CN111505532A - Online detection method for early internal short circuit of series lithium battery pack based on SOC correlation coefficient - Google Patents

Online detection method for early internal short circuit of series lithium battery pack based on SOC correlation coefficient Download PDF

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CN111505532A
CN111505532A CN202010350255.6A CN202010350255A CN111505532A CN 111505532 A CN111505532 A CN 111505532A CN 202010350255 A CN202010350255 A CN 202010350255A CN 111505532 A CN111505532 A CN 111505532A
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soc
short circuit
internal short
correlation coefficient
battery pack
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来鑫
易威
郑岳久
周龙
孙涛
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University of Shanghai for Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
    • G01R31/52—Testing for short-circuits, leakage current or ground faults
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
    • G01R31/56—Testing of electric apparatus

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Abstract

The invention provides an online early internal short circuit detection method of a series lithium battery pack based on SOC correlation coefficient, which is used for early warning internal short circuit faults of the series lithium battery pack in an electric automobile and comprises the following steps: step 1, acquiring the voltage, the total current and the temperature of each single battery in a series lithium battery pack in real time through a battery management system of an electric automobile, and estimating the SOC value of each single battery through an SOC estimation algorithm; step 2, calculating the SOC correlation coefficient r of the nth single battery and the two adjacent single batteries in a fixed time domain in a window moving moden‑1,nAnd rn,n+1(ii) a Step 3, correlating the SOC coefficient rn‑1,nAnd rn,n+1And a set threshold value rthrComparing, when the nth single battery cell is electrically connected with two adjacent single batteriesSOC correlation coefficient r of pooln‑1,nAnd rn,n+1Are all smaller than the threshold r at the same timethrAnd judging that the single battery has an internal short circuit, and carrying out internal short circuit alarm by the battery management system.

Description

Online detection method for early internal short circuit of series lithium battery pack based on SOC correlation coefficient
Technical Field
The invention belongs to the field of battery safety management, and particularly relates to an early internal short circuit online detection method of a series lithium battery pack based on SOC (system on chip) correlation coefficients.
Background
With the increasing prominence of energy and environmental problems, the vigorous development of new energy automobiles has become a necessary trend. Lithium ion batteries are widely used in the electric vehicle and energy storage industries due to their advantages in energy density, power density, and self-discharge rate. In order to meet the range requirements of electric vehicles, many battery manufacturers seek larger capacity batteries, which can be met by filling the batteries with more active materials, but which will make the lithium ion batteries more susceptible to internal short circuit failure. In recent years, many lithium ion battery safety accidents have occurred due to internal short circuits. In 2013, a power battery pack fire accident occurs due to the fact that a wave sound 787, and the accident is caused by the fact that a battery pack fires due to the fact that a certain battery monomer is subjected to internal short circuit; in 2016, the Samsung Note7 mobile phone has a large-scale explosion accident, and after investigation, the negative electrode material of the battery is extruded to cause lithium precipitation, and lithium dendrites penetrate through a diaphragm to form an internal short circuit. Internal short circuits are highly likely to cause thermal runaway, but have long latencies. Therefore, the early effective diagnosis of the internal short circuit has great significance for improving the safety of the lithium battery.
In order to realize online identification of internal short circuits, researchers have proposed many early internal short detection algorithms based on the influence of internal short circuits on the electrochemical and thermal characteristics of lithium ion batteries. In summary, the existing internal short detection methods can be classified into 4 types: (1) based on a comparison of the voltages of the cells of the series battery. And comparing the actually measured voltage with the model predicted voltage, and judging that the internal short circuit occurs when the voltage is lower than the predicted value to a certain degree. However, the voltage signal collected on the automobile is affected by the accuracy of the sensor and has larger noise, so the method of directly comparing the actually measured voltage data needs to set a larger threshold value to prevent false alarm, and the setting of the larger threshold value means that the degree of the internal short circuit is increased or the detection time is prolonged. (2) Based on a comparison of the series battery pack uniformity. The internal short circuit resistance is calculated by calculating the amount of leakage current of the battery over a period of time. At present, the internal short circuit detection method based on the consistency of the series battery pack is mature, and can basically detect the internal short circuit of 100 ohms or less in a certain time. However, when the internal short circuit resistance is 100 ohms, the short circuit current is small, and a long time is required for the algorithm to reach the recognizable degree. (3) Based on a comparison of leakage amounts. The internal short circuit resistance is calculated by calculating the amount of leakage current of the battery over a period of time. (4) The internal short circuit detection method based on the change of the residual charging capacity among the batteries is characterized in that the residual charging capacity of other batteries when the charging cut-off voltage is reached is calculated by taking the first fully charged battery in the series battery pack as a reference, and the internal short circuit is detected by comparing the continuous two-time residual charging capacity change of each single body. The method can accurately detect and quantify the internal short circuit without establishing a model, and greatly reduces the calculation and storage space of the BMS, but has the defect that the method only works under the charging working condition and is influenced by the charging habit of a user. (5) Based on a comparison of the particular circuit configurations. The internal short circuit resistance value is obtained by comparing the ratio of the current flowing through a certain branch circuit by adopting a ring-shaped symmetrical circuit structure. The method can realize the internal short circuit detection of the parallel battery pack and improve the detection time from hour to second. However, due to the adoption of a special circuit structure, the cost may be too high during application, and the sampling precision of the method on the current cannot be met under a dynamic working condition.
It can be seen that most of the conventional lithium battery internal short circuit detection methods are based on specific working conditions, some methods cannot detect early internal short circuits or have long detection time, and some methods are not suitable for online calculation due to complicated calculation. Therefore, it is important to provide a method for quickly detecting the early internal short circuit of the lithium battery suitable for online use.
Disclosure of Invention
The present invention is made to solve the above problems, and an object of the present invention is to provide an online detection method for an early internal short circuit of a series lithium battery pack based on an SOC correlation coefficient.
The invention provides an online early internal short circuit detection method of a series lithium battery pack based on SOC correlation coefficient, which is used for early warning internal short circuit faults of the series lithium battery pack in an electric automobile and has the characteristics that the online early internal short circuit detection method comprises the following steps: step 1, acquiring the voltage, the total current and the temperature of each single battery in a series lithium battery pack in real time through a battery management system of an electric automobile, and estimating the SOC value of each single battery through an SOC estimation algorithm;
step 2, calculating the SOC correlation coefficient r of the nth single battery and the two adjacent single batteries in a fixed time domain in a window moving moden-1,nAnd rn,n+1；
Step 3, correlating the SOC coefficient rn-1,nAnd rn,n+1And a set threshold value rthrComparing the correlation coefficient r of the SOC of the nth unit cell and the SOC of the two adjacent unit cellsn-1,nAnd rn,n+1Are all smaller than the threshold r at the same timethrAnd judging that the single battery has an internal short circuit, and carrying out internal short circuit alarm by the battery management system.
The method for detecting the short circuit in the series lithium battery pack in the early stage based on the SOC correlation coefficient, provided by the invention, can also have the following characteristics: in step 2, the single batteries at the head end and the tail end of the series lithium battery pack are used as adjacent single batteries to calculate the SOC correlation coefficient.
The method for detecting the short circuit in the series lithium battery pack in the early stage based on the SOC correlation coefficient, provided by the invention, can also have the following characteristics: wherein, in the step 2, the SOC correlation coefficient is calculated as formula (1),
Figure BDA0002471581730000041
multiplying the numerator and denominator of the formula (1) by the number n of the single batteries to obtain a formula (2),
Figure BDA0002471581730000042
the formula (2) is simplified to obtain a formula (3),
Figure BDA0002471581730000043
then calculating the SOC correlation coefficient in a fixed time domain by a moving window mode, such as formula (4),
Figure BDA0002471581730000044
in the formula (1), X, Y is two variables representing the SOC value, μ, of the corresponding unit cellX、μYRespectively, the mean values of two variables, in formula (3), a represents an accumulation term of the product of the two variables, b represents an accumulation term of the variable X, d represents an accumulation term of the variable Y, f represents an accumulation term of the power of the variable X, g represents an accumulation term of the power of the variable Y, and in formula (4), subscript k represents time, and n represents the size of a moving window.
Action and Effect of the invention
According to the online detection method for the early internal short circuit of the series lithium battery pack based on the SOC correlation coefficient, disclosed by the invention, because whether the internal short circuit occurs can be judged by calculating the SOC correlation coefficient of each single battery and two adjacent single batteries in the series lithium battery pack and comparing the two SOC correlation coefficients with the set threshold value, the early internal short circuit can be detected in real time and is not limited by the running working condition of an automobile, the detection speed is high, and the detection method is simple and does not increase the calculation burden of a battery management system.
Drawings
FIG. 1 is a flow chart of a method for online detection of an early internal short circuit of a series lithium battery pack based on SOC correlation coefficients in an embodiment of the invention;
FIG. 2 is a schematic diagram illustrating the calculation of SOC correlation coefficients in a series lithium battery pack according to an embodiment of the present invention;
FIG. 3 is a diagnostic schematic in an embodiment of the invention;
FIG. 4 is a schematic diagram of an equivalent internal short circuit experimental circuit topology in an embodiment of the present invention;
FIG. 5 is a voltage graph of an equivalent internal short circuit experiment in an embodiment of the present invention;
FIG. 6 is a SOC plot of an equivalent internal short circuit experiment in an embodiment of the present invention;
fig. 7 is a calculation result diagram of the SOC correlation coefficient of the battery module in the embodiment of the invention.
Detailed Description
In order to make the technical means and functions of the present invention easy to understand, the present invention is specifically described below with reference to the embodiments and the accompanying drawings.
Fig. 1 is a flowchart of an early internal short circuit online detection method for a series lithium battery pack based on SOC correlation coefficients in an embodiment of the present invention.
As shown in fig. 1, the online detection method for the early internal short circuit of the series lithium battery pack based on the SOC correlation coefficient according to the embodiment is used for early warning the internal short circuit fault of the series lithium battery pack in the electric vehicle, and includes the following steps:
step 1, acquiring the voltage, the total current and the temperature of each single battery in the series lithium battery pack in real time through a battery management system of the electric automobile, and estimating the SOC value of each single battery through an SOC estimation algorithm.
Step 2, calculating the SOC correlation coefficient r of the nth single battery and the two adjacent single batteries in a fixed time domain in a window moving moden-1,nAnd rn,n+1。
Fig. 2 is a schematic diagram illustrating the calculation of SOC correlation coefficients in a series lithium battery pack according to an embodiment of the present invention.
As shown in fig. 2, in step 2, the single batteries at the head end and the tail end of the series lithium battery pack are used as adjacent single batteries to calculate SOC correlation coefficients, and each single battery obtains two correlation coefficients rn-1,nAnd rn,n+1And in order to ensure that each single battery of the series battery pack calculates two correlation coefficients, the single batteries at the head end and the tail end carry out correlation coefficient calculation to obtain rn,1。
In step 2, the SOC correlation coefficient is calculated as formula (1),
Figure BDA0002471581730000071
in the formula (1), X, Y is two variables representing the SOC value, μ, of the corresponding unit cellX、μYRespectively, represent the mean of two variables.
In this embodiment, since the obtained SOC value is a time series data, the variables and the mean value in the formula (1) are always changed, which causes a great obstacle to iterative calculation, the formula (1) needs to be correspondingly deformed, the numerator and the denominator of the formula (1) are simultaneously multiplied by the number n of the single batteries to obtain the formula (2), and the deformed formula (2) does not need to subtract each variable from the mean value any more, so that the method is more suitable for iterative calculation,
Figure BDA0002471581730000072
the formula (2) is simplified to obtain a formula (3),
Figure BDA0002471581730000073
in the formula (3), a represents an accumulation term of a product of two variables, b represents an accumulation term of a variable X, d represents an accumulation term of a variable Y, f represents an accumulation term of a power of the variable X, and g represents an accumulation term of a power of the variable Y.
In this embodiment, since the early internal short-circuit resistor has a large resistance value and a low power consumption, the SOC value of the battery with the early internal short-circuit is not much different from that of the normal battery, and a long period of time is required for the SOC value to be different to a recognizable degree. When the SOC correlation coefficient is calculated, if the segments with small differences are included, the detection sensitivity is affected, so that the number of variables participating in calculation needs to be controlled within a certain range, and the SOC correlation coefficient in a fixed time domain is calculated in a moving window mode, so that past data are forgotten, as shown in formula (4),
Figure BDA0002471581730000081
in equation (4), the subscript k denotes time and n denotes the size of the moving window.
In this embodiment, if n is too large, the sensitivity of the algorithm may be reduced, and if n is too small, the false alarm rate of the algorithm may be increased, so that n is 600 in this embodiment.
Step 3, correlating the SOC coefficient rn-1,nAnd rn,n+1And a set threshold value rthrComparing the correlation coefficient r of the SOC of the nth unit cell and the SOC of the two adjacent unit cellsn-1,nAnd rn,n+1Are all smaller than the threshold r at the same timethrAnd judging that the single battery has an internal short circuit, and carrying out internal short circuit alarm by the battery management system.
Fig. 3 is a diagnostic schematic in an embodiment of the invention.
As shown in FIG. 3, the right side of the diagram is a schematic diagram of the series-connected battery pack, and the left side of the diagram is a SOC graph and t1Time t and2and the SOC correlation coefficient at the moment is shown schematically. Three cells connected in series are taken as an example in the figure, wherein it is assumed that the third cell has an internal short circuit. Three curves in the left side curve chart respectively correspond to the SOC of each monomer, and due to the characteristic that the charge and discharge energy of each monomer of the series battery pack is consistent, the SOC curves of each monomer tend to be parallel when discharging, and the SOC reduction rate of the internal short circuit monomer is higher due to extra electric quantity loss; similarly, under the charging condition, the SOC of other normal single batteries tends to be parallel, and the SOC rising rate of the internal short-circuit single battery is slower due to the power loss. And the difference between the curves increases with time. Therefore, the calculation of the SOC correlation coefficient is real-time, so that the internal short circuit detection under the full working condition can be realized, and the sensitivity is good when the internal short circuit is detected to a lower degree.
In the left graph of fig. 3, t is the current time, L is the range of the moving window, each time, the data of the fixed time range (L) is taken for the calculation of the correlation coefficient, and as the time advances, the data in the moving window is updated in real time1+1), forgets the data of the first moment (t1L) in FIG. 3, the upper part of the left graph represents t1Time t and2SOC correlation coefficient at time. The SOC correlation coefficient between the normal cells is close to +1, and the SOC correlation coefficient between the cell in which the internal short circuit occurs and the normal cell is close to 0. In order to increase the robustness of the algorithm and reduce the probability of false alarm of the system, the SOC correlation coefficients of two adjacent single batteries are used as judgment standards. Through calculation, each single battery in the series battery pack obtains two SOC correlation coefficients, such as r of the second single battery in the right side of FIG. 31,2And r2,3And the unit cells at the head and tail ends are mutually calculated, such as r3,1. Only when two correlation coefficients of a certain single battery are smaller than a set threshold value r at the same momentthrWhen, e.g., t2In the SOC correlation coefficient diagram at time, r2,3And r3,1Are all less than a set threshold value rthrAnd at the moment, judging that the third monomer has an internal short circuit, and sending an alarm by the battery management system.
FIG. 4 is a schematic diagram of an equivalent internal short circuit experimental circuit topology in an embodiment of the invention.
As shown in fig. 4, in the present example, a 50Ah ternary lithium ion battery was also investigated, and the charge cut-off voltage was 4.25V and the discharge cut-off voltage was 2.8V. A battery module is formed by connecting 7 monomers in series, and an internal resistance simulation internal short circuit with the resistance value of 10 ohms is connected in parallel at two ends of the second monomer. Namely RISC10 Ω. Adopt the dicaron to carry out charge-discharge to battery module, gather electric current and each monomer voltage simultaneously, precision is 0.5% when its high-power (exceeding 40kW) is exported, and precision is 0.1% when the miniwatt (being less than 40kW) is exported, and sampling frequency is 1 Hz.
First, corresponding to the first step, the voltage curve of each single battery obtained through the equivalent internal short circuit experiment is shown in fig. 5. The SOC values of the respective unit cells estimated by the equivalent circuit model are shown in fig. 6. It can be seen that over time, the voltage versus SOC curve for the inner shorted cell differentiates from the normal cell.
Then, the SOC correlation coefficient of the adjacent cells in the battery module is calculated, as shown in fig. 7, with the SOC correlation coefficient of the battery module on the left side in the figureThe right side of the graph is a partial enlarged view of the graph when the internal short circuit is detected, and r is shown on the right side1,2Representing the SOC correlation coefficient of monomer No. 1 and monomer No. 2, and so on. The black dotted line in the right graph is a set correlation coefficient threshold, and when both SOC correlation coefficients of a single body are smaller than the set correlation coefficient threshold at the same time, it indicates that an internal short circuit has occurredthrSet to 0.7. Since the estimated SOC value may deviate from the actual value at the initial time, and the SOC correlation coefficient may fluctuate greatly, the control algorithm compares the SOC correlation coefficient with the threshold value after the vehicle is started for 1000 s. From the right graph, it can be seen that the SOC correlation coefficient of the normal cell is substantially maintained above 0.8, while the correlation coefficient of the cell with the internal short circuit in the low SOC interval is suddenly decreased, even the SOC correlation coefficient is 0. As shown on the right, where r1,2Represents the correlation coefficient of monomers No. 1 and No. 2, r2,3The correlation coefficients of the No. 2 and No. 3 monomers are represented, the triangle represents the moment when the internal short circuit is detected, the two correlation coefficients of the No. 2 monomer are both smaller than a set threshold value at the moment, namely the internal short circuit of the No. 2 monomer is detected, and the battery management system gives an alarm.
Effects and effects of the embodiments
According to the online detection method for the early internal short circuit of the series lithium battery pack based on the SOC correlation coefficient, whether the internal short circuit occurs can be judged by calculating the SOC correlation coefficient of each single battery and two adjacent single batteries in the series lithium battery pack and comparing the two SOC correlation coefficients with the set threshold value, so that the early internal short circuit can be detected in real time and is not limited by the running condition of an automobile, the detection speed is high, and the detection method is simple and does not increase the calculation burden of a battery management system.
The above embodiments are preferred examples of the present invention, and are not intended to limit the scope of the present invention.

Claims (3)

1. An early internal short circuit online detection method of a series lithium battery pack based on SOC correlation coefficients is used for early warning internal short circuit faults of the series lithium battery pack in an electric automobile, and is characterized by comprising the following steps:
step 1, acquiring the voltage, the total current and the temperature of each single battery in the series lithium battery pack in real time through a battery management system of the electric automobile, and estimating the SOC value of each single battery through an SOC estimation algorithm;
step 2, calculating the SOC correlation coefficient r of the nth single battery and two adjacent single batteries in a fixed time domain in a mode of moving a windown-1,nAnd rn,n+1；
Step 3, the SOC correlation coefficient r is obtainedn-1,nAnd rn,n+1And a set threshold value rthrComparing the SOC correlation coefficients r of the nth unit cell and the adjacent two unit cellsn-1,nAnd rn,n+1Are all smaller than the threshold r at the same timethrAnd judging that the single battery has an internal short circuit, and carrying out internal short circuit alarm by the battery management system.
2. The SOC correlation coefficient-based online early internal short circuit detection method for the series lithium battery pack is characterized in that:
in the step 2, the single batteries at the head end and the tail end of the series lithium battery pack are used as adjacent single batteries to calculate the SOC correlation coefficient.
3. The SOC correlation coefficient-based online early internal short circuit detection method for the series lithium battery pack is characterized in that:
wherein, in the step 2, the SOC correlation coefficient is calculated as formula (1),
Figure FDA0002471581720000021
multiplying the numerator and denominator of the formula (1) by the number n of the single batteries to obtain a formula (2),
Figure FDA0002471581720000022
the formula (2) is simplified to obtain a formula (3),
Figure FDA0002471581720000023
and then calculating the SOC correlation coefficient in the fixed time domain by means of a moving window, as shown in formula (4),
Figure FDA0002471581720000024
in the formula (1), X, Y is two variables representing the SOC value, μ, corresponding to the unit cellX、μYRespectively, represent the mean of two variables,
in the formula (3), a represents an accumulation term of a product of two variables, b represents an accumulation term of a variable X, d represents an accumulation term of a variable Y, f represents an accumulation term of a power of the variable X, g represents an accumulation term of a power of the variable Y,
in equation (4), the subscript k denotes time and n denotes the size of the moving window.
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Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112014746A (en) * 2020-09-08 2020-12-01 上海理工大学 Fault diagnosis method for distinguishing internal and external micro short circuits of series battery packs
CN112180266A (en) * 2020-09-21 2021-01-05 上海理工大学 Tracking early warning method for whole process of short circuit in battery
CN112462274A (en) * 2020-11-23 2021-03-09 哈尔滨理工大学 Battery self-discharge effect-based method for diagnosing short-circuit fault in grouped batteries
CN112793465A (en) * 2021-01-22 2021-05-14 合肥国轩高科动力能源有限公司 Thermal runaway early warning method and application of ternary lithium ion battery system
CN112946522A (en) * 2021-02-05 2021-06-11 四川大学 On-line monitoring method for short-circuit fault in battery energy storage system caused by low-temperature working condition
CN113552496A (en) * 2021-06-29 2021-10-26 哈尔滨理工大学 Voltage cosine similarity-based diagnosis method for short circuit fault in battery series module
CN113884922A (en) * 2021-10-28 2022-01-04 同济大学 Battery internal short circuit quantitative diagnosis method based on voltage and electric quantity outlier coefficient
CN114035086A (en) * 2021-12-16 2022-02-11 上海交通大学 Battery pack multi-fault diagnosis method based on signal processing
CN114091404A (en) * 2022-01-20 2022-02-25 天津力神电池股份有限公司 Electricity-heat model of lithium ion battery
CN114114039A (en) * 2021-12-06 2022-03-01 湖北亿纬动力有限公司 Method and device for evaluating consistency of single battery cells of battery system
CN117341476A (en) * 2023-12-04 2024-01-05 湖南行必达网联科技有限公司 Battery differential pressure fault early warning method and system

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107831443A (en) * 2017-10-20 2018-03-23 开沃新能源汽车集团有限公司 Battery system short trouble diagnostic method based on coefficient correlation
CN108445343A (en) * 2018-03-12 2018-08-24 北京理工大学 A kind of power battery internal short-circuit detection method and system
CN110712528A (en) * 2019-10-25 2020-01-21 优必爱信息技术（北京）有限公司 Real-time monitoring method and device for power battery pack

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107831443A (en) * 2017-10-20 2018-03-23 开沃新能源汽车集团有限公司 Battery system short trouble diagnostic method based on coefficient correlation
CN108445343A (en) * 2018-03-12 2018-08-24 北京理工大学 A kind of power battery internal short-circuit detection method and system
CN110712528A (en) * 2019-10-25 2020-01-21 优必爱信息技术（北京）有限公司 Real-time monitoring method and device for power battery pack

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
BINGXIA ET AL.: "A correlation based fault detection method for short circuits in battery packs", 《JOURNAL OF POWER SOURCES》 *
姜君等: "串联锂离子电池组仿真技术研究", 《电源技术》 *

Cited By (18)

* Cited by examiner, † Cited by third party
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CN112014746A (en) * 2020-09-08 2020-12-01 上海理工大学 Fault diagnosis method for distinguishing internal and external micro short circuits of series battery packs
CN112180266A (en) * 2020-09-21 2021-01-05 上海理工大学 Tracking early warning method for whole process of short circuit in battery
CN112462274A (en) * 2020-11-23 2021-03-09 哈尔滨理工大学 Battery self-discharge effect-based method for diagnosing short-circuit fault in grouped batteries
CN112793465A (en) * 2021-01-22 2021-05-14 合肥国轩高科动力能源有限公司 Thermal runaway early warning method and application of ternary lithium ion battery system
CN112946522A (en) * 2021-02-05 2021-06-11 四川大学 On-line monitoring method for short-circuit fault in battery energy storage system caused by low-temperature working condition
CN113552496A (en) * 2021-06-29 2021-10-26 哈尔滨理工大学 Voltage cosine similarity-based diagnosis method for short circuit fault in battery series module
CN113552496B (en) * 2021-06-29 2024-04-02 哈尔滨理工大学 Battery series module internal short circuit fault diagnosis method based on voltage cosine similarity
CN113884922A (en) * 2021-10-28 2022-01-04 同济大学 Battery internal short circuit quantitative diagnosis method based on voltage and electric quantity outlier coefficient
CN113884922B (en) * 2021-10-28 2022-11-18 同济大学 Battery internal short circuit quantitative diagnosis method based on voltage and electric quantity outlier coefficient
CN114114039A (en) * 2021-12-06 2022-03-01 湖北亿纬动力有限公司 Method and device for evaluating consistency of single battery cells of battery system
CN114114039B (en) * 2021-12-06 2023-10-03 湖北亿纬动力有限公司 Method and device for evaluating consistency of single battery cells of battery system
CN114035086A (en) * 2021-12-16 2022-02-11 上海交通大学 Battery pack multi-fault diagnosis method based on signal processing
CN114035086B (en) * 2021-12-16 2023-08-11 上海交通大学 Multi-fault diagnosis method for battery pack based on signal processing
CN114091404A (en) * 2022-01-20 2022-02-25 天津力神电池股份有限公司 Electricity-heat model of lithium ion battery
CN114091404B (en) * 2022-01-20 2022-05-17 天津力神电池股份有限公司 Electric-thermal model of lithium ion battery
CN117341476A (en) * 2023-12-04 2024-01-05 湖南行必达网联科技有限公司 Battery differential pressure fault early warning method and system
CN117341476B (en) * 2023-12-04 2024-02-27 湖南行必达网联科技有限公司 Battery differential pressure fault early warning method and system

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Application publication date: 20200807