not change when the voltage is the same, then the SOC of the battery at two times when the voltage is equal during the charging stage is also equal, that is, the amount of charge stored in the two batteries is equal.

[0106] Figure 2 The curves of voltage and charge amount of two adjacent charge and discharge cycles obtained by constant current and constant voltage charging and constant current discharging for a lithium cobalt oxide battery. In each charge and discharge cycle, the battery is first charged to 4.2V at a constant current rate of 0.5C, then switched to constant voltage 4.2V charging until the current decreases to 0.02C, and then discharged at a constant current rate of 1C to the lower cut-off voltage of 2.75V. Figure 2 As shown, when the same voltage point t is specified 1 and t 2 When t 1 Time and t 2 The charge difference of the lithium-ion battery between the times ΔQ cell (t 1 ,t 2 )=0.

[0107] The above assumption only considers the invariance of the battery capacity between adjacent charge and discharge cycles, and does not consider the specific value of the battery capacity. Therefore, the above calculation is valid regardless of the degree of battery aging.

[0108] When the battery charge and discharge current changes dramatically, the battery voltage becomes unstable due to the chemical reaction during the battery polarization process. Therefore, it is chosen to observe the battery characteristics under a steady state of constant current.

[0109] When ΔQ cell (t 1 ,t 2 )=0, formula (7) can be used to calculate the 1 Time and t 2 ΔQ between moments ISC .

[0110]

[0111] Among them, T sample Indicates the sampling time interval, I L is the current of the series connected battery pack.

[0112] The calculation method of short-circuit current is shown in formula (8):

[0113]

[0114] Where Δt represents t 1 Time and t 2 The time interval between moments;

[0115] The calculation formula of micro short circuit resistance is:

[0116]

[0117] In order to illustrate the method we used, a battery pack consisting of 12 21700 lithium batteries, a charge and discharge tester, and a micro short circuit resistance simulation board were used as an implementation case to simulate the process of micro short circuit of battery cells in a series of lithium-ion battery packs. The parameters of the batteries used are shown in Table 1. The structure of the entire experimental platform is shown in Figure 3 shown.

[0118] Table 1 Basic parameters of lithium-ion batteries

[0119]

[0120]

[0121] The lithium-ion battery pack operates under constant current charging and dynamic stress test (DST) discharge conditions to simulate the actual operation of the lithium-ion battery pack. The battery operates 2 charge and discharge cycles under the above conditions.

[0122] At present, the experimental methods for inducing micro-short circuits in lithium-ion batteries can be mainly divided into three categories: the abuse condition method, the artificially designed internal defect method, and the equivalent resistance method. The abuse condition method is close to the real micro-short circuit, but it is not repeatable and is more resource-intensive; although the artificially designed internal defect method is repeatable, the experimental battery manufacturing process is complicated and has poor repeatability, and there is also the problem of damaging the integrity of the battery. Although the equivalent resistance method cannot observe the real damage of the battery micro-short circuit to the internal materials, the fault diagnosis algorithm of the present invention only requires the external characteristics of the internal short-circuited battery, such as voltage, current, etc., and the experiment of this method is repeatable, the battery can be reused, and it is more environmentally friendly. In summary, the present invention uses the equivalent resistance method to simulate the internal short circuit failure of the battery.

[0123] In summary, 100Ω, 50Ω and 10Ω resistors are connected in parallel at both ends of No. 2, No. 6 and No. 10 batteries to simulate the micro short circuit of the battery from mild to severe. The voltage curve of the 12-cell battery charge and discharge is as follows Figure 4 , Figure 5 , Figure 6 shown.

[0124] According to the above method, the fault degree of the micro-short-circuited battery is detected and quantitatively evaluated. After the battery has a micro-short circuit, the voltage change trend is different from that of the normal battery. If the DTW distance between the normalized battery cell voltage and the median of the battery pack voltage is higher than the threshold, the cell is diagnosed as faulty. The diagnosis result is as follows: Figure 7 , Figure 8 , Fig. 9 After offline testing, it was determined that the DTW distance for diagnosing a micro-short-circuited battery was 60.

[0125] After detecting the location of the micro-short-circuited battery, the micro-short-circuit resistance of the faulty cell is quantitatively calculated using formulas (7), (8) and (9). When the short-circuit resistance is 100Ω and 50Ω, the voltage corresponding to the same voltage point is 3.9V, and the SOC of the corresponding battery is about 50%; when the short-circuit resistance is 10Ω, due to the limitation of the experimental equipment, the voltage point is selected as 4.05V. The calculation results are shown in Table 2.

[0126] Table 2 Calculation results of micro short circuit resistance

[0127]

[0128] Embodiment 2

[0129] The present invention also provides a system for detecting and quantitatively evaluating micro-short circuits in a series-connected lithium-ion battery pack, the system being used to implement any one of the methods described, the system comprising: a detection module and an evaluation module;

[0130] A detection module, used for performing micro-short circuit detection on lithium-ion battery packs based on a dynamic time warping algorithm;

[0131] The evaluation module is used to quantitatively evaluate the micro-short circuit degree of the lithium-ion battery based on the charge and discharge cycle voltage characteristics and charge amount difference method after the location of the micro-short circuit battery is detected.

[0132] In this embodiment, the detection module includes: a calculation unit and a detection unit;

[0133] A calculation unit, used for calculating the median of the normalized voltage of the series battery group and the warping path between the voltages of each cell in the series battery group by using a dynamic time warping algorithm;

[0134] The detection unit is used to detect the position of the faulty single cell in the lithium-ion battery pack according to the calculation result, and complete the micro-short circuit detection of the lithium-ion battery pack.

[0135] In this embodiment, the process of using the dynamic time warping algorithm to calculate the normalized median of the voltage of the series-connected battery group and the distance between the voltages of each cell in the series-connected battery group includes:

[0136] The two charging voltage curves within a preset time interval are represented as time series L x (x 1 ,x 2 ,…,x n ) and L y (y 1 ,y 2 ,…y n );

[0137] Build the distance matrix D to calculate the time series L x (x 1 ,x 2 ,…,x n ) and L y (y 1 ,y 2 ,…y n ), namely:

[0138]

[0139] Among them, the value of each element in the n-order distance matrix is ​​expressed as: x i is the voltage curve L x At time t i The voltage value, y i is the voltage curve L y At time t j Voltage value;

[0140] Based on the values ​​of each element in the n-order distance matrix, the regularized path W is calculated, that is,

[0141] Calculate the normalized median voltage of the series battery pack and the regular path W between the voltages of each cell in the series battery pack, and compare them with a preset threshold;

[0142] If the distance between the normalized median voltage of the series battery pack and the voltage of each cell in the series battery pack is greater than a preset threshold, the corresponding cell is determined to be a fault location in the lithium-ion battery pack.

[0143] In this embodiment, the process of quantitatively evaluating the micro-short circuit degree of the lithium-ion battery based on the charge-discharge cycle voltage characteristics and charge amount difference method includes:

[0144] V t,cell (Cap,SOC,T,I L )=V OCV (Cap,SOC,T)+V impedance (Cap,SOC,T,I L )

[0145] Among them, V t,cell is the terminal voltage of the battery; Cap is the capacity of the battery; T is the temperature of the battery; I L Indicates the charge and discharge current of the battery; V OCV is the open circuit voltage; V impedance is the internal impedance voltage of the battery.

[0146] In this embodiment, if the same voltage point t is specified 1 and t 2 When t 1 Time and t 2 The charge difference of the lithium-ion battery between the times ΔQ cell (t 1 ,t 2 )=0;

[0147] When ΔQ cell (t 1 ,t 2 )=0, calculate the 1 Time and t 2 The charge ΔQ leaked from the battery due to a micro short circuit between the times ISC , the calculation formula is:

[0148] ΔQ ISC (t 1 ,t 2 )=ΔQ load (t 1 ,t 2 )

[0149]

[0150] Among them, T sample Indicates the sampling time interval, I L is the current of the series battery pack, ΔQ load The amount of charge that the battery obtains or releases from the outside;

[0151] The short-circuit current is calculated as:

[0152]

[0153] Where Δt represents t 1 Time and t2 The time interval between moments;

[0154] The calculation formula of micro short circuit resistance is:

[0155]

[0156] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for detecting and quantitatively evaluating micro short circuits in a series-connected lithium-ion battery pack, characterized in that: The method comprises: Micro short circuit detection of lithium-ion battery packs based on dynamic time warping algorithm; After detecting the location of the micro-short-circuited battery, the degree of micro-short-circuit of the lithium-ion battery is quantitatively evaluated based on the charge and discharge cycle voltage characteristics and charge difference method.

2. The method according to claim 1, characterized in that The method for detecting micro short circuit of lithium-ion battery pack based on dynamic time warping algorithm includes: The dynamic time warping algorithm is used to calculate the median of the normalized voltage of the series battery group and the warping path between the voltages of each cell in the series battery group; The position of the faulty cell in the lithium-ion battery pack is detected according to the calculation result, and the micro-short circuit detection of the lithium-ion battery pack is completed.

3. The method according to claim 2, characterized in that The method of using the dynamic time warping algorithm to calculate the median of the normalized series battery group voltage and the distance between the voltages of each cell in the series battery group includes: The two charging voltage curves within a preset time interval are represented as time series L x (x1,x2,…,x n ) and L y (y1,y2,…y n ); Build the distance matrix D to calculate the time series L x (x1,x2,…,x n ) and L y (y1,y2,…y n ), namely: Among them, the value of each element in the n-order distance matrix is ​​expressed as: x i is the voltage curve L x At time t i The voltage value, y i is the voltage curve L y At time t j Voltage value; Based on the values ​​of each element in the n-order distance matrix, the regularized path W is calculated, that is, Calculate the normalized median voltage of the series battery pack and the regular path W between the voltages of each cell in the series battery pack, and compare them with a preset threshold; If the distance between the normalized median voltage of the series battery pack and the voltage of each cell in the series battery pack is greater than a preset threshold, the corresponding cell is determined to be a fault location in the lithium-ion battery pack.

4. The method according to claim 1, characterized in that: The quantitative evaluation of the micro-short circuit degree of lithium-ion batteries based on the charge-discharge cycle voltage characteristics and charge difference methods includes: V t,cell (Cap,SOC,T,I L )=V OCV (Cap,SOC,T)+V impedance (Cap,SOC,T,I L ) Among them, Cap represents the capacity of the battery, T represents the temperature of the battery, and I L Indicates the battery charge and discharge current, V OCV is the open circuit voltage, V impedance is the internal impedance voltage of the battery.

5. The method according to claim 4, characterized in that If the same voltage points t1 and t2 are specified, the charge difference ΔQ of the lithium-ion battery between t1 and t2 can be calculated by calculating the integral of the current over time. cell (t1, t2) = 0; When ΔQ cell When (t1, t2) = 0, calculate the charge ΔQ of the battery leaked due to micro short circuit between t1 and t2 ISC , the calculation formula is: ΔQ ISC (t1,t2)=ΔQ load (t1,t2) Among them, T sample Indicates the sampling time interval, I L is the current of the series battery pack, ΔQ load The amount of charge that the battery obtains or releases from the outside; The short-circuit current is calculated as: Wherein, Δt represents the time interval between time t1 and time t2; The calculation formula of micro short circuit resistance is:

6. A system for detecting and quantitatively evaluating micro short circuits in a series-connected lithium-ion battery pack, the system being used to implement the method described in any one of claims 1 to 5, characterized in that: The system comprises: a detection module and an evaluation module; The detection module is used to perform micro-short circuit detection on the lithium-ion battery pack based on a dynamic time warping algorithm; The evaluation module is used to quantitatively evaluate the micro-short circuit degree of the lithium-ion battery based on the charge-discharge cycle voltage characteristics and the charge amount difference after detecting the position of the micro-short circuit battery.

7. The system according to claim 6, characterized in that The detection module includes: a calculation unit and a detection unit; The calculation unit is used to calculate the median of the normalized voltage of the series battery group and the warping path between the voltages of each cell in the series battery group using a dynamic time warping algorithm; The detection unit is used to detect the position of the faulty cell in the lithium-ion battery pack according to the calculation result, and complete the micro-short circuit detection of the lithium-ion battery pack.

8. The system according to claim 7, characterized in that The process of using the dynamic time warping algorithm to calculate the normalized median voltage of the series battery pack and the DTW distance between the voltages of each cell in the series battery pack includes: The two charging voltage curves within a preset time interval are represented as time series L x (x1,x2,…,x n ) and L y (y1,y2,…y n ); Build the distance matrix D to calculate the time series L x (x1,x2,…,x n ) and L y (y1,y2,…y n ), namely: Among them, the value of each element in the n-order distance matrix is ​​expressed as: x i is the voltage curve L x At time t i The voltage value, y i is the voltage curve L y At time t j Voltage value; Based on the values ​​of each element in the n-order distance matrix, the regularized path W is calculated, that is, Calculate the normalized median voltage of the series battery pack and the regular path W between the voltages of each cell in the series battery pack, and compare them with a preset threshold; If the distance between the normalized median voltage of the series battery pack and the voltage of each cell in the series battery pack is greater than a preset threshold, the corresponding cell is determined to be a fault location in the lithium-ion battery pack.

9. The system according to claim 6, characterized in that The process of quantitatively evaluating the micro-short circuit degree of lithium-ion batteries based on the charge-discharge cycle voltage characteristics and charge difference method includes: V t,cell (Cap,SOC,T,I L )=V OCV (Cap,SOC,T)+V impedance (Cap,SOC,T,I L ) Among them, Cap represents the capacity of the battery, T represents the temperature of the battery, and I L Indicates the battery charge and discharge current, V OCV is the open circuit voltage, V impedance is the internal impedance voltage of the battery.

10. The system according to claim 9, characterized in that If the same voltage points t1 and t2 are specified, the charge difference ΔQ of the lithium-ion battery between t1 and t2 can be calculated by calculating the integral of the current over time. cell (t1, t2) = 0; When ΔQ cell When (t1, t2) = 0, calculate the charge ΔQ of the battery leaked due to micro short circuit between t1 and t2 ISC , the calculation formula is: ΔQ ISC (t1,t2)=ΔQ load (t1,t2) Among them, T sample Indicates the sampling time interval, I L is the current of the series battery pack, ΔQ load The amount of charge that the battery obtains or releases from the outside; The short-circuit current is calculated as: Wherein, Δt represents the time interval between time t1 and time t2; The calculation formula of micro short circuit resistance is:

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